Organizers:
Adji Bousso Dieng · Aleksandrina Goeva · Anshul Kundaje · Barbara Engelhardt · Chang Liu · David Van Valen · Debora Marks · Edward Boyden · Eli N Weinstein · Elizabeth Wood · Lorin Crawford · Mor Nitzan · Ray Jones · Romain Lopez · Tamara Broderick · 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. 

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. ProfessorEngelhardt 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 statistical 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 leverages and pioneers the 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 Worlds ”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 Broad Institute of Harvard/MIT. She is also the Founder and Director of Project Clio, a 501(c)(3) non-profit organization focused on the generation of safe and effective cell-based therapeutics for the treatment of autoimmune diseases and CEO of JURA Bio, Inc., a venture-backed startup with the same aim. She has developed a number of probabilistic 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 served as the 2020 WiML Finance and Sponsorship Chair. 

Eli Weinstein is a graduate student at Harvard University in the Biophysics Program, advised by Debora Marks in the Department of Systems Biology at Harvard Medical School and Jeffrey Miller in the Department of Biostatistics at Harvard School of Public Health. He is a Hertz Foundation Fellow.

Lorin Crawford PhD is a Senior Researcher at Microsoft Research New England. He also holds a faculty position as the RGSS Assistant Professor of Biostatistics at Brown University with an affiliation in the Center for Computational Molecular Biology. His scientific research interests involve the development of novel and efficient computational methodologies to address complex problems in statistical genetics, cancer pharmacology, and radiomics (e.g., cancer imaging). The central aim of Dr. Crawford's research program is to build machine learning algorithms and statistical tools that aid in the understanding of how nonlinear interactions between genetic features affect the architecture of complex traits and contribute to disease etiology. An overarching theme of the research done in the Crawford Lab group is to take modern computational approaches and develop theory that enable their interpretations to be related back to classical genomic principles. Before joining both MSR and Brown, Dr. Crawford received his PhD from the Department of Statistical Science at Duke University where he was formerly co-advised by Drs. Sayan Mukherjee and Kris C. Wood. He also received his Bachelors of Science degree in Mathematics from Clark Atlanta University. 

Mor Nitzan PhD is a Senior Lecturer (Assistant Professor) in the School of Computer Science and Engineering, and affiliated to the Institute of Physics and the Faculty of Medicine, at the Hebrew University of Jerusalem. Her research is at the interface of Computer Science, Physics, and Biology, focusing on the representation, inference and design of multicellular systems. Her group develops computational frameworks to better understand how cells encode multiple layers of spatiotemporal information, and how to efficiently decode that information from single-cell data. They do so by employing concepts derived from diverse fields, including machine learning, information theory and dynamical systems, while working in collaboration with experimentalists and capitalizing on vast publicly available data. Nitzan aims to uncover organization principles underlying information processing, division of labor, and self-organization of multicellular systems such as tissues, and how cell-to-cell interactions can be manipulated to optimize tissue structure and function. Prior to joining the Hebrew University as a faculty member, Nitzan was a John Harvard Distinguished Science Fellow and James S. McDonnell Fellow at Harvard University. She completed a BSc in Physics, and obtained a PhD in Physics and Computational Biology at the Hebrew University, working with Profs. Hanah Margalit and Ofer Biham, on the interplay between structure and dynamics in 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). Dr. Nitzan is a recipient of the Azrieli Foundation Early Career Faculty Fellowship, Google Research Scholar Award, Researcher Recruitment Award by the Israeli Ministry of Science and Technology, John Harvard Distinguished Science Fellowship, James S. McDonnell Fellowship, and the Schmidt Postdoctoral Award for Women in Mathematical and Computing Sciences.

Tamara Broderick PhD is an Associate Professor in the Department of Electrical Engineering and Computer Science at MIT. She is a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), the MIT Statistics and Data Science Center, and the Institute for Data, Systems, and Society (IDSS). She completed her Ph.D. in Statistics at the University of California, Berkeley in 2014. Previously, she received an AB in Mathematics from Princeton University (2007), a Master of Advanced Study for completion of Part III of the Mathematical Tripos from the University of Cambridge (2008), an MPhil by research in Physics from the University of Cambridge (2009), and an MS in Computer Science from the University of California, Berkeley (2013). Her recent research has focused on developing and analyzing models for scalable Bayesian machine learning. She has been awarded selection to the COPSS Leadership Academy (2021), an Early Career Grant (ECG) from the Office of Naval Research (2020), an AISTATS Notable Paper Award (2019), an NSF CAREER Award (2018), a Sloan Research Fellowship (2018), an Army Research Office Young Investigator Program (YIP) award (2017), Google Faculty Research Awards, an Amazon Research Award, the ISBA Lifetime Members Junior Researcher Award, the Savage Award (for an outstanding doctoral dissertation in Bayesian theory and methods), the Evelyn Fix Memorial Medal and Citation (for the Ph.D. student on the Berkeley campus showing the greatest promise in statistical research), the Berkeley Fellowship, an NSF Graduate Research Fellowship, a Marshall Scholarship, and the Phi Beta Kappa Prize (for the graduating Princeton senior with the highest academic average).

Thouis Jones ScD is a senior computational scientist working in the lab of Eric Lander at the Broad Institute. He was a cofounder of the CellProfiler project.  His 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. 

Wouter Boomsma PhD is an Associate Professor in Machine Learning at the University of Copenhagen. Originally 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 is a 2020 LSA Collegiate Fellow (Statistics), starting at the University of Michigan in Fall 2021. Yixin Wang works in the fields of Bayesian statistics, machine learning, and causal inference. Her research interests lie in the intersection of theory and applications. She completed her PhD in statistics at Columbia working with David Blei and her undergraduate in mathematics and computer science at the Hong Kong University of Science and Technology. One thread of Dr. Wang's research is designing fair machine learning algorithms that automate decision-making while reliably repairing historical discriminations. In addition to theoretical correctness, machine learning algorithms are often required to be fair in order to be deployed in practice. To this end, she is designing algorithms that operationalize equal opportunity and affirmative action in college admissions and loan decisions using counterfactual predictions.Her research centers around developing practical and trustworthy machine learning algorithms for large datasets that can enhance scientific understandings and inform daily decision-making. 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.