Publications

Our fellows publish foundational advances in machine learning and new computational methods for biology in leading journals — and present their work at conferences around the world.
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2023
Cross-modal autoencoder framework learns holistic representations of cardiovascular state

Radhakrishnan, A., Friedman, S. F., Khurshid, S., Ng, K., Batra, P., Lubitz, S. A., Philippakis, A., and Uhler, C.

Nature Communications
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2023
2022
“Prospective study of circulating metabolomic signatures and breast cancer incidence among predominantly premenopausal women

Wang T., Zeleznik, O., McGee, E.E., Brantley, K.D., Balasubramanian, R., Rosner, B.A., Willett, W.C., Clish, C.B., and Eliassen, A.H.

Cancer Research
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2023
Critical bias in critical care devices

Charpignon, ML., Byers, J., Cabral, S., Celi, L.A., Fernandes. C., Gallifant, J., Lough, M.E., Mlombwa, D., Moukheiber, L., Ong, B.A., Panitchote, A., William, W., Wong, A.I., and Nazer, L.

Critical Care Clinics
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2023
Preprints and the future of scientific publishing: In favor of relevance

Glymour, M.M., Charpignon, M.L., Chen, Y.H., and Kiang, M.

American Journal of Epidemiology
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2023
Feature learning in neural networks and kernel machines that recursively learn features

Radhakrishnan, A., Beaglehole, D., Pandit, P., and Belkin, M.

2022
Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia

Charpignon, ML., Vakulenko-Lagun, B., Zheng, B., Magdamo, C., Bowen, S., Evans, K., Rodriguez, S., Sokolov, A., Boswell, S., Sheu, Y., Somai, M., Middleton, L., Hyman, B., Betensky, R.A., Finkelstein, S.N., Welsch, R.E., Tzoulaki, I., Blacker, D., Das, S., and Albers, M.W.

Nature Communications
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2022
Transfer learning with kernel methods

Radhakrishnan, A., Ruiz Luyten, M., Prasad, N., and Uhler, C.

2022
Towards automated crystallographic structure refinement with a differentiable pipeline

Li, M. and Hekstra, D.

Machine Learning for Structural Biology Workshop, NeurIPS
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2022
Fisher information lower bounds for sampling

Chewi, S., Gerber, P., Lee, H., and Lu, C.

Proceedings of The 34th International Conference on Algorithmic Learning Theory
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2022
Splenic red pulp macrophages provide a niche for CML stem cells and induce therapy resistance

Bührer, E., Amrein, M.A., Forster, S., Isringhausen, S., Schürch, C., Bhate, S., Brodie, T., Zindel, J., Stroka, D., Al Sayed, M., Nombela-Arrieta, C., Radpour, R., Riether, C., ad Oschbein, A.F.

2022
Active learning for optimal intervention design in causal models

Zhang, J., Cammarata, L., Squires, C., Sapsis, T.P., and Uhler, C.

2022
Causal structure learning: A combinatorial perspective

Squires, C. and Uhler, C.

Foundations of Computational Mathematics
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2022
Antibody-antigen docking and design via hierarchical equivariant refinement

Jin, W., Barzilay, R., and Jaakola, T.

Proceedings of the 39th International Conference on Machine Learning
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2022
Benign, tempered, or catastrophic: A taxonomy of overfitting

Mallinar, N., Simon, J. B., Abedsoltan, A., Pandit, P., Belkin, M., and Nakkiran, P.

2022
Causal structure discovery between clusters of nodes induced by latent factors

Squires, C., Yun, A., Nichani, E., Agrawal, R., and Uhler, C.

Proceedings of Machine Learning Research
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2022
Causal structure discovery between clusters of nodes induced by latent factors

Squires, C., Yun, A., Nichani, E., Agrawal, R., and Uhler, C.

Proceedings of Machine Learning Research
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2022
ETAB: A benchmark suite for visual representation learning in echocardiography

Alaa, A., Philippakis, A., and Sontag, D.

NeurIPS 2022 Conference Paper
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2022
Leadership
Our co-directors have spent their careers bridging the gap between data science and biology.
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Areas of Focus
Our research projects at the intersection of machine learning and biology aim to illuminate the programs of life.
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