NECB 2026

MIT Department of Biology. His research develops deep learning methods for protein structure prediction and design, contributing to widely used tools such as ColabFold and modern approaches to evolutionary protein modeling.

ETH Zurich and the Paul Scherrer Institute. His group studies how mechanical forces on the cell nucleus regulate chromatin organization and gene expression, linking mechanobiology to cell fate decisions.

Professor at MIT and Co-Director of the Eric and Wendy Schmidt Center at the Broad Institute. Her research bridges statistics, machine learning, and biology, with a focus on causal inference for gene regulation and single-cell analysis.

Professor of Genetics at Harvard Medical School and Founding Director of the Center for Cancer Systems Biology (CCSB) at Dana-Farber Cancer Institute. His group pioneered systems-scale mapping of the human protein–protein interactome to understand how mutations perturb networks in disease.

Li Weibo Professor of Biomedical Research and founding Chair of the Department of Genomics and Computational Biology at UMass Chan Medical School. Her group develops computational methods for functional annotation of the human genome and leads the data analysis center of the ENCODE Consortium.

Senior Researcher at Microsoft Research New England. His work explores how machine learning — particularly self-supervised methods — can extract new biological insights from cellular imaging and molecular data.

Assistant Professor in the Division of Gastroenterology at Boston Children's Hospital and the Department of Pediatrics at Harvard Medical School. His lab uses quantitative approaches to study transcription factor function and developmental gene regulatory programs.

Yale University, working at the interface of physics, evolution, and immunology. Her group develops theoretical and computational models of how immune repertoires adapt in response to pathogens and disease.

Assistant Professor of Mathematics and Computer Science at Tufts University. Her group designs mathematical and computational methods to infer fitness landscapes and describe evolutionary processes, with applications to protein sequence and structure analysis.

Princeton University, in the Department of Computer Science and the Lewis-Sigler Institute for Integrative Genomics. His group develops computational methods for single-cell and spatial genomics to study immune cell function in cancer and infection.

Department of Biostatistics at the Harvard T.H. Chan School of Public Health. She develops statistical methods for high-dimensional inference and dimension reduction, with applications to single-cell genomics and computational biology.