Accelerate AI Business Summit 2019 Speakers

Hear from though leaders, AI experts, and executive speakers how AI is transforming industry
Mike Stonebreaker
Mike Stonebreaker

A.M. Turing Award Laureate, Professor, Co-founder

MIT CSAIL, Tamr

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Abstract

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Bio

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Regina Barzilay
Regina Barzilay

Professor, MacArthur Fellow

MIT CSAIL

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Abstract

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Bio

Regina Barzilay is a professor in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. Her research interests are in natural language processing. Currently, Prof. Barzilay is focused on bringing the power of machine learning to oncology. In collaboration with physicians and her students, she is devising deep learning models that utilize imaging, free text, and structured data to identify trends that affect early diagnosis, treatment, and disease prevention. Prof. Barzilay is poised to play a leading role in creating new models that advance the capacity of computers to harness the power of human language data.

Regina Barzilay is a recipient of various awards including an NSF Career Award, the MIT Technology Review TR-35 Award, Microsoft Faculty Fellowship and several Best Paper Awards in top NLP conferences. In 2017, she received a MacArthur fellowship, an ACL fellowship and an AAAI fellowship.

Prof. Barzilay received her MS and BS from Ben-Gurion University of the Negev. Regina Barzilay received her PhD in Computer Science from Columbia University, and spent a year as a postdoc at Cornell University.

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 Manager

Domino Data Lab

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 who has spent over a decade 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 additionally works as a Customer Success Manager at Domino Data Lab, helping customers make the most of their Data Science platform and guiding them through building teams and processes to be successful. Previously he has successfully built out analytics functions at multiple insurance companies. This includes building out data and analytics platforms, Business Intelligence capabilities, and Data Science 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

Coming Soon.

Bio

Ben Vigoda, Founder & CEO
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

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

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

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

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

Data Scientist

Capco

Predictive Analytics for Wealth Management

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 Capco 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 a data scientist and senior consultant at Capco. 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

Dr. Kirk Borne
Dr. Kirk Borne

Principal Data Scientist

Booze Allen Hamilton

Human-Centered Data Science - When the left brain meets the right brain

Abstract

We will present two different dimensions of the practice of data science, specifically data storytelling (including data visualization) and data literacy. There will be short presentations, integrated with interactive sessions, group activities, and brief moments of brain and body exercise. The combination of these various activities is aimed at demonstrating and practicing the concepts being presented. The Data Literacy theme component will include a section on “data profiling – having a first date with your data”, focusing on getting acquainted with all the facets, characteristics, features (good and bad), and types of your data. This theme will also include a section on matching models to algorithms to data types to the questions being asked. The Data Storytelling theme component will include sections on the neuroscience of visual displays of evidence (visual analytics) for decision-making and include a component on user-centered design in data science. Design thinking, empathy, consultative practice, and the BI Dashboard Formula (BIDF) methodology will be emphasized. The combination of the two themes (data literacy and data storytelling) will be made more concrete through exercises in small breakout groups. Each group will be given a sample problem, then asked to take a data science approach (modeling, visualization, storytelling) to address the three fundamental questions that we should always consider in our projects: What? So what? Now what? The workshop participant will come away with design tips, tricks, and tools for better human-centered data science. The goal is for your next data science project and presentation to be your best ever. As Maya Angelou said so eloquently, “people will forget what you said, people will forget what you did, but people will never forget how you made them feel.” Make your data science matter by demonstrating why and how it matters.

Bio

Kirk Borne is a data scientist and an astrophysicist who has used his talents at Booz Allen since 2015. He was professor of astrophysics and computational science at George Mason University (GMU) for 12 years. He served as undergraduate advisor for the GMU data science program and graduate advisor in the computational science and informatics Ph.D. program.
Kirk spent nearly 20 years supporting NASA projects, including NASA’s Hubble Space Telescope as data archive project scientist, NASA’s Astronomy Data Center, and NASA’s Space Science Data Operations Office. He has extensive experience in large scientific databases and information systems, including expertise in scientific data mining. He was a contributor to the design and development of the new Large Synoptic Survey Telescope, for which he contributed in the areas of science data management, informatics and statistical science research, galaxies research, and education and public outreach.

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
Jennifer Kloke

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.