Tamara Broderick
Associate Professor of EECS
Develops machine learning and Bayesian methods to quickly and reliably quantify uncertainty and robustness in data analysis.
Tamara Broderick's lab at MIT works on trustworthy & safe ML, probabilistic & causal inference and visualization & visual analytics. 30 papers since 2023, 11 at top venues, mostly JMLR and AISTATS. 17 alumni. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 37%
- PhD students
- 0
- MS students
- 0
- alumni listed
- 17
Papers by yearfrom the lab homepage, 2023–2026
- 8 papers2026 · T1 1 · collecting
- 8 papers2025 · T1 5
- 5 papers2024 · T1 2
- 9 papers2023 · T1 3
T1 venuesOtherVenues: JMLR, AISTATS, ICLR, TMLR, ICML
Research focuspaper titles that use each topic's words
- Probabilistic & causal inference8 papers
- Trustworthy & safe ML0 papers
- Visualization & visual analytics0 papers
- bayesian methods0 papers
- quantify0 papers
Paper list
From the lab homepage.
20268 papers
- Philosophical Transactions of the Royal Society AAn automatic finite-sample robustness metric: when can dropping a little data change conclusions? Part I: definitions and experiments
- Philosophical Transactions of the Royal Society AAn automatic finite-sample robustness metric: when can dropping a little data change conclusions? Part II: theory and intuition
- Bulletin of the American Meteorological SocietyAre Hourly PM2.5 Forecasts Sufficiently Accurate to Plan Your Day? Individual Decision-Making in the Face of Increasing Wildfire Smoke
- COLMDo LLMs Benefit From Their Own Words?
- ICLR (T1)Dropping Just a Handful of Preferences Can Change Top Large Language Model Rankings
- CHI WorkshopIt’s Complicated: On the Design and Evaluation of AI-Powered AAC Interfaces
- TS-LIMITSPrediction-powered inference for time series across space
- Symposium on Probabilistic Machine LearningQuit While You’re Ahead: Sequential Training Can Harm Simulation-Based Inference
20258 papers
- TMLR (T1)Approximations to worst-case data dropping: unmasking failure modes
- Learning Meaningful Representations of Life at ICLRBeyond Schrödinger Bridges: A Least-Squares Approach for Learning Stochastic Dynamics with Unknown Volatility
- AISTATS (T1)Consistent Validation for Predictive Methods in Spatial Settings
- JMLR (T1)How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences
- AISTATS (T1)Multi-marginal Schrödinger Bridges with Iterative Reference Refinement
- NeurIPS (T1)Smooth Sailing: Lipschitz-Driven Uncertainty Quantification for Spatial Association
- ICLR WorkshopWild posteriors in the wild
- Reliable ML from Unreliable DataWrong Model, Right Uncertainty: Spatial Associations for Discrete Data with Misspecification
20245 papers
- TMLR (T1)Are you using test log-likelihood correctly?
- JMLR (T1)Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box
- NeurIPS WorkshopCommon Functional Decompositions Can Mis-attribute Differences in Outcomes Between Populations
- ICLR WorkshopLearning a vector field from snapshots of unidentified particles rather than particle trajectories
- ICML WorkshopUsing gradients to check sensitivity of MCMC-based analyses to removing data
20239 papers
- ASSETSA Usability Study of Nomon: A Flexible Interface for Single-Switch Users
- Journal of the American Statistical AssociationConfidently comparing estimators with the c-value
- ICLR (T1)Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem
- Bayesian AnalysisEvaluating Sensitivity to the Stick-Breaking Prior in Bayesian Nonparametrics (with Discussion)
- ICML (T1)Gaussian processes at the Helm(holtz): A more fluid model for ocean currents
- Bayesian AnalysisIndependent finite approximations for Bayesian nonparametric inference
- BernoulliLocal Exchangeability
- JMLR (T1)The SKIM-FA Kernel: High-Dimensional Variable Selection and Nonlinear Interaction Discovery in Linear Time
- Science AdvancesToward a Taxonomy of Trust for Probabilistic Machine Learning
Where alumni went17 listed
Apply to MITFall 2027
MIT EECS Computer Science PhD
- Round
- Single round
- Opens
- —
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- See program page
- English
- TOEFL/IELTS
- Fee
- USD 90
Confirmed on the program page.
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