May 9th - 11th, 2023
Machine Learning for Biotech, Pharma, & Healthcare
Learn the latest advancements, trends, and topics from top practitioners at intersection of AI and Biotech, Pharma, and Healthcare
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 Past 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)
Sadid Hasan, PhD
Sadid Hasan is a Senior Director for AI at CVS Health leading the team responsible for AI-enabled clinical care plan initiatives in Aetna. His recent work involves solving problems related to clinical information extraction, paraphrase generation, natural language inference, and clinical question answering using Deep Learning. Sadid has over 60 peer-reviewed publications in the top NLP/Machine Learning venues, where he also regularly serves as a program committee member/area chair including ACL, IJCAI, EMNLP, NeurIPS, ICML, COLING, NAACL, AMIA, MLHC, MEDINFO, ICLR, ClinicalNLP, TKDE, JAIR etc.
AI for Clinical Care Planning and Decision Support(Business Talk)
Jiayi Cox, PhD
Jiayi Cox is a data scientist who delivers deep learning solutions on biologics at Novartis Institutes for BioMedical Research (NIBR). Her research interests include using graph neural network to model protein interactions and using language models to find protein binding site. Jiayi is experienced in several fields of studies including human genetics, molecular biology, and NGS data analysis. Prior to NIBR, she helped prioritize biomarkers nomination as a machine-learning scientist at a Boston-based pharmaceutical start-up. She obtained her PhD degree from Boston University on computational biology.
John Peach
A modern polymath, John holds advanced degrees in mechanical engineering, kinesiology and data science, with a focus on solving novel and ambiguous problems. As a senior applied data scientist at Amazon, John worked closely with engineering to create machine learning models to arbitrate chatbot skills, entity resolution, search, and personalization.
As a principal data scientist for Oracle Cloud Infrastructure, he is now defining tooling for data science at scale. John frequently gives talks on best practices and reproducible research. To that end, he has developed an approach to improve validation and reliability by using data unit tests and has pioneered Data Science Design Thinking. He also coordinates SoCal RUG, the largest R meetup group in Southern California.
Tired of Cleaning your Data? Have Confidence in Data with Feature Types(Workshop)
Evie Fowler
Evie Fowler is a data scientist based in Pittsburgh, Pennsylvania. She currently works in the healthcare sector leading a team of data scientists who develop predictive models centered on the patient care experience. She holds a particular interest in the ethical application of predictive analytics and in exploring how qualitative methods can inform data science work. She holds an undergraduate degree from Brown University and a master’s degree from Carnegie Mellon.
Data Science and Contextual Approaches to Palliative Care Need Prediction(Talk)
Jennifer Dawn Davis, PhD
Jennifer Davis, Ph.D. is a Staff Field Data Scientist at Domino Data Labs, where she empowers clients on complex data science projects. She has completed two postdocs in computational and systems biology, trained at a supercomputing center at the University of Texas, Austin, and worked on hundreds of consulting projects with companies ranging from start-ups to the Fortune 100. Jennifer has previously presented topics at conferences for Association for Computing Machinery on LSTMs and Natural Language Generation and at conferences across the US and in Italy. Jennifer was part of a panel discussion for an IEEE conference on artificial intelligence in biology and medicine. She has practical experience teaching both corporate classes and at the college level. Jennifer enjoys working with clients and helping them achieve their goals.
Large Scale Deep Learning using the High-Performance Computing Library OpenMPI and DeepSpeed(Workshop)
Max Urbany
As Max progresses through his Master’s Program, he is particularly interested in intelligent digital accessibility design, along with the ethical analysis of existing predictive models. His passion for creating quality user-centered tools drives him to understand as much as he can about end users while leveraging what data can reveal.
Z by HP Panel Discussion on the Diverse Role of Data Science in Education(Talk)
Dan Chaney
Dan Chaney is the VP, Enterprise AI / Data Science Solutions, for Future Tech Enterprise, Inc., an award-winning global IT solutions provider. He oversees all sales, marketing, and technical activities focused on Future Tech’s comprehensive range of AI and data science workstation solutions. Prior to joining Future Tech, Dan spent 20 years at Northrop Grumman, most recently serving as the company’s Enterprise Director of IT Solution Architecture & Engineering. Dan earned his bachelor’s and master’s degrees in communication and computer science from the University of Kentucky. Dan is a Certified Information Systems Security Professional (CISSP) and adjunct instructor for the University of Louisville’s cybersecurity workforce program sponsored by the National Centers of Academic Excellence in Cybersecurity.
Z by HP Panel Discussion on the Diverse Role of Data Science in Education(Talk)
Kristin Hempstead
Kristin has been with HP for 11 years and is currently the North America business development manager for HP’s data science and artificial intelligence solutions focusing on federal, education, and public sector customers. She has an MBA from University in South Florida with a specialization in Finance and MIS and a BS in Agriculture from the University of Georgia.
Z by HP Panel Discussion on the Diverse Role of Data Science in Education(Talk)
Gregory Ryslik, PhD
Greg Ryslik is a data science and machine learning executive experienced in leading AI and engineering initiatives ranging across the biotech, healthtech and fintech domains. As senior vice president at COMPASS Pathways his work focuses on developing technology to improve the therapeutic journey for patients with treatment resistant depression. Concurrently, Greg is an assistant professor of practice in the Department of Computer Science & Engineering at Ohio State University as well as an Instructor at Stanford Continuing Studies where he lectures on statistics for artificial intelligence.
Greg started his career as an actuary at PricewaterhouseCoopers in New York City. After his PhD he spent several years in Silicon Valley at companies such as Tesla Motors, Mindstrong Health and Genentech. He is a fellow of the Casualty Actuarial Society, as well as a member of the American Academy of Actuaries. In September 2020, he was named one of Boston Business Journal’s “40 under 40”.
Greg holds a PhD from Yale University in biostatistics, a master’s degree in statistics from Columbia University and an undergraduate degree in mathematics, computer science and finance from Rutgers University.
Utilizing NLP in the Context of COMP360 Psilocybin Therapy for Treatment Resistant Depression(Talk)
Veysel Kocaman, PhD
Veysel is a well known thought leader in healthcare NLP and works as a Lead Data Scientist and ML Engineer at John Snow Labs, improving the Spark NLP for the Healthcare library and delivering hands-on projects in Healthcare and Life Science. He is a seasoned data scientist with a strong background in every aspect of data science including NLP, machine learning, deep learning, and big data with over ten years of experience. He’s also pursuing his Ph.D. in ML at Leiden University, Netherlands, and delivers graduate-level lectures in Auto ML and Distributed Data Processing. Veysel has broad consulting experience in Statistics, Data Science, Software Architecture, MLOps, Machine Learning, and AI to several start-ups, boot camps, and companies around the globe. He also speaks at Data Science & AI events, conferences and workshops, and has delivered more than a hundred talks at international as well as national conferences and meetups.
Spark NLP for Healthcare: Modular Approach to Solve Problems at Scale in Healthcare NLP(Half-Day Training)
Andrew Giessel, PhD
Andrew Giessel is the Director of Data Science and AI at Moderna, where he leads a team of data scientists applying machine learning to solve a diverse set of science, business, and engineering problems. While at Moderna, he and his team have helped the company grow from a 300 employee start-up to a commercial organization producing hundreds of millions of vaccine doses all over the world. Prior to Moderna, Andrew earned a Ph.D. and did postdoc in the Neurobiology department at Harvard Medical School. Andrew lives in the Boston area with his family.
Speakers Coming Soon
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 2023 | May 9th-11th
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