Healthcare and Finance
Take part in a series of interactive sessions that focus on tackling pressing industry challenges & opportunities with Senior Data & Analytics leaders.
- 09:30 - 09:40 PDTIntroduction
- 09:40 - 10:05 PDTMachine Learning in Computational Biology using single-cell RNA-seq data and spatial transcriptomic images
Dr. Sandhya Prabhakaran, Research Scientist at Moffitt Cancer Center
Dr. Sandhya Prabhakaran is a Research Scientist at the Integrated Mathematical Oncology department, Moffitt Cancer Centre, Florida. Before that, she was a Research Scientist at Memorial Sloan Kettering Cancer Centre and Columbia University. Her Ph.D. in Computer Science is from the University of Basel and her Masters in Intelligent Systems (Robotics) is from the University of Edinburgh. Her research deals with developing statistical theory, mechanistic mathematical models, and Bayesian inference models, particularly to problems in Cancer Biology and Computer Vision. Prior to academics, she was an Assembler programmer working with the Mainframe Operating System (z/OS) at IBM Software Laboratories and has developed Mainframe applications. She has completed 4 out of the 6 World Marathon Majors.
- 10:05 - 10:10 PDT5 min break
- 10:10 - 10:35 PDTHealthcare Finance Beyond Excel: R for Automation, Time Series Forecasting, and Interactive Reporting
Javier Orraca, Data Scientist at Health Net
Javier Orraca is a Data Scientist for Health Net, a Centene Corporation health insurance company, where he supports the Product Performance and Strategic Insights organization. With over 15 years of experience working for EY, PG&E, KPMG, and Health Net, Javier leverages the R programming language for financial forecasting, automation, machine learning, and identifying major risks and opportunities. Javier works closely with Centene's data engineering teams to deploy to production predictive algorithms and web apps used by Health Net's executives and sales teams. Javier facilitates knowledge sharing at work by co-hosting a monthly Centene R User Group for the company’s analysts, statisticians, and data scientists.
- 10:35 - 10:40 PDT5 min break
- 10:40 - 11:05 PDTHow to create advanced analytics services for your company and business partners on top of CDP
Ilya Katsov, Head of Data Science at Grid Dynamics
Ilya joined Grid Dynamics in 2009, and since then has been leading engagements with a number of major retail and technology companies, focusing primarily on Big Data, Machine Learning, and Economic Modeling. He is currently managing the Industrial AI consulting practice that helps clients to become successful AI adopters and deliver innovate AI solutions. Prior to joining Grid Dynamics, Ilya worked at Intel Research on emerging wireless communication technologies. He is the author of several scientific articles and international patents, and also authored a book, “Introduction to Algorithmic Marketing: Artificial Intelligence for Marketing Operations” (2017).
- 11:05 - 11:10 PDT5 min break
- 11:10 - 11:35 PDTSearch & Recommender Systems for Healthcare
Faizan Javed, Senior Director of Search, Data Science and Analytics at Kaiser Permanente
Faizan is currently Sr Director of Search, Data Science and Analytics at Kaiser Permanente Digital where he is leading engineering and data science teams for search and recommender systems to power digital healthcare. He was previously at HomeDepot.com where he built and led the Search Science team. Before that, Faizan led multiple data science and engineering teams at CareerBuilder across a wide range of data science initiatives for the online recruitment domain. Faizan has published and presented several papers at top-tier conferences such as ACM KDD and AAAI including Best Deployed Application Awards at AAAI/IAAI’17 and AAAI/IAAI’21 and has filed for and been awarded several patents. He has also organized workshops at leading conferences such as IEEE ICDM, ECML-PKDD, and SIAM SDM and most recently co-organized the Knowledge Graphs and E-commerce workshop at ACM KDD’20. Faizan earned his MSc in Bioinformatics, Certificate in Technology Entrepreneurship, and Ph.D. in Computer Science, all from the University of Alabama at Birmingham (UAB).
- 11:35 - 11:50 PDT15 min break
- 11:50 - 12:15 PDTData Science in the next Generation of Risk Management
Colin Chen, Executive Director at JPMorgan & Chase
Data Science, including Artificial Intelligence (AI) applications and Machine Learning methods, has started its full-force influence on the financial industry from daily customer services and business operations to high-level strategy and decision making. In this talk, I will focus on the developments of data science in risk management. First, I will illustrate how data science can help to improve the current risk management frameworks by closing some gaps between business and risk functions, by automating operational processes, and by integrating dynamic controls. In the meantime, data science also introduces new risks into risk management either as complex procedures or well-defined models. Some thoughts on how to control such risks are discussed.
Colin Chen is currently the director and founder of Data Science and Analytics Consulting, focusing on data science projects from the financial and media industries. He has over 12 year’s experience in financial risk management. He worked at JPMorgan Chase as an Executive Director leading the Operational Risk modeling group and at Bank of America as a Director of Model Risk Management. He also worked at Wells Fargo and Fannie Mae on credit and market risk models with mortgages, credit cards, auto loans, mortgage servicing rights and hedging. Colin also worked for the SAS institute for 10 years as a senior software developer. He was the 2013 Chair of the Section of Statistical Programmers and Analysts under the American Statistics Association. Colin holds a Ph.D. in Statistics and a Master in Computer Science, both from Purdue University, and published over 20 papers in professional journals. Topic: Data Science in the next Generation of Risk Management In this talk, I will cover some new developments in Financial Risk Management and the roles Data Science can plan in these new developments.
- 12:15 - 12:20 PDT5 min break
- 12:20 - 12:45 PDTTechnical Debts in Machine Learning Systems
Pragati Awashti, AVP Decision Science & Analytics at PNC
Pragati Awashti is an experienced professional with Master of Science in Business Analytics from LeBow College of Business, Drexel University Philadelphia. Data! Data! Data! I can’t make bricks without clay!- Perfect lines by Sir Arthur Conan Doyle. How can one even think of doing business without data. Data, being an essential part at whatever we do inspires me a lot to give it a deep insight and an overall different view.
- 12:45 - 12:50 PDT5 min break
- 12:50 - 13:15 PDTJourney to AI at Scale: The View from the CDAO’s Desk
Jesse Bishop at Dataiku & Mike Berger at Mount Sinai
About the Speakers
Michael Berger is a data scientist and industrial engineer by schooling but has had three distinct but interconnected careers in technology and management:
-Process engineer and ERP consultant both in the Big 6 and as an independent
-DotCom entrepreneur (of the non-serial variety)
-Analytics leader at large health systems and payers
Michael's main focus is to learn how operational analytics, cognitive machine learning, and infinite computing can help healthcare tackle its biggest problems. He has a life-long appreciation for classic enterprise data warehousing but has become a major advocate for leveraging cloud technologies to complement those solutions.
Jesse Bishop is an enterprise account executive at Dataiku. He received his Ph.D. in econometric analysis from the University of Minnesota and has over 10 years of experience as a practicing economist and data scientist.
- 13:15 - 13:20 PDT5 min break
- 13:20 - 13:45 PDTResponsible AI in Health: From Principles to Practice
Tempest van Schaik, Sr Machine Learning Engineer (Healthcare) at Microsoft
AI has made amazing technological advances possible; as the field matures, the question for AI practitioners has shifted from “can we do it?” to “should we do it?”. In this talk, Dr. Tempest van Schaik will share her Responsible AI (RAI) journey, from ethical concerns in AI projects to turning high-level RAI principles into code, and the foundation of an RAI review board that oversees projects for the team. She will share some of the practical RAI tools and techniques that can be used throughout the AI lifecycle, special RAI considerations for healthcare, and the experts she looks to as she continues in this journey.
Tempest is passionate about improving lives using sensors, data, and AI. Some of the ways she's driven impact have been through her startup, SoilCards, which aims to make mobile soil testing accessible to the world’s poorest farmers in order to improve their livelihood and protect the environment. She has also developed novel ways to measure cognitive function and mood in people with depression using wearables. She has used data science to improve physiotherapy for children with cystic fibrosis and has put principles of responsible AI into practice to build predictive ICU models which treat different patient groups fairly. She is currently a Senior Machine Learning Engineer in Microsoft’s Commercial Software Engineering (CSE) team, where she is an ML Lead for collaborations with some of Microsoft’s biggest healthcare customers. She is a member of CSE’s Responsible AI board and a CSE ambassador for Diversity & Inclusion because she believes in promoting positive change as a leader in the industry. She has a Ph.D. in Bioengineering from Imperial College London, with an internship at MIT, and an Imperial College Rector's Award. She is a Technical Advisory Board member of Ultromics Ltd as well as a TEDx and SXSW speaker. Her research has received awards from Innovate UK and the US National Academies of Science Engineering and Medicine.
- 13:45 - 13:50 PDT5 min break
- 13:50 - 14:15 PDTStandardizing the Model Development Process
Miriam Friedel, Director, Software Engineering - Center for Machine Learning at Capital One
In the last 15 years, data science and machine learning have gone from a nice-to-have business function to one that is essential to the core of many businesses, including Capital One. As part of this evolution, the process of building a machine learning model has also needed to evolve. While experimentation remains at the heart of this process, running a series of experiments and sharing the end result is no longer sufficient. In order to leverage a model as part of a critical business decision or product, the steps in the model building process itself must be cataloged and shared in a clear, organized way. In this talk, I will discuss the why and the how behind Rubicon, an open-source tool we developed to help standardize the model development process. By leveraging Rubicon, data scientists and ML engineers are able to easily create standard, complete, and automated experiment tracking, providing clarity, audibility, and transparency as they help bring models from proof-of-concept to operational reality.
Miriam is a Director of Software Engineering at the Capital One Center for Machine Learning, where she leads a team of engineers and data scientists building tools and solutions to solve ML problems across the enterprise. Prior to Capital One, she was Head of Data Science at Skafos, an eCommerce start-up based in Charlottesville, VA. She has spent over fifteen years in scientific and technical fields spanning theoretical physics, software engineering, transportation, neuroscience, management consulting, and machine learning. Miriam received her ScB in Physics from Brown University and her Ph.D. in Physics from the University of California, Santa Barbara, and is a co-author on over fifteen peer-reviewed articles.
- 14:15 - 14:20 PDT5 min outro
- Marketing spend optimization and attribution
- Customer analytics and personalization
- Fraud detection and risk scoring
- ML for quantitative equity portfolio management
- Price and promotion optimization
- Churn prevention
- AI/ML for IT Operations (AIOps)
- Data Science platforms and MLOps
- Deep learning for enterprise applications
- Reinforcement learning in personalization and trading
- and more...