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Learning Meaningful Representations of Life (LMRL)
Elizabeth Wood · Adji Bousso Dieng · Aleksandrina Goeva · Anshul Kundaje · Barbara Engelhardt · Chang Liu · David Van Valen · Debora Marks · Edward Boyden · Eli N Weinstein · Lorin Crawford · Mor Nitzan · Romain Lopez · Tamara Broderick · Ray Jones · Wouter Boomsma · Yixin Wang

Tue Dec 14 05:59 AM -- 02:00 PM (PST) @ None
Event URL: https://lmrl.org »

One of the greatest challenges facing biologists and the statisticians that work with them is the goal of representation learning to discover and define appropriate representation of data in order to perform complex, multi-scale machine learning tasks. This workshop is designed to bring together trainee and expert machine learning scientists with those in the very forefront of biological research for this purpose. Our full-day workshop will advance the joint project of the CS and biology communities with the goal of "Learning Meaningful Representations of Life" (LMRL), emphasizing interpretable representation learning of structure and principle.

We will organize around the theme "From Genomes to Phenotype, and Back Again": an extension of a long-standing effort in the biological sciences to assign biochemical and cellular functions to the millions of as-yet uncharacterized gene products discovered by genome sequencing. ML methods to predict phenotype from genotype are rapidly advancing and starting to achieve widespread success. At the same time, large scale gene synthesis and genome editing technologies have rapidly matured, and become the foundation for new scientific insight as well as biomedical and industrial advances. ML-based methods have the potential to accelerate and extend these technologies' application, by providing tools for solving the key problem of going "back again," from a desired phenotype to the genotype necessary to achieve that desired set of observable characteristics. We will focus on this foundational design problem and its application to areas ranging from protein engineering to phylogeny, immunology, vaccine design and next generation therapies.

Generative modeling, semi-supervised learning, optimal experimental design, Bayesian optimization, & many other areas of machine learning have the potential to address the phenotype-to-genotype problem, and we propose to bring together experts in these fields as well as many others.

LMRL will take place on Dec 13, 2021.

Tue 5:59 a.m. - 6:00 a.m.
All LMRL Events are being held online in Gather.Town (click any link below)  link »
Tue 6:00 a.m. - 7:00 a.m.
Poster Session  link »
Tue 7:00 a.m. - 7:10 a.m.
Opening remarks  link »
Tue 7:10 a.m. - 7:35 a.m.
Panel I (Panel)  link »
Tue 7:35 a.m. - 9:00 a.m.
Talks I (Talks)  link »
Tue 9:05 a.m. - 10:00 a.m.
Talks II (Talks)  link »
Tue 10:05 a.m. - 11:00 a.m.
Talks III (Talks)  link »
Tue 11:05 a.m. - 12:00 p.m.
Panel II (Panel)  link »
Tue 12:05 p.m. - 12:55 p.m.
Talks IV (Talks)  link »
Tue 12:55 p.m. - 1:00 p.m.
Closing remarks  link »
Tue 1:00 p.m. - 2:00 p.m.
Poster Session  link »

Author Information

Elizabeth Wood (Broad Institute)

Elizabeth Wood co-founded and co-runs JURA Bio, Inc., an early-stage therapeutics start up focusing on developing and delivering cell-based therapies for the treatment of autoimmune and immune-related neurodegenerative disease. Before founding JURA, Wood was a post-doc in the lab of Adam Cohen at Harvard, after completing her PhD studies with Angela Belcher and Markus Buehler at MIT, and Claus Helix-Neilsen at The Technical University of Denmark. She has also worked at the University of Copenhagen’s Biocenter with Kresten Lindorff-Larsen, integrating computational methods with experimental studies to understand how the ability of proteins to change their shape help modulate their function. Elizabeth Wood is a visiting scientist at the Broad Institute, where she serves on the steering committee of the Machine Inference Algorithm’s Initiative.

Adji Dieng (Columbia University)
Aleks Goeva (Broad Institute)
Anshul Kundaje (Stanford University)
Barbara Engelhardt (Princeton University)
Chang Liu (UC Irvine)

Professor Liu’s research is in the fields of synthetic biology, chemical biology, and directed evolution. He is particularly interested in engineering specialized genetic systems for rapid mutation and evolution of genes in vivo. These systems can then be widely applied for the engineering, discovery, and understanding of biological function.

David Van Valen (Caltech)
Debora Marks (Harvard University)

Debora is a mathematician and computational biologist with a track record of using novel algorithms and statistics to successfully address unsolved biological problems. She has a passion for interpreting genetic variation in a way that impacts biomedical applications. During her PhD, she quantified the pan-genomic scope of microRNA targeting - the combinatorial regulation of protein expression and co-discovered the first microRNA in a virus.  As a postdoc she made a breakthrough in the classic, unsolved problem of ab initio 3D structure prediction of proteins using undirected graphical probability models for evolutionary sequences. She has developed this approach to determine functional interactions, biomolecular structures, including the 3D structure of RNA and RNA-protein complexes and the conformational ensembles of apparently disordered proteins. Her new lab at Harvard is interested in developing methods in deep learning to address a wide range of biological challenges including designing drug affinity libraries for large numbers of human genes, predicting epistasis in antibiotic resistance, the effects of genetic variation on human disease etiology and drug response and sequence design for biosynthetic applications.

Edward Boyden (Massachusetts Institute of Technology)
Eli N Weinstein (Harvard)
Lorin Crawford (Microsoft Research)

I am a Senior Researcher at Microsoft Research New England. I also maintain a faculty position in the School of Public Health as the RGSS Assistant Professor of Biostatistics with an affiliation in the Center for Computational Molecular Biology at Brown University. The central aim of my 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. Some of my most recent work has landed me a place on Forbes 30 Under 30 list and recognition as a member of The Root 100 Most Influential African Americans in 2019. I have also been fortunate enough to be awarded an Alfred P. Sloan Research Fellowship and a David & Lucile Packard Foundation Fellowship for Science and Engineering. Prior to joining both MSR and Brown, I received my PhD from the Department of Statistical Science at Duke University where I was co-advised by Sayan Mukherjee and Kris C. Wood. As a Duke Dean’s Graduate Fellow and NSF Graduate Research Fellow I completed my PhD dissertation entitled: "Bayesian Kernel Models for Statistical Genetics and Cancer Genomics." I also received my Bachelors of Science degree in Mathematics from Clark Atlanta University.

Mor Nitzan (The Hebrew University of Jerusalem)

Mor Nitzan 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. Mor 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, Mor 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) and Prof. Aviv Regev (Broad Institute). Mor 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.

Romain Lopez (Genentech & Stanford University)
Tamara Broderick (MIT)
Ray Jones (Broad Institute)
Wouter Boomsma (University of Copenhagen)
Yixin Wang (Columbia University)

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