Gregory Wornell
Sumitomo Electric Industries Professor in Engineering
Studies where information and learning theory meet the physical world, including statistical inference, machine learning, and computational imaging.
Gregory Wornell's lab at MIT works on learning theory & optimization, computational imaging & photography and efficient training & inference. 34 papers since 2023. 52 alumni. Applications close .
At a glance
- papers, 2023–2026
- 9/ year
- at T1 venues
- 0%
- PhD students
- —
- MS students
- —
- alumni listed
- 52
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 7 papers2025 · T1 0
- 12 papers2024 · T1 0
- 15 papers2023 · T1 0
T1 venuesOther
Research focuspaper titles that use each topic's words
- Learning theory & optimization11 papers
- Computational imaging & photography2 papers
- Efficient training & inference2 papers
- physical world0 papers
- statistical inference0 papers
Paper list
From the lab homepage.
20257 papers
- Learning (ICML- , (Vancouver, Canada), JulyA Unified View on Learning Unnormalized Distributions via Noise-Contrastive Estimation
- Acoust., Speech, Signal Processing (ICASSP- , (Hyperabad, India), AprilEstimating the Number and Locations of Boundaries in Reverberant Environments with Deep Learning (opens in a new tab)
- Acoust., Speech, Signal Processing (ICASSP- , (Hyperabad, India), AprilExtremum Encoding for Joint Baseband Signal Compression and Time-Delay Estimation for Distributed Systems (opens in a new tab)
- 2417-2432, MayJoint Data Compression and Time-Delay Estimation for Distributed Systems via Extremum Encoding (opens in a new tab)
- 4083-4100, AprilRF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge (opens in a new tab)
- Learning (ICML- , (Vancouver, Canada), , JulySatori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search
- Learning (ICML- , (Vancouver, Canada), JulyScore-of-Mixture Training: One-Step Generative Model Training Made Simple via Score Estimation of Mixture Distributions
202412 papers
- 65-80, JulyA Deep Learning Method for Reflective Boundary Estimation (opens in a new tab)
- Proc. SystAre Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage? (opens in a new tab)
- 8455-8466, DecemberCommunication Over Discrete Channels Subject to State Obfuscation (opens in a new tab)
- Proc.ACM ComputFaceFolds: Meshed Radiance Manifolds for Efficient Volumetric Rendering of Dynamic Faces (opens in a new tab)
- Proc. IntGambling-Based Confidence Sequences for Bounded Random Vectors (opens in a new tab)
- Proc. MachGibbs-based Information Criteria and the Over-Parameterized Regime
- Proc. IntGroup Fairness with Uncertainty in Sensitive Attributes (opens in a new tab)
- 1-12, JanuaryImaging Privacy Threats From an Ambient Light Sensor (opens in a new tab)
- 632-655, JanuaryInformation-Theoretic Characterizations of Generalization Error for the Gibbs Algorithm (opens in a new tab)
- Commun., Contr., Computing (Champaign, Illinois), SeptemberOn the Capacity of Scalar Gaussian Channels Subject to State Obfuscation (opens in a new tab)
- Proc. IntOperator SVD with Neural Networks via Nested Low-Rank Approximation (opens in a new tab)
- Proc. MachThermometer: Towards Universal Calibration for Large Language Models (opens in a new tab)
202315 papers
- (MLSP- , (Rome, Italy), September2D-Radar Imaging with Deep Convolutional Neural Networks (opens in a new tab)
- Theory (ISIT- , (Taipei, Taiwan), JuneA Bilateral Bound on the Mean-Square Error for Estimation in Model Mismatch (opens in a new tab)
- A375, MayAn Architecture for Passive Joint Localization and Environment Learning in Shallow-Water Underwater Acoustic Settings
- 665-677, JanuaryAn Architecture for Passive Joint Localization and Structure Learning in Reverberant Environments (opens in a new tab)
- 2364-2384, AprilAsynchronous Massive Access and Neighbor Discovery Using OFDMA
- (WACV- , (Waikoloa, HI), JanuaryCan Shadows Reveal Biometric Information? (opens in a new tab)
- (ICASSP- }, (Rhodes, Greece), JuneLearning Environmental Structure Using Acoustic Probes with a Deep Neural Network (opens in a new tab)
- Learning (ICML- , (Honolulu, HI), JulyOn Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation (opens in a new tab)
- A177, MayOn Data-Driven Underwater Acoustic Direct Localization: Design Considerations of a Deep Neural Network-Based Solution
- (ICASSP- , (Rhodes, Greece), JuneOn Neural Architectures for Deep Learning-Based Source Separation of Co-Channel OFDM Signals (opens in a new tab)
- Theory (ISIT- , (Taipei, Taiwan), JuneOn the Generalization Error of Meta Learning for the Gibbs Algorithm (opens in a new tab)
- (AAAI- , (Washington, DC), FebruaryPost-hoc Uncertainty Learning using a Dirichlet Meta-Model (opens in a new tab)
Where alumni went52 listed
Apply to MITFall 2027
MIT EECS Computer Science PhD
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