Speakers

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Daniel has expressed a lifelong fascination in the blend of healthcare and computing. Learning how to program when 8-bit CPUs were still fashionable, his love for elegant algorithmic solutions has deeply influenced his way of thinking and appreciation for every single bit (or millisecond) to this very day. However, only in Daniel’s mind this deep love for technology translated into becoming a clinically trained MD with primary focus on human genetics and working experience in both, pediatrics and infectious disease. In a seemingly intentional evasion of having to pick a profession after all, Daniel completed a subsequent Masters program in Computer Science with focus on AI and parallel computing. Later on, Daniel figured it would somehow be a good idea to add another Masters in Cybersecurity to his already chaotic life. Daniel’s professional career includes senior scientist positions at the National Institutes of Health and in academia as well as executive leadership positions in various Silocon Valley startups. As the Head of Informatics & Artificial Intelligence (AI) at Genentech, Daniel currently overseas five distinct teams, i.e. Information Management (IMO), Technology Development (TechDev), Systems Operations (SysOps), Predictive Analytics (gPA), and Artificial Intelligence (AI). Regarding his work style, Daniel is a proud and longstanding Linux desktop user, strong advocate for open source, and absolutely passionate about servant leadership. In his free time he likes to build autonomous robots, develop 3D rendering engines from scratch, or cook "old world" food dishes that he learned from his dad.

Tudor Oprea, MD PhD is Professor of Medicine and Pharmaceutical Sciences, and Chief, Translational Informatics Division, at the Department of Internal Medicine, University of New Mexico School of Medicine in Albuquerque, NM (USA). He is also Guest Professor at the Institute of Medicine, Gothenburg University in Sweden and at the Center for Protein Research, University of Copenhagen, Denmark. Dr. Oprea has co-authored over 200 publications, 8 US patents, and 3 books (as editor). Dr. Oprea is PI for the Illuminating the Druggable Genome Knowledge Management Center, a NIH Common Fund initiative (druggablegenome.net). His computational work led to clinical trials in two types of cancer, most notably for R-ketorolac, which shows promise in ovarian cancer; and G-1, the first G-protein estrogen receptor agonist, currently under therapeutic investigation for melanoma. His most current research is in the development of machine learning and artificial intelligence models for target and drug discovery, by combining numerical and free-text information to model human health.

Shruthi Bharadwaj is a Senior Scientist at Novartis within the Informatics and Analytics group. She received her Ph.D in Biomedical Engineering from the University of Florida and continued her research as a post-doctoral fellow at the MD Anderson Cancer Center. Shruthi has been interested and involved in utilizing AI and machine learning approaches in Pharma. She has a patent that involves machine-learning approach to predict the onset of colon cancer in patients with Inflammatory Bowel Disease. She has won several NIH grants that supported her research in leveraging AI approaches in healthcare. She has published several research articles, book chapters and abstracts that focus on AI approaches in diagnosis and drug development. With the increase in availability of clinical trial data, AI and Machine Learning Approaches are becoming imperative in mining and finding clinically significant insights. In this talk, I will provide an overview of the various approaches currently used to tackle the big-data problem in pharma.

Sándor Szalma is Global Head, Computational Biology in Takeda Pharmaceuticals. He is responsible for computational biology, computational and statistical genetics, machine learning and informatics approaches supporting target discovery and validation/reverse translation and forward translation/biomarker and patient stratification in neuroscience, gastroenterology, rare disease and oncology. He serves as a member of the governance board of Open Targets and leads the Takeda engagement in the Regeneron Whole Exome Sequencing of UK Biobank Consortium. Before joining Takeda, he was head of Translational Informatics and External Innovation, R&D IT in Janssen Research & Development, LLC. Previously, he was member of the industry advisory committee of ELIXIR, member of the board of the Pistoia Alliance, member of the Translational Medicine Advisory Committee of the PhRMA Foundation and led the Data & Knowledge Management Strategic Governance Group of Innovative Medicine Initiative. His past positions included president of MeTa Informatics, general manager of QuantumBio and senior director of Computational Biology and Bioinformatics at Accelrys, Inc. He was co-founder of Acheuron Pharmaceuticals, Inc. He lectured at UCSD Extension and was adjunct professor at Rutgers University in the Computational Biology and Molecular Biophysics program. He is the author of 45 scientific publications and book chapters and two patents. He received his doctoral degree in physical organic chemistry from A. Szent-Györgyi Medical University in Szeged, Hungary.

Dr. Olynick joined ATOM, a consortium for AI driven drug discovery, at its founding in October 2017 as Head of Business Development and Operations. At ATOM,  Dr. Olynick is responsible for creating high value public-private partnerships and understanding the AI, data, and experimental landscape that will drive the future of early drug discovery. In the data space, Dr. Olynick has been working with the ATOM team to enable the AI community with sharable curated and newly generated drug discovery data. Dr. Olynick has an MBA from Wharton and a PhD in Materials Science and Engineering from the University of Illinois at Urbana-Champaign.

"Bowen Liu is a doctoral candidate in the department of chemistry at Stanford University, advised by Vijay Pande and Jure Leskovec. His work centers on developing machine learning approaches for drug discovery applications. He received his Bachelor’s in chemistry and applied mathematics from the University of Auckland"

Liling Warren is Head of Translational Medicine Statistics at Teva Pharmaceuticals. She is responsible for building and applying computational biology, statistical genetics, biomarker analytics to support drug discovery and development projects at Teva. She served as a member of the scientific committee in GRC (Genomic Research Consortium) to evaluate and assess drug discovery and drug repurposing opportunities using Real World Data (RWD)-based GWAS and PheWAS studies. Before joining Teva, she was Director of Statistics at Roivant, Head of US biostatistics at Acclarogen and led many statistical genetics projects at GSK. She received her Ph.D. in Bioinformatics and MS in Statistics from North Carolina State University.

Senior Computational Scientist with 18 years of experiences in developing sophisticated, analytical, bioinformatics algorithms, pipelines, and portals as well as applying them to understand complex biological data. Credo: the properly conducted analysis of combined clinical, molecular and genomic data can be easily translated into clinical applications to increase a success rate of drug developments and to provide personalized medicine solution to patients.

" I turn complexity into clarity. I lead interdisciplinary teams of data scientists, biologists, software engineers, and computational chemists that turn large quantities (petabytes!) of complex data into actionable insights in drug discovery and healthcare. Central to this challenge is developing and deploying machine learning algorithms that 1) accurately answer the *right* questions, 2) are integrated smoothly at scale, and 3) provide analysis that can be easily understood through intuitive user interfaces. My people-first philosophy: Building long-term strategy and implementing quarterly tactics is hard, and I am obsessed with how to get groups to embrace ideation, mutual respect and vigorous debate, while still driving to clear and timely decisions that scale our business. What motivates me: My career thread has been working with groups that push the limits of new science and emerging tech. I am excited by the human impact that new tools can have as long as we embrace approaches that truly are nuanced, balanced and thoughtful. Beyond the office: I write about women in tech, startup best practices and open science and have been published in the NY Times, Washington Post and Science. I am also a regular speaker on AI in biopharma. And as a final surprise twist, I lead biyearly commercial backpacking trips in Alaska! "

Huanyu Zhou is Senior Director, Head of Translational and Non-clinical Statistics in Teva Pharmaceutical Industries. His team is responsible for 1) statistical genetics/genomics, computational biology, and bioinformatics on target discovery/validation, biomarker identification and patient stratification; 2) pre-clinical and non-clinical statistics in cell-line development, biologics process development, bioassays, animal studies, anti-drug antibody detection, Chemistry, Manufacturing and Controls, and biosimilar and generics development; 3) applying machine learning and artificial intelligence approaches in support of various R&D activities such as patient stratification, cell-line and biologics process development, and clinical trial monitoring. Before joining Teva, he worked at Pfizer and Merck leading translational and genomic analytical activities. He received his doctoral degree in population genetics from Harvard School of Public health.

" Biotech/Pharma Executive with more than 15 years experience in R&D. As a bioengineer by training I am passionate about applying computational approaches (machine learning/AI as well as Systems Biology/Systems Pharmacology) to drug discovery and development. Currently, I serve a Global Head of Modeling and Simulation, PK Sciences at Novartis. Before joining Novartis, I served as interim Senior Vice President, Scientific Value, at GNS Healthcare. I focussed on how causal machine learning can drive strategic decision making in drug development. Prior, I established the use of Systems Biology (quantitative Biology combined with mathematical modeling and simulation) at Merrimack Pharmaceuticals where I held assignments of increasing responsibility from 2003 to 2017. As Head of Discovery and Early Development, I was responsible for all target identification and Ph1/2 clinical development activities. I received my Diplom Ingenieur (M. Sc.) in Chemical Engineering from the Technical University in Karlsruhe, Germany, and her Ph.D. in Biological Engineering from the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg. She was a post-doctoral fellow in the laboratories of Douglas Lauffenburger and Peter Sorger in the Biological Engineering Department at MIT. "

Andrew Weber is a Research Director with the ATOM Consortium, a public-private consortium developing accelerated workflows for the discovery of small molecule cancer therapeutics. His current research focus areas are on generative networks for optimization of chemical structure in high performance compute workflows, predictive pharmacokinetics, and drug induced liver injury. Andrew has over 10 years of drug discovery and development experience at GlaxoSmithKline, deploying computational modelling techniques applied to pharmacokinetics (including physiologically based pharmacokinetics), pharmacodynamics, transnational medicine, systems toxicology, route of delivery, and manufacturing throughput and cost of goods. Andrew has a M.S. in Chemical Engineering from Villanova University and a B.S. in Chemical Engineering from Bucknell University.

Brandon Allgood is the CTO and cofounder at Numerate, Inc, an AI driven drug discovery company. At Numerate, he manages the development and application of Numerate's AI platform and is responsible for Numerate’s technological vision. Brandon has also served as Director of Computational Science at Numerate and as a Research Scientist at Pharmix. He received a B.S. in Physics from the University of Washington, Seattle, and a Ph.D. in Computational Physics from the University of California, Santa Cruz. Brandon has authored scientific publications in astrophysics, solid-state physics, and computational biology and has 15 years of experience in AI, mathematical modeling, and large scale cloud and distributed computing. He is a member of the Forbes Technology Council and is a UCSC Foundation Trustee.

Senior Scientist protein purification and characterization at Merck. Direct focus on IMRs and biologics, in charge of the Analytical Ultra-Centrifugation (AUC) and Capillary Electrophoresis (CE-SDS PAGE) platforms at the MRL Palo Alto. Purification and characterization of antibodies, FABs, F(Ab')2 and bispecifics, performing stress studies and quality control. Technologies: SEC, RP, HIC, LAL, IEF, CE-SDS PAGE (glycans and proteins charge variants), SV-AUC, SE-AUC, DSC, SEC-MALS, DLS, SLS (HT).

Dr. Higgins has served in corporate, hospital, federal and academic research settings. He has 2 doctorates and an MD (psychiatry), but does not practice medicine. He has served as Chief of Molecular Neurobiology at the National Institutes of Health (NIND; NIA), U.S. VP of R&D for Roche, CMIO at MedStar Health (9 hospitals including Georgetown and Washington Hospital Ctr), IPA at the Defense Advanced Research Projects Agency (DARPA), and most recently as VP of Pharmacogenomics at Assurex Health, now a subsidiary of Myriad Genetics. He also co-founded SimQuest International, served as President and CEO, and sold the company to Laerdal Medical Corporation. He has received both federal grants and contracts (24), and raised substantial private equity funding. He has over 200 peer-reviewed publications.

36 years of experience in Healthcare life sciences and Health Economics and Outcomes Research. Lead author on two 2019 peer reviewed publications. Oct 2019 paper in The Lancet Digital Health utilized machine learning to identify undiagnosed individuals with familial hypercholesterolemia in national healthcare encounter and structured EHR data sets. July 2019 paper in Circulation: Cardiovascular Quality and Outcomes quantified the impact of health outcomes for restrictive prior authorization policies. We utilize machine learning and advanced analytics to identify individuals and populations with the highest unmet medical need for a given disease or condition.

Alex Aronov received his PhD in bioorganic chemistry from the University of Washington in 1998. He completed his postdoctoral training at UCSF, working on structure-based design and synthesis of novel anti-parasitics, and holds an MBA from MIT Sloan. He started his industrial career in molecular modeling at Vertex Pharmaceuticals in Cambridge, MA. From 2007 to 2012, he led a cross-functional team to deliver a novel immuno-oncology therapy BVD-723. He is currently Director and Head, Early Discovery Modeling & Informatics at Vertex, with responsibility for global Modeling & Informatics support of Vertex’s early discovery efforts. He is an author of 30 peer-reviewed manuscripts, and is an inventor on over 40 international patent applications.

Guido Lanza is the President and CEO of Numerate. Formerly, Guido was the co-founder and Chief Technical Officer of Pharmix, where he served on the company’s Board of Directors for five years and was named one of Business Week’s “Tech’s Best Young Entrepreneurs under-30” in 2006. Prior to Pharmix, Guido was a research scientist with Professor Koza of Stanford University with whom he developed applications of genetic programming in the area of bioinformatics and computational biology. Guido is the author of 10 scientific publications and inventor of 4 issued patents. He received a B.A. in Molecular and Cell Biology and Integrative Biology from the University of California, Berkeley and an M.Sc. in Bioinformatics from the University of Manchester (UK).

As Head of Innovation Management and Scouting and as a member of Takeda’s Digital Strategy team Ronald helps Takeda with its technology and innovation strategy. Earlier, at the Life Science division of PA Consulting, Ronald led projects for some of the world’s top 10 pharmaceutical companies that included projects around strategy, commercialization and digital health and at his company BioFrontline he provided management, strategy and commercial advice to life sciences companies in the US, Europe and Asia. After obtaining MAs in Biotechnology and Molecular Biology and a PhD in Pharmaceutical Biology at the University Medical Center Groningen in the Netherlands Ronald spent six years at Harvard studying Parkinson’s, Schizophrenia and the genetics of aggressive behavior, before making the transition to industry. As a consultant Ronald got involved in projects related to artificial intelligence and he has been following the field over the last 10 years. He is frequently invited to deliver presentations and keynotes related to AI in Pharma and Healthcare and has presented at numerous events internationally. He is also the founder of the LinkedIn groups ‘AI – Artificial Intelligence’, ‘Small Molecules’, ‘Boston Biotech’ and ‘Golden Triangle Biotech’ that serve over 10,000 members.