Keynotes - Recomb 2022

Keynote Speakers

Read more about our speakers by clicking on their name below.

Regina Barzilay

Regina Barzilay is a School of Engineering Distinguished Professor for AI and Health in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. She is an AI faculty lead for Jameel Clinic, an MIT center for Machine Learning in Health. Her research interests are in machine learning models for molecular modeling with applications to drug discovery and clinical AI. She also works in natural language processing. She is a recipient of various awards including the NSF Career Award, the MIT Technology Review TR-35 Award, Microsoft Faculty Fellowship and several Best Paper Awards at NAACL and ACL. In 2017, she received a MacArthur fellowship, an ACL fellowship and an AAAI fellowship. In 2020, she was awarded the Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity. She received her PhD in Computer Science from Columbia University, and spent a year as a postdoc at Cornell University. Prof. Barzilay received her undergraduate degree from Ben-Gurion University of the Negev, Israel.

Howard Y. Chang

Howard Y. Chang M.D., Ph.D. is the Virginia and D.K. Ludwig Professor of Cancer Research and Director of the Center for Personal Dynamic Regulomes at Stanford University. He is a Howard Hughes Medical Institute Investigator; he is also Professor of Dermatology and of Genetics at Stanford University School of Medicine. Chang earned a Ph.D. in Biology from MIT, M.D. from Harvard Medical School, and completed Dermatology residency and postdoctoral training at Stanford University. His research addresses how large sets of genes are turned on or off together, which is important in normal development, cancer, and aging. Chang discovered a new class of genes, termed long noncoding RNAs, that can control gene activity throughout the genome, illuminating a new layer of biological regulation. He invented ATAC-seq and other new methods for defining DNA regulatory elements genome-wide and in single cells. The long term goal of his research is to decipher the regulatory information in the genome to benefit human health.

Dr. Chang is a Member of the National Academy of Sciences, National Academy of Medicine, and American Academy for the Arts and Sciences. Dr. Chang’s honors include the NAS Award for Molecular Biology, Outstanding Investigator Award of the National Cancer Institute, Paul Marks Prize for Cancer Research, Judson Daland Prize of the American Philosophical Society, and the Vilcek Prize for Creative Promise. His work was honored by the journal Cell as a Landmark paper over the last 40 years and by Science as “Insight of the decade”.

John Chodera

The Chodera lab focuses on reimagining the way we develop small molecule drugs and pair therapeutics with individual patient tumors by bringing physical modeling and structure-informed machine learning into the cancer genomics era. By combining novel algorithmic advances to achieve orders-of-magnitude efficiency gains with powerful but inexpensive GPU hardware, machine learning, and distributed computing technologies, the lab is developing next-generation approaches and open source software for predicting small molecule binding affinities, designing small molecules with desired properties, predicting drug sensitivity or resistance of clinical mutations, and understanding the detailed structural mechanisms underlying oncogenic mutations. The Chodera lab co-develops the OpenMM GPU-powered molecular simulation framework, which powers numerous biomolecular modeling and simulation applications using physical modeling and machine learning. As a core member of the Folding@home Consortium, the lab harnesses the largest computing platform in the world—the first to reach an exaFLOP/s—pooling the efforts of a million volunteers around the world to study functional implications of mutations and new opportunities for therapeutic design against cancer targets and global pandemics. Dr. Chodera co-founded the Open Force Field Initiative, a scientific collaboration funded by the NIH and an industry consortium consisting of dozens of scientists working to develop modern open source infrastructure for building and applying high-quality biomolecular force fields. Dr. Chodera is also a co-founder of the COVID Moonshot, an open science patent-free drug discovery effort that has developed a patent-free small molecule therapy effective against COVID-19 now funded to reach IND-enabling studies by the WHO ACT-A program to provide a low-cost antiviral treatment targeted at low- and middle-income countries.

Lenore Cowen

Dr. Lenore J. Cowen is a Professor in the Computer Science Department at Tufts University, where she directs the NSF-funded Tufts T-Tripods institute, in the foundations of data science. She also has a courtesy appointment in the Tufts Mathematics Department, and in the Department of Genetics in the Tufts Graduate School of Biomedical Sciences. She received a BA in Mathematics from Yale and a Ph.D. in Mathematics from MIT. . Dr. Cowen’s research interests span three areas: Discrete Mathematics (since high school), Algorithms (since 1991 in graduate school) and most recently Computational Molecular Biology, where her research group now focuses mainly on predicting protein function from structural and biological network information. She led a team that won the DREAM Disease Module Identification challenge in 2016. In 2020, she was awarded both the CRA-E Undergraduate Research Faculty Mentoring Award from the Computing Research Association, and the NCWIT Undergraduate Research Mentoring Award from the National Center for Women and Information Technology.

John Marioni

Professor John Marioni became Head of Research at EMBL-EBI in 2021, in charge of developing and implementing the research vision of the institute. He shares a joint appointment at the Wellcome Sanger Institute and the Cancer Research UK Cambridge Institute within the University of Cambridge. He is heavily involved in the scientific organisation and governance of the Human Cell Atlas (HCA) project where he sits on the Organising Committee (OC) and co-chairs the HCA Analysis Working Group (AWG).

Professor Marioni joined EMBL-EBI as Research Group Leader in Computational and Evolutionary Genomics in 2010. His group develops the computational and statistical tools necessary to exploit high-throughput genomics data, with the aim of understanding the regulation of gene expression and modelling developmental and evolutionary processes. Within this context, the Marioni group focuses on understanding how the divergence of gene expression levels is regulated, using gene expression as a definition of the molecular fingerprint of individual cells to study the evolution of cell types, and modelling spatial variability in gene-expression levels within a tissue or organism.

Professor Marioni obtained his PhD in Applied Mathematics in the University of Cambridge in 2008 and did his postdoctoral research in the Department of Human Genetics, University of Chicago.

Bing Ren

Dr. Bing Ren is Director of the Center for Epigenomics and Professor of Cellular and Molecular Medicine at the University of California, San Diego (UCSD). He is also a Member of the Ludwig Institute for Cancer Research (LICR). Dr. Ren obtained his Ph.D. in Biochemistry from Harvard University in 1998, and joined the faculty at LICR and UCSD in 2001, after completing postdoctoral training at the Whitehead Institute.

Dr. Ren has made seminal contributions to the study of genome organization and regulation in mammalian cells. In particular, he has greatly advanced our understanding of the dynamic and cell-type specific function of transcriptional regulatory elements. He developed transformative technologies that allowed identification and characterization of these sequences in the genome. As an investigator of the NIH ENCODE consortium, he led the production of first maps of transcriptional regulatory sequences in the human and mouse genomes. He also made groundbreaking discoveries about the chromatin architecture. He found that the mammalian genome is partitioned into magabase-sized topologically associating domains (TADs), which serve to constrain the chromatin interactions between enhancers and promoters. By producing and characterizing high-resolution maps of chromatin contacts in the human genome during stem cell differentiation and across diverse cell types, he further elucidated general principles of chromatin organization and annotated target genes of enhancers. In recent years, Dr. Ren also developed single cell multiomic approaches for analysis of cell’s epigenome. His pioneering work in epigenome analysis has laid the foundation for characterization of the gene regulatory programs underlying mammalian development and aging, and helped interpret non-coding sequence variants contributing to complex traits and disease in humans.

He is a recipient of the Chen Award for Distinguished Academic Achievement in Human Genetic and Genomic Research, and an elected fellow of the American Association for the Advancement of Science.

Wenyi Wang

Dr. Wenyi Wang is Professor of Bioinformatics and Computational Biology and Biostatistics at The University of Texas MD Anderson Cancer Center and Adjunct Professor in Statistics at Rice University and Texas A&M University. Dr. Wang’s group focuses on the development and application of computational methods to study the evolution of the human genome as well as the cancer genome, and to further develop risk prediction models to accelerate the translation of biological findings to clinical practice. She has received multiple research grants from federal and state agencies including the National Cancer Institute. She serves on the editorial board of the Journal of the American Statistical Association Applications and Case Studies.

Dr. Wang is a pioneer in developing statistical methods, such as DeMixT, for transcriptomic deconvolution in heterogeneous tumor samples. She developed MuSE for accurate subclonal mutation calling, which won second place in the Mutation Calling Dream Challenge. MuSE calls are incorporated into the consensus mutation calls for whole-exome sequencing data from ~13,000 samples in The Cancer Genome Atlas and whole-genome sequencing data from ~2,700 samples in the Pan-Cancer Analysis of Whole Genomes (PCAWG). Most recently, she co-led a pan-cancer characterization of intratumor genetic heterogeneity in subclonal selection using PCAWG data.

Concurrently, Dr. Wang has 19 years of experience building risk prediction models for inherited cancer syndromes. Her work on these models and their clinical implications has been published in several high-impact oncology and statistics journals. The corresponding computer tools for risk counseling are freely available and used worldwide. She is currently leading a Cancer Prevention and Research Institute of Texas (CPRIT) grant to disseminate her software tool LFSPRO to genetic counselors. Her group also developed other new methods, such as Famdenovo for the identification of deleterious de novo mutations in rare inherited cancer syndromes.

Dr. Wang joined MD Anderson as an assistant professor in 2010. She received her PhD in biostatistics from Johns Hopkins University in 2007 and did her postdoctoral training in statistical genomics at UC Berkeley with Terry Speed and, concurrently, in genome technology at Stanford with Ron Davis.

RECOMB 2022, LA JOLLA, CA, USA, MAY 22-25, 2022

Email: recomb2022@ucsd.edu