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Learning Meaningful Representations of Life
Elizabeth Wood · Adji Bousso Dieng · Aleksandrina Goeva · Alex X Lu · Anshul Kundaje · Chang Liu · Debora Marks · Ed Boyden · Eli N Weinstein · Lorin Crawford · Mor Nitzan · Rebecca Boiarsky · Romain Lopez · Tamara Broderick · Ray Jones · Wouter Boomsma · Yixin Wang · Stephen Ra

Fri Dec 09 05:00 AM -- 02:00 PM (PST) @ Virtual
Event URL: http://lmrl.org »

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 Bousso Dieng (Princeton University & Google AI)
Aleksandrina Goeva (Broad Institute)
Alex X Lu (Microsoft Research)

I’m a Senior Researcher at Microsoft Research New England, in the BioML group. I’m interested in how machine learning can help us discover new insights from biological data, by finding patterns that are too subtle or large-scale to identify unassisted. I primarily focus on biological images, and my research often designs self-supervised learning methods, as I believe these methods are unbiased by prior knowledge.

Anshul Kundaje (Stanford 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.

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.

Ed Boyden
Eli N Weinstein (Columbia University)
Lorin Crawford (Microsoft)
Mor Nitzan (The Hebrew University of Jerusalem)
Rebecca Boiarsky (MIT)
Romain Lopez (Genentech & Stanford University)
Tamara Broderick (Massachusetts Institute of Technology)
Ray Jones (Broad Institute)
Wouter Boomsma (University of Copenhagen)
Yixin Wang (University of Michigan)
Stephen Ra (Prescient Design / Genentech)

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