NYC 2019 Speakers

Hear from though leaders, AI experts, and executive speakers how AI is transforming industry
Babak Hodjat, PhD
Babak Hodjat, PhD

Siri Co-Inventor, VP Evolutionary AI, Founder

Cognizant | Sentient Technologies

Accelerate AI Keynote; Creating an AI Powered Organization

Abstract

Enterprise leaders across every industry are seeking better ways to understand their business. They need new ways to model the data that flows through their systems – the life blood of their information architecture. Currently, the models are taken and used by human decision makers, or rigid algorithms are developed to make use of the models in an attempt to optimize one or more outcomes. But, what how can we get AI to drive the decision making? How do we AI-enable a fast paced business operating in an ever changing environment? How do we create smart, responsive businesses able to predict the organization’s need in real-time? We try to answer these questions, and provide real world examples of how to apply AI to businesses to create end-to-end intelligence across a variety of industries.

Bio

Babak Hodjat (https://en.wikipedia.org/wiki/Babak_Hodjat) is VP of Evolutionary AI at Cognizant, and former co-founder and CEO of Sentient, responsible for the core technology behind the world’s largest distributed artificial intelligence system. Babak was also the founder of the world’s first AI-driven hedge-fund, Sentient Investment Management. Babak is a serial entrepreneur, having started a number of Silicon Valley companies as main inventor and technologist. Prior to co-founding Sentient, Babak was senior director of engineering at Sybase iAnywhere, where he led mobile solutions engineering. Prior to Sybase, Babak was co-founder, CTO and board member of Dejima Inc. Babak is the primary inventor of Dejima’s patented, agent-oriented technology applied to intelligent interfaces for mobile and enterprise computing – the technology behind Apple’s Siri. Babak is a published scholar in the fields of Artificial Life, Agent-Oriented Software Engineering, and Distributed Artificial Intelligence, and has 31 granted or pending patents to his name. He is an expert in numerous fields of AI, including natural language processing, machine learning, genetic algorithms, distributed AI, and has founded multiple companies in these areas. Babak holds a PhD in Machine Intelligence from Kyushu University, in Fukuoka, Japan.

John C. Havens
John C. Havens

Executive Director, Member, Contributing Writer

The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems | WEF, Global Future Council on Human Rights and Technology | Mashable, The Guardian, and Slate

Accelerate AI Keynote

Abstract

Coming soon!

Bio

Coming soon!

Usama Fayyad, PhD
Usama Fayyad, PhD

Co-Founder & CTO, Co-Founder, Former Chief Data Officer

OODA Health | KDD Conference | Barclays Bank

Accelerate AI Keynote

Abstract

Coming soon!

Bio

Coming soon!

Marsal Gavalda, PhD
Marsal Gavalda, PhD

Head of Machine Learning

Square

Adopting a machine learning mindset: How to discover, develop, and deliver automation solutions company-wide

Abstract

As Machine Learning becomes a core component of any forward-looking company, how can we engage the entire workforce to help with ML and automation initiatives? This talk will cover how at Square we have adopted a “machine learning mindset” by 1. providing training to all employees (both technical and non-technical folks) on what ML is and how it works, including current applications and ethical considerations, 2. conducting structured brainstorming sessions to elicit automation opportunities, where everyone can contribute what ML could mean for their team or their customers, and 3. implementing a subset of those ideas by partnering with infrastructure, operations, and product teams, resulting in improved risk management, more efficient internal operations, and novel customer-facing product features.

Bio

Marsal Gavalda is a senior R&D executive with deep expertise in speech, language, and machine learning technologies. Marsal currently leads the Commerce Platform Machine Learning team at Square, where he applies machine learning for economic empowerment and financial inclusion. Previously, Marsal headed the Machine Intelligence team at Yik Yak, where he developed natural language processing and machine learning services to analyze the content of messages, discover trends, and make recommendations at scale and across languages. Prior to that, Marsal served as the Director of Research at MindMeld (acquired by Cisco), where he applied the latest advances in speech recognition, language understanding, information retrieval, and machine learning to the MindMeld conversational and anticipatory computing platform. Marsal has also extensive experience in the customer interaction and speech analytics space, as he has served as VP and Chief of Research at Verint Systems and as VP of Research and Incubation at Nexidia (acquired by NICE), where he developed disruptive speech analytics solutions for the call center, intelligence, and media markets. Marsal holds a PhD in Language Technologies and a MS in Computational Linguistics, both from Carnegie Mellon University, and a BS in Computer Science from BarcelonaTech. Marsal is the author of over thirty technical and literary publications, thirteen issued patents, and is fluent in six languages. He is also a frequent speaker at academic and industry conferences and organizes, every summer, a science and humanities summit in Barcelona on topics as diverse as machine translation, music, or the neuroscience of free will.

Conor Jensen
Conor Jensen

Customer Success Team Lead

Dataiku

Building and Managing World Class Data Science Teams (Easier said than done)

Abstract

Despite the promise and opportunities of data science, many organizations are failing to see a return on their investment. The key issue holding organizations back is a lack of good data science management. This manifests in failure to effectively build and manage teams. In this workshop, we will go through a methodological approach for helping managers identify the needs of their organization and build the appropriate team. We will learn how to:
1- put in place the appropriate foundational elements
2- select and recruit the right team
3- develop and manage that team to success
4- create pipelines of good data science managers and technical rock stars

Bio

Conor Jensen is an experienced Data Science executive with over 15 years working in the analytics space across multiple industries as both a consumer and developer of analytics solutions. He is the founder of Renegade Science, a Data Science strategy and coaching consultancy and works as a Customer Success Team Lead at Dataiku, helping customers make the most of their Data Science platform and guiding them through building teams and processes to be successful. He has worked at multiple Data Science platform startups and has successfully built out analytics functions at two multinational insurance companies. This includes building out data and analytics platforms, Business Intelligence capabilities, and Data Science teams serving both internal and external customers.
Before moving to insurance, Conor was a Weather Forecaster in the US Air Force supporting operations in Southwest Asia. After leaving the military, Conor spent a number of years in store management at Starbucks Coffee while serving as an Emergency Management Technician in the Illinois Air National Guard.
Conor earned his Bachelor of Science degree in Mathematics from the University of Illinois at Chicago.

Ben Vigoda, PhD
Ben Vigoda, PhD

CEO

Gamalon

Continuous Improvement of Chat, Social, and Survey Interactions Using AI “Idea analysis”

Abstract

How does customer experience/digital marketing know what customers are saying to our human chat, bot chat, survey, or social? Why are they not satisfied or not moving to the next action? The first step is to deeply analyze customer conversations. A new generation of AI technology makes this possible, extracting the ideas contained in text to summarize, organize, and display for analysis.

Bio

Ben is the CEO and Founder of Gamalon. He was previously the co-founder and CEO of Lyric Semiconductor, the first microprocessor architectures for statistical machine learning, growing out of Ben’s PhD at MIT. Lyric was acquired by Analog Devices, and Lyric’s technology is deployed in leading smartphones and consumer electronics, medical devices, wireless base stations, and automobiles. He has authored over 120 patents and academic publications, and his work has been featured in the Wall Street Journal, New York Times, EE Times, Scientific American, Wired, TechCrunch, and other media.

Ben has been an Intel Student Fellow, Kavli Foundation/National Academy of Sciences Fellow, served on the DARPA Information Science and Technology (ISAT) steering committee, and has held research appointments at MIT, Hewlett Packard, Mitsubishi, and the Santa Fe Institute. He also co-founded Design That Matters, a not-for-profit that for the past decade has helped solve engineering and design problems in underserved communities and has saved thousands of infant lives by developing low-cost, easy-to-use medical technology such as infant incubators, UV therapy, pulse oximeters, and IV drip systems that have been fielded in 20 countries.

Arun Verma, PhD
Arun Verma, PhD

Head-Quant Research Solutions

Bloomberg

Quantamental Factor Investing Using Alternative Data and Machine Learning

Abstract

To gain an edge in the markets quantitative hedge fund managers require automated processing to quickly extract actionable information from unstructured and increasingly non-traditional sources of data. The nature of these “alternative data” sources presents challenges that are comfortably addressed through machine learning techniques. We illustrate use of AI and ML techniques that help extract derived signals that have significant alpha or risk premium and lead to profitable trading strategies.

This session will cover the following topics:
• The broad application of machine learning in finance
• Extracting sentiment from textual data such as news stories and social media content using machine learning algorithms
• Construction of scoring models and factors from complex data sets such as supply chain graph, options (implied volatility skew, term structure), Geolocational datasets and ESG (Environmental, Social and Governance)
• Robust portfolio construction using multi-factor models by blending in factors derived from alternative data with the traditional factors such as fama-french five-factor model.

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.

Olivier Blais
Olivier Blais

Head of Data Science

Moov AI

Democratizing Artificial Intelligence in a business context

Abstract

Artificial intelligence is the buzzword that is everywhere in the media in 2018. Although this subject is the focus of attention, it still raises many questions about how this impacts businesses and how can they better leverage AI. More specifically, Olivier Blais, experienced data scientist will demonstrate how to develop an agile data science community as he has developed practice communities in various companies from different industries. He will also prove why Artificial Intelligence is not as complex as everybody thinks and why you should start deploying artificial intelligence capabilities now.

During this presentation, Olivier will:
* Introduce key concepts such as artificial intelligence, machine learning and deep learning
* Present great use cases, in which machine learning can give companies an edge in enhanced productivity, cost reduction or revenue generation
* Demonstrate strategies to enable your company to become more data driven
* Detail useful techniques on how you can make it easy to deploy artificial intelligence in your company.

Bio

Olivier Blais is the Head of Data Science for Moov AI and an experienced Data Scientist with many years of business transformation experience using innovative approaches in different industries, such as financial services, technologies, aerospace, and consumer products. He is a strong believer that a good dose of data, mixed with a pinch of human instinct, can shed some light on what makes a business successful.

Hillary Green-Lerman
Hillary Green-Lerman

Senior Curriculum Lead

DataCamp

Building an Analytics Team

Abstract

Based on her experience of building analytics teams from the ground up, Hillary will walk through the process of creating an analytics team.
We’ll begin by examining why analytics teams exist and how they are different from Data Science teams. Next, we’ll discuss possible structures for analytics team, including embedded, independent, and hybrid structures.
We’ll talk about best practices in hiring a diverse and talented analytics team, including good interview questions, and interview tools, such as CoderPad to ensure that applicants have the necessary skill set.
Once the team is up and running, it needs to integrate with Product teams. Creating best practices around data creation and experimental design, can make sure that your team is involved early, before problems can surface.
Success can bring challenges, such as too many under-defined requests. Creating a ticketing system unique to your team can ensure that ad hoc requests can be handled in a systematic and efficient manner. This is key to scaling an analytics team.
There are many approaches to becoming the voice of data at a company. Building a data reporting ecosystem ensure that all internal clients have access to what they need when they need it. The talk will cover dashboarding, alert systems, and data newsletters. Finally, we’ll discuss promoting responsible data conception through continuous training in statistics and tooling for all members of an organization.

Bio

Hillary is a Senior Curriculum Lead at DataCamp. She is an expert in creating a data-driven product and curriculum development culture, having built the Product Intelligence team at Knewton and the Data Science team at Codecademy. She enjoys explaining data science in a way that is understandable to people with both PhDs in Math and BAs in English.

Adam Jenkins, PhD
Adam Jenkins, PhD

Data Science Lead

Biogen

Integrating Data Science Into Commercial Pharma: The good, The Bad, and The Validated

Abstract

One of the most difficult industries for data science to take hold and gain effectiveness is the world of commercial pharma/biotech. Due to regulation of FDA, lack of identifiable patient data, and one of the last industries that uses a “traveling salesperson” approach, data science is still taking hold in this industry. This talk will talk in depth about steps that companies in this space can take to make the most out of their data science teams and out of their data in general. These steps will include standardizing internal data, utilizing 3rd party data in unique methodologies, bearing the course during marketing and sales initiatives, and creating validation methods.
We will dive into these issues through the context of how to bring the industry from one of “old school” sales and marketing techniques into one where machine learning can make tangible top and bottom line impacts. Through this lens we will identify areas of opportunity that should first be tackled by any organization and those areas which are often pitfalls (even though they may seem lucrative). Additionally, an ideal team make-up and time line will be outlined so that these companies can level-set where they are and where they can improve their data science processes.

Bio

Adam Jenkins is a Data Science Lead 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.

Sam Ransbotham, PhD
Sam Ransbotham, PhD

Guest Editor

MIT Sloan Management Review

Artificial Intelligence in Business Gets Real

Abstract

While humanoids and computer wizardry attract attention, how are normal businesses currently using artificial intelligence? Leading organizations are deepening their commitments to AI and are eager to scale AI. But many companies have discovered, often to their surprise, that it is easy to apply AI and get quick results. What is not so easy is building a system of AI applications along with associated data pipelines that interact and are reliable. I will share the results of research that combines a global survey with 3,076 respondents and in-depth interviews with 36 business executives. The research tells a story of measurable benefits from current AI initiatives, increased investments, and determined efforts to expand AI across the enterprise.

Bio

Sam Ransbotham is an associate professor of information systems and McKiernan Distinguished Fellow at Boston College. While everyone seems to be talking about artificial intelligence, Sam is curious about what businesses are really doing—How are normal businesses using AI now and how will they use AI later? To learn more, he serves as editor for the MIT Sloan Management Review initiative on Artificial Intelligence in Business. Recently, he co-authored a research report “Artificial Intelligence in Business Gets Real” based on a global survey of 3,076 executives and dozens of interviews. Sam earned a Bachelor’s degree in Chemical Engineering, an MBA, and a PhD all from the Georgia Institute of Technology.

Richard Palmer
Richard Palmer

CTO & Co-Founder

Gravyty

Expanding Nonprofit Workforce with Deep Learning

Abstract

Many of the 1.5M nonprofits in the US face increasing pressure to achieve their missions due to fundraising inefficiencies, lack of access to talent pools, and budget constraints. This limits the social good that can be cultivated to improve our world.

Through this lens, we’ll explore how artificial intelligence has played a crucial role in expanding the capabilities of nonprofit organizations, allowing them to build more and deeper relationships with supporters without having to hire more people (or fire anyone). As practitioners of vertical AI products, we’ll show how we’ve utilized open source technologies including TensorFlow, NLTK, CRFsuite, SKlearn, and others to determine who is most likely to give a donation to an organization and learn from communications to mimic the cognitive functions of fundraisers to expand their reach.

The discussion will center around case studies involving the College of Charleston, where AI was used to increase their workforce throughput by 160%, and University of Delaware, where AI uncovered a $50M donor. Similar examples will be pulled from cancer research, human rights, and Alzheimer’s funding.

The talk is intended for beginner-to-intermediate attendees. The main takeaways will be an exploration of AI in SaaS products, how behavioral psychology affects AI adoption, and insight into the nonprofit industry.

Bio

Rich is the co-founder and CTO of Gravyty, the first company focused on applying artificial intelligence to the social good industry. Rich believes that technology and companies are at their best when they augment people and allow them to do things that were previously impossible in simple, cost-effective and elegant ways.
As a brain aneurysm survivor, Rich has a deeply rooted belief that technology should be used for the positive benefit of people. He strives to scale Gravyty to help nonprofits of all shapes and sizes gain the resources they need to achieve their missions. Prior to Gravyty, Rich was senior product manager at RelSci, a $120M business connection intelligence platform. He was also the head of portfolio analytics for CapitalIQ and led product development for their quantitative solutions team. In between, Rich has founded four technology-based companies and was the winner of the 2015-2016 Entrepreneurship Award from Babson College.
He has a BS in Economics and Information Technology from Rensselaer Polytechnic Institute and an MBA from Babson College.

David Woodruff
David Woodruff

Associate Vice President and Chief Operating Officer for Resource Development

MIT

Expanding Nonprofit Workforce with Deep Learning

Abstract

Many of the 1.5M nonprofits in the US face increasing pressure to achieve their missions due to fundraising inefficiencies, lack of access to talent pools, and budget constraints. This limits the social good that can be cultivated to improve our world.

Through this lens, we’ll explore how artificial intelligence has played a crucial role in expanding the capabilities of nonprofit organizations, allowing them to build more and deeper relationships with supporters without having to hire more people (or fire anyone). As practitioners of vertical AI products, we’ll show how we’ve utilized open source technologies including TensorFlow, NLTK, CRFsuite, SKlearn, and others to determine who is most likely to give a donation to an organization and learn from communications to mimic the cognitive functions of fundraisers to expand their reach.

The discussion will center around case studies involving the College of Charleston, where AI was used to increase their workforce throughput by 160%, and University of Delaware, where AI uncovered a $50M donor. Similar examples will be pulled from cancer research, human rights, and Alzheimer’s funding.

The talk is intended for beginner-to-intermediate attendees. The main takeaways will be an exploration of AI in SaaS products, how behavioral psychology affects AI adoption, and insight into the nonprofit industry.

Bio

David Woodruff is Associate Vice President and Chief Operating Officer for Resource Development at Massachusetts Institute of Technology (MIT) and he has served in this capacity since June 2012. There he oversees front-line and support operations of a development team that raises more than $500 million per year. MIT recently launched the public phase of its $6 billion comprehensive campaign, The MIT Campaign for a Better World.
David first worked at MIT between 1984 and 2002. His assignments included corporate fundraising and individual giving and he led the major gifts team in MIT’s successful $2 billion campaign in 1997-2004, Calculated Risks/Creative Revolutions. Between 2002 and 2008, David was Chief Development Officer at Harvard School of Public Health where he headed up initial planning for the School’s portion of a Harvard University campaign. From 2008 to 2012, David held the post of Executive Director and Chief Operating Officer for Development at Massachusetts General Hospital (MGH) where he oversaw overall development operations and guided the execution of the hospital’s successful $1.5 billion campaign, The Campaign for the Third Century of Medicine.
David received his bachelor’s degree from MIT and master’s degree from Stanford University. David also earned his MBA from Babson College. David has been a frequent presenter at conferences held by CASE, AFP and AHP and serves on a number of nonprofit boards. In 2017 he was the recipient of CASE’s Quarter Century Circle Award for fundraising service in higher education. David is also a Certified Fundraising Executive. David is currently president of the Massachusetts Chapter of the Association of Fundraising Professionals and began his two-year term in 2019. David serves on the newly created AI in Advancement Advisory Council.

Jeffrey Saltz
Jeffrey Saltz

Associate Professor

Syracuse University

Leading Data Science Teams: A framework to help guide data science project managers

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 provide a framework managers can use to help ensure a successful data science project. The focus of this framework is not on which specific algorithm a team should use, but rather, how to ensure that the data science effort is progressing effectively and efficiently.
Key aspects of the framework, that will be discussed, include:
Forming Data Science Teams:
1. Staffing the team
2. Roles on Data Science Projects
3. Training data science teams
4. Structuring and coordinating data science functions/capabilities – Coordinating IT, analytic and client teams

Establishing Processes for Developing Analytical Solutions
1. Agile data science process methodologies
2. Balancing “research” vs “getting something useful”
3. Analytic model life-cycle management
4. Quality – Help ensure to ensure accurate insights
5. Tools and platforms to support modular data science practices –
Modular analytics development (re-use, reproducibility, efficient team)

Risk Management
1. Model documentation and transparency
2. Data transparency
3. Model compliance management
4. Analytics regulatory risks and risk mitigation
5. Deployment risk
6. Framework to ensure ethical data usage and analytics development
7. Data and model ownership

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.

Bobbie Carlton
Bobbie Carlton

Founder

Innovation Women

More Women in Data Science: Creating the Pipeline

Abstract

Many events and conferences have gender and general diversity issues. These can be a product of a long history of predominately male speakers – the Known Quantity Factor– or, of an industry where there is little diversity at the top (where speakers often come from) or in the ranks.
An #allmalepanel can get you mocked, ridiculed and harassed on social media. There’s a “Congrats you have an all-male panel” Tumblr. Gender Avenger (@GenderAvenger) looks for events light on female speakers and creates social media-ready images with your event hashtag. The Gender Avenger community can bury your event’s social media stream in a flash.
Speaking at conferences is an important path to career and business success. Speaking at conferences gets you access to prospective customers, partners, employees and investors. Meanwhile, more diverse conferences are more interesting and better attended.
So, how to do you create a pipeline for future success and a diverse conference? You likely already have a number of diversity speakers inside your own community. We help educate your existing community of women and diverse candidates on what it takes to be a speaker – how to source speaking opportunities, write abstracts, pitch and then rock the gig itself. We also help them understand the benefits of public speaking through this hands-on workshop. (We can also create a special “event inside an event” that will increase your number of female speakers AND create a pipeline for the future.)
We talk to professional women about:
• The Speaker’s Paradise (why there are so many speaking opportunities today)
• The 5 “C’s” – the benefits of public speaking
• Rebranding their fear – getting over the nervousness of public speaking
• How to handle the “no” – getting turned down to speak.

Bio

Bobbie Carlton is a parallel founder and an award-winning speaker, marketing, PR and social media professional. Her marketing and PR firm, Carlton PR & Marketing is a boutique firm that supports tech startups and small to medium-sized companies. Her second company, Mass Innovation Nights (MIN), is a social media powered new product showcase which has launched more than 1200 new products which have received more than $3 billion in collective funding. Company number three is Innovation Women, an online speaker bureau for entrepreneurial and technical women. It helps connect event managers and awesome speakers who just happen to be women. Follow Bobbie on Twitter as @BobbieC @MassInno @WomenInno or @CarltonPRM.

Arturo Amador, PhD
Arturo Amador, PhD

Senior Consultant

Capgemini Norway

Big Data and Mobility Analytics: What can we learn from the way things (and humans!) move?

Abstract

Things (IoT) will mean that the amount of devices that connect to the Internet will rise massively. This is already giving rise to the creation of massive amounts of data. Spatial and temporal mobility patterns of things and societies as a whole can be characterized based on the interactions that we are able to capture from the IoT sensors.
In this presentation, we will review what we can learn from human mobility patterns, how they can be used to optimize traffic, city planning and tourist attractions. We will review the challenges associated with privacy and security regulation when analyzing mobility patterns. As an application we will present an study on AIS data that describes the locations of vessels traveling in Norwegian seas. We will close the presentation with an overview of the kind of AI techniques we can apply to analyze mobility patterns.

Bio

Arturo Opsetmoen Amador is a senior data scientist working as a consultant for Capgemini. He specializes in the application of AI technologies to solve practical problems that have positive effects on our society. He has experience as a lead data scientist in Smart Digital, a division part of Telenor Norway – Business. He was in charge of bringing the Big Data service «Mobility Analytics» to provide insights into human mobility patterns to the Norwegian market. He has a scientific background and holds a Ph.D. in physics from the Norwegian University of Science and Technology. His interests include Big Data, AI and ML technologies, and how their ethic implementation can improve our society.

Anjali Shah, PhD
Anjali Shah, PhD

Senior Data Scientist

IBM

Accelerate AI Development with Transfer Learning

Abstract

Deep Learning is an incredibly powerful technique, which has found uses in wide range of applications such as image object detection, speech translation, natural language processing and time series modeling. However, training deep neural network models requires a tremendous amount of time, training data and compute resources. A technique called transfer learning allows data scientists to increase their productivity dramatically by sharing neural network architectures and model weights. Reuse of a pre-trained model on a different but related task enables training of deep neural networks with comparatively less data. In this talk, you will learn the details of how transfer learning works and will see demonstrations in both financial and healthcare domains. We will talk about specific use cases and lessons learned that are applicable to many other industry sectors.

Bio

Anjali is a Senior Data Scientist at IBM aligned to insurance and financial services industry. She has worked across healthcare, financial services and telecommunications industries. Her expertise in applying cutting-edge technology to analyze structured and unstructured data has helped her clients convert data into actionable business insights. Her early career in software engineering focused on managing complex projects with strict deadlines (having delivered multiple technology solutions). Prior to joining IBM, she has delivered 80+ lectures as Assistant Professor in Health Information Management. She has a Ph.D. in Biomedical Informatics and Applied Statistics, Master’s and Bachelor’s degrees in Computer Science.

Steve Geringer
Steve Geringer

Data Science Solutions Architect

IBM

Accelerate AI Development with Transfer Learning

Abstract

Deep Learning is an incredibly powerful technique, which has found uses in wide range of applications such as image object detection, speech translation, natural language processing and time series modeling. However, training deep neural network models requires a tremendous amount of time, training data and compute resources. A technique called transfer learning allows data scientists to increase their productivity dramatically by sharing neural network architectures and model weights. Reuse of a pre-trained model on a different but related task enables training of deep neural networks with comparatively less data. In this talk, you will learn the details of how transfer learning works and will see demonstrations in both financial and healthcare domains. We will talk about specific use cases and lessons learned that are applicable to many other industry sectors.

Bio

Steve is a Data Science Solutions Architect on the IBM Analytics team covering Healthcare, State/Local Government. Steve works with clients to understand their big data and analytics goals and helps design data driven solutions to fit their needs. When not engaged with clients, Steve can be found keeping up with all the latest breakthroughs in data science. Steve achieved the “Kaggle Competitions Expert” designation by his high performance in Kaggle Machine Learning Competitions and received his Bachelor of Science in Computer Science and Applied Mathematics from the University at Albany.

Meina Zhou
Meina Zhou

Lead Data Scientist

Indellient

Predictive Analytics for Wealth Management and Beyond

Abstract

In order to understand customer behaviors and provide better services, wealth management firms have started to invest vastly in data analytics. During this session, Meina Zhou will discuss how she has successfully helped different financial institutions implement in-house predictive analytics solutions to improve wealth management services. She will also address multiple use cases that built at different stages of the customer journey, including customer acquisition, customer personalization, and customer retention. She will discuss both the analytics components and the challenges involved throughout the implementation process.

Bio

Meina is the Lead Data Scientist at Indellient. Her core expertise lies in the application of proven data science tools and techniques to conduct business analytics and predictive modeling. Meina has used her business acumen and data science skills to solve business problems. Meina is a thought leader in the data science world and is an active conference speaker. She enjoys public speaking and sharing innovative data science ideas with other people. Meina received her Master of Science in Data Science from New York University and her Bachelor of Arts in Mathematics and Economics from Agnes Scott College.

Adam McElhinney
Adam McElhinney

Chief of Machine Learning & AI Strategy

Uptake

Recent Advances in Machine Learning with Applications to Internet of Things (IoT)

Abstract

The proliferation of sensor technologies has resulted in more connected machines than ever before. This change is resulting in huge quantities of sensor data becoming available for analysis. Machine learning algorithms have resulted in a mixed track record of success with these data sources. This talk will give an overview of the state of machine learning as applied to IoT and industrial equipment. It will discuss some of the challenges with current approaches, exciting theoretical advancements and some “lessons learned” from the field.

Specifically:
What do we mean by IoT?
What is failure prediction and prognostics?
What is the value of IoT?
Differences between physics based approaches to IoT and data-driven approaches to IoT
What are the challenges from applying data-driven approaches to IoT?
How can recent advances in machine learning help with the unique challenges of IoT?
Real-case study that illustrates the application of deep-learning, gradient boosting, transfer learning and other machine learning techniques for IoT applications
What are the opportunities for future enhancements and exciting research in this area?

Bio

Adam McElhinney is currently the Head of Data Science at Uptake Technologies, where he leads a team of 75 Data Scientists building cutting-edge industrial data analytics tools. Additionally, Adam is an Adjunct Professor in the Computer Science and Mathematics departments at Illinois Institute of Technology. Additionally, Adam has filed 18 patents for his research in machine learning, internet of things (IOT), software engineering and big data technology. Adam was recognized by the Illinois Technology Association as the 2018 Technologist of the Year.

Saisi Peter
Saisi Peter

Product Manager, Artificial Intelligence

Facebook

Social Recommendation in a world of Infinite Content

Abstract

Imagine time as currency. Finite. Fiat. Hard to earn. Harder to yield. In a world of infinite content, why would anyone use your service or app? The answer is to provide unobtrusive, transparent return on investment. The initial milestone for A.I. was connecting people to relevant content. The new harder requirement is recommending the right content, at the right time, and in the right medium to the right person. Given that natural ceiling on digital screen time, A.I has to transcend into real life. How do you extend your digital service to have real life touchpoints? If you work in the social recommendations space e.g. news, social networking, how do you prepare for this new age? We will walk through how to leverage A.I to make the content glut tractable for a picky audience with finite time

Bio

Saisi is a Product Manager in Facebook’s Artificial Intelligence org. Specifically, leveraging Artificial Intelligence / Machine Learning at scale to personalize experiences for 2 Billion Facebook users. He has first-hand experience in driving business and consumer outcomes across the Facebook family that includes Facebook, Instagram and WhatsApp. At Facebook, he has also worked providing Internet access to underconnected and Facebook’s eCommerce vertical

Nicole Alexander
Nicole Alexander

Professor

New York University

Data Ethnography

Abstract

Data Enthography is a look at data as a means to combat bias and produce data to intervene in larger civic and private networks and engagements. This presentation uses insights from machine learning analytics and design thinking to challenge those developing algorithms and data sets that are to be representative of diverse populations but rarely are. The talk is largely based on the concept that to illuminate bias within machine learning, the ‘removal of bias’ itself has to be manifested into a ‘thing’ to teach or sway the algorithms. The idea aims to initiate a standard for equity and equality, by centering collaboration in the creation of this data set. The application has affects within areas of biometrics of accurate facial recognition, predictive analytics and finance and credit issuances.

Bio

Nicole Alexander is an Adjunct Professor at New York University. She is also a Lecturer at Columbia University and an Advisory Council Subject Matter Expert at Gerson Lehrman Group. Over the past 16 years Nicole has held leadership roles across marketing and technology which included Vice President, Innovation Practice at Nielsen China, Vice President at Marketing Evolution, and Vice President, at Pointlogic.

Nicole holds a global executive M.B.A. jointly awarded by New York University Stern School of Business, HEC Paris and London School of Economics and Political Science. She earned a Bachelor’s degree in International Business from New York University.

Jennifer Kloke, PhD
Jennifer Kloke, PhD

VP of Product innovation

Ayasdi

AI and Value-Based Care: Reducing Costs and Enhancing Patient Outcomes

Abstract

Politics aside, value-based care is the model that is transforming the practice and compensation of healthcare in the United States. Once laggards, payers and providers are increasingly becoming sophisticated enterprises when it comes to data and the implications for healthcare are staggering. What lies within that data has the power to cure disease, reduce readmissions, enable precision medicine, improve population health, detect fraud and reduce waste.

Take Flagler Hospital, a 335-bed hospital in St. Augustine, Florida. They don’t have a single data scientist on staff. Nonetheless, they have orchestrated one of the most successful deployments of artificial intelligence in healthcare — delivering cost savings of more than 30%, reducing the length of stay by days and reducing readmissions by a factor of more than 7X.

In this talk, Dr. Jennifer Kloke, VP of Product Innovation at Ayasdi, will walk through how healthcare institutions small and large will be able to apply artificial intelligence in the pursuit of value based care. She can discuss the strategy, implementation, and results seen to date and go over how these advances are transforming the healthcare industry.

Bio

Dr. Jennifer Kloke is the VP of Product Innovation at Ayasdi. For the last three years, she has been responsible for the automation and algorithm development for the entire Ayasdi codebase and led many efforts to development cutting edge analysis techniques utilizing TDA and AI. During that time, she was the principal investigator for a Phase 2 DARPA SBIR developing automation and data fusion capabilities. These have led to breakthroughs in the field and several patents. Jennifer also served five years as a Senior Data Scientist analyzing a wide variety of data including point cloud, text, and networks from diverse industries including large military contractors, finance, bio-tech, and electronics manufacturing. Her work includes developing prediction algorithms for reducing the number of false alarms for a large military jet manufacturer as well as developing and deploying a predictive program management application at a large government contractor.

Jennifer received her Ph.D. in Mathematics from Stanford University with an emphasis on topological data analysis. She has collaborated with chemists at Lawrence Berkeley National Laboratory and UC Berkeley to develop topological methods to mine large databases of chemical compounds to identify energy efficient compounds for carbon capture. She also developed a de-noising algorithm to efficiently process high dimensional data and has published in the Journal of Differential Geometry.

Michael Radwin
Michael Radwin

Vice President Data Science

Intuit

Data Science + Design Thinking: A perfect blend to achieve the best user experience

Abstract

As data scientists, we invest much of our time on the business problem, the data, the statistics, the algorithm and the model. But we can’t afford to overlook one very important component: the customer! A great AI/ML model with a poorly designed user experience is ultimately is going to fail. The world’s best data products are born from a perfect blend of data science and an amazing user experience.

Design thinking is a methodology for creative problem solving developed at the Stanford University d.school. The methodology is used by world class design firms like IDEO and many of the world’s leading brands like Apple, Google, Samsung and GE.

Michael Radwin, VP of Data Science at Intuit, will offer a recipe for how to apply design thinking to the development of AI/ML products. Your team will learn how to get deep customer empathy & fall in love with the customer’s problem (not the team’s solution). Next, you will learn to go go broad to go narrow, focusing on what matters most to customers. Finally, learn how to get customers involved in the development process by running rapid experiments and quick prototypes.

These lessons of blending data science & design thinking can be applied to products that leverage supervised and unsupervised machine learning models, as well as “old-school” AI expert systems.

Case study examples will include:
Mint users lose $250 million in overdraft fees every year. Using the data from Mint’s 10 million users, we applied a machine learning algorithm that predicts if you are likely, within three days, to have an overdraft. Mint alerts you in time, with helpful suggestions on how to avoid the exorbitant Non-Sufficient-Funds fee.
Business or personal? Mobile mileage tracking for QuickBooks Self-Employed: ML model + UX = automatic categorization of individual trips easy to accurately rack up potential tax deductions.
Americans spend 7 billion hours every year filing taxes. TurboTax’s Tax Knowledge Engine, which uses advanced AI to translate the 80,000+ pages of US tax requirements and instructions into a software oracle that can explain computations to DIY tax filers so that they have greater confidence in the calculations in their returns.

Bio

Michael Radwin is a Vice President of Data Science at Intuit, responsible for leading a team dedicated to using artificial intelligence and machine learning models for security, anti-fraud and risk. Prior to Intuit, Radwin was VP Engineering of Anchor Intelligence, which used machine learning ensemble methods to fight online advertising fraud. He also served as Director of Engineering at Yahoo!, where he built ad-targeting and personalization algorithms with neural networks and naïve Bayesian classifiers, and scaled web platform technologies Apache and PHP. Radwin holds an ScB in Computer Science from Brown University.

Frank Zhao
Frank Zhao

Senior Director, Quantamental Research

S&P Global Market Intelligence

Natural Language Processing: Deciphering the Message within the Message – Stock Selection Insights using Corporate Earnings Calls

Abstract

Given the growing interest in NLP among investors, this session will demystify common NLP terms and provide an overview of general steps in NLP. NLP can be used to quantify the sentiment of earnings calls. We will discuss how sector-level sentiment trends are generated, providing insights around inflection points and accelerations, stock-level sentiment changes and forward returns as well as language complexity in earnings calls.

Bio

Frank is a Senior Director and a key member of S&P Global Market Intelligence’s Quantamental Research group. His primary focus is to conduct systematic alpha research on global equities with publications on natural language processing, newly discovered stock selection anomalies, event-driven strategies and industry-specific signals. Frank has a master’s degrees in Financial Engineering from UCLA Anderson and in Finance from Boston College Carroll, and has undergraduate degrees in Computer Science and Economics from University of California, Davis.

Halim Abbas
Halim Abbas

Chief AI Officer

Cognoa

AI to revolutionize child behavioral diagnostics and therapeutics

Abstract

Artificial intelligence is revolutionizing many industries from manufacturing to automated driving, and the healthcare industry, though relatively recent to penetrate, is no exception. But diagnostics and therapeutics of child behavioral conditions like autism, ADHD, and language disorders remain relatively behind. This is unfortunate because early detection of such disorders is proven to improve the prospects of affected children dramatically. It also happens to be a healthcare context where there is a significant unmet need for streamlining and scalability of services.

We discuss the potential of artificial intelligence to disrupt that domain and talk about specific artificial intelligence techniques, challenges, and outcomes of experiments that have been applied successfully in practice.

We present the challenges, solutions, insights, and validation results of our user-facing solution that screens for autism and ADHD by combining multiple predictors based on various media inputs while allowing for inconclusive determination on hard-to-screen subjects. We will talk about incorporating signal from parental questionnaires, expert analysis of short video uploads, as well as short doctor questionnaires. We will get into leveraging automatable, complementary signals like audio-video streams from interactive storytelling and gaming sessions on tablets and mobile phones. Finally, we will explore the potential of deep learning to help with this problem-setting.

This talk is intended to raise awareness of the potential benefit of applying AI to the field of healthcare in general and child behavioral diagnostics in particular, and share with the audience a concrete example of a context where proper application of artificial intelligence has yielded demonstrably superior results to the traditional standard of practice.

Bio

Halim is a high tech innovator who spearheaded world-class data science projects at game changing techs like eBay and Teradata. Formally educated in Machine Learning, his professional expertise span Information Retrieval, Natural Language Processing, and Big Data. Halim has a proven track record of applying state of the art data science techniques across industry verticals such as eCommerce, web & mobile services, airline, BioPharma, and the medical technology industry. He currently leads the AI department at Cognoa, a data driven behavioral healthcare startup in Palo Alto.

Benn Stancil
Benn Stancil

Chief Analyst

Mode Analytics

Thirty minutes to answers: Data science's Great Compression, and its next frontier

Abstract

If we look back at the major developments in data over the last decade, we often think about advances in data storage, in AI, and in machine learning. While these are significant, over the last several years, a series of quiet revolutions have been driving equally big changes. A new class of data tools are closing the technology gap between the world’s leading companies and everyone else, making data science far more accessible to a much wider range of companies. This is opening up new opportunities for companies to serve and sell to an emerging “middle class” of data consumers.

As these technologies spread, we’ve collectively become remarkably skilled at processing, modeling, analyzing, and learning from data. And yet, despite being we’re massively more informed than ever before, we haven’t seen comparable improvements in outcomes.

Why? Because we’re rarely in the room where these decisions happen. We often hand off our work, and tell executives “you take it from here.” Our tools and analyses, no matter how powerful or clever, will always be restricted by how they’re wielded – and who wields them.

In this talk, I’ll outline the three technological changes that are driving this quiet transformation of data science. I’ll show how any company, regardless of stage or resources, can and should take advantage of these changes. I’ll discuss the problem technology hasn’t yet solved – how to put data science in the rooms where decisions are made. Finally, I’ll survey the opportunities we have to address this challenge.

Bio

Benn Stancil is a co­founder and Chief Analyst at Mode, a company building collaborative tools for analysts. Benn is responsible for overseeing Mode’s internal analytics efforts. Benn is also an active contributor to the data science community, frequently helping data science teams build their technology stacks and establish data-driven cultures within their companies. In addition, Benn provides strategic oversight and guidance to Mode’s product direction as a member of the product leadership team.
Prior to Mode, Benn was a senior analyst at Microsoft and Yammer, where he helped lead product analytics. Benn also worked as an economic analyst at the Carnegie Endowment for International Peace, a think tank in Washington, DC.

 Robbie Allen
Robbie Allen

CEO

Infinia ML

The Machine Learning Problem You Don’t Know You Have . . . Yet

Abstract

So you’re doing some machine learning. But have you really thought about what needs to happen once you put it into production? This is the challenge lurking behind every promising machine learning initiative: making it work in the real world.
Robbie Allen, author of the book Machine Learning in Practice and the CEO of two data-centered companies, breaks the challenge down into key production issues like:
On-Prem Deployment. If you’re not in the cloud, deployment on your own servers or hardware can be difficult.
Workflow Integration. When companies work with legacy and/or closed systems, connecting prediction API’s into products/workflows is hard.
Who Owns Deployment and Maintenance? Is DevOps responsible for maintenance and deployment of ML models into production? Is this a job for data scientists? Engineers? When data scientists build models, will they see the light of day?
Monitoring. Today’s tools lack the ability to measure model drift and identify when a model’s production data is no longer representative of training data.
Still Learning? Machine learning is not “learning” unless there is a continuous feedback loop of data from production; many ML solutions do not have an established method for doing this.
Changing It Up. Measuring and adjusting machine learning models is challenging enough; these tasks are even harder when dealing with changing models, parameters, data labels, and goals.

Bio

Robbie Allen is the CEO of Infinia ML, a team of advanced machine learning experts focused on making business impact. The company helps Fortune 500 companies and cutting-edge startups reduce costs, increase efficiency, and achieve breakthroughs with data science. Infinia ML serves industries from manufacturing and healthcare to marketing and human resources. The company’s capabilities include natural language processing, recommendation engines, object detection, 3D image modeling, and anomaly detection.
Previously, Robbie founded and led Automated Insights, whose natural language generation software helps automate content production for The Associated Press, Yahoo!, and many others. Automated Insights was successfully acquired by Vista Equity Partners in 2015, and Robbie currently serves as the company’s Executive Chairman. Before starting Automated Insights, Robbie was a Distinguished Engineer at Cisco. Robbie has authored or coauthored eight software books, owns six patents, and has spoken at a variety of conferences including the O’Reilly AI Conference, Strata, SXSW, and the MIT Sloan CIO Symposium. He holds two Master’s degrees from MIT and is completing his Ph.D. in computer science at UNC-Chapel Hill.

Kaitlin Andryauskas
Kaitlin Andryauskas

Business Intelligence Manager

Wayfair

Improving Data Quality for Superior Results

Abstract

Have you ever been asked to create a model or derive insights out of data that is inaccurate, missing, or unreliable? As data experts, we know that no matter how good our model is, the results will be unreliable if the source data is unreliable. As the saying goes– Junk in, junk out.

This talk will explore the approaches that Wayfair’s Business Intelligence Team has used to improve the quality of source data, and in turn increase the accuracy of reporting, models, and insights.

Yard Arrival Date – Wayfair has increased data capture from <10% to > 99% for Yard Arrival Date. Yard Arrival date is the date that an SPO (Stock Purchase Order) arrives at our warehouse yard. Knowing the exact date that an order arrived in the yard is essential to creating predictive models that will allow us to predict when future orders will arrive. We found that the capturing Yard Arrival required staff to perform extra work that they did not see the value in. There also was no accountability – no one was checking if the staff was entering the data, and it didn’t impact their work, so there was no incentive to do this seemingly needless work. By explaining the usefulness of this data, working with the warehouse staff to improve the data capture process, and creating a public dashboard that drove accountability, we drove Yard Arrival Date entry compliance from less than 10% to over 99%.

ETA to Port – In our International Supply Chain many data points, including ETA to port, are manually entered by Wayfair staff. Like any manual data entry process, this leads to the possibility of overlooked data, data that has been accidently mistyped (“fat fingered”), and other sources of manual error. When it came time for the end of month report, the team would inevitably be hit with a barrage of weird data that they needed to track down and corrected. To avoid a mad rush at the end of the month, the Business Intelligence team created a reporting portal that would allow the team to identify shipments with dates that were questionable – for instance, a shipment that left Asia in late January and arrived in the US in early January (did they have a time machine?). This has improved data quality yielding reliable insights throughout the month. Additionally, we moved to an EDI data source for some data inputs, which reduces some of the risks associated with a manual data entry processes (as our business scales, manual data entry is no longer possible).

Estimated Yard Arrival Date – Now that we had a reliable yard arrival date, we could start improving the model that would estimate yard arrival date. To do this, we looked at the original model and realized that the field had two meanings – some people used it as arrival to the yard, while others interpreted it as the date the shipment was unloaded into the warehouse. Often these are the same things, but in times of high inbound volume, some containers can sit in the yard for weeks. We aligned on a consistent definition of yard arrival, and began to break down the model by comparing Actual and Estimated Yard Arrival. The data revealed that appointment date, which we had assumed was correct 100% of the time, was incorrect 40% of the time. Once this was revealed, we worked with the operations teams for process changes; if an appointment was made more than a month ago, the team should call the carrier and confirm that the product was still scheduled to arrive on time. We also created a model that uses machine learning to take current appointment times and predict when the actual arrival will occur.

Bio

Kaitlin Andryauskas is a Business Intelligence Manager at Wayfair supporting Wayfair’s global supply chain. She approaches her work by identifying the problem that will unlock the largest potential, and then find the data that will provide the required insights. She is passionate about solving complex problems requiring both analytical skill and business acumen, as well as using data to solve pressing social issues. Kaitlin has an undergraduate degree in Sociology from The University of Texas at Austin, a Master’s in Business Analytics from Bentley University and 24 credits towards a Master’s in Education at Johns Hopkins University. Kaitlin is former high school history teacher and Teach for America Baltimore Alum.

Yuval Greenfield
Yuval Greenfield

Developer Relations

MissingLink.ai

DeepOps: Building an AI First Company

Abstract

Data scientists spend 30% of their time building shoddy infrastructure. Our data shows that many AI teams can accelerate their progress by 10x at least. Deep Learning brings with it enormous amounts of data, complicated experiment results and intense compute requirements. Decades of experience in moving code to production yielded best practices in engineering that have not yet found their place in deep learning teams. Breaking silos to foster trust, a transparent culture, and shared responsibility – we introduce DeepOps – deep learning ops. A set of methodologies, tools and culture where data engineers and scientists collaborate to build a faster and more reliable deep learning pipeline.
Surveying hundreds of AI companies, we’ve learned that adopting DeepOps practices helped them ship faster, with more confidence and improved customer experiences.
In this talk, Yuval Greenfield, Deep Learning Developer Relations at MissingLink.ai, will discuss:
DeepOps checklist – insights from leading AI teams and how to bring them to your team.
Increase productivity within data science teams
Reduce time to market
Increase deployment speed
Increase visibility and transparency across teams.

Bio

Yuval Greenfield has been an engineer and data enthusiast for the past 13 years in the fields of military cybersecurity, computer vision medical diagnostics, gaming, 360 cameras, and deep-learning tools. He holds a B.Sc. in Physics and Mathematics from the Hebrew University of Jerusalem as part of the IDF Talpiot program. At MissingLink, Yuval is in charge of developer relations, using the MissingLink platform for deep learning research, building tutorials, marketing content, and technical presentations.

Greg Michaelson, PhD
Greg Michaelson, PhD

Chief Success Coordinator

DataRobot

Abstract

The advent of the “citizen data scientist” has been both concerning and irritating to many so-called “real” data scientists at work in the business world today. Some argue that the prospect of new, less well-trained AI-builders has been made established data scientists threatened, insecure, and paranoid. Others argue that using untrained modelers to build AI is a recipe for disaster. In this talk, Dr. Greg Michaelson will discuss the truth and the hype of the citizen data scientist and will propose a framework for utilizing both skilled data scientists and citizen data scientists in the same organization.

Bio

Greg Michaelson is the Chief Success Coordinator for DataRobot. Prior to that role, he led the data science practice at DataRobot, working clients across the world to ensure their success using the DataRobot platform to solve their business problems. 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.

Kerstin Frailey
Kerstin Frailey

Senior Data Scientist

Metis

Building an Effective Data Science Project Portfolio for your Business

Abstract

Each day, AI and data science become less of a competitive advantage for businesses and more of a requirement for survival. Yet, many companies struggle to effectively leverage their data science and AI investments. New Vantage Partners reports that although 92% of businesses are increasing investment in AI, 77% continue to face challenges with adoption. What makes some companies reap the benefits of AI while others struggle to see return on investment? The difference is strategy.

Whether your teams are distributed or centralized, many or few, sophisticated or just starting, your company needs a unified data strategy. But, data science and AI bring new challenges to strategic planning. The nature of their output demands that data science and AI projects undergo a more risk-aware scoping process. To scale smoothly and evolve quickly, a data strategy needs to provide broad and specific standards. Finally, proposed projects need to be contextualized within the larger data ecosystem in order to amplify their impact.

This talk is designed for business leaders, data science managers, and decision makers that want to ensure the effectiveness of the AI and data science capabilities they are building. Attendees will leave equipped with the tools to:
Critically evaluate pitched projects and select the most strategic ones;
Build an effective, impactful, and high yield data science project portfolio;
Evolve your data science roadmap to quickly adapt to new opportunities.

Bio

Kerstin is passionate about bringing data science from the edge of business to the center of it. She has data science experience in all three sectors: for-profit, non-profit, and government. Currently, she is a Senior Data Scientist at Metis where develops and delivers curriculum to accelerate data science learning for teams. As Director of Data Science, she founded the Guidestar data science team and brought machine learning to the largest nonprofit data warehouse. At Postmates she used her broad data science toolkit to support marketing, growth, finance, and fleet team needs. As a University of Chicago Data Science for Social Good Fellow she helped uncover early signals for delays in education. She holds graduate degrees in statistics, mathematical statistics, and mathematical computer science from Cornell University and University of Illinois at Chicago. As an undergraduate she studied psychology and anthropology at Yale University.

Seph Mard
Seph Mard

Head of Model Validation

DataRobot

Validating AI/ML Models - Lessons Learned from the Banking Industry

Abstract

In today’s financial services industry, competitors are rushing to enable the AI-driven enterprise by making strategic investments in AI and machine learning technology, financial institutions not investing in AI and machine learning technology risk losing their competitive edge. However, due to an increased reliance on AI and machine learning models with everyday business processes and for strategic decisions, model risk must not be ignored and must be effectively managed. If left unchecked, the consequences of model risk can be severe; where model risk is defined as the risk of financial or reputation loss due to errors in the development, implementation or use of models.
Therefore, AI and machine learning models require constant monitoring and effective validation. This is not only a regulatory requirement, but it is also sound business practice. In this session, Seph will present the cornerstones of effective modern model risk management in the age of AI and machine learning by first providing an overview of AI and machine learning in the financial serves industry, summarizing the regulatory background and the machine learning model lifecycle, and then finally presenting the challenges and emerging best practice for the validation of models, in an ever-changing world of AI and machine learning.

Bio

Coming soon!

Brian Carrier, PhD
Brian Carrier, PhD

CTO

Basis Technology

Machine Learned Ranking for LegalTech

Abstract

The amounts of data in digital investigations are ever increasing and new approaches are needed for finding the relevant items amongst the noise. For too long, the focus on digital investigation software has been on parsing and extracting any possible piece of data and displaying it to the user. But, with the increasing amount of data, the focus needs to be on showing only the most relevant items.

Machine learning techniques can help identify which items the user should see first and therefore save them time. This talk will outline how these techniques can be used to rank documents, executables, and other files found during a digital investigation.

Bio

Brian Carrier leads the digital forensics team at Basis Technology, which builds software for incident response, digital forensics, and custom mission needs. He is the author of the book File System Forensic Analysis and developer of several open source digital forensics analysis tools, including The Sleuth Kit and Autopsy. Brian has a Ph.D. in computer science from Purdue University and worked previously for @stake as a research scientist and the technical lead for their digital forensics lab and incident response team. Brian is the chairperson for the Open Source Digital Forensics Conference (OSDFCon) and involved with many conferences, workshops and technical working groups, including the Annual DFRWS Conference and the Digital Investigation Journal.

Mario Vuksan
Mario Vuksan

CEO

ReversingLabs Corporation

Machine Learned Ranking for LegalTech

Abstract

The amounts of data in digital investigations are ever increasing and new approaches are needed for finding the relevant items amongst the noise. For too long, the focus on digital investigation software has been on parsing and extracting any possible piece of data and displaying it to the user. But, with the increasing amount of data, the focus needs to be on showing only the most relevant items.

Machine learning techniques can help identify which items the user should see first and therefore save them time. This talk will outline how these techniques can be used to rank documents, executables, and other files found during a digital investigation.

Bio

Mario Vuksan is the Co-Founder and Chief Executive Officer at ReversingLabs Corporation. Mr. Vuksan served as a Director of Research and Knowledgebase Services at Bit9 Inc. He also served as Program Manager and Consulting Engineer at Groove Networks (acquired by Microsoft), working on Web based solutions, P2P management, and integration servers. Before Groove Networks, Mr. Vuksan developed one of the first Web 2.0 applications at 1414c, a spin-off from PictureTel. He is a regular presenter at RSA, Black Hat, Defcon, Caro Workshop, Virus Bulletin, CEIC, FSISAC, and AVAR Conferences, and has also authored numerous texts on security. He supports AMTSO, IEEE Malware Working Group and CTA, and holds a BA from Swarthmore College and an MA from Boston University.

David Talby, PhD
David Talby, PhD

CTO

Pacific AI

What to expect when you’re putting AI in production

Abstract

Machine learning and “AI” systems often fail in production in unexpected ways. This talk shares real-world case studies showing why this happens and explains what you can do about it, covering best practices and lessons learned from a decade of experience building and operating such systems at Fortune 500 companies across several industries.

The covered topics include concept drift (identifying and correcting for model decay due to changes in the distribution of data in production), common pitfalls in A/B testing (like the primacy and novelty effects), offline versus online measurements, and systems that learn in production (such as adversarial learning use cases). This talk is intended for executives, technical leaders and product managers who want to learn from others’ mistakes how to best set up their teams & products for success.

Bio

David Talby has been building real-world big data analytics systems in healthcare, finance and e-commerce for over a decade. David has extensive experience in building and operating web-scale data science and business platforms, as well as building world-class, Agile, distributed teams. Prior to joining the startup world, he was with Microsoft’s Bing group, where he led business operations for Bing Shopping in the US and Europe. Earlier, he worked at Amazon both in Seattle and the UK, where he built and ran distributed teams that helped scale Amazon’s financial systems. David holds a PhD in computer science and master’s degrees in both computer science and business administration.

Alex Ermolaev
Alex Ermolaev

Director of AI

Change Healthcare

Major Applications of AI in Healthcare

Abstract

The latest AI advances have the potential to massively improve our health and well being. However, most of the work is yet to be done. In this talk, we will explore the most important opportunities for AI in healthcare. For example, we will explore how AI can diagnose major life-threatening conditions even before those conditions emerge. We will talk about AI ability to recommend dramatically more effective and less harmful treatment plans based on AI understanding of patient’s medical history and current conditions. Finally, we will talk about AI role in making our healthcare system effective and affordable for everyone.

Bio

Alex Ermolaev, Director of AI at Change Healthcare, has developed and led a variety of AI projects over the last 20 years, including enterprise AI, NLP, AI platforms/tools, imaging and self-driving cars. Alex is one of the most frequent “AI in Healthcare” speakers in the Silicon Valley. Change Healthcare is one of the largest healthcare technology companies in the world.