Organizers:
Adji Bousso Dieng • Alán Aspuru-Guzik • Anshul Kundaje • Barbara Engelhardt • Chang Liu • David Van Valen • Debora Marks • Ed Boyden • Elizabeth Wood • Kresten Lindorff-Larsen • Mor Nitzan • Orr Ashenberg • Smita Krishnaswamy • Thouis Jones • Wouter Boomsma • Yixin Wang

Submissions Committee: Alan Amin • Max Shen • Miriam Shiffman

Adji Bousso Dieng PhD is a Researcher in Artificial Intelligence and Statistics. Her work bridges probabilistic graphical models and deep learning, both on the modeling and algorithmic fronts. She holds a PhD in Statistics from Columbia University where she was advised by David Blei and John Paisley. She co-organized a workshop on deep generative models at ICLR in 2019 and the symposium on advances in approximate Bayesian inference in the same year.

Alán Aspuru-Guzik PhD is a professor of Chemistry and Computer Science at the University of Toronto and is also the Canada 150 Research Chair in Theoretical Chemistry and a Canada CIFAR AI Chair at the Vector Institute. He is a CIFAR Lebovic Fellow in the Biologically Inspired Solar Energy program. Alán also holds a Google Industrial Research Chair in Quantum Computing. Alán began his independent career at Harvard University in 2006 and was a Full Professor at Harvard University from 2013-2018. He received his B.Sc. from the National Autonomous University of Mexico (UNAM) in 1999 and obtained a PhD from the University of California, Berkeley in 2004, where he was also a postdoctoral fellow from 2005-2006. Alán conducts research in the interfaces of quantum information, chemistry, machine learning and chemistry. He was a pioneer in the development of algorithms and experimental implementations of quantum computers and quantum simulators dedicated to chemical systems. He has studied the role of quantum coherence in the transfer of excitonic energy in photosynthetic complexes and has accelerated the discovery by calculating organic semiconductors, organic photovoltaic energy, organic batteries and organic light-emitting diodes. He has worked on molecular representations and generative models for the automatic learning of molecular properties. Currently, Alán is interested in automation and “autonomous” chemical laboratories. Among other recognitions, he received the Google Focused Award for Quantum Computing, the Sloan Research Fellowship, The Camille and Henry Dreyfus Teacher-Scholar award, and was selected as one of the best innovators under the age of 35 by the MIT Technology Review. He is a member of the American Physical Society and an elected member of the American Association for the Advancement of Science (AAAS) and received the Early Career Award in Theoretical Chemistry from the American Chemical Society.

Anshul Kundaje PhD is an Assistant Professor of Genetics and Computer Science at Stanford University. The Kundaje lab develops statistical and machine learning methods for integrative analysis of large-scale functional genomic data to decode regulatory elements and pathways across diverse cell types and tissues and understand their role in cellular function and disease. Anshul led the integrative analysis efforts for two of the largest functional genomics consortia - The Encyclopedia of DNA Elements (ENCODE) and The Roadmap Epigenomics Projects. The Kundaje lab has developed several interpretation methods for deep learning models in genomics. The Kundaje lab has also recently co-developed the first model repository for machine learning models in genomics http://kipoi.org/. Anshul is a recipient of the 2019 HUGO Chen Award, 2016 NIH Director’s New Innovator Award and 2014 Alfred Sloan Foundation Fellowship. Anshul is also a member of the NIH Director’s Advisory Committee for Artificial Intelligence in Biomedical Research.

Barbara Engelhardt PhD is an Associate Professor in the Princeton University Computer Science Department and the Center for Statistics and Machine Learning. Previously, she was at Duke University, where she had been an assistant professor in Biostatistics and Bioinformatics and Statistical Sciences. She graduated from Stanford University and received her Ph.D. from the University of California, Berkeley, advised by Professor Michael Jordan. She did postdoctoral research at the University of Chicago, working with Professor Matthew Stephens. Interspersed among her academic experiences, she spent two years working at the Jet Propulsion Laboratory, a summer at Google Research, and a year at 23andMe. Professor Engelhardt received an NSF Graduate Research Fellowship, the Google Anita Borg Memorial Scholarship, and the Walter M. Fitch Prize from the Society for Molecular Biology and Evolution. As a faculty member, she received the NIH NHGRI K99/R00 Pathway to Independence Award, a Sloan Faculty Fellowship, and an NSF CAREER Award. Professor Engelhardt’s research interests involve developing sta- tistical models and methods for the analysis of high-dimensional biomedical data, with a goal of understanding the underlying biological mechanisms of complex phenotypes and human disease.

Chang Liu PhD is an Assistant Professor in the Departments of Biomedical Engineering, Chemistry, and Molecular Biology Biochemistry at the University of California Irvine. His lab developed orthogonal DNA replication (Or- thoRep), a powerful genetic system for the continuous evolution of user-selected genes entirely in vivo. OrthoRep has been applied to the rapid evolution of several enzymes, proteins, and antibodies in hundreds of replicate experiments, in the process generating large datasets that may become synergistic with machine learning approaches for protein engineering. More broadly, Liu is interested in synthesizing specialized genetic systems that go beyond what nature’s genetic systems can do in order to accelerate evolution, reinterpret the genetic code, and record transient information as heritable genetic mutations. These systems are then applied to the discovery of useful biomolecules, biopolymers, and therapeutics; the study of molecular evolution; and the study of cell and developmental biology. Liu received his BA in Chemistry from Harvard, his PhD in Chemical Biology from The Scripps Research Institute, and was a Miller Fellow in Bioengineering at UC Berkeley before starting his lab at UC Irvine in 2013. Liu has taken leadership roles in the synthetic biology and protein engineering communities, for example as a council member in the Engineering Biology Research Consortium and co-chair of the 9th International Conference on Biomolecular Engineering.

David Van Valen PhD is faculty in the Division of Biology and Bioengineering at the California Institute of Technology. His research group’s long-term interest is to develop a quantitative understanding of how living systems process, store, and transfer information, and to unravel how this information processing is perturbed in human disease states. To that end, his group leveragesand pioneersthe latest advances in imaging, genomics, and machine learning to produce quantitative measurements with single-cell resolution as well as predictive models of living systems. Prior to joining the faculty, he studied mathematics (BS 2003) and physics (BS 2003) at the Massachusetts Institute of Technology, applied physics (PhD 2011) at the California Institute of Technology, and medicine at the David Geffen School of Medicine at UCLA (MD 2013).

Debora Marks PhD is an Associate Professor in the Department of Systems Biology at Harvard Medical School. As a mathematician and computational biologist, Marks develops novel algorithms, statistical methods and machine learning to successfully address unsolved biological problems. Her passion is to understand, predict and design biomolecular systems in a way that impacts biomedical applications and synthetic biology at many scales, with a focus on developing new methods in probabilistic modeling that exploit the huge and increasing corpus of natural and synthetic sequence diversity. Over the past five years, her lab has developed methods that accelerate structural biology with applications to cryoEM, crystallography, protein design and computed 3D structures of thousands of proteins with unknown folds, protein complexes and RNA interactions, as well as flexible, dynamic and even disordered protein ensembles. To address new challenges in protein design, Marks has adapted and developed NLP-inspired deep neural methods for (i) designing libraries of high affinity, specific nanobodies, antibodies that bypass the need for expensive rounds of selection or labor intensive specificity assays and (ii) design and prediction of proteins that induce membranes compartmentalization and potentially biostasis in human cells and (iii) predicting the effect of genetic variation on disease and drug response. In 2016, Marks received the ICSB Overton Award for outstanding accomplishment in the early-to-mid career with significant contribution to the field of computational biology, and in 2018 the Chan Zuckerberg Initiative Ben Barres Early Career Acceleration Award in the Neurodegeneration Challenge.

Ed Boyden PhD is Y. Eva Tan Professor in Neurotechnology at MIT, professor of Biological Engineering and Brain and Cognitive Sciences at MIT’s Media Lab and McGovern Institute for Brain Research, and was recently selected to be an Investigator of the Howard Hughes Medical Institute (2018). He leads the Synthetic Neurobiology Group, which develops tools for analyzing and repairing complex biological systems such as the brain, and applies them systematically to reveal ground truth principles of biological function as well as to repair these systems. These technologies include expansion microscopy, which enables complex biological systems to be imaged with nanoscale precision; optogenetic tools, which enable the activation and silencing of neural activity with light; robotic methods for directed evolution that are yielding new synthetic biology reagents for dynamic imaging of physiological signals, such as neural voltage; novel methods of noninvasive focal brain stimulation; and new methods of nanofabrication using shrinking of patterned materials to create nanostructures with ordinary lab equipment. He co-directs the MIT Center for Neurobiological Engineering, which aims to develop new tools to accelerate neuroscience progress. Amongst other recognitions, he has received the Croonian Medal (2019), the Lennart Nilsson Award (2019), the Warren Alpert Foundation Prize (2019), the Rumford Prize (2019), the Canada Gairdner International Award (2018), the Breakthrough Prize in Life Sciences (2016), the BBVA Foundation Frontiers of Knowledge Award (2015), the Carnegie Prize in Mind and Brain Sciences (2015), the Jacob Heskel Gabbay Award (2013), the Grete Lundbeck Brain Prize (2013), the NIH Director’s Pioneer Award (2013), the NIH Director’s Transformative Research Award (three times, 2012, 2013, and 2017), and the Perl/UNC Neuroscience Prize (2011). He was also named to the World Economic Forum Young Scientist list (2013) and the Technology Review World’s ”Top 35 Innovators under Age 35” list (2006), and is an elected member of the National Academy of Sciences (2019), the American Academy of Arts and Sciences (2017), the National Academy of Inventors (2017), and the American Institute for Medical and Biological Engineering (2018). His group has hosted hundreds of visitors to learn how to use new biotechnologies, and he also regularly teaches at summer courses and workshops in neuroscience, and delivers lectures to the broader public (e.g., TED (2011), TED Summit (2016), World Economic Forum (2012, 2013, 2016)).

Elizabeth Wood PhD is Visiting Scientist at the Stanley Center for Psychiatry at the Broad Institute of Harvard/MIT. She is also Co-Founder and Director of JURA Bio, Inc., a startup focused on the generation of safe and effective cell-based therapeutics for the treatment of autoimmune diseases. She has developed a number of proba- bilistic models for immune receptors with complex phenotypic data. Her previous work has focused on the generation of fully Bayesian models for RNA structure prediction as well as the computational design and synthesis of a host of engineered applications of synthetic biology: membrane-channel and membrane-displayed proteins tools in neurons, yeast, and co-block polymer lipids for use in optogenetics, carbon-capture and storage, and forward-osmosis water filtration. She serves as the 2020 WiML NeurIPS Finance and Sponsorship Co-Chair.

Kresten Lindorff-Larsen PhD is a Professor at the Linderstrøm-Lang Centre, University of Copenhagen. He is the director of a Novo Nordisk Foundation challenge programme center, PRISM (Protein Interactions and Stability in Medicine and Genomics), aimed towards using biophysical models and high-throughput protein chemistry experi- ments to study how genomic variation causes disease. He also directs the BRAINSTRUC structural biology initiative in which experiments and computation are combined to study the structure and dynamics of biomolecular assemblies in the brain. Both of these interdisciplinary projects integrate biophysical experiments and computational methods to solve complex problems in biology. Lindorff-Larsen trained as a biochemist at the University of Copenhagen and Carlsberg Laboratory, and completed his Ph.D. at the University of Cambridge in 2004. He then moved on to become an assistant professor in Copenhagen before joining D. E. Shaw Research in NewYork in 2007, and returned to Copen- hagen in 2011, where he now serves as a Professor of Computational Protein Biophysics. He received the Danish Independent Research Councils’ Young Researchers’ Award in 2006, was a co-recipient the 2009 Gordon Bell Prize, and has received several prestigious grants and scholarships. He has organized several conferences including Con- formational Ensembles from Experimental Data and Computer Simulations (http://tinyurl.com/BPS-berlin-2017) focused on integration of computational modelling and biophysics experiments. His current research interests include developing and applying computational methods for integrative structural biology, and the integration of biophysics and genomics research.

Mor Nitzan PhD is currently a John Harvard Distinguished Science Fellow James S. McDonnell Fellow at Harvard University, studying representation, inference and design of multicellular systems. Specifically, developing computational frameworks for learning organization principles underlying the ways in which cells encode multiple layers of information, and how to efficiently decode that information, as well as learning design rules for multicellular self-organization and division of labor. Nitzan completed a BSc in Physics, and obtained a PhD in Physics and Computational Biology at the Hebrew University, with Prof. Hanah Margalit and Prof. Ofer Biham, working on the interplay between structure and dynamics in multi-layered gene regulatory networks. She was then hosted as a postdoctoral fellow by Prof. Nir Friedman (Hebrew University), in collaboration with Prof. Aviv Regev (Broad Institute). In 2020 she will be joining the faculty of the School of Computer Science and Engineering at the Hebrew University of Jerusalem.

Orr Ashenberg PhD is a computational biologist at the Klarman Cell Observatory within the Broad Institute and the computational lead for the Human Tumor Atlas Pilot Program. His focus is the comprehensive characterization of cells in healthy and diseased tissue. This area has been transformed by recent breakthroughs in single-cell profiling that allow identification of cell type, developmental state, and function for thousands of cells. As a computational biologist, Orr works together with experimental biologists to develop and carry out the analyses needed to build these cell profiles, as well as to guide the next set of biological questions. Orr is particularly excited to use these approaches to deepen our understanding of the immune system response in cancers like glioblastoma and neuroblas- toma. Orr received his A.B. in chemistry and chemical biology from Harvard College and his Ph.D. in computational and systems biology from MIT. Orr moved to the West Coast to complete his postdoc at the Fred Hutchinson Cancer Research Center, and returned to Massachusetts for his position at the Broad Institute.

Thouis (Ray) Jones PhD is a senior computational scientist working in the lab of Eric Lander at the Broad Institute. He was a cofounder of the CellProfiler project whose research interests include understanding mechanisms of gene regulation and automatic image analysis for high-throughput cell imaging systems. He has a PhD in Computer Science from MIT.

Smita Krishnaswamy PhD is an Assistant professor in Genetics and Computer Science, and the Kingsley fellow at the Yale School of Medicine. She is affiliated with the applied math program, computational biology program, Yale Center for Biomedical Data Science and Yale Cancer Center. Her lab works on the development of machine learning techniques to analyze high dimensional high throughput biomedical data. Her focus is on deep representation learn- ing, specifically manifold learning and deep learning techniques for detecting structure and patterns in data. She has developed algorithms for non-linear dimensionality reduction and visualization, learning data geometry, denoising, imputation, inference of multi-granular structure, and inference of feature networks from big data. Her group has applied these techniques to many data types such as single cell RNA-sequencing, mass cytometry, electronic health record, structural protein data, and connectomic data from a variety of systems. Specific application areas include immunology, immunotherapy, cancer, neuroscience, developmental biology and health outcomes. Prior to Yale, Smita was a postdoctoral fellow at Columbia University in the Systems Biology department. Smita has a Ph.D. in Computer Science and Engineering from the University of Michigan where her dissertation “Design Analysis and Test of Logic Circuits under Uncertainty,” won an outstanding dissertation award and was published as a book by Springer in 2013.

Wouter Boomsma PhD is an Associate Professor in Machine Learning at the University of Copenhagen. Orig- inally trained in statistical modelling, structural bioinformatics and molecular simulation, his group has in recent years focused on learning compact representations of molecular structure and sequence-structure relationships. He is a reviewer and regular attendee of NeurIPS and was co-organizer of the 2017 Machine Learning and Molecules meeting (https://mlmol.github.io/) – a fruitful early attempt to bring together researchers from biology, chemistry and machine learning to highlight important challenges and developments in the field.

Yixin Wang PhD was formerly a student in the Statistics Department of Columbia University, advised by Professor David Blei. Her research interests lie in Bayesian statistics, machine learning, and causal inference. Prior to Columbia, she completed undergraduate studies in mathematics and computer science at the Hong Kong University of Science and Technology. Her research has received several awards, including the INFORMS data mining best paper award, student paper awards from American Statistical Association Biometrics Section and Bayesian Statistics Section, and the ICSA conference young researcher award.