MARBLE: Machine Learning and Artificial Intelligence for Biologics Engineering

Tommi Jaakkola (MIT)

Tommi Jaakkola received the M.Sc. degree in theoretical physics from Helsinki University of Technology, Finland, and Ph.D. from MIT in computational neuroscience. Following a postdoctoral position in computational molecular biology (DOE/Sloan fellow, UCSC) he joined the MIT EECS faculty 1998. He received the Sloan Research Fellowship 2002. His research interests include many aspects of machine learning, statistical inference and estimation, and algorithms for various modern estimation problems such as those involving multiple predominantly incomplete data sources. His applied research focuses on problems in computational biology such as transcriptional regulation.

Max Welling (University of Amsterdam and Microsoft Research)

Max is a research chair in Machine Learning at the University of Amsterdam and a Distinguished Scientist at MSR. He is a fellow at the Canadian Institute for Advanced Research (CIFAR) and the European Lab for Learning and Intelligent Systems (ELLIS) where he also serves on the founding board. His previous appointments include VP at Qualcomm Technologies, professor at UC Irvine, postdoc at U. Toronto and UCL under supervision of prof. Geoffrey Hinton, and postdoc at Caltech under supervision of prof. Pietro Perona. He finished his PhD in theoretical high energy physics under supervision of Nobel laureate prof. Gerard‘t Hooft.

Max has served as associate editor in chief of IEEE TPAMI from 2011-2015, he serves on the advisory board of the Neurips foundation since 2015 and has been program chair and general chair of Neurips in 2013 and 2014 respectively. He was also program chair of AISTATS in 2009 and ECCV in 2016 and general chair of MIDL 2018. Max is recipient of the ECCV Koenderink Prize in 2010 and the ICML Test of Time award in 2021. He directs the Amsterdam Machine Learning Lab (AMLAB) and co-directs the Qualcomm-UvA deep learning lab (QUVA) and the Bosch-UvA Deep Learning lab (DELTA).

Aaron Chevalier (Amazon)

Aaron is scientist and engineer specializing in high-throughput computational discovery, design, and testing of therapeutic and diagnostic proteins. He is currently leading an R&D team applying computational modeling to problems in natural sciences, building entirely new products for Amazon.

Debora Marks (Harvard Medical School, Broad Institute of Harvard and MIT)

Debora Marks (PI) is a world leading computational biologist with a focus on developing new AI methods to accelerate biotherapeutic discovery and now focusing on viral genome forecasting and immune response. In 2016, Dr. Marks received the ICSB Overton Award for outstanding accomplishment to the field of computational biology, in 2018 the Chan Zuckerberg Initiative Ben Barres Early Career Acceleration Award in the Neurodegeneration Challenge Design and in 2020 an NIH Director's Transformative award for antibody design.

Dr. Wengong Jin (PDRA, Broad Institute)

Wengong is a Postdoctoral Associate at Eric and Wendy Schmidt Center of Broad Institute. He finished his Ph.D. in MIT CSAIL, advised by Regina Barzilay and Tommi Jaakkola. His research seeks to develop novel machine learning algorithms for biology, including drug discovery, immunology, genetic engineering, and synthetic biology. He is particularly interested in deep generative models and graph neural networks.

Victor Greiff

Victor Greiff is an Associate Professor at the University of Oslo (Department of Immunology) since January 2018. His group develops machine learning, computational and experimental tools for analyzing antibody and T-cell repertoires to facilitate the in silico design of immune receptor-based immunodiagnostics and immunotherapeutics. 

Michael Adrian Jendrusch (PhD Student, EMBL Heidelberg)

Michael is a biology Ph.D. student in Jan Korbel's group at EMBL Heidelberg. He is developing machine learning methods for applications in the lab, ranging from bioimage analysis to protein design. In particular, he is focusing on generative models of protein sequences and structures to design proteins and specific protein-protein interactions.

Le Song (Chief Scientist of AI, Biomap and Professor, Mohamed bin Zayed University of AI)

Le's principal research interests lie in the development of machine learning models and algorithms, especially in the area of representation learning and graph neural networks. He is fascinated by the synergy between AI and other science areas, such as drug design and materials science