Speakers

Hear from though leaders, AI experts, and executive speakers how AI is transforming industry
Alex 'Sandy' Pentland
Alex 'Sandy' Pentland

Professor

MIT Media Lab

Blockchain AI, or Future Data Systems Must Be Built Differently

Bio

Professor Alex “Sandy” Pentland directs the MIT Connection Science and Human Dynamics labs and previously helped create and direct the MIT Media Lab and the Media Lab Asia in India. He is one of the most-cited scientists in the world, and Forbes recently declared him one of the “7 most powerful data scientists in the world” along with Google founders and the Chief Technical Officer of the United States. He has received numerous awards and prizes such as the McKinsey Award from Harvard Business Review, the 40th Anniversary of the Internet from DARPA, and the Brandeis Award for work in privacy

Amy O'Connor
Amy O'Connor

Chief Data and Information Officer

Cloudera

Building a Big Data Center Of Excellence - The Secret Sauce to your Data Journey — The People.

Bio

Amy O’Connor is Chief Data and Information Officer at Cloudera. She advises customers globally as they introduce Big Data solutions and adopt enterprise-wide Big Data delivery capabilities. Amy was recently named by Information Management as one of the “10 Big Data Experts to Know”. Prior to joining Cloudera, Amy built and ran Nokia’s Big Data team, developing and managing Nokia’s data asset and leading a team of data scientists to drive insights. Previously Amy was vice president of services marketing and also led strategy for the software and storage business units of Sun Microsystems. Amy has an extensive background in the Hadoop ecosystem, in building big data skills and teams, and in developing business-focused analytical use cases. She holds her M.B.A. from Northeastern University and her bachelor degrees in computer science and electrical engineering from the University of Connecticut.

Dr Lynda Chin
Dr Lynda Chin

Associate Vice Chancellor & Chief Innovation Officer

The University of Texas System

AI and Big data in Medicine: Trust, Transparency and Transformation

Bio

An elected member of the National Academy of Medicine, the Association of American Physicians, and the American Society for Clinical Investigation,  Dr. Chin is a world renowned cancer genomic scientist who had authored or co-authored over 200 peer-reviewed publications and spoken in over 300 national and international conferences including Tedmed.  At Dana-Farber Cancer Institute where she was a professor at Harvard Medical School and a senior associate member at the Broad Institute of MIT and Harvard, Dr. Chin conducted research spanning mouse models of human cancers, cancer genomics, functional genomics and personalized medicine.  Outside of her laboratory, Dr. Chin held multiple leadership positions in The Cancer Genome Atlas (TCGA) project in the U.S., including serving on the executive subcommittee and leading the development of Firehose pipeline as co-PI at the Broad Institute.  Internationally, Dr. Chin has been active in the International Cancer Genome Consortium since its inception, currently sitting on the ICGC-ARGO Steering Committee and chairing its Phenomics Working Group.  In 2011, Dr. Chin created the first Department of Genomic Medicine at MD Anderson Cancer Center with a mission to accelerate the translation of scientific advances and democratize the best care to patients outside of academic centers with technologies, data and AI analytics.  In 2015, foreseeing the need for a new model to harness the potential of real-world health and health-related data, Dr. Chin launched the Institute for Health Transformation as the Chief Innovation Officer for University of Texas System. She designed and developed REDI (Real-world Education, early Detection and Intervention), an infrastructure platform for a connected health data and care delivery ecosystem.  Collaborating with leaders from different industries, including PwC, AT&T and Walmart, engaging local stakeholders from federally qualified health center to community-based programs, Dr. Chin operationalized such an ecosystem in one of the poorest communities of South Texas to demonstrate how purposeful data sharing across public and private entities is feasible and how insight-driven actions can improve chronic disease management for a vulnerable population.   A mother of three teenagers, Dr. Chin is an entrepreneur who has founded or co-founded  cancer therapeutic, biomarker and AI companies.

Dr. Kirk Borne
Dr. Kirk Borne

Principal Data Scientist and Executive Advisor

Booz-Allen Hamilton

Current and Future Trends in AI, Machine Learning, and Data Science

Bio

Dr Kirk Borne is a principal Data Scientist and Executive Advisor at Booz-Allen Hamilton where he provides leadership and mentoring to multi-disciplinary teams of data scientists; Former Professor of Astrophysics and Computational Science in the George Mason University. 

Gary Marcus
Gary Marcus

Scientist, Bestselling Author & Entrepreneur

New York University

Keynote Speaker

Bio

Cognitive scientist, author, and award-winning Professor of Psychology and Neural Science at NYU. Former Director of Uber AI Labs who acquired his AI startup, Genomic Intelligence

Andrew Casey
Andrew Casey

Head of Analytics- Business & Industrial

Google

Marketing in a Machine Learning World

Abstract

The Machine Learning era is upon us. Machine Learning will have a profound impact on digital marketing and the way in which companies will need to leverage their first party data.  Come and learn about how this shift will impact marketing, and ways that you can position your organization for success.

Bio

Drew is currently a Head of Analytics at Google, managing a team of marketing analysts that service Google’s largest clients in the Business & Industrial space.  These clients include global leaders in Shipping & Logistics, MRO Supply, Custom Printing, and Oil & Gas.  He has accrued over 10 years of experience working with B2B clients, ranging from the largest enterprise technology companies to the largest industrial supply companies.  Prior to Google, Drew spent 6 years with the advertising agency Ogilvy & Mather.  At Ogilvy, the majority of his time was spent servicing IBM as an Analytics Consultant, advising IBM on how to properly measure digital marketing and working with their teams to implement measurement frameworks within their measurement platforms.  Drew is based out of Google’s NYC office and holds an undergraduate degree from Harvard.

Dr Catherine Havasi
Dr Catherine Havasi

CEO & Co-Founder

Luminoso Inc

Transfer Learning: Applications for natural language understanding

Abstract

Deep learning has been transformational in many aspects of AI and powers many business use cases. However, there are limits to what can be done with rote application of deep learning. One such limitation appears when you have a specific domain to understand, but all the available training data is for a different or more general domain, and acquiring the vast amount of data necessary to train a new system is infeasible. This is where transfer learning provides new opportunities — using the insights of one system to enrich another.

ML pioneer Andrew Ng has called transfer learning “the next driver of ML commercial success.” Transfer learning makes powerful systems more reusable, and reduces the amount of training data, compute, and professional services needed. Is it ready for business deployment or is it still emerging technology? How is it used in business today? This talk focuses on language related use cases for customer service, search, question answer, self-help and consumer finance. We’ll also have some fun with applications of transfer learning.

Bio

Dr. Catherine Havasi is the CEO and Co-Founder of Luminoso Technologies, an Artificial Intelligence (AI), natural language processing (NLP) company in Cambridge, MA. Luminoso was founded on nearly a decade of research at the MIT Media Lab on how NLP and machine learning could be applied to text analytics. For over 15 years she has been researching language and learning and was a research scientist in artificial intelligence and computational linguistics at the MIT Media Lab where she ran the Digital Intuition group. In the late 90s, she co-founded the Common Sense Computing Initiative, or ConceptNet, a big-data lexical resource used in over two thousand academic projects.

Aleksandra Przegalinska
Aleksandra Przegalinska

Research fellow

MIT (Center for Collective Intelligence)

Towards a more advanced Human-Bot Interaction

Abstract

Our research is carried out in the context of the ongoing process of introducing artificial intelligence in the area of social interaction with people, with a particular emphasis on interactions in the professional sphere. In this presentation, we provide details of our own methodology of researching human-bot interaction. We describe our experiment using electromyography as well as other psychophysiological data and a detailed set of questionnaires focused on assessing interactions and willingness to collaborate with a virtual assistant. Our purpose is to thoroughly examine the character of the human/non-human interaction process.

Bio

ALEKSANDRA PRZEGALINSKA – PhD in philosophy of artificial intelligence, Assistant Professor at Kozminski University, currently Research Fellow at the Center for Collective Intelligence at Massachusetts Institute of Technology (MIT) in Boston. Recent visiting scholar at The New School for Social Research/ Brown University in New York City (2014).  In 2011 Aleksandra worked as the Chairman of Media Regulation Working Party at the Council of European Union in Brussels. As a William J. Fulbright Scholar Aleksandra also majored in Sociology at The New School for Social Research in New York (2012), where she participated in research on identity in virtual reality, with particular emphasis on Second Life. 

Aleksandra’s current primary research interest include consequences of introducing artificial intelligence systems to people’s social and professional sphere as well as wearable technologies and human/bot interaction
Eyal Pfeifel
Eyal Pfeifel

Co-Founder & CTO

imperson

Look Who’s Talking: A Deep Dive into the Medium of the Future — Natural Language Conversation

Abstract

According to Business Insider, 80% of companies will want an AI-driven chatbot by 2020. When you take into consideration their ability to help companies reach, recognize, and stay connected to individuals with meaningful conversations in real-time, it’s no wonder why business are clamoring to use this new medium. While there are currently two primary types of chatbots on the market – utilitarian bots, which are based on decision-tree logic, and bots that are driven solely by natural language process (NLP), which are intelligent bots like Apple’s Siri and Microsoft’s Cortana – neither can fully understand language, syntax, and are unable to adapt to a conversation. Enter natural language understanding (NLU). NLU based bots are some of the most difficult to create, having to learn turn-taking, comprehending language, and responding like a human — something that’s missing from a majority of bots on the market. As a result, this makes NLU invaluable because it’s able to adapt to a specific person’s speech and yet still able to have a more natural conversation than utilitarian bots. Drawing from his experiences creating premium bots for some the world’s biggest brands, Eyal Pfeifel, CTO & Co-Founder of imperson, will discuss how business can utilize NLU bots to build authentic relationships between brands and consumers. He will explain why companies are opting for conversational interfaces over other bots, the current capabilities of bot technologies, and what he see for the future — including bots that can lead conversations and those that have their own motivations.

Bio

Eyal Pfeifel is the CTO and Co-Founder of imperson, a Disney Accelerator alum and developers of conversational AI technology that power premium conversational bots via text, voice, and video.
Pfeifel is a technology visionary with more than 20 years of experience in software development. At imperson, he leads the charge in commercializing sophisticated language algorithms for building conversational bots that have personality, intent, natural learning capabilities, and relationship memory. Pfeifel has been the technology architect behind the earliest bot personas to come on the scene, bringing iconic characters like Miss Piggy and Doc Brown to life (even before Facebook F8, 2016) as well as recent chatbots that includes Genius (Albert Einstein) for National Geographic, Disney Pandora Park experience and Chandon consumer-facing chatbot.
Prior to imperson, Eyal was CTO of Magic Software Enterprises, the first Israeli software company to go public on the NASDAQ

 

 
Jeffrey Saltz
Jeffrey Saltz

Associate Professor

Syracuse University

Workshop: Key questions to ask when managing data science projects

Abstract

Data science managers (and senior leaders managing data science teams) need to think through many questions relating to how to best execute their data science efforts. For example, what is the most effective way to lead a data science project? How to make sure my data science team does not expose my organization to issues relating to the misuse of data and/or algorithms? How do I validate the results provided by the data science team?

This workshop will explore these questions and many others. The focus of this not on which specific algorithms a team should use, but rather, how to ensure an effective and efficient data science team. Areas to be explored include:

Managing Data Science Teams, such as:
o Structuring and coordinating data science functions/capabilities
o Coordinating IT, analytic and client teams
o Staffing/training data science teams
o Ensuring ethical data usage and model development/deployment
o Enabling data and model transparency

Improving processes to develop and deploy analytical models, such as:
o Tools and platforms to support modular data science practices
o Agile data science process methodologies
o Analytic model workflow management
o Analytic model life-cycle management

Exploring Chief Data Officer & Chief Analytics Officer responsibilities, such as:
o Exploring innovative data science governance approaches
o Business value of analytics and analytical governance
o Managing project and deployment risk
o Ensuring data and model ownership
o Designing, staffing and directing data & analytics governance

Ensuring data and model asset management, including:
o Model management platforms
o Model documentation and transparency
o Model compliance management
o Analytics regulatory risks and risk mitigation

Bio

 Jeff Saltz is an Associate Professor at Syracuse University, where his research and consulting focus on helping organizations leverage data science and big data for competitive advantage. Specifically, his work identifies the key challenges, and potential solutions, relating to how to manage, coordinate and run data science / big data projects within and across teams. In order to stay connected to the real world, Jeff consults with clients ranging from professional football teams to Fortune 500 organizations. In his last full-time corporate role, at JPMorgan Chase, he reported to the firm’s Chief Information Officer and drove technology innovation across the organization.  Saltz received his B.S. in computer science from Cornell University, an M.B.A. from The Wharton School at the University of Pennsylvania and a Ph.D. in Information Systems from the NJIT.
Greg Michaelson
Greg Michaelson

General Manager of Banking

DataRobot

Workshop: ML and AI for Executives

Abstract

In this workshop, Greg will discuss what modern executives need to know in order to take full advantage of the data they’re already collecting and build a competitive advantage using AI.  Topics to be covered will be: a short introduction to AI and ML, How to spot opportunities to you use AI in business, and common blockers faced by organizations seeking to adopt AI.

Bio

Greg Michaelson is the General Manager of Banking for DataRobot. Prior to joining DataRobot, Greg led modeling teams at Travelers and Regions Financial, focusing on pricing and risk modeling. He earned his Ph.D. in applied statistics from the Culverhouse College of Business Administration at the University of Alabama. Greg lives in Charlotte, NC with his wife and four children and their pet tarantula.

Aarthi Srinivasan
Aarthi Srinivasan

Director, Product Management - Personalization & Machine Learning

Target

Future of AI

Abstract

This topic will cover Trends in Startups as well as corporate investments in Machine Learning & Artificial Intelligence. Based on analysis of market data, I will share my view on AI hot topic areas. The takeaways from this talk are:

AI macro themes in startups and Corporations & How to create a product backed AI vision

Bio

Aarthi enjoys building dynamic teams to launch scalable products. She brings over 15+ years of combined experience in product management, consulting and engineering. Currently she leads the personalization team at Target to create unique customer experiences powered by machine learning algorithms. She enables her teams to embrace the power of customer research & product analytics to create a product strategy that drives positive business results. Aarthi’s experience in Financial technology encompassed the launch of award winning Social Security and Income Planning solutions that saved billions of dollars for retirees. She has also worked at Intuit, Oracle and JP Morgan in her earlier years. On the academic front, she holds an MBA from Wharton, MS in Computer Science from Stony Brook University and BS from Madras University.

Niranjan Thomas
Niranjan Thomas

General Manager, Platform and Technology Partnerships

Dow Jones, Professional Information Business

Using unstructured data and machine learning to understand loss events

Abstract

Data is big news. Big, small or in between. Organizations are building out their data capabilities accordingly. Most organizations are historically adept at working with structured data, but as there is more data is available than ever before, its this explosion in unstructured data that provides great opportunity.

Using news and event data to identify the reality around an entity of interest (company, person or event), understanding how that reality changes with new data and how this impacts that entity is where business value can be found. These insights represent opportunities or risks. Next, taking action on this insight is imperative to realize this business value.

Machine learning technology can be applied to great effect to unstructured datasets to identify these changing realities. In the financial services segment particularly within insurance and asset management this includes the ability to identify the network effect of loss events which is the focus of this presentation.

Bio

Niranjan Thomas is General Manager, Platform and Technology Partnerships, Professional
Information Business, at Dow Jones. He is responsible for the DNA data platform including product strategy, go to market, customer solutions and driving technology ecosystem
partnerships.

Niranjan has over 16 years of experience in Technology leadership roles across software design and development, software project management, cybersecurity, risk and business management. He has spent significant time delivering solutions for the manufacturing, 
consulting, technology, telecommunications, media and financial services industries.
Prior to joining Dow Jones he served as Head of Technology at AMP, Australia’s leading specialist
wealth management and life insurance company.

Niranjan holds a Bachelor’s degree in Business Information Systems from RMIT University,
Melbourne, Australia.

Manoj Saxena
Manoj Saxena

Executive Chairman

CognitiveScale

How to win with AI and Blockchain: Lessons from the early adopters

Abstract

Over the course of the past year, we’ve seen artificial intelligence move from futuristic application to business imperative. Organizations are starting to recognize AI as a strategic capability and a practical tool that powers critical business processes and transforms user experiences to drive large scale efficiency and innovation.

CEOs and board members are now asking: What impact will AI have on my organization? How do I go about applying AI to drive business results and prevent potential business model disruption?

To solve real business problems with AI, enterprises need both an AI software platform and a partner with a fundamentally deep understanding of their specific industry and technology requirements. Employing a horizontal data science platform or consuming a general-purpose API-based cognitive service will not adequately solve complex business problems leading to expensive and time consuming AI or data science experiments that never make it into production. Simply put, using an industry-specific AI software platform speeds implementation and ultimately results in a faster return-on-investment.

In this presentation, Manoj Saxena will share practical examples where global businesses have realized breakthrough value from AI and Blockchain initiatives while also highlighting some of the common myths and short coming of Enterprise AI.  Manoj will then open up the conversation for Q&A and discussion around how audience members are already using AI in their organization, major challenges with adoption of Enterprise AI at scale, and the opportunities for augmenting business processes with industry-specific AI.

Bio

Manoj Saxena is the Executive Chairman of CognitiveScale and a founding managing director of The Entrepreneurs’ Fund IV, a $100m seed fund focused exclusively on the cognitive computing and machine intelligence market with eight active investments. 

 

Previously, he was IBM’s first general manager of IBM Watson (2011-14), where his team built the world’s first cognitive systems for healthcare, retail, and financial services.  He received the IBM Chairman’s award for Watson commercialization and helped with the formation of Watson Business Group in January 2014 with a $1B investment from IBM.

 

Prior to IBM, he successfully founded, scaled, and sold two venture-backed software companies within a five-year span. Webify, an emerging leader in industry-specific SOA middleware, was acquired by IBM in 2006, and Exterprise, a business process collaboration company, was purchased by Commerce One in 2001.

Saxena also served as Chairman for The Federal Reserve Bank of Dallas, San Antonio Branch through December, 2017, and oversees the Saxena Family Foundation.  He holds nine software patents.

Saxena holds a masters degree in business administration from Michigan State University, and a masters in management sciences from the Birla Institute of Technology & Science in Pilani, India.

Richard Tibbetts
Richard Tibbetts

CEO

Empirical Systems

Making Business Bayesian: From Uncertainty to Action

Abstract

Businesses have spent decades trying to make better decisions through the complete understanding of data. New technologies make Bayesian inference and generative modeling more accessible to business analysts. But the ability to rapidly quantify uncertainty, simulate new data, and understand direction, magnitude, and confidence of effects creates new communications challenges.

This talk presents techniques for capturing domain knowledge and making findings actionable for decision makers utilizing the explanatory powers of transparent AI.

Bio

Richard Tibbetts is the CEO of Empirical Systems. Prior to Empirical, he was founder and CTO at StreamBase, the leading real-time analytics platform, which merged with TIBCO in 2013. Subsequently, Richard was a visiting scientist at the Probabilistic Computing Project at MIT and lead the open source release of BayesDB. He holds MEng and SB degrees in computer science from MIT.

 

Arun Verma, PhD
Arun Verma, PhD

Head, Quant Research Solutions

Bloomberg

Extracting embedded alpha from social & news data using statistical arbitrage & machine learning

Abstract

The high volume and time sensitivity of news and social media stories requires automated processing to quickly extract actionable information, while the unstructured nature of textual information presents challenges that are comfortably addressed through machine learning techniques. This talk will cover the following topics:
♦Extracting actionable information in the high volume, time-sensitive environment of news stories and social media content using machine learning
♦ Quantitative techniques for assigning aggregated sentiment scores and other derived metrics (e.g., sentiment dispersion)
♦ Demonstrating that using sentiment scores in your trading strategy ultimately helps achieve higher risk-adjusted returns
♦ Illustrating variation in sensitivity of sentiment with respect to industry sector, market cap, trading volume, etc.
♦ Uncovering topic codes that are more relevant for return prediction, as well as those which lead to noisy sentiment extraction and a weaker predictive signal

Bio

Dr. Arun Verma joined the Bloomberg Quantitative Research group in 2003. Prior to that, he earned his Ph.D from Cornell University in the areas of computer science & applied mathematics. At Bloomberg, Mr. Verma’s work initially focused on Stochastic Volatility Models for Derivatives & Exotics pricing. More recently, he has enjoyed working at the intersection of diverse areas such as data science (with structured & unstructured data), innovative quantitative models across all asset classes & using machine learning methods to help reveal embedded signals in financial data.

 

Sam Ransbotham
Sam Ransbotham

Guest Editor

MIT Sloan Management Review

The Adoption of AI in Business: Opportunities and Challenges:

Abstract

The Adoption of AI in Business: Opportunities and Challenges: While corporate expectations for artificial intelligence are sky-high across industry and geography, few organizations have mastered integrating the technology into their business processes and offerings — and many who want to don’t fully understand the work that lies ahead. MIT Sloan Management Review’s recent research on artificial intelligence and business strategy offers a “state of the state” of AI adoption inside corporations.
This session will provide an overview of organizational readiness for and adoption of AI across sectors.

Bio

Sam Ransbotham is an associate professor of information systems at Boston College and the editor for two initiatives at MIT Sloan Management Review (one on Data and Analytics, the other on Artificial Intelligence).  In 2014, he was awarded an NSF CAREER Award for his analytics-based research in information security.  Prior to his joining the faculty at Boston College, Ransbotham founded a software company with a globally diverse client base.  Sam earned a Bachelors in Chemical Engineering, an MBA, and a PhD all from the Georgia Institute of Technology. 

Ambrish Roy
Ambrish Roy

Data Scientist

Vertex Pharmaceuticals

Harnessing the power of recommender system for drug off-target activity prediction

Abstract

Recommender system has become a widely popular technique in social network and e-commerce services. In this work, we have applied recommender system in the drug discovery setting, and the first test case we tried was in virtual chemical biology. Chemical biology studies interactions between chemical matter and biological targets, typically proteins, with an eye toward answering questions around new biological pathways, their potential role in disease treatment, and their potential liabilities as drug targets. Most small molecule drugs are known to interact with multiple proteins, effectively a lack of fine-tuning with respect to the biological system at large. This phenomenon is sometimes referred to as polypharmacology. Unfortunately, experimentally available bioactivity data is often limited to only a few of the proteins.
First, we assembled a large knowledge base of small molecule–protein interactions that covers millions of compounds and thousands of proteins, yet despite our best efforts the resulting matrix is very sparse. By analogy to e-commerce, we can view small molecules and proteins as users and products, respectively, treating drug-target interaction (DTI) prediction as a recommendation task, and predicting billions of connections between drugs and possible targets within the DTI network.
The resulting hybrid model we came up with used content-based filtering to narrow down the search space, while collaborative filtering modeled the probability of interaction between a drug and a target through neighborhood-regularized logistic function of drug-specific and target-specific latent vectors that represented their properties. The model significantly outperformed our previous non-network-based benchmarks on holdout large scale data sets.

Bio

Ambrish is a data scientist at Vertex Pharmaceuticals, working with business partners to re-shape their analytic capabilities and implement innovative technology and solutions. Ambrish holds a Ph.D. in Bioinformatics and his areas of interest include application development, data analytics and machine learning. He has developed multiple scientific applications in the field of computational structural biology and chemistry.

Marc Maleh

Marc Maleh

Global Director

Havas

Cross Industry Panel- User Design and Experience with AI

Abstract

Data is no longer just the input that informs and measures a consumer’s brand experience; it is increasingly the product, the content, and even the campaign with its own creative identity.

AI and machine learning is transforming vast reams of raw data into a whole new breed of brand engagements and disrupting the way brands are connecting with their consumers. Data now has a face, voice, a look and a language that speaks to consumers in a precise way. For creators, that means the old rules of creativity no longer apply. What does the creative process look like in this new landscape? As brand guardians, how can we win consumers hearts with these innovations? What are the challenges and pitfalls of operationalizing these initiatives? The discussion will be based on real world successes and challenges.

Bio

Marc has over 13 years of experience in interactive and emerging technology. Prior to his role as Global Director of Cognitive at Havas Marc served as VP, Managing Director at R/GA, where he helped grow a team of Data Scientists, technologists, and creatives who built data driven platforms and campaigns for Nike, LA Dodgers, MD Anderson Cancer Research Center, Samsung and Verizon.  Marc has also managed international design and technology teams in New York and Shanghai for Screampoint, working with clients that included Apple, AIG, World Trade Center Development, Hudson Yards Development and Jamba Juice.   Marc has been a Judge at the Cannes International Festival of Creativity and has spoken at Fast Company Innovation Festival, Venture Beat AI Conference,  NYC Media Lab, General Assembly, NYU, Parsons and Montana State University. 

Matthieu Lorrain

Matthieu Lorrain

Head of Creative Innovation

Google

Cross Industry Panel- User Design and Experience with AI

Abstract

Data is no longer just the input that informs and measures a consumer’s brand experience; it is increasingly the product, the content, and even the campaign with its own creative identity.

AI and machine learning is transforming vast reams of raw data into a whole new breed of brand engagements and disrupting the way brands are connecting with their consumers. Data now has a face, voice, a look and a language that speaks to consumers in a precise way. For creators, that means the old rules of creativity no longer apply. What does the creative process look like in this new landscape? As brand guardians, how can we win consumers hearts with these innovations? What are the challenges and pitfalls of operationalizing these initiatives? The discussion will be based on real world successes and challenges.

Bio

Matthieu Lorrain is a NYC-based creative leader specialized in brand innovation and creative technology. His recent work explores the relationship between physical and digital universes with the intent of using our environment as a creative canvas (AR, VR, geo-localized services, etc…).

Matthieu has worked with brands from all industries including L’Oréal, J&J, Playstation, Oreo, Warner Bros, Google, Mattel, Hershey and many more. He has been a featured speaker at major events such as the Cannes Lions, 4A’s Createtech, KIKK Festival and NYC Tech Forum.

Matthieu currently leads creative innovation at the ZOO, Google’s creative think tank for brands and agencies. His mission is to explore the creative potential of the latest Google technologies, evangelize top agency and brand partners and drive the development of new interactive consumer experiences.

Amy Chen

Amy Chen

Director of Entrepreneurship Programs

NYC Media Lab

Cross Industry Panel- User Design and Experience with AI

Abstract

Data is no longer just the input that informs and measures a consumer’s brand experience; it is increasingly the product, the content, and even the campaign with its own creative identity.

AI and machine learning is transforming vast reams of raw data into a whole new breed of brand engagements and disrupting the way brands are connecting with their consumers. Data now has a face, voice, a look and a language that speaks to consumers in a precise way. For creators, that means the old rules of creativity no longer apply. What does the creative process look like in this new landscape? As brand guardians, how can we win consumers hearts with these innovations? What are the challenges and pitfalls of operationalizing these initiatives? The discussion will be based on real world successes and challenges.

Bio

Amy Chen is building a community of practice in innovation and entrepreneurship at NYC Media Lab. She connects industry executives with university faculty and students.

Cat Kolodij

Cat Kolodij

Business Leader Marketing and Experience Strategy

Progressive Insurance

Cross Industry Panel- User Design and Experience with AI

Abstract

Data is no longer just the input that informs and measures a consumer’s brand experience; it is increasingly the product, the content, and even the campaign with its own creative identity.

AI and machine learning is transforming vast reams of raw data into a whole new breed of brand engagements and disrupting the way brands are connecting with their consumers. Data now has a face, voice, a look and a language that speaks to consumers in a precise way. For creators, that means the old rules of creativity no longer apply. What does the creative process look like in this new landscape? As brand guardians, how can we win consumers hearts with these innovations? What are the challenges and pitfalls of operationalizing these initiatives? The discussion will be based on real world successes and challenges.

Bio

Cat and her team combine audience insights, strategy and analytics to
deliver brand strategies and experience that drive new business growth and foster stronger, longer-lasting relationships with customers. More than market research and strategy, the team’s mission is to foster a customer-first approach to strategy development and problem-solving across the enterprise. 

Her team’s efforts have resulted in successful launch of the first
Young Homeowner campaign, “Parentamorphosis”, creation of the STAY creative platform designed to catch the customer brand up with the
consumer brand launch of the Voice of Customer insight platform, maturation of Marketing analytics, and deeper collaboration between
Marketing and Innovation Services to deliver Marketable Innovations.

Kazuhiro Shimbo

Kazuhiro Shimbo

Chief Investment Officer

Mizuho Alternative Investments, LLC

AI and Data Science in Investment

Abstract

The quantitative approach in investment has been the key trend in asset management industries for the last few decades. Asset managers have developed quantitative models for return prediction, risk assessment, and portfolio construction typically with hypothesis-driven approaches. It is worth noting that quantitative investment has not been interacting in a meaningful manner with its non-quant counterpart, i.e., a fundamental approach relying on a qualitative analysis by human beings. Evolution of AI and data science not only reinforce the emphasis on the quantitative approach, it also changes the way the industry sees the world and approaches. For example, a combination of quant and fundamental, namely quantamental becomes a hot topic. There is much more emphasis on data-driven approach, which was typically considered to be a bad practice. However, there are many challenges and naïve application of AI techniques typically fails. We are more successful in some fields than others depending on the nature of problems. In this overview, I will first describe how and where AI and Data Science can be applied to quantitative investment and their future potentials. The emphasis is on the framework rather than a specific investment strategy. We start with the brief description of traditional framework and their limitation. Then we see how AI and Data Science can improve and enhance the capacity. Then we move on to the discussion on difficulties and challenges in applying AI and Data Science to investment and how we should approach. There are technical aspects and social aspects in the difficulties we face. On a technical side, we cover a few stylized facts about market data and theories developed in financial engineering, which is key for the effective implementation of AI to quant investment.

Bio

Kazuhiro Shimbo serves as MAI’s Chief Investment Officer. He is responsible for overseeing all aspects of the firm’s quantitative investment programs including the research process, portfolio management, execution and risk management. Mr. Shimbo manages MAI’s quantitative investment team. Mr. Shimbo joined MAI at its inception as the Head of Risk Management. In that role, he was instrumental in the development and improvement of the firm’s quantitative models and technological infrastructure. Prior to joining MAI, Mr. Shimbo was employed at the Industrial Bank of Japan (IBJ) for over seven years. For the last three years of his tenure at IBJ, Mr. Shimbo served as Quantitative Researcher and then Portfolio Manager at the bank’s derivatives market making desk. Mr. Shimbo earned his Ph.D. in Applied Probability from the School of Operations Research and Information Engineering at Cornell University. He also holds a M.Sc in Financial Economics from the University of London and a B.S. in Physics from Kyoto University in Japan.

Soroush Abbaspour
Soroush Abbaspour

Chief of Staff

IBM Watson Health

Intersection of AI & BlockChain in Healthcare

Abstract

The amount and variety of healthcare data are growing at a very rapid pace. By some estimates, there are 150+ Exabytes of data in healthcare today and it is doubling every 24 months! In addition to the data, the amount of medical knowledge available in the form of publications is doubling every 18 months. The most important challenge organizations are facing is how to cope with these increasing amounts of data and knowledge and how to derive insights that matter in making decisions across the healthcare and life sciences applications.

The era of Big Data in healthcare provides opportunities for applying computational methods for gleaning insights regarding practice patterns variations (practice-based evidence), adherence to suggested care regimens, behavior modification, and personalization of care to the individual needs. BlockChain enables the ecosystem of data in healthcare to have more fluidity, and AI allows us to extract insights from the data.

In this presentation, Dr. Abbaspour will share ideas on applications of AI and BlockChain in healthcare and life sciences.

Bio

Dr. Soroush Abbaspour is the Program Director and Chief of Staff, Innovations in AI & BlockChain for HealthCare and Life Science Organization in IBM Watson Health. He is responsible for Innovations Global Operations, Strategic Initiatives, and Business Development.
 
Prior to joining IBM Watson Health in June 2015, Dr. Abbaspour was the IBM Research Global Business Development Executive, leading go-to-market strategy and execution of Smarter Energy and Cognitive solutions.
 
Dr. Abbaspour received his PhD in Electrical Engineering from University of Southern California (USC) and his MBA from New York University (NYU) Stern Business School. He has served as a member of technical program committees for several IEEE/ACM conferences and as the vice-chair of ACM SIGDA Physical Design Technical Committee. His work on nonlinear simulation of VLSI circuits has received IBM Research Division Award. He has seventeen (17) US patents, and has published more than twenty (20) journal and conference papers.
 
QuHarrison Terry
QuHarrison Terry

Director of Marketing

Redox

What will you do with democratized health data?

Abstract

Connecting to an application programming interface (API) is a relatively trivial task these days. Exchanging healthcare data as part of a business workflow, however, is a completely different story. The issues: security, privacy, interoperability, modernizing infrastructure, scaling and more. Hear case studies on how healthcare and digital health companies are flowing data through a new API platform to accelerate their business growth. Their experience may be the new benchmark, raising your expectations of what democratized health data can do for you.

Bio

QuHarrison Terry is the Marketing Director and in-house “Futurist” at Redox, the platform for healthcare data exchange. Named twice as a “LinkedIn Top Voice” in Technology, Terry focuses on the future of technology and how it will influence human behavior. Before joining the Redox team, Terry founded 23VIVI, the world’s first digital art marketplace powered by the blockchain. He attended the University of Wisconsin-Madison and currently lives in Madison, WI.

Adam Jenkins
Adam Jenkins

Sr. Data Scientist

Biogen

The Eternal Struggle: Rectifying Business Need and Analytical Perfection

Abstract

Throughout the course of many data science projects, there often occurs tension between the technical capabilities of a team and what the immediate inherent business needs are for a company. While the scoping of a project may be based upon analytical need of a business, the technical team often sees the future and what “could be done” as a more important task to tackle. This rift can often cause projects to expand in size, nature, and length of time to complete, ultimately decreasing ROI of the project and subjecting the trust of the team to unnecessary stress. Newly formed data science teams and incorrectly managed teams are often at higher risk for this to scenario. With the ultimate possibility that constant straining of the team can result in cuts and abandonment of the initiative, solutions are paramount. During this talk we will explore the main causes of this rift, from both the technical point of view and the business side, in terms of data science team management and project scoping. Mitigation of these problems can be achieved through a fine lined combination of forward thinking projects and business based projects. The inclusion of this will satiate both the need to adapt/implement emerging technologies of the technical team and allow the business side to have ROI based metrics to value their team and time spent. A second cause that will be discussed revolves around the inherent disconnect that often occurs between what the terms “value” and “ROI” indicate for the various members that are at the table during the initiation of a data science project. While many high level managers think purely in business context, the injection of highly technical, skilled employees into the mix often brings a mix of uncertainty and disconnect among many other team members.

Bio

Adam is the Lead Data Scientist at Biogen, where he works on optimizing commercial outcomes through marketing, patient outreach and field force infrastructure utilizing data science and predictive analytics. Biogen is a leader in the treatment and research of neurological diseases for 40 years.  Prior to being commercial lead, Adam was part of their Digital Health team where he worked on next generation application of wearable and neurological tests.  Holding a PhD in genomics, he also teaches management skills for data science and big data initiatives at Boston College.

Michael Johansson
Michael Johansson

Biologist

Centers for Disease Control and Prevention

The Epidemic Prediction Initiative: Forecasting challenges to support public health

Abstract

Recent epidemics of pathogens such as influenza viruses, MERS coronavirus, chikungunya virus, Ebola virus, and Zika virus, highlight the importance of epidemics on local and global scales. Statistical and mathematical modeling have long been used as a conceptual tools to describe epidemic dynamics and assess possible interventions, yet their use to forecast the trajectory and spread of epidemics is relatively new to public health, and the use of forecasts in decision making remains limited. To help close this gap, the Centers for Disease Control and Prevention Epidemic Prediction Initiative (EPI) is building links between the research and decision-making communities to ensure that forecasts address specific public health needs, facilitate the sharing of data and knowledge about that data, establish standards for assessing and communicating forecast skill, compare different forecasting approaches, and identify effective communication strategies for forecasts. As part of this work, EPI hosted a forecasting challenge for dengue epidemics in 2015 and has hosted an annual prospective seasonal influenza forecasting challenge since 2013. In these open challenges participating groups submit forecasts for short-term activity and for longer-term seasonal targets (e.g. epidemic peak). The challenges have highlighted strengths and weaknesses in current forecasting approaches. For example, the accuracy of forecasts for short-term activity tends to be much higher than for seasonal targets, which often have lead times of several weeks or months. Current forecasts are therefore most useful for situational awareness and less so for key seasonal events more than a few weeks away. Nonetheless, for both kinds of targets simple ensemble forecasts outperformed expectations based on historical data alone, demonstrating that current forecasts add information beyond data alone. Through the forecasting challenges, EPI has opened a path for real-time forecasting of epidemics, allowing both researchers and decision makers to identify and address the challenges of making and disseminating forecasts during epidemics. Forecasting increasingly has an opportunity to contribute to evidence-based public health decision making, and as the science of epidemic forecasting continues to evolve, new opportunities for engagement are emerging.

Bio

Michael Johansson is a Biologist and the Modeling Unit Lead at the Centers for Disease Control and Prevention Dengue Branch in San Juan, Puerto Rico. He uses statistical and mathematical modeling to improve surveillance, prevention, and control of arboviral diseases including chikungunya, dengue, yellow fever, and Zika. He also works to improve the use of quantitative models to support decision making related to infectious disease outbreaks more broadly as co-founder of the CDC Epidemic Prediction initiative and co-chair of the US interagency Pandemic Prediction and Forecasting Science and Technology working group.

Alexander Tolpygo
Alexander Tolpygo

President & COO

SFL Scientific

The Bottom Line: Deep Learning and AI Pipelines for Biotechnology & Healthcare

Abstract

The goal of this session is to demonstrate and learn about building end-to-end pipelines for biotechnology and healthcare applications. Artificial intelligence is poised to make radical changes in healthcare, transforming areas such as diagnosis, medical imaging, genomics, and drug discovery. Pragmatic technical implementation can have the potential to lower costs, identify more effective treatments, develop new tools and products, while driving company growth through automation and competitive advantage. In this session we will describe: The significance of data-strategy and its impact on core business goals; case studies exploring the business value and impact of transforming divisions with AI-driven methods employing deep learning; and how the final AI-framework is integrated into a company’s internal workflow. We highly encourage executives and leaders of all backgrounds to join (no AI/healthcare background necessary).

Bio

Alexander Tolpygo is the President & COO of SFL Scientific, a data science consulting firm that specializes in solving complex data, automation, and R&D problems. His firm develops, integrates, and manages sophisticated Artificial Intelligence systems by leveraging emerging technologies in data engineering, deep learning, and predictive analytics. A biomedical engineer and statistician by training, his experience spans multiple industries including healthcare, biotech, pharmaceuticals, medical devices, insurance, and manufacturing. He helps business leaders narrow the gap in development, identifying where digital transformation and big data can accelerate the accurate decisions that lead to innovation and revenue growth.

Glenn Butcher
Glenn Butcher

Sr. Director, Global Cystic Fibrosis Marketing

Vertex Pharmaceuticals

Improving Patient Adherence with Recommendation Systems

Abstract

Proper use of medications can significantly improve patient outcomes and reduce healthcare costs, yet among patients with chronic illness, approximately 50% do not take medications as prescribed. Recent estimates suggest that a lack of adherence causes nearly 125,000 deaths, 10 percent of hospitalizations and costs the already strained healthcare system between $100–$289 billion a year. The factors that determine whether a patient will adhere to their therapy are complex and involve multiple stakeholders.
Artificial intelligence techniques offer several compelling opportunities to improve medication adherence. The complex interplay of multiple stakeholders coupled with the unstructured nature of conversations and communications provides a rich source of data to analyze for sentiment and intent. When coupled with outcomes and actual adherence data, reinforcement learning can tailor specific recommendations and interventions to the unique challenges facing any patient adherence situation. This talk will examine the current application of AI techniques to improve patient adherence and share one example where natural language processing, predictive analytics and machine learning are being piloted to help case managers support patients living with a chronic rare disease.

Bio

Glenn Butcher is a global thought leader in digital and multi-channel marketing for biotech and
pharmaceutical companies. He has led global, regional, and country digital campaigns across most
therapeutic areas in companies small and large. His diverse experience has helped over 20 brands and 10 companies establish winning digital strategies and multi-channel campaigns over the past 17
years.

Glenn is credited with building out digital and multi-channel centers of excellence in several leading
biotech and pharmaceutical companies. He has also led numerous innovation programs to explore and implement social media, artificial intelligence, and digital health. He is a passionate advocate
for innovating on top of a solid foundation of core capabilities to ensure immediate impact and 
sustainable growth.

As Senior Director, Global Cystic Fibrosis Marketing at Vertex Pharmaceuticals, Glenn is leading the charge to establish and grow digital skills, teams, and platforms throughout the commercial
organization.

Gil Blander
Gil Blander

Chief Scientific Officer

InsideTracker

Longitudinal Analysis Of Clinical Biomarker Data From a Personalized Nutrition Platform in Healthy Subjects

Abstract

To facilitate increasingly personalized approaches to health and medicine, there is increasing enthusiasm for the collection of high-dimensional datasets on individuals to facilitate population stratification and targeted intervention strategies. However, it is not always clear whether insights derived from studies in unhealthy populations or in controlled trial settings are transferable to the general population. An observational analysis was conducted on longitudinal blood biomarker data from 1033 individuals, generated using an automated, web-based personalized nutrition and lifestyle platform.

First, the longitudinal dataset was used to construct a correlation network as a powerful tool for biological hypothesis generation. Both expected and novel biological correlations were uncovered, including a connection between neutrophil and triglyceride levels that has been previously proposed as relevant to cardiovascular risk but insufficiently investigated to date. Further, biomarker changes from baseline to follow-up were assessed relative to platform use. Across many biomarkers measured, there was a notable change toward normalcy in those who had out-of-range values at baseline. In addition, preliminary evidence was found for the “real world” effectiveness of attempts to increase oatmeal and dairy consumption on LDL cholesterol levels.

Bio

Dr. Gil Blander is internationally recognized for his research in the basic biology of aging and translating research discoveries into new ways of detecting and preventing age-related conditions. He leads a team of biology, nutrition & exercise physiology experts, and computer scientists at InsideTracker.

Dr. Gil Blander received a Ph.D. in biology from the Weizmann Institute of Science and completed his Post Doctoral fellowship at MIT, before going on to found InsideTracker. The InsideTracker platform analyzes key biochemical and physiological markers and applies algorithms and large scientific databases to determine optimal zones for each marker. The system then provides nutrition, exercise, supplements and lifestyle interventions that empower people to optimize their markers, increasing vitality, improving overall health, as well as athletic performance and extending life.

Norma A. Padrón
Norma A. Padrón

Assistant Professor Of Health Economics

Thomas Jefferson University

Algorithmic Transparency and Health Care --Where do we go from here?

Abstract

Establishing an active, collaborative community of all those involved in the ecosystem of health and health care (experts, practitioners, patients, entrepreneurs, hospital administrators, caregivers) is crucial in order to realize the potential of newer technologies and availability of data in a way that is transparent, fair and accountable. We put forth here ideas and strategies that we hope are part of the groundwork to engage these stakeholders.

 

Bio

Dr. Padron is a trained health economist with a focus on research design and data analysis. 

Her academic background is in Health Policy and Management (PhD, Yale ’14), Public health (MPH, Pompeu Fabra ’09) and Economics (Duke, ’08). For the last decade, her research agenda has focused on to identifying methodological strategies in which the use of large (open) datasets can be used to improve the design, implementation and evaluation of population health strategies for health systems.

Her goal is to bring together methodologies generally used in economics, data science, public health research and systems design to reduce health disparities and improve the effectiveness of health delivery models.

Dr Anthony Chang
Dr Anthony Chang

Chief Intelligence And Innovation Officer

Children's Hospital of Orange County

A Physician-Data Scientist Grand Vision: A Virtual Medical Oracle

Abstract

The ultimate accomplishment for deep learning and cognitive architecture in medicine will be the creation of a “virtual medical oracle”: a real-time, all knowing medical and healthcare resource based on not only deep learning of existing medical data (such as radiological and pathological images) but also cognitive architecture based on expert opinions and patient experiences. With deep learning as well as cloud computing, a virtual medical imaging center can now be built as a valuable resource for all clinicians and patients. This imaging center requires GPUs and sophisticated deep reinforcement and/or one-shot learning as well as the novel capsule network architecture. While deep learning is currently deployed frequently in artificial intelligence in medicine, a future man-machine cognitive synergy involving more clinicians will be needed for the synthesis of the highest level of artificial/human intelligence in medicine and healthcare. 

The 10 necessary steps for this astounding pinnacle of “medical” intelligence to be realized will be outlined; among these requisite steps are: an AI-inspired acquisition of medical data; an universal common medical data repository; an internet of everything strategy for medical data; a close physician-data scientist collaboration; and a true deep learning/cognitive architecture hybrid structure for medical knowledge. 

Bio

Dr. Chang attended Johns Hopkins University for his B.A. in molecular biology prior to entering Georgetown University School of Medicine for his M.D. He then completed his pediatric residency at Children’s Hospital National Medical Center and his pediatric cardiology fellowship at Children’s Hospital of Philadelphia.

He is currently the Chief Intelligence and Innovation Officer and Medical Director of the Heart Failure Program at Children’s Hospital of Orange County. 

He recently completed his Masters of Science (MS) in Data Science with a sub-specialization in artificial intelligence from Stanford School of Medicine. He is also a computer scientist-in-residence at Chapman University.

He is known for several innovations in pediatric cardiac care, including introducing the cardiac drug milrinone and co-designing (with Dr. Michael DeBakey) an axial-type ventricular assist device in children. He has helped to build a successful cardiology practice as a company and was able to complete a deal on Wall Street. He is the founder and medical director of the nascent Medical Intelligence and Innovation Institute (MI3) that is supported by the Sharon Disney Lund Foundation. He intends to build a clinician-computer scientist interface to enhance all aspects of data science and artificial intelligence in health and medicine. He currently lectures widely on big data and artificial intelligence in medicine (he has been called “Dr. A.I.” and has given a TEDx talk and on the faculty of Singularity University)

Dr Rhoda Au
Dr Rhoda Au

Director of Neuropsychology

Framingham Heart Study

Healthcare Panel: Accelerating Discovery and Innovation for Precision Brain Health?

Abstract

Despite the depth and breadth of Alzheimer’s Disease research, effective methods for treatment remain elusive. Technology is changing what we can do and how we can do it. Digital technologies have translated into a plethora of wearable sensors and smart home devices flooding the market, with more devices in the pipeline. They offer a solution for monitoring and detecting modifiable brain health related risk factors and behavioral symptoms well before changes meet the threshold for clinical diagnosis. The more transformative opportunity, however, lies in a shift from a medical intervention model of “personalized medicine” to a more comprehensive focus on “personalized brain health” that focuses on preventing disease altogether. Current and emerging artificial intelligence/data science approaches are critical to meeting this scientific vision but are highly dependent on access to high quality healthcare data resources. We address the challenge of shifting longstanding clinical research policies and practices to one centered on lowering the barriers to data sharing and embracing as well as incentivizing team science.

Bio

Rhoda Au is Professor of Anatomy & Neurobiology, Neurology and Epidemiology at Boston University Schools of Medicine and Public. Since 1990, she has conducted research related to cognitive aging and dementia at the Framingham Heart Study, and has recently included integrating digital technology into the cognitive assessment process to identify digital biomarkers as surrogate indices to more expensive and invasive fluid and imaging biomarkers and using “big data” analytics to identify novel AD pathways and treatments. She is also building multi-sector ecosystems to generate solutions that move the focus on precision medicine to one centered more broadly on precision health.

Sanji Fernando
Sanji Fernando

Vice President, Optum Labs Center for Applied Data Science

UnitedHealth Group

Healthcare Panel: Accelerating Discovery and Innovation for Precision Brain Health?

Abstract

Despite the depth and breadth of Alzheimer’s Disease research, effective methods for treatment remain elusive. Technology is changing what we can do and how we can do it. Digital technologies have translated into a plethora of wearable sensors and smart home devices flooding the market, with more devices in the pipeline. They offer a solution for monitoring and detecting modifiable brain health related risk factors and behavioral symptoms well before changes meet the threshold for clinical diagnosis. The more transformative opportunity, however, lies in a shift from a medical intervention model of “personalized medicine” to a more comprehensive focus on “personalized brain health” that focuses on preventing disease altogether. Current and emerging artificial intelligence/data science approaches are critical to meeting this scientific vision but are highly dependent on access to high quality healthcare data resources. We address the challenge of shifting longstanding clinical research policies and practices to one centered on lowering the barriers to data sharing and embracing as well as incentivizing team science.

Bio

SANJI FERNANDO is a Vice President and Head of the OptumLabs Center for Applied Data. The Center for Applied Data Science (CADS) is a new kind of resource within Optum,
focused on the application of new data science methods to solve complex health care challenges
faced by UnitedHealthCare and Optum. Sanji joined UnitedHealth Group in 2014 from Nokia, where he created the Data Science team for
Nokia. 
Prior to Nokia, Sanji was a co-founder and VP of Engineering of a venture-backed mobile software
company, Vettro. Sanji began his career in consulting with Viant and Accenture.
Sanji is a graduate of the Trinity College with a bachelor’s degree in Computer Science. He lives in
the Boston area with his wife, Michelle, and their three boys, Ben, Lucas and Axel. In his free time,
Sanji enjoys coaching his sons in basketball and baseball. He also serves on the board of his local
Little League.

 

John Kelley
John Kelley

Chair & CEO

CereScan

Healthcare Panel: Accelerating Discovery and Innovation for Precision Brain Health?

Abstract

Despite the depth and breadth of Alzheimer’s Disease research, effective methods for treatment remain elusive. Technology is changing what we can do and how we can do it. Digital technologies have translated into a plethora of wearable sensors and smart home devices flooding the market, with more devices in the pipeline. They offer a solution for monitoring and detecting modifiable brain health related risk factors and behavioral symptoms well before changes meet the threshold for clinical diagnosis. The more transformative opportunity, however, lies in a shift from a medical intervention model of “personalized medicine” to a more comprehensive focus on “personalized brain health” that focuses on preventing disease altogether. Current and emerging artificial intelligence/data science approaches are critical to meeting this scientific vision but are highly dependent on access to high quality healthcare data resources. We address the challenge of shifting longstanding clinical research policies and practices to one centered on lowering the barriers to data sharing and embracing as well as incentivizing team science.

Bio

John A. Kelley, Jr., has been the Chairman and CEO of CereScan, a functional brain diagnostics company headquartered in Denver, Colorado since mid-2009. Previously, Mr. Kelley served as the Chairman, President, and Chief Executive Officer of McDATA Corporation, a provider of storage networking and data infrastructure solutions until Brocade Communications Systems, Inc. acquired McDATA in early 2007. Prior to joining McDATA, he served as Executive Vice President of Networks at Qwest Communications International from July 2000 to December 2000, after Qwest acquired US West in 2000. He was the President of Wholesale Markets for US West from May 1998 to July 2000. From 1995 to April of 1998, Mr. Kelley served as Senior Vice-President and General Manager of Large Business and Government Accounts as well as President of the Federal Systems Group. Before his employment at US West, he was the Area President for Mead Corporations Southwest Region from 1991 to 1994 and Vice President and General Manager of the Industrial Products Division for Mead in 1995. He has held senior leadership positions at NBI Corp., Alcatel/Friden, and Xerox Corporation. Mr. Kelley has been a member of the board of directors of Polycom, Inc. (NASDAQ) since March of 2000 and currently serves as the Chair of the Nominating and Governance Committee. He is also on the Board of Directors of Emulex Corporation (NYSE), and began that assignment in 2014. His private company board work has included Aztek Networks, Stored IQ, Circadence Corporation, and 3 Leaf Networks. Additionally, he has participated at the board level on several local and national non-profits. Mr. Kelley holds a BS in Business Management from the University of Missouri, St. Louis. He has served in the US Army (1970-1972) and also played varsity baseball at the University of Missouri, Columbia.

Dr Rong Xu
Dr Rong Xu

Associate Professor Of Biomedical Informatics

Case Western Reserve University

Healthcare Panel: Accelerating Discovery and Innovation for Precision Brain Health?

Abstract

Despite the depth and breadth of Alzheimer’s Disease research, effective methods for treatment remain elusive. Technology is changing what we can do and how we can do it. Digital technologies have translated into a plethora of wearable sensors and smart home devices flooding the market, with more devices in the pipeline. They offer a solution for monitoring and detecting modifiable brain health related risk factors and behavioral symptoms well before changes meet the threshold for clinical diagnosis. The more transformative opportunity, however, lies in a shift from a medical intervention model of “personalized medicine” to a more comprehensive focus on “personalized brain health” that focuses on preventing disease altogether. Current and emerging artificial intelligence/data science approaches are critical to meeting this scientific vision but are highly dependent on access to high quality healthcare data resources. We address the challenge of shifting longstanding clinical research policies and practices to one centered on lowering the barriers to data sharing and embracing as well as incentivizing team science.

Bio

Rong Xu is an Associate Professor of Biomedical Informatics in the Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University. Dr.Xu earned her BS degree in Biology from Peking University, M.S degree in Biology from Case Western Reserve University, M.S in Computer Science and Ph.D. in Biomedical Informatics from Stanford University

Dr Xu is a leader and innovator, conducting cutting edge research in the field of Biomedical Informatics. Dr. Xu’s research seeks both to reveal the mechanisms that underlie human diseases and discover new drug treatments to combat them. Dr. Xu develops natural language
processing (NLP), artificial intelligence, machine learning, data mining, statistical learning, systems biology, and other advanced computational techniques that can create, integrate, and analyze large amounts of heterogeneous and complex biological and health data. Her research interests in biomedical sciences include: drug discovery, disease gene discovery, human gut, microbiome, drug toxicity prediction, pharmacogenetics
 and pharmacogenomics, and post-
market drug safety surveillance. As evidence of her creativity and innovation, Dr Xu has recently
received national recognition with three important awards: (1) The NIH Director’s New Innovator
Award (2014), (2) The Landon-AACR Innovator Award for Cancer Prevention Research (2015), and (3) The American Medical Informatics Association (AMIA) New Investigator Award (2016).
Dr.Xu was featured on 2016 Crain’s Cleveland Business “Who to Watch Health Care”, which highlights innovators in Northeast Ohio’s medical fields.

Daniel Shenfeld
Daniel Shenfeld

VP, Data Science

eviCore Healthcare

Product-Data Fit: The Lean Startup Methodology and Healthcare Data Products

Abstract

The lean startup methodology is one of the most popular product development paradigms. It emphasizes the concept of product-market fit, and quick build-measure-learn iterations to reduce the uncertainty about how the market will react to the product.

In most traditional software products, there is no uncertainty around the ability to build the product as specified: software engineering is deterministic. But nowadays, more and more products are heavily dependent on machine learning and AI components. This adds a second source of uncertainty to the product development process: we can’t guarantee model performance in advance.

This added uncertainty raises new product development challenges. For example, what are the acceptance criteria for a machine learning model? What is the Definition of Done? If a modeling effort is unsuccessful, should it lead to a product pivot? The goal of this talk is to provide a unifying framework to tackle these challenges. We will focus specifically on examples from data science applications in healthcare, where poor data quality often amplifies these challenges. 

We introduce the concept of product-data fit and describe how modeling iterations should interact with the product development cycle, emphasizing the flow of validated learning from the product side to the data side and vice versa and how product development drives the determination of prediction value and guides the choice of modeling metrics. We will discuss several use cases from the healthcare industry and highlight guiding questions and principles to help achieve product-data fit and product-market fit, as well as common pitfalls. We also illustrate how this approach can shorten time to market and help achieve financial business goals of AI driven products in the healthcare space.

Bio

Daniel is a data science executive with over 8 years of experience leading product-oriented data science organizations in building products from inception through scaling. Daniel has deep expertise in the healthcare technology and biotech spaces, having led data science and R&D teams in companies from early stage ventures to large organizations. As VP of data science at eviCore healthcare, he currently oversees multiple strategic data initiatives resulting in multimillion-dollar savings. Daniel also consults to companies across various verticals on data and product strategy. He holds a PhD in mathematics from Princeton University, and his computational biology contributions have been published in Cell, Nature Biotechnology, and other top journals.

 

Afik Gal
Afik Gal

VP, Product Innovation

eviCore Healthcare

Co-Presenter- Product-Data Fit: The Lean Startup Methodology and Healthcare Data Products

Abstract

The lean startup methodology is one of the most popular product development paradigms. It emphasizes the concept of product-market fit, and quick build-measure-learn iterations to reduce the uncertainty about how the market will react to the product.

In most traditional software products, there is no uncertainty around the ability to build the product as specified: software engineering is deterministic. But nowadays, more and more products are heavily dependent on machine learning and AI components. This adds a second source of uncertainty to the product development process: we can’t guarantee model performance in advance.

This added uncertainty raises new product development challenges. For example, what are the acceptance criteria for a machine learning model? What is the Definition of Done? If a modeling effort is unsuccessful, should it lead to a product pivot? The goal of this talk is to provide a unifying framework to tackle these challenges. We will focus specifically on examples from data science applications in healthcare, where poor data quality often amplifies these challenges. 

We introduce the concept of product-data fit and describe how modeling iterations should interact with the product development cycle, emphasizing the flow of validated learning from the product side to the data side and vice versa and how product development drives the determination of prediction value and guides the choice of modeling metrics. We will discuss several use cases from the healthcare industry and highlight guiding questions and principles to help achieve product-data fit and product-market fit, as well as common pitfalls. We also illustrate how this approach can shorten time to market and help achieve financial business goals of AI driven products in the healthcare space.

Bio

Afik Gal is a healthcare executive, physician, Duke MBA and an entrepreneur with over 13 years of cross functional experience (technology, clinical and business) in healthcare analytics and digital health. Afik is currently a VP for technology innovation at eviCore healthcare after previously leading product andBizDev at QPIDHealth(acquired by eviCore). Prior to joining QPID, Afik led PWC’s healthcare innovation lab where he focused on business model innovation and matching technologies with payers, providers and pharma needs. As an entrepreneur Afik founded a boutique consulting firm which helped hospitals and tech vendors innovate their products and business models (client included Amdocs, SAP, Orbotech, Wix,NetApp). Afik also founded a clinical analytics startup and consulted multiple other  digital healthcare companies.

Alexander Statnikov
Alexander Statnikov

Vice President, Machine Learning Solutions and Global Line Modeling

American Express

Machine Learning Powers Better Decisioning in Financial Services

Abstract

American Express has always been at the cutting-edge of analytics and decision science. Today, our cutting-edge Machine learning and Artificial Intelligence capabilities are critical to driving decisions that better serve Card Members and grow the business. If selected, Alexander could inform attendees about how the company is leveraging machine learning models across the business to provide accurate credit assessments and industry-leading fraud prevention.

Alexander will describe American Express’ successes /learning, and he will highlight the company’s research, modeling and validation processes that help sustain the high performance of machine learning models.

Bio

Alexander Statnikov is Vice President of Digital Modeling and Machine Learning at American Express. He plays an essential role in leading Machine Learning and Big Data Analytics activities for the company. Alexander is currently managing teams of data scientists responsible for developing a variety of machine learning models and data products for customer acquisition, underwriting, credit and fraud management. Prior to joining American Express, Alexander was an Associate Professor at New York University specializing in causal discovery and various areas of data science and machine learning. Alexander is an author of more than 50 journal articles, 5 books and monographs, and inventor of 13 U.S. patents (issued or pending).

Dr. Marcell Vollmer
Dr. Marcell Vollmer

Chief Digital Officer

SAP Ariba

Cross Industry Panel: The impact of AI across the enterprise

Bio

Marcell Vollmer is Chief Digital Officer for SAP Ariba and is responsible defining and driving Digital Transformation for customers of SAP Ariba globally. A thought leader in procurement, supply chain, finance and shared services, Marcell’s expertise lies in defining digital transformation strategy and to make Run Simple a reality for global customers and consumers by delivering high cost and additional procurement savings.

Previously, he was Chief Operating Officer for SAP Ariba where he successfully developed and led global business development, procurement, go to market, sales operations, and enablement. And prior to that Marcell wasChief Procurement Officer of SAP and was responsible for the reorganization and process optimization for and end-to-end source-to-pay organization. Since joining SAP in 2005 he has held various leadership roles involving restructuring, improving project efficiency and execution of global programs in finance, procurement, sales, human resources and post merger integrations.

Andrew Gilman
Andrew Gilman

Chief Customer Officer

Immuta

Cross Industry Panel: The impact of AI across the enterprise

Bio

Andrew Gilman is the Chief Customer Officer at Immuta, a pioneer in data management for AI, where he’s responsible for global sales and marketing. He brings nearly 15 years of experience leading go-to-market (GTM) strategy and operations at successful venture-backed and publicly-traded enterprise technology companies. Prior to Immuta, he was Actifio’s General Manager and led GTM operations for the SaaS and Software portfolio. Previously at Actifio, Andrew was the founding member of the marketing team, where he built and operated the demand generation, inside sales, corporate and channel marketing teams globally – growing the company from pre-revenue to more than $1B valuation in 2014, while scaling to over 1,000 customers across 30 countries.  Before Actifio, Andrew led GTM strategy and virtualization programs for EqualLogic (acquired by Dell for $1.4B). Before Dell, he held a variety of product marketing leadership roles at EMC including leading the mid-range storage software portfolio, and graduated from the Executive Leadership Development Program (MLDP). Previously Andrew worked in both technical and marketing capacities at several successful startups. He frequently contributes articles to industry publications, can be found speaking at industry events and has served as an advisor/mentor to emerging technology startups. Andrew earned a B.S. in Business Administration from the Boston University School of Management. He lives just outside of Boston with his wife, son and daughter.

Michael Hayes
Michael Hayes

Special Projects

MIT Computer Science and Artificial Intelligence Lab (CSAIL)

Cross Industry Panel: The impact of AI across the enterprise

Bio

Michael is a senior executive with over 20 years of experience building and leading high-growth teams. 

Prior to FeatureX, Michael was founder and CEO of Tripleshot, the first game-proof block trading solution for institutional traders. Michael has a patent on the market construction technique used by Tripleshot (US 8,732,065).

Previously, Michael ran a variety of sales, marketing, and client service organizations in fast-growing enterprise software companies. He also has significant experience creating business alliances between companies of all sizes, including between US and international companies.

Michael started his career as an officer aboard the USS La Jolla (SSN-701), a nuclear-powered fast attack submarine.

Hannah Arnold
Hannah Arnold

Fintech VC

F-Prime Capital

Cross Industry Panel: The impact of AI across the enterprise

Bio

Hannah is an Associate at F-Prime Capital, where she focuses on early-stage investments in FinTech and Enterprise Technology. Prior to joining F-Prime, Hannah worked with startups in Johannesburg as an associate with Secha Capital and as a consultant with Bain & Company in Atlanta, where she served large tech clients and worked extensively with the Private Equity practice.

In her role, Hannah interacts regularly with startups working to bring AI to the enterprise, particularly in the financial sector. As a result, she has a close read on which AI use cases are getting the most traction within financial services today, and will share her perspective on the strategies that will define winners and losers in this emerging field.

Hannah received her BA in Public Policy from Duke University.

Tomasz Adamusiak
Tomasz Adamusiak

Director of Engineering

Thomson Reuters

Knowledge Graphs in Financial Technology - Future or Hype

Abstract

Gartner states that “graph analysis is possibly the single most effective competitive differentiator for organisations pursuing data-driven operations and decisions.” Graphs are becoming an increasingly popular and useful tool in the information world – but they are by no means new.

In the world of financial data, Thomson Reuters considerable data assets are contributing to the formation of a Thomson Reuters Knowledge Graph. This will help our customers to identify both inferred and factual relationships previously unknown. For example, Thomson Reuters has been tracking movements of officers and directors of companies for over 30 years.

Our Deals database spans a similar period. By mapping organisations and people in both data sets to common permanent identifiers (PermIDs), a graph representation is formed exploring which executives are associated with which deals through time. Graphs like this can also be easily connected to other graphs as long as the graph databases share some common standards – typically around how entities (like people or companies) and relationships are represented. Graph use cases range from relationship management and business development to alpha and idea generation. Risk is perhaps the biggest category as graph databases can help identify hidden or complex relationships, which go to the core of fraud detection, supply chain risk analysis and exposure to sanctioned entities. The documents leaked in the Panama Papers case is one such example that helped identify hidden connections and the importance of connecting the dots as part of the research process to expose political corruption and offshore financial secrecy.

Bio

Tomasz A Adamusiak MD PhD has a unique blend of business acumen and technology expertise spanning multiple industries from finance to
healthcare. He leads an organization developing Thomson Reuters Big
Data solutions for top financial, pharma, and research institutions –
Data Fusion and Graph Feed. Dr. Adamusiak has a wealth of experience in the linked data and data science space backed up by the leadership and advisory roles within the American Medical Informatics Association (AMIA), and the SNOMED International.

Delin Shen, PhD
Delin Shen, PhD

Head of Predictive Modeling

AIG

Mission Analytics: Common pitfalls and how to avoid them (A journey in an insurance company)

Abstract

Data is in fashion, and rightly so. However, many organizations struggle to “carry” it properly. The promise of data and data analytics is immense, but its actual implementation needs more than just data science PhDs and Hadoop clusters. It requires a mindset shift. What is the right mix of talent to make that happen? What kind of projects need to be undertaken and how to phase them? How to separate the hype of advanced techniques like machine learning from what will work for business in the now and here? Why is scaling important and how does it usually get undermined? As you already have realized while solving this for your organizations, the approach requires a mix of EQ and IQ. While there is no silver bullet, in this session we will discuss how we can be proactively aware of the common pitfalls, and avoid being blindsided by them on our journey.

Bio

Delin Shen joined AIG in September 2016 to build a predictive modeling team and drive personal insurance growth and profitability with modern machine learning and analytics. His responsibilities include building and training a predictive modeling team and promoting best practice modeling guidelines, partnering with business partners to identify potential high impact analytics problems and design solutions, and exploring new data sources and tools to improve modeling effectiveness.

 

Prior to AIG, Delin served as a Senior Director of Statistical Modeling at LexisNexis Risk Solutions, where he was responsible for designing and developing innovative products and analytical solutions for Insurance Data Services, focusing on marketing analytics and telematics.

Delin holds a Ph.D. from the Harvard-MIT Division of Health Sciences and Technology on exploratory data analysis and a B.A. from Tsinghua University with a double major of Biomedical Engineering and Mechanical Engineering.

Sarah Biller
Sarah Biller

Angel Investor / Co-Founder & Board Member

FinTech Sandbox

Finance Panel: Demystifying AI for the Enterprise

Abstract

The panel will focus on the legitimacy of solutions claiming they are using artificial intelligence and how enterprises integrate these solutions. 

Bio

Sarah Biller is an Educator, Investor, and Entrepreneur in the FinTech sector. She teaches a graduate level course on the evolution of Financial Technology at Brandeis University and is a frequent guest lecturer at MIT.  She is an LP in Mountain State Capital, a venture capital fund investing in the promising start-ups from across sectors in the West Virginia, Ohio and Pennsylvania, and sits on the firm’s Board.  Ms. Biller is the co-Founder of the FinTech Sandbox, a Boston-based not for profit that accelerates the product development cycle of FinTech start-ups, and currently sits on its Board of Directors.   

Ms. Biller was also most recently the Head of Innovation Ventures at State Street Bank’s Global Exchange division and its Chief Operating Officer for Innovation. Prior to joining State Street, Ms. Biller has been a Principal in the launch of several successful venture-backed start-ups in the FinTech and Life Sciences sectors.  Most recently she was the Co-Founder and President of Capital Market Exchange (CMX), a venture-backed predictive analytics platform utilizing investor sentiment to help bond investors anticipate near-term changes in spreads.  CMX’s clients were large institutional firms managing in excess of $6 trillion of Fixed Income assets.

She is the Independent Director for Finomial, a Boston-based technology firm automating the compliance and regulatory requirements for a global set of Hedge Funds.  Ms. Biller also is on the Board of the Nashville-based FinTech back office technology providerCore10.  She also sat on the board of a trading platform for Bitcoin Options, Alt-Options, LLC until its acquisition in 2016.

Ms. Biller chairs Ernst & Young’s National selection committee for its Financial Services Entrepreneur of the Year award. She is an active mentor in the Barclays Techstars and StartupBootcamp FinTech accelerators as well as the Founding Advisor for MassChallenge’s new FinTech Lab initiative.  Ms. Biller has also held roles at Fidelity Investments; worked on MCI’s corporate venture team; and launched and led research divisions for Fortune 500 CFO’s and treasurers at the Corporate Executive Board.  She is also supportive of the arts and plays tennis competitively. 

Ms. Biller studied Finance at West Virginia University and George Washington University.

Meredith Moss
Meredith Moss

CEO

Finomial

Finance Panel: Demystifying AI for the Enterprise

Abstract

The panel will focus on the legitimacy of solutions claiming they are using artificial intelligence and how enterprises integrate these solutions. 

Bio

Meredith Moss founded Finomial with a vision to automate compliance practices that impede revenue growth, add spiraling operational costs, and expose financial institutions to regulatory risk. Under her leadership, global banks adopted Finomial’s innovative cloud platform for Compliant Client Lifecycle Management. Meredith’s perspective on regulatory compliance is sought after by industry practitioners, as she synthesizes regulatory requirements into technology-driven operational practice. In founding Finomial, Meredith brings 20 years of fintech experience from Credit Suisse, Lehman Brothers and Reuters, as well as strategy expertise from Monitor Company. She earned her MBA at Harvard Business School and her BA at Brown University.

Ram Ravichandran
Ram Ravichandran

Director, Analytics & A/B Testing

Visa Inc

Building An Effective AI Practice

Abstract

Artificial Intelligence is the next stage in progression of an organization Analytics Maturity Curve. It bridges the gap between knowing and acting in speed, scale, complexity and variety with a significant bottom line impact. There are no cookie cutter approaches for building a successful practice today – it is an iterative exercise for every domain, organization, function and team. It depends on the specific needs, the stage of analytical maturity, team & organization, infrastructure, funding & leadership support. We will touch upon our journey -our approach to building it, mistakes we made/lessons we learnt, where we are and want to be. We want to hear back from the audience their feedback, inputs and ideas to optimize our progression *This talk will not use any business confidential data, policies and is not official representation of Visa’s policies or roadmap. It is a personal participation and generic domain knowledge. It does not represent VISA,Inc. in any form or matter.

Bio

As the Director of Analytics & A/B Testing, I am responsible for enabling a Data Driven Decision Making culture via quantifiable impact and actionable insights. The team’s responsibilities span facilitating Strategy, optimizing execution and driving value with the Analytics Value Chain for stakeholders across Product, Marketing and Relationship Management. Critical focus areas are Conversion Rate Optimization, Customer Lifecycle Management and Voice of Customer. A fervent believer in power of data, importance of iterative learning, valuing customer feedback and delivering easy to adopt solutions. Hit me up on value of Big Data, Responsible leadership with Data and Artificial Intelligence applications.  

Antonio Alvarez
Antonio Alvarez

Director of Data innovation

Santander UK Technology

A viral model for scalable adoption of data science in a large financial organization

Abstract

The cultural change to adopt Machine learning and data science as a mainstream approach, particularly in a 150 years old organisation with thousands of employees, is massive.

In Santander we have successfully used a biological approach that has allowed us to create critical mass in a natural and effective way. By following an iterative, value driven approach focused on building capabilities as use case are delivered, on creating a platform as patterns are detected and gathering a community as people are involved we are spreading the data science virus throughout the company. We will discuss our model, its advantages and challenges and lessons learned.

Bio

Antonio Alvarez is the director of Data innovation at Santander UK. Antonio has 18 years of experience in financial services across 4 countries focused in technology, change management and data. After years in leadership roles in the integration of the banks acquired by Santander, Antonio now drives the transformation of Santander UK beyond a digital organization and into a data-driven organization through the creation of a collaborative self service environment for analytics and data science that empowers teams in all areas and at all levels to get close to customers and be innovative.

 

Rohit Arora
Rohit Arora

CEO & CoFounder

Biz2Credit

AI and Convergence in Banking and FinTech

Abstract

Biz2Credit is a leading small business credit marketplace that works with banks and non-banks; over the last year and a half the company has delved deeper into how artificial intelligence and data can help in small business lending and support the 5,000 loan applications Biz2Credit sees a month. The company has found that when businesses provide Biz2Credit access to their data, that giving them something in return such as benchmarking or a cash flow analysis can be very beneficial when it comes to the stickiness of the customer. Increasingly, machine learning can be predictive of repayment behavior including voice analytics, application information and Biz2Credit’s digital site. Machine learning has many powerful applications for enhancing quality of credit decisions. Digital Site visits and other data including voice analytics based on CSR interactions, application data (such as intended use of funds), and behavioral data (time to complete application, amount requested, etc.) can be incorporated into a scorecard that results in improved assessments and risk-based pricing. visits.

Bio

Rohit Arora, CEO of Biz2Credit, is one of America’s top experts in small business lending and the use of FinTech to streamline the funding process. In 2011, he was named New York City’s “Top Entrepreneur” by Crain’s New York Business, which named Biz2Credit among NYC’s “Fast 50” of 2014 and 2016. He meets regularly with top executives from the Federal Reserve and the Small Business Administration (SBA) and has updated the President’s Council of Economic Advisors at The White House on matters related to small business lending.  

Since its inception in 2007, Biz2Credit has arranged more than $1.7 billion in funding and has over 200,000 registered small and mid-sized company clients. The platform handles more than 5,000 new loan applications each month, on average.

Rohit is a frequently quoted source on banking and technology trends, the emergence of institutional investors in small business finance, convergence in payments and funding companies, and other topics. He is often quoted by The Wall St. Journal, CNBC, Fox Business, Washington Post, NY Times, Bloomberg, Fortune, American Banker, and others. He is also a columnist writing about small business matters for Forbes, Inc., and the New York Daily News.

 

Rohit oversees the widely reported Biz2Credit Small Business Lending Index, a monthly report that analyzes loan approval rates, as well as the Top 25 Small Business Cities in America, the Biz2Credit Latino Lending Report, and an annual Women in Small Business Study.

Previously, Rohit worked for Deloitte Consulting, Goldman Sachs, and Silkroute, a Singapore-based private equity fund. He holds a Master’s Degree in International Finance from Columbia University and co-authored Beyond Cost Reduction: The Risks and Rewards of Global Service Sourcing, a report funded by the Alfred Sloan Foundation and the Chazen Institute of International Business.

Jack Klinck
Jack Klinck

Managing Partner

Hyperlane

Finance Panel- Demystifying AI for the Enterprise

Abstract

The panel will focus on the legitimacy of solutions claiming they are using artificial intelligence and how enterprises integrate these solutions. 

Bio

Jack Klinck is currently an active venture capitalist at Boston based Hyperplane VC focused on seed investments in AI/ML/IOT firms.  From 2006 to 2015, Mr. Klinck was Executive Vice President and Head of Global Strategy and New Ventures at State Street Corporation, where he served on that firm’s management committee and ran several business lines including Alternative Investment Solutions, Credit Services, Global Exchange and Corporate Strategy.  Before joining State Street, Mr. Klinck was Vice Chairman and President of the Investment Manager Solutions Group at Mellon Financial Corporation.  Before joining Mellon in 1997, Mr. Klinck held various management positions at American Express.  Mr. Klinck holds a B.A. from Middlebury College and an M.B.A from the Fuqua School of Business at Duke University. Mr Klinck chairs the Board of the local Boy Scouts of America – The Spirit of Adventure Council and also sits on the Boards of the Boston Symphony Orchestra and the National Association of Corporate Directors – New England Chapter.

Stephen Lawrence
Stephen Lawrence

Head of Quantextual Research

State Street

Applied Finance - The Third Culture

Abstract

What can financial analysts learn from computer scientists about data science? And can computer scientists change their approach to appeal to finance professionals? Data scientists typically argue about the relative merits of the statistical and algorithmic approaches to data mining (Leo Breiman – The Two Cultures). Anyone who has done data science in applied finance (trading, sell side research, portfolio management) recognizes that there is a third culture that is radically different from the other two. In this session we explore these cultures and why it is important that we bridge the gap between the traditional data science cultures and applied finance – the third culture.

Bio

Stephen Lawrence is the Head of QuantextualSM Research at State Street Global ExchangeSM. He oversees research aggregation services for clients by innovatively blending machine learning and human knowledge. His team helps accelerate the implementation of investment ideas based on academic research and provides an “Idea Lab” platform to help summarize a wide range of research including academic and sell-side research. Since joining State Street in 2003, he has been involved in the development of FX investor behavior indicators and quantitative investment strategies based on those measures.

Stephen is also a TED speaker with a 2015 talk titled “The future of reading: it’s fast.”

Bobby Brennan
Bobby Brennan

Co-Founder

DataFire.io

Finance Panel- How Data Science is Opening New Frontiers for the Insurance Industry.

Abstract

Insurance is one of the modern world’s oldest industries. Since the beginning, an insurers success has hinged on its ability to leverage statistics and actuarial science to analyze risk. But the last decade has created a perfect storm of technological evolution causing the industry to reinvent its core disciplines. On our panel, we’ll speak with some of the insurance industry’s leading data scientists to learn more about many topics including new lines of insurance driven by our tech-centric society (e.g. the rise of cyber insurance) and the impact vast amounts of available data sources has on modeling risk.

Bio

Robert Brennan is a software engineer working in Boston Massachusetts. He holds a B.S. in Machine Leaning, as well as a B.A. in Mathematics and Linguistics. He spent three years at Google working on Natural Language Processing as a Senior Engineer, and is now running a team of Data Science consultants in Boston. 

Bobby Brennan
Bobby Brennan

Director of Data Science

BitSight Technologies

Finance Panel- How Data Science is Opening New Frontiers for the Insurance Industry.

Abstract

Insurance is one of the modern world’s oldest industries. Since the beginning, an insurers success has hinged on its ability to leverage statistics and actuarial science to analyze risk. But the last decade has created a perfect storm of technological evolution causing the industry to reinvent its core disciplines. On our panel, we’ll speak with some of the insurance industry’s leading data scientists to learn more about many topics including new lines of insurance driven by our tech-centric society (e.g. the rise of cyber insurance) and the impact vast amounts of available data sources has on modeling risk.

Bio

Marc heads the data science group at BitSight which strives to keep BitSight Ratings the best in the business, enable new features and products, provide thought leadership in the cyber security industry, and improve other BitSight business functions where possible.  Marc started his career as a data scientist at BBN Technologies in the 80s while studying Cognitive Science at MIT. Venturing out of the Hub, Marc’s path included earning a CS PhD in upstate NY, working on machine translation in Germany, search at the MITRE Corporation in Bedford, bioinformatics at the University of Iowa, natural language processing at Thomson Reuters in Minnesota, electricity market forecasting at WindLogics, electricity theft detection for Florida Power & Light, and predictive analytics for Industrial IoT at Honeywell. He is very happy to be working on risk at BitSight and to be back in the Boston area.

Andrew Campbell
Andrew Campbell

Director, Analytics and Insights

Sun Life Financial

Finance Panel- How Data Science is Opening New Frontiers for the Insurance Industry.

Abstract

Insurance is one of the modern world’s oldest industries. Since the beginning, an insurers success has hinged on its ability to leverage statistics and actuarial science to analyze risk. But the last decade has created a perfect storm of technological evolution causing the industry to reinvent its core disciplines. On our panel, we’ll speak with some of the insurance industry’s leading data scientists to learn more about many topics including new lines of insurance driven by our tech-centric society (e.g. the rise of cyber insurance) and the impact vast amounts of available data sources has on modeling risk.

Bio

Andrew Campbell joined Sun Life Financial in 2013, to help build out the predictive modeling function for the US-based group benefits business. He leads a team of data scientists who build algorithms and decision support tools that help claim examiners, underwriters, and customer support personnel more effectively do their job.

Prior to Sun Life, Andrew worked as a consultant at LexisNexis on a team that helped auto and home insurers set prices based on demand elasticity.

John Langton
John Langton

Co-Founder

Threat Effect

Finance Panel- How Data Science is Opening New Frontiers for the Insurance Industry.

Abstract

Insurance is one of the modern world’s oldest industries. Since the beginning, an insurers success has hinged on its ability to leverage statistics and actuarial science to analyze risk. But the last decade has created a perfect storm of technological evolution causing the industry to reinvent its core disciplines. On our panel, we’ll speak with some of the insurance industry’s leading data scientists to learn more about many topics including new lines of insurance driven by our tech-centric society (e.g. the rise of cyber insurance) and the impact vast amounts of available data sources has on modeling risk.

Bio

John Langton is co-founder of Threat Effect where he applies machine learning methods to cyber security data for improved risk analytics. He has a Ph.D. in computer science and a background in both AI and cyber security. John began his career researching advanced analytics for cyber security at the Department of Defense. He then applied that experience in the private sector by founding a company called VisiTrend. VisiTrend was acquired by Carbon Black in 2015 where John served as Director of Data Science.  John has several peer-reviewed publications and regularly participates in the local, Boston data science community.

Satadru Sengupta
Satadru Sengupta

General Manager, Insurance

DataRobot

Finance Panel- How Data Science is Opening New Frontiers for the Insurance Industry.

Abstract

Insurance is one of the modern world’s oldest industries. Since the beginning, an insurers success has hinged on its ability to leverage statistics and actuarial science to analyze risk. But the last decade has created a perfect storm of technological evolution causing the industry to reinvent its core disciplines. On our panel, we’ll speak with some of the insurance industry’s leading data scientists to learn more about many topics including new lines of insurance driven by our tech-centric society (e.g. the rise of cyber insurance) and the impact vast amounts of available data sources has on modeling risk.

Bio

Satadru Sengupta, General Manager, Insurance at DataRobot, is a practicing data scientist for more than ten years. Before DataRobot, Satadru led the Global Distribution Analytics at AIG Science Team and previously worked with Liberty Mutual Insurance and Deloitte Consulting. In his current role as the GM Insurance, Satadru runs the Insurance Practice at DataRobot where he spends 50% time on strategy, thought leadership, and client advisory and remaining time on hands-on work in the front-line.

Andy Palmer
Andy Palmer

Co-Founder & CEO

Tamr

DataOps: Enterprise Data that Doesn't Suck

Abstract

We’re at a unique point in time when fundamental changes in data management in the enterprise, the low barrier to cloud migration and the volume and value of enterprise data combine into a massive opportunity to create next generation data engineering pathways. During his talk, Andy will highlights the converging factors that allow non-data native companies transform their data engineering organizations to catch up with data-native companies like Facebook, Google and Amazon. 

Bio

Andy Palmer is co-founder and CEO of data analytics start-up Tamr, Inc. , a company that he founded with fellow serial entrepreneur and 2014 Turing Award winner Michael Stonebraker, PhD, adjunct professor at MIT CSAIL; Ihab Ilyas, University of Waterloo;and others. Previously, Palmer was co-founder and founding CEO of Vertica Systems, a pioneering big data analytics company (acquired by HP). He also founded Koa Labs, a shared start-up space for entrepreneurs in Cambridge’s Harvard Square, and holds a Research Affiliate position at MIT CSAIL. In February 2016, he and Michael Stonebraker will present their online course, “Startup Success: How to Launch a Technology Company in 6 Steps,” for MIT on edX. During his career as an entrepreneur, Palmer has served as founding investor, BOD member or advisor to more than 50 start-up companies in technology, healthcare and the life sciences. He also served as Global Head of Software and Data Engineering at Novartis Institutes for BioMedical Research (NIBR) and as a member of the start-up team and Chief Information and Administrative Officer at Infinity Pharmaceuticals (NASDAQ: INFI). Previously, he held positions at innovative technology companies Bowstreet, pcOrder.com and Trilogy.

Li Haifeng
Li Haifeng

SVP Risk and Finance Technology

Bank of America

Finance Panel- Demystifying AI for the Enterprise

Abstract

The panel will focus on the legitimacy of solutions claiming they are using artificial intelligence and how enterprises integrate these solutions. 

Bio

Haifeng Li, SVP, Risk and Finance Technology at Bank of America, is an innovative leader in AI and business transformation. He is the author of the widely popular open source machine learning engine SMILE.