Selling Out Soon 20% Offer | Ends In
FOCUS AREA OVERVIEW
Over the past several years, more and more Pharma, Healthcare, and Biotech companies have realized the importance of investing in AI, and we are already seeing the fruit of some of those investments in the fields of disease identification, drug discovery, clinical trials, and much more. This new focus area will cover some of the technology and recent developments underpinning these applications.
TOPICS YOU'LL LEARN
AI for Biotech & Pharma
Natural Language Parsing for Healthcare Data
AI for Drug Discovery & Design
Machine Learning for Better Patient Outcomes
AI for Treatment Discovery
Machine Learning for Clinical Data Diagnosis
Bioinformatics and Machine Learning
AI for Treatment Discovery
Deep Learning applications for Healthcare
Responsible AI
Ethical and Legal Consequences of Unsafe Machine Learning
Transparency & Explainability in Machine Learning
Idenifying Bias in Machine Learning
Differential Privacy & Federated Learning
Realiabilty in Critical Machine Learning Systems
Introduction to Advanced Machine Learning (multiple sessions)
Introduction to Advanced Deep Learning (multiple sessions)
Introduction to Advanced NLP (multiple sessions)
Some of Our Current Machine Learning for Biotech, Pharma, & Healthcare Speakers
Tomasz Adamusiak, MD, PhD
Tomasz Adamusiak MD Ph.D. is a Chief Scientist in the Clinical Insights & Innovation Cell at MITRE. He leads a multi-disciplinary group driving high-impact contributions to private and public sectors in Clinical and Genomic Data Science. Before MITRE, Tomasz was the Head of Data Science in the Pfizer Innovation Research (PfIRe) Lab. His team was responsible for developing novel digital endpoints, designing decentralized approaches for clinical trials, and applying AI/machine learning methods to generate novel insights from clinical data. Tomasz served in leadership and advisory roles in the American Medical Informatics Association, the SNOMED International, and the Epic Research Data Network.
Unlocking the Potential of Protein Prediction in Drug Discovery(Business Talk)
Mélissa Rollot
Mélissa is a data scientist engineer. Over the past 7 years working at Quinten Health in the healthcare sector as a Project Manager in data science, she has participated in the development of several decision support solutions powered by AI, e.g. for rare disease diagnosis, disease progression modelling and endotyping, or evaluation of population heterogeneity. She leds multiple studies of real-world data using advanced analytics methods to characterize phenotypes and disease progression for neurological conditions, cardiovascular diseases, and oncology, for pharma companies, research organizations and care providers. Currently, she is managing at Quinten Health the development of AI-powered solutions to support R&D decisions using RW data for our client.
A Natural Language Processing (NLP) Approach to Automate Patients’ Testimonials Analysis(Tutorial)
Rebecca Vislay-Wade, PhD
Rebecca Vislay-Wade is a Principal Data Scientist at Moderna, where she leads a team of scientists developing AI applications for clinical operations, regulatory science, and pharmacovigilance. Prior to Moderna, she worked as Senior Research Data Scientist at Highmark Health. Rebecca holds a PhD in biochemistry from Harvard University and did postdoctoral work in neuroscience at the NIH and Children’s National Medical Center in Washington, DC. She currently lives in the Boston area with her family.
Data Science @ Moderna: Accelerating Regulatory Communication with Natural Language Processing(Talk)
Robert F. Dougherty, PhD
As the Vice President of Digital Health Research at COMPASS Pathways, Bob is leading the data science and machine learning efforts aimed at improving the safety, efficacy, and scalability of psilocybin therapy. He is an accomplished neuroscientist and engineer with deep expertise in measuring human brain and behavior, and building data-driven solutions to mental health care challenges. Prior to joining COMPASS Pathways, Bob was VP of Research at Mindstrong, leading the research and data science teams in the development of digital biomarkers for mental health. Prior to Mindstrong, Bob was the Research Director of the Stanford Center for Neurobiological Imaging. He has published over one hundred peer-reviewed articles in the fields of psychology, psychiatry, neuroscience, statistics, and magnetic resonance technology over his 30+ year scientific career. Bob completed his PhD in Experimental Psychology at the University of California at Santa Cruz, and postdoctoral fellowships at the University of British Columbia and Stanford University.
Jesse Johnson
Jesse Johnson is Vice President of Data Science and Data Engineering at Dewpoint Therapeutics, a drug development Biotech startup founded in 2019 around a scientific field called biomolecular condensates. In this role, Jesse’s diverse set of experiences from academic math departments, engineering teams at Google, and data science teams at large, medium and small life science companies provide a unique perspective on the ways that data and wet lab teams communicate differently, or sometimes don’t communicate at all.
Development Principles for Biotech Data Teams(Business Talk)
Jenna Reps, PhD
Jenna Reps is a Director at Janssen Research and Development where she is focusing on developing novel solutions to personalize risk prediction. Jenna’s areas of expertise include applying machine learning and data mining techniques to develop solutions for various healthcare problems. She is currently working within the patient level prediction OHDSI workgroup with the aim of developing open source and user friendly software for developing risk models using data sets in the OMOP Common Data Model format. Prior to joining Janssen Research and Development, Jenna was a Senior Research Fellow at the University of Nottingham where she developed supervised learning techniques to signal adverse drug reactions using UK primary care data and acted as a data consultant to other researchers within the University. Jenna received her BSc in Mathematics and MSc in Mathematical Biology at the University of Bath and her PhD in Computer Science at the University of Nottingham.
Patient Level Prediction with Supervised Learning Models in Federated Data Networks(Tutorial)
Frank DeFalco
Frank DeFalco is the Director of Epidemiology Analytics at Janssen Research and Development where he architects software solutions and data platforms for the analysis and application of observational data sources. He is currently the leader and Benevolent Dictator of the OHDSI open source architecture working group. Frank is a presenter and panelist at OHDSI symposiums and has served as faculty for OHDSI symposium tutorials classes on architecture and common data model vocabulary. In addition to leading the OHDSI Architecture working group Frank initiated development of a standardized platform for observational analytics known as ATLAS. He is an active contributor to the open source software repositories developed and released by OHDSI including ATLAS, WebAPI, Achilles, Circe, Arachne, Visualizations, Hermes, Helios and others. Frank’s areas of expertise include computation epidemiology, large scale data platforms, software development and architecture, data visualization and informatics. Prior to joining Janssen Research and Development, Frank held the position of Senior Principal and Director of Collaboration and Analytics at British Telecom where he was a strategic advisor for multiple Fortune 100 companies across sectors including Consumer Products, Telecommunications and Pharmaceuticals. Frank received his undergraduate degrees in Computer Science and Psychology at Rutgers University.”
Patient Level Prediction with Supervised Learning Models in Federated Data Networks(Tutorial)
Joshy George, PhD
Joshy George is a bioinformatics researcher with a Ph.D. in Bioinformatics from the University of Melbourne, Australia, and a Master's in Computer Science from the Indian Institute of Science. With his background in data science and machine learning, Dr. George has co-authored over 100 peer- reviewed scientific articles, showcasing expertise in developing principled methods to solve complex biological problems. In his current role, he leads a team that is focused on building predictive models for cancer precision medicine and understanding the molecular mechanisms leading to diseases.
Is Machine Learning Necessary to Solve Problems in Biology(Talk)
You Will Meet
Top speakers and practitioners in Biotech, Pharma, and Healthcare
Data Scientists, Machine Learning Engineers, and AI Experts interested in risk in Biotech, Healthcare, and Pharma
Healthcare, Biotech, and Pharma industry professionals who want to understand safe machine learning
Core contributors in the fields of Machine Learning and Deep Learning
Software Developers focused on building safe machine learning and deep learning
Technologists seeking to better understand AI and machine learning application in the filed of Biotech, Healthcare, and Pharma
CEOs, CTOs, CIOs and other c-suite decision makers
Data Science Enthusiasts interested in making a difference
Why Attend?
Immerse yourself in talks, tutorials, and workshops on Machine Learning and Deep Learning tools, topics, models and advanced trends
Expand your network and connect with like-minded attendees to discover how Machine Learning and Deep Learning knowledge can transform not only your data models but also your business and career
Meet and connect with the core contributors and top practitioners in the expanding and exciting fields of Machine Learning and Deep Learning
Learn how the rapid rise of intelligent machines is revolutionizing how we make sense of data in the real world and its coming impact on the domains of business, society, healthcare, finance, manufacturing, and more
ODSC EAST 2024 - April 23-25th
Register your interest for East 2024ODSC Newsletter
Stay current with the latest news and updates in open source data science. In addition, we’ll inform you about our many upcoming Virtual and in person events in Boston, NYC, Sao Paulo, San Francisco, and London. And keep a lookout for special discount codes, only available to our newsletter subscribers!