W4: AI for Pharma
cambridge complex

TUESDAY, APRIL 16 | 8:00AM-11:30AM

ABOUT THIS WORKSHOP:

Have you ever been curious to apply machine learning to large amounts of data but was not sure of the concepts to use? Have you ever wondered on how to get started on AI? Well, you have come to the right place! This workshop will help you learn AI like never before (our instructors guarantee it!). The workshop is a crash-course on AI where you will learn the fundamentals and applications of AI/ML in Pharma. Come join us, learn, and network!

AGENDA AND TOPICS COVERED INCLUDE:

8:00 am Workshop Introduction & Chairperson Remarks

Cindy Crowninshield, RDN, LDN, HHC, Executive Event Director, Cambridge Healthtech Institute
Bino John, PhD, Associate Director, AstraZeneca

8:15 Introduction and Fundamentals of AI: From KNN to DNN

Bino John, PhD, Associate Director, AstraZeneca

9:10 Demo: Making Sense of Your Data - Part 1

John Van Hemert, PhD, Research Scientist, Bioinformatics, Corteva Agri Science, A Dow-Dupont Division

9:30 Coffee Break

9:40 Demo: Making Sense of Your Data - Part 2

John Van Hemert, PhD, Research Scientist, Bioinformatics, Corteva Agri Science, A Dow-Dupont Division

10:10 OrgaQuant: Application of Deep Learning for 3D Imaging

Timothy Kassis, PhD, Lead Instructor, New Engineering Education Transformation (NEET), Massachusetts Institute of Technology

10:30 Add a Little Structure to Your Life: Deep Learning for Text and Images

Peter Henstock, PhD, Machine Learning & AI Technical Lead, Business Technology, Pfizer, Inc.

10:50 AI-Driven Drug Discovery & Development

Ken Aoshima, PhD, Executive Director, Data Science Laboratory, hhc Data Creation Center, Eisai Co., Ltd

11:10 AI’s Key Role in Unlocking How Genes Regulate Collective Behaviors

Wenlong Tang, PhD, Research Investigator II, Novartis Institutes for BioMedical Research

11:30 Workshop Ends

INSTRUCTORS:

Bino John, PhD, Associate Director, AstraZeneca

Bino John, PhD, currently leads a variety of Artificial Intelligence (AI) Initiatives at AstraZeneca (AZ). As an Associate Director at AZ, he is leveraging Deep learning and other advanced AI approaches to accelerate Drug development. Before joining AZ in 2018, Bino led a variety of computational biology initiatives and teams at Dow and then Dow-DuPont. In those roles, his efforts included enabling machine learning/AI and integrative big-data informatics capabilities for genomics research for the Agricultural Sector. He earned an Integrated Master’s degree in Chemistry from the Indian Institute of Technology (Mumbai) in 2000 and subsequently received his PhD from The Rockefeller University in Biomedical Sciences in 2003. His thesis research in computational structural biology with Dr. Andrej Sali was followed by postdoctoral studies in computational genomics with Dr. Chris Sander at the Memorial Sloan-Kettering Cancer Centre. In 2005, Bino joined the University of Pittsburgh as a faculty, where he focused on using high-throughput methods for cancer biomarker discovery, resulting in the discovery of novel molecules and molecular pathways.

John Van Hemert, PhD, Research Scientist, Bioinformatics, Corteva Agri Science, A Dow-Dupont Division

Formally trained in Computational Biology, Multivariate Statistics, and Machine Learning, I’ve worked in Discovery R&D at DuPont Pioneer since 2011. Projects are balanced between developing computational methods that enrich the discovery pipeline and characterize its products-- some of which appear in scientific and IP literature. I’ve led statisticians, computational biologists, and data scientists around the world, working on everything from experiment design, to drone imagery analysis, to molecular biotechnology development. Before that, I was a Staff Scientist at Iowa State University in the Crop Genome Informatics Laboratory where I led modernization of the Plant Expression DataBase (PlExDB). Prior, I received my PhD (2010) from the Electrical and Computer Engineering Department at Iowa State University in Bioinformatics and Computational Biology, where I worked on Systems Biology of the Grapevine for an international consortium of researchers and published several papers in biological network analysis and mining. I also hold Bachelors’ degrees (2006) from the University of Northern Iowa, by the Computer Science Department and Business College.

Wenlong Tang, PhD, Research Investigator II, Novartis Institutes for BioMedical Research

Dr. Wenlong Tang leads the preclinical animal behavior research group as a lab head at Novartis Institutes for Biomedical Research. His team applies cutting edge artificial intelligence technologies to accelerate drug discovery, specifically in neuropsychiatric diseases. His research interests include behavioral health informatics, locomotion analysis, signal processing, and pattern recognition. Before joining Novartis, he completed his postdoctoral training in Electrical and Computer Engineering at the University of Alabama and in Biomedical Engineering at Tulane University. He received his Ph.D. from the University of Maryland Baltimore County in 2010. He also holds a Masters’ degree (2007) from Beijing University of Technology, and a Bachelors’ degree (2004) from Beihang University (Beijing, China).

Timothy Kassis, PhD, Lead Instructor, New Engineering Education Transformation (NEET), Massachusetts Institute of Technology

Dr. Timothy Kassis completed his postdoctoral training under Profs. Linda Griffith (BE) and David Trumper (MechE) at MIT. Prior to that, Dr. Kassis obtained a PhD in Bioengineering and an M.S. in Mechanical Engineering from the Georgia Institute of Technology in Atlanta, GA, and a B.Eng. in Electronic and Communications Engineering from the University of Nottingham, UK. Dr. Kassis has lived for extended amounts of time in the Philippines, Canada, UK, Lebanon, Syria, and since 2008, the United States. Dr. Kassis is currently the lead instructor for the School of Engineering's New Engineering Education Transformation (NEET) Living Machines (LM) thread and is also the instructor for 20.051, 20.052 and 20.053 which are the three classes entitled 'Living Machines' required by all students participating in the LM thread. Dr. Kassis' research interests lie at the convergence of engineering, biology, and computation. He is particularly interested in creating engineering tools to answer difficult biological questions. Dr. Kassis has worked on a variety of interdisciplinary research projects from elucidating the role of lymphatics in lipid transport to designing organ-on-chip microfluidic models to developing deep convolutional networks for biomedical image processing.

Ken Aoshima, PhD, Executive Director, Data Science Laboratory, hhc Data Creation Center, Eisai Co., Ltd.

His main research interests are in real world health care big data analysis, biostatistics, bioinformatics and biomarker researches. He has held the responsibilities for biostatistics and leading translational medicine using biomarkers in clinical development. Dr. Aoshima has been a director, head of Biomarker & Personalized Medicine Unit and focusing on bioinformatics and biomarker researches in preclinical research for more than 9 years, since 2016 he is in charge of leading ICT and data driven drug discovery and development by combining various types of big data and utilizing advanced data analysis technologies including artificial intelligence (AI) in global Eisai.

Peter Henstock, PhD, Machine Learning & AI Technical Lead, Business Technology, Pfizer, Inc.

Peter Henstock is the AI and Machine Learning Technical Lead at Pfizer. He served as the site statistician for nearly 10 years while also developing analytics software. Working with scientists and managers, he concluded that visualization was the ideal common ground where everyone could understand and discuss results, so he went on to develop most of Pfizer’s internal visualization and analysis software. Prior to joining Pfizer, Peter worked at the MIT Lincoln Laboratory in image processing and computational linguistics projects. He holds a PhD in AI from Purdue University, and Masters degrees in Linguistics, Software Engineering, Biology, and Statistics. He currently teaches graduate level AI and Machine Learning, and Software Engineering at Harvard.

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