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.

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.

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.

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 is Senior Director and Head of Data Science at Vertex Pharmaceuticals, a function he co-founded in 2017 to address data-centric problems across the entire business through application of advanced analytics and machine learning. Previously, Alex led the Discovery Informatics team at Vertex, with responsibility for HTS content and enrichment, the global chemogenomics initiative, as well as the company’s toxicogenomics efforts. From 2007 to 2012, he led a cross-functional team from lead discovery to late preclinical development that delivered a novel immuno-oncology candidate BVD-723. Alex started his career at Vertex as a computer-aided drug design scientist in 2001, and was involved in projects across a range of therapeutic areas from oncology to inflammation, and from rare diseases to antivirals. He serves on the Drug Discovery for the Nervous System committee at the Center for Scientific Review/NIH, and is a board member for MIT delta v entrepreneurship bootcamp at the MIT Martin Trust Center. Alex is an author of over 30 peer-reviewed manuscripts, and is an inventor on over 40 international patent applications. He holds a PhD in Chemistry from the University of Washington, and completed postdoctoral training at the University of California, San Francisco School of Pharmacy. He received an MBA from MIT Sloan School of Management.

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 former Head of Innovation Management and Scouting and member of Takeda’s Digital Strategy team Ronald helped 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 around strategy, commercialization and digital health. As founder of BioFrontline he provides 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 in the Netherlands Ronald led research projects on Parkinson’s disease, 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 for over 10 years. He is frequently invited to deliver presentations and keynotes related to AI in Pharma and Healthcare and has presented at national and international events. Ronald founded the LinkedIn groups ‘AI – Artificial Intelligence’, ‘Small Molecules’, ‘Boston Biotech’ and ‘Golden Triangle Biotech’ that serve over 10,000 members.

Cao (Danica) Xiao is the Director of Machine Learning at Analytics Center of Excellence of IQVIA. She is leading IQVIA’s North America machine learning team to drive next generation healthcare AI. Her team works on various projects on disease prediction, in silico drug modeling (e.g., adverse drug reaction detection, drug repositioning and de novo design) and clinical trial recruitment prediction. Her research focuses on using ML/AI approaches to solve diverse real world healthcare challenges. Particularly, she is interested in phenotyping on electronic health records, data mining for in-silico drug modeling, patient segmentation for neuro-degenerative diseases. Her research has been published in leading AI conferences including KDD, NIPS, ICLR, AAAI, IJCAI, SDM, ICDM, WWW and top health informatics journals such as Nature Scientific Reports and JAMIA. Prior to IQVIA, she was a research staff member in the AI for Healthcare team at IBM Research from 2017 to 2019 and served as member of the IBM Global Technology Outlook Committee from 2018 to 2019. She acquired her Ph.D. degree from University of Washington, Seattle in 2016.

Jimeng Sun is a professor of Computer Science Department at University of Illinois Urbana-Champaign (UIUC). Prior to UIUC, he worked at Georgia Tech and IBM Research. His research is on artificial intelligence (AI) for healthcare. And the core topics include 1) Deep learning for drug discovery, 2) Clinical trial optimization, 3) Computational phenotyping, 4) Clinical predictive modeling, 5) Treatment recommendation and 6) Health monitoring. Dr. Sun has been collaborating with many healthcare organizations.He published over 200 papers with h-index 59 and filed over 20 patents. He has received SDM/IBM early career research award 2017, ICDM best research paper award in 2008, SDM best research paper award in 2007, and KDD Dissertation runner-up award in 2008. In 2019, he was recognized as Top 100 AI Leaders in Drug Discovery and Advanced Healthcare. Dr. Sun received B.S.~and M.Phil.~in Computer Science from Hong Kong University of Science and Technology in 2002 and 2003, M.Sc and PhD in Computer Science from Carnegie Mellon University in 2006 and 2007.

Dr. Tom Chittenden is Chief Data Science Officer and Founding Director of the WuXi NextCODE Advanced Artificial Intelligence Research Laboratory. He is responsible for development and execution of the NextCODE global AI/ML R&D strategy. The principal focus of Dr. Chittenden’s work is the development and application of integrated systems biology models to investigate evolutionary factors of human disease. His work has been published in top-tier scientific journals, including featured articles in Nature and Science. In 2019, Dr. Chittenden was named among the top 100 A.I. Leaders in Drug Discovery and Advanced Healthcare. He is regarded as one of the world’s leading authorities on A.I. and causal statistical machine learning in precision medicine. Dr. Chittenden is an Accredited Professional Statistician™ with the American Statistical Association. In addition to his position at WuXi NextCODE, he holds academic faculty appointments at Boston Children’s Hospital and the Harvard Medical School. He is a Senior Fellow and Chief Statistical Sciences Advisor for the International Society for Philosophical Enquiry. Dr. Chittenden holds a PhD in Molecular Cell Biology and Biotechnology from Virginia Tech and a DPhil in Computational Statistics from the University of Oxford. His multidisciplinary postdoctoral training includes experimental investigations in Molecular and Cellular Cardiology from the Dartmouth Medical School and Computational Biology and Biostatistics from the Dana-Farber Cancer Institute and the Harvard School of Public Health.

Lucas Glass is the Global Head, Analytics Center of Excellence. The ACOE is responsible for researching, developing, and operationalization machine learning and data science solutions in the R&D business. Lucas started his career in pharmaceutical data science 15 years ago at Center (Galt) working on pharmacovigilance data mining algorithms. Since then he has worked at the US Department of Justice in healthcare fraud, several small startups, and TTC, llc which was acquired by IMS In 2012. Lucas holds a BA is Physics from Boston University, a MS in biostatistics from Drexel University, and is a PhD Candidate at Temple University where he is researching deep learning embedding techniques on large scale healthcare data.

Dr. Sharma has over 15 years of experience in applying and managing data science and machine learning approaches to solving complex problems in the healthcare space. Yugal was a scientist for the National Institutes of Health (NIH), focusing on developing early disease detection algorithms. Since NIH, Yugal helped found a technology startup, followed by a business consulting startup. Most recently, he applied his background as a government consultant focusing on analytics strategy for NIH and FDA clients.