Timezone: »
The last decade has seen both machine learning and biology transformed: the former by the ability to train complex predictors on massive labelled data sets; the latter by the ability to perturb and measure biological systems with staggering throughput, breadth, and resolution. However, fundamentally new ideas in machine learning are needed to translate biomedical data at scale into a mechanistic understanding of biology and disease at a level of abstraction beyond single genes. This challenge has the potential to drive the next decade of creativity in machine learning as the field grapples with how to move beyond prediction to a regime that broadly catalyzes and accelerates scientific discovery.
To seize this opportunity, we will bring together current and future leaders within each field to introduce the next generation of machine learning specialists to the next generation of biological problems. Our full-day workshop will start a deeper dialogue with the goal of Learning Meaningful Representations of Life (LMRL), emphasizing interpretable representation learning of structure and principles. The workshop will address this challenge at five layers of biological abstraction (genome, molecule, cell, system, phenome) through interactive breakout sessions led by a diverse team of experimentalists and computational scientists to facilitate substantive discussion.
We are calling for short abstracts from computer scientists and biological scientists. Submission deadline is Friday, September 20. Significant travel support is also available. Details here:
https://lmrl-bio.github.io/call
https://lmrl-bio.github.io/travel
Fri 8:30 a.m. - 8:45 a.m.
|
Opening Remarks
(
Welcome Address
)
Opening Remarks by Francis Collins, Director, NIH (by video) |
🔗 |
Fri 8:45 a.m. - 9:00 a.m.
|
Opening Remarks
(
Remarks
)
Opening remarks by Francis Collins (Director, National Institutes of Health) via video and Krishna Yeshwant, General Partner at Google Ventures. |
Krishna Yeshwant 🔗 |
Fri 9:00 a.m. - 9:30 a.m.
|
Keynote - Bio/ML
(
Keynote
)
link »
Aviv Regev. Professor of Biology; Core Member, Broad Institute; Investigator, Howard Hughes Medical Institute. Aviv Regev pioneers the use of single-cell genomics and other techniques to dissect the molecular networks that regulate genes, define cells and tissues, and influence health and disease. |
Aviv Regev 🔗 |
Fri 9:30 a.m. - 10:00 a.m.
|
Keynote - ML
(
Keynote
)
link »
Max Welling is a research chair in Machine Learning at the University of Amsterdam and a VP Technologies at Qualcomm. |
Max Welling 🔗 |
Fri 10:00 a.m. - 10:30 a.m.
|
In conversations: Daphne Koller and Barbara Englehardt
(
Keynote
)
Daphne Koller is the Rajeev Motwani Professor in the Computer Science Department at Stanford University and founder of insitro. |
Daphne Koller · Barbara Engelhardt 🔗 |
Fri 10:30 a.m. - 10:45 a.m.
|
Coffee Break
|
🔗 |
Fri 10:45 a.m. - 12:00 p.m.
|
Molecules and Genomes
(
Panel
)
David Duvenaud & Alan Asparu-Guzik; Michael Keiser & Jennifer Wei; David Jones & John Jumper; David Haussler & Alex D'Amour speak on jointly identified challenges. |
David Haussler · Djork-Arné Clevert · Michael Keiser · Alan Aspuru-Guzik · David Duvenaud · David Jones · Jennifer Wei · Alexander D'Amour 🔗 |
Fri 12:00 p.m. - 12:30 p.m.
|
Synthetic Systems
(
Panel
)
Pamela Silver, Debora Marks, and Chang Liu in conversation. |
Pamela Silver · Debora Marks · Chang Liu · Possu Huang 🔗 |
Fri 12:30 p.m. - 1:15 p.m.
|
Poster Session I (Lunch Provided)
(
Poster
)
Yixin Wang and Alex D'Amour in conversation. |
🔗 |
Fri 1:15 p.m. - 3:00 p.m.
|
Phenotype
(
Panel
)
Challenge Presenters: Casey Greene, Dylan Kotliar, Smita Kirshnaswamy Conversation Facilitators: Alex Wiltschko, Aurel Nagy, Brendan Bulik-Sullivan, Casey Greene, David Kelley, Dylan Kotliar, Eli van Allen, Gokcen Eraslan, James Zou, Matt Johnson, Meromit Singer, Nir Hacohen, Samantha Morris, Scott Linderman, Smita Krishnaswamy |
Nir HaCohen · David Reshef · Matthew Johnson · Sam Morris · Aurel Nagy · Gokcen Eraslan · Meromit Singer · Eliezer Van Allen · Smita Krishnaswamy · Casey Greene · Scott Linderman · Alexander Wiltschko · Dylan Kotliar · James Zou · Brendan Bulik-Sullivan
|
Fri 3:00 p.m. - 3:15 p.m.
|
Coffee Break
|
🔗 |
Fri 3:15 p.m. - 5:00 p.m.
|
Cell
(
Panel
)
Anne Carpenter, Hui Ting Grace Yeo, Jian Zhou, Maria Chikina, Alexander Tong, Benjamin Lengerich, Aly O. Abdelkareem, Gokcen Eraslan, Andrew Blumberg, Stephen Ra, Daniel Burkhardt, Emanuel Flores Bautista, Frederick Matsen, Alan Moses, Zhenghao Chen, Marzieh Haghighi, Alex Lu, Geoffrey Schau, Jeff Nivala, Luke O'Connor, Miriam Shiffman, Hannes Harbrecht and Shimbi Masengo Wa Umba Papa Levi present in a lightning round. |
Anne Carpenter · Jian Zhou · Maria Chikina · Alexander Tong · Ben Lengerich · Aly Abdelkareem · Gokcen Eraslan · Stephen Ra · Daniel Burkhardt · Frederick A Matsen IV · Alan Moses · Zhenghao Chen · Marzieh Haghighi · Alex Lu · Geoffrey Schau · Jeff Nivala · Miriam Shiffman · Hannes Harbrecht · Levi Masengo Wa Umba · Joshua Weinstein
|
Fri 5:00 p.m. - 5:45 p.m.
|
Closing Remarks
(
Closing
)
Chris Sander, Ila Fiete, and Dana Pe'er present. |
Chris Sander · Ila Fiete · Dana Peer 🔗 |
Fri 5:45 p.m. - 6:00 p.m.
|
Last Look at Posters (Drinks Provided)
(
Poster
)
|
🔗 |
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.
Yakir Reshef (Harvard University)
Jonathan Bloom (Broad Institute of MIT and Harvard)
Jasper Snoek (University of Toronto)
Barbara Engelhardt (Princeton University)
Scott Linderman (Stanford University)
Suchi Saria (Johns Hopkins University)
Suchi Saria is an assistant professor of computer science, health policy and statistics at Johns Hopkins University. Her research interests are in statistical machine learning and computational healthcare. Specifically, her focus is in designing novel data-driven computing tools for optimizing decision-making. Her work is being used to drive electronic surveillance for reducing adverse events in the inpatient setting and individualize disease management in chronic diseases. She received her PhD from Stanford University with Prof. Daphne Koller. Her work has received recognition in the form of two cover articles in Science Translational Medicine (2010, 2015), paper awards by the the Association for Uncertainty in Artificial Intelligence (2007) and the American Medical Informatics Association (2011), an Annual Scientific Award by the Society of Critical Care Medicine (2014), a Rambus Fellowship (2004-2010), an NSF Computing Innovation fellowship (2011), and competitive awards from the Gordon and Betty Moore Foundation (2013), and Google Research (2014). In 2015, she was selected by the IEEE Intelligent Systems to the ``AI's 10 to Watch'' list. In 2016, she was selected as a DARPA Young Faculty awardee and to Popular Science's ``Brilliant 10’’.
Alexander Wiltschko (Google Brain)
Casey Greene (University of Pennsylvania)
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.
Kresten Lindorff-Larsen (University of Copenhagen)
Kresten 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 New York in 2007. He returned to Copenhagen 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 of the 2009 Gordon Bell Prize, and has received several prestigious grants including a Hallas-Møller stipend (2011), a Sapere Aude grant (2012), and most recently a Novo Nordisk Foundation challenge programme grant (2019). His current research interests include developing and applying computational methods for integrative structural biology, and the integration of biophysics and genomics research.
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.
More from the Same Authors
-
2021 : Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning »
Zachary Nado · Neil Band · Mark Collier · Josip Djolonga · Mike Dusenberry · Sebastian Farquhar · Qixuan Feng · Angelos Filos · Marton Havasi · Rodolphe Jenatton · Ghassen Jerfel · Jeremiah Liu · Zelda Mariet · Jeremy Nixon · Shreyas Padhy · Jie Ren · Tim G. J. Rudner · Yeming Wen · Florian Wenzel · Kevin Murphy · D. Sculley · Balaji Lakshminarayanan · Jasper Snoek · Yarin Gal · Dustin Tran -
2021 : Multi-Group Reinforcement Learning for Maternal Health in Childbirth »
Barbara Engelhardt · Promise Ekpo -
2022 : Multi-fidelity Bayesian experimental design using power posteriors »
Andrew Jones · Diana Cai · Barbara Engelhardt -
2022 : Sequential Gaussian Processes for Online Learning of Nonstationary Functions »
Michael Minyi Zhang · Bianca Dumitrascu · Sinead Williamson · Barbara Engelhardt -
2022 : Multi-group Reinforcement Learning for Electrolyte Repletion »
Promise Ekpo · Barbara Engelhardt -
2022 : How can we use natural evolution and genetic experiments to design protein functions? »
Ada Shaw · June Shin · Debora Marks -
2022 : TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness Prediction »
Pascal Notin · Lodevicus van Niekerk · Aaron Kollasch · Daniel Ritter · Yarin Gal · Debora Marks -
2022 : Kernelized Stein Discrepancies for Biological Sequences »
Alan Amin · Eli Weinstein · Debora Marks -
2022 : scPerturb: Information Resource for Harmonized Single-Cell Perturbation Data »
Tessa Green · Stefan Peidli · Ciyue Shen · Torsten Gross · Joseph Min · Samuele Garda · Jake Taylor-King · Debora Marks · Augustin Luna · Nils Blüthgen · Chris Sander -
2022 : Designing and Evolving Neuron-Specific Proteases »
Han Spinner · Colin Hemez · Julia McCreary · David Liu · Debora Marks -
2023 Poster: Switching Autoregressive Low-rank Tensor Models »
Hyun Dong Lee · andrew warrington · Joshua Glaser · Scott Linderman -
2023 Poster: Causal-structure Driven Augmentations for Text OOD Generalization »
Amir Feder · Yoav Wald · Claudia Shi · Suchi Saria · David Blei -
2023 Poster: ProteinNPT: Improving protein property prediction and design with non-parametric transformers »
Pascal Notin · Ruben Weitzman · Debora Marks · Yarin Gal -
2023 Poster: NAS-X: Neural Adaptive Smothing via Twisting »
Dieterich Lawson · Michael Li · Scott Linderman -
2023 Poster: Convolutional State Space Models for Long-Range Spatiotemporal Modeling »
Jimmy Smith · Shalini De Mello · Jan Kautz · Scott Linderman · Wonmin Byeon -
2023 Poster: ProteinGym: Large-Scale Benchmarks for Protein Fitness Prediction and Design »
Pascal Notin · Aaron Kollasch · Daniel Ritter · Lodevicus van Niekerk · Nathan Rollins · Steffanie Paul · Ada Shaw · Ruben Weitzman · Jonathan Frazer · Mafalda Dias · Dinko Franceschi · Rose Orenbuch · Han Spinner · Yarin Gal · Debora Marks -
2022 Workshop: 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 -
2022 : Neural encoding and decoding of facial movements »
Scott Linderman -
2022 Poster: SIXO: Smoothing Inference with Twisted Objectives »
Dieterich Lawson · Allan Raventós · andrew warrington · Scott Linderman -
2022 Poster: Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs »
Julia Costacurta · Lea Duncker · Blue Sheffer · Winthrop Gillis · Caleb Weinreb · Jeffrey Markowitz · Sandeep R Datta · Alex Williams · Scott Linderman -
2022 Poster: Non-identifiability and the Blessings of Misspecification in Models of Molecular Fitness »
Eli Weinstein · Alan Amin · Jonathan Frazer · Debora Marks -
2022 Poster: JAWS: Auditing Predictive Uncertainty Under Covariate Shift »
Drew Prinster · Anqi Liu · Suchi Saria -
2021 : Invited talk (ML) - Barbara Engelhardt »
Barbara Engelhardt -
2021 Workshop: Your Model is Wrong: Robustness and misspecification in probabilistic modeling »
Diana Cai · Sameer Deshpande · Michael Hughes · Tamara Broderick · Trevor Campbell · Nick Foti · Barbara Engelhardt · Sinead Williamson -
2021 Workshop: 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 -
2021 : Discussion: Aleksander Mądry, Ernest Mwebaze, Suchi Saria »
Aleksander Madry · Ernest Mwebaze · Suchi Saria -
2021 : Dataset Shifts: 8 Years of Going from Practice to Theory to Policy and Future Directions »
Suchi Saria -
2020 : Chang Liu »
Chang Liu -
2020 Workshop: Learning Meaningful Representations of Life (LMRL.org) »
Elizabeth Wood · Debora Marks · Ray Jones · Adji Bousso Dieng · Alan Aspuru-Guzik · Anshul Kundaje · Barbara Engelhardt · Chang Liu · Edward Boyden · Kresten Lindorff-Larsen · Mor Nitzan · Smita Krishnaswamy · Wouter Boomsma · Yixin Wang · David Van Valen · Orr Ashenberg -
2020 Poster: Point process models for sequence detection in high-dimensional neural spike trains »
Alex Williams · Anthony Degleris · Yixin Wang · Scott Linderman -
2020 Oral: Point process models for sequence detection in high-dimensional neural spike trains »
Alex Williams · Anthony Degleris · Yixin Wang · Scott Linderman -
2020 Poster: Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations »
Joshua Glaser · Matthew Whiteway · John Cunningham · Liam Paninski · Scott Linderman -
2020 Poster: Evaluating Attribution for Graph Neural Networks »
Benjamin Sanchez-Lengeling · Jennifer Wei · Brian Lee · Emily Reif · Peter Wang · Wesley Qian · Kevin McCloskey · Lucy Colwell · Alexander Wiltschko -
2020 Affinity Workshop: Women in Machine Learning »
Xinyi Chen · Erin Grant · Kristy Choi · Krystal Maughan · Xenia Miscouridou · Judy Hanwen Shen · Raquel Aoki · Belén Saldías · Mel Woghiren · Elizabeth Wood -
2019 Workshop: Program Transformations for ML »
Pascal Lamblin · Atilim Gunes Baydin · Alexander Wiltschko · Bart van Merriënboer · Emily Fertig · Barak Pearlmutter · David Duvenaud · Laurent Hascoet -
2019 : Phenotype »
Nir HaCohen · David Reshef · Matthew Johnson · Sam Morris · Aurel Nagy · Gokcen Eraslan · Meromit Singer · Eliezer Van Allen · Smita Krishnaswamy · Casey Greene · Scott Linderman · Alexander Wiltschko · Dylan Kotliar · James Zou · Brendan Bulik-Sullivan -
2019 : Synthetic Systems »
Pamela Silver · Debora Marks · Chang Liu · Possu Huang -
2019 : In conversations: Daphne Koller and Barbara Englehardt »
Daphne Koller · Barbara Engelhardt -
2019 Poster: BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos »
Eleanor Batty · Matthew Whiteway · Shreya Saxena · Dan Biderman · Taiga Abe · Simon Musall · Winthrop Gillis · Jeffrey Markowitz · Anne Churchland · John Cunningham · Sandeep R Datta · Scott Linderman · Liam Paninski -
2019 Poster: Mutually Regressive Point Processes »
Ifigeneia Apostolopoulou · Scott Linderman · Kyle Miller · Artur Dubrawski -
2018 : Invited Talk Session 2 »
Debora Marks · Olexandr Isayev · Tess Smidt · Nathaniel Thomas -
2018 : Barbara Engelhardt »
Barbara Engelhardt -
2018 : Research Panel »
Sinead Williamson · Barbara Engelhardt · Tom Griffiths · Neil Lawrence · Hanna Wallach -
2018 : Panel on research process »
Zachary Lipton · Charles Sutton · Finale Doshi-Velez · Hanna Wallach · Suchi Saria · Rich Caruana · Thomas Rainforth -
2018 : TBC 4 »
Debora Marks -
2018 Poster: Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming »
Bart van Merriënboer · Dan Moldovan · Alexander Wiltschko -
2017 : Invited talk: Is interpretability and explainability enough for safe and reliable decision making? »
Suchi Saria -
2017 Workshop: Machine Learning for Health (ML4H) - What Parts of Healthcare are Ripe for Disruption by Machine Learning Right Now? »
Jason Fries · Alex Wiltschko · Andrew Beam · Isaac S Kohane · Jasper Snoek · Peter Schulam · Madalina Fiterau · David Kale · Rajesh Ranganath · Bruno Jedynak · Michael Hughes · Tristan Naumann · Natalia Antropova · Adrian Dalca · SHUBHI ASTHANA · Prateek Tandon · Jaz Kandola · Uri Shalit · Marzyeh Ghassemi · Tim Althoff · Alexander Ratner · Jumana Dakka -
2016 : Estimating What-if Outcomes for Targeting Interventions in a Clinical Setting »
Suchi Saria -
2016 Workshop: Machine Learning in Computational Biology »
Gerald Quon · Sara Mostafavi · James Y Zou · Barbara Engelhardt · Oliver Stegle · Nicolo Fusi -
2016 Tutorial: ML Foundations and Methods for Precision Medicine and Healthcare »
Suchi Saria · Peter Schulam -
2015 Workshop: Machine Learning For Healthcare (MLHC) »
Theofanis Karaletsos · Rajesh Ranganath · Suchi Saria · David Sontag