Sanjay Shakkottai
Professor
Directs the Center for Generative AI, working on diffusion models for image editing and inverse problems, bandits and statistical learning theory.
Sanjay Shakkottai's lab at UT Austin works on learning theory & optimization, generative & foundation models and image & video generation. 30 papers since 2023, 1 at top venues, mostly SIGCOMM. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 3%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 7 papers2026 · T1 0 · collecting
- 7 papers2025 · T1 0
- 8 papers2024 · T1 0
- 8 papers2023 · T1 1
T1 venuesOtherVenues: SIGCOMM
Research focuspaper titles that use each topic's words
- Generative & foundation models6 papers
- Learning theory & optimization5 papers
- diffusion models3 papers
- Image & video generation2 papers
- image editing0 papers
Paper list
From the lab homepage.
20267 papers
- ACM Transactions on Modeling and Performance Evaluation of Computing Systems (AMC TOMPECS), . An earlier version appeared in the Proceedings of the ACM Workshop on Machine Learning for NextG Networks (ACM MLNextG), Washington DC, November .CIPAT: A Two-stage Configuration Impact Prediction Analysis Toolkit for Cellular Networks (opens in a new tab)
- Proceeding of the Fourteenth International Conference on Learning Representations (ICLR), Rio de Janeiro, Brazil, April .Efficient Approximate Posterior Sampling with Annealed Langevin Monte Carlo (opens in a new tab)
- Proceeding of the Fourteenth International Conference on Learning Representations (ICLR), Rio de Janeiro, Brazil, April .Fine-Tuning Diffusion Models via Intermediate Distribution Shaping (opens in a new tab)
- To appear in the Proceedings of the Forty-Third International Conference on Machine Learning (ICML), Seoul, South Korea, July .Temper-Then-Tilt: Principled Unlearning for Generative Models through Tempering and Classifier Guidance (opens in a new tab)
- To appear in the Proceedings of the Forty-Third International Conference on Machine Learning (ICML), Seoul, South Korea, July . ProjectTest-Time Anchoring for Discrete Diffusion Posterior Sampling (opens in a new tab)
- To appear in the Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI), Renton, WA, May .Towards Performance Robustness for Microservices (opens in a new tab)
- To appear in the Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI), Renton, WA, May .UNUM: A New Framework for Network Control (opens in a new tab)
20257 papers
- Proceedings of the Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS), San Diego, CA, December . ProjectAnchored Diffusion Language Model (opens in a new tab)
- Proceedings of the Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS), San Diego, CA, December .Constrained Posterior Sampling: Time Series Generation with Hard Constraints (opens in a new tab)
- Proceedings of The Thirteenth International Conference on Learning Representations (ICLR), Singapore, April .Infilling Score: A Pretraining Data Detection Algorithm for Large Language Models (opens in a new tab)
- Proceedings of the Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS), San Diego, CA, December .Machine Unlearning under Overparameterization (opens in a new tab)
- Proceedings of the Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS), San Diego, CA, December .Meta-Learning Provably Learns Adaptable Foundation Models (opens in a new tab)
- Proceedings of The Thirteenth International Conference on Learning Representations (ICLR), Singapore, April . ProjectRB-Modulation: Training-Free Personalization of Diffusion Models using Stochastic Optimal Control (opens in a new tab)
- Proceedings of The Thirteenth International Conference on Learning Representations (ICLR), Singapore, April . ProjectSemantic Image Inversion and Editing using Stochastic Rectified Differential Equations (opens in a new tab)
20248 papers
- —A Lyapunov Theory for Finite-Sample Guarantees of Markovian Stochastic Approximation (opens in a new tab)
- IEEE Trans. on Automatic ControlAdaptive KL-UCB Based Bandit Algorithms for Markovian and I.I.D. Settings (opens in a new tab)
- Transactions on Machine Learning Research (TMLR), November .Bandits with Mean Bounds (opens in a new tab)
- ACM Trans. Model. Perform. Eval. Comput. Syst. (ACM TOMPECS), 9, 3, Article 12, September .Bandits with Stochastic Experts: Constant Regret, Empirical Experts and Episodes (opens in a new tab)
- IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR), Seattle, WA, June . ProjectBeyond First-Order Tweedie: Solving Inverse Problems using Latent Diffusion (opens in a new tab)
- Proceedings of the Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December .In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness (opens in a new tab)
- Proceedings of the Annual International Conference on Mobile Computing and Networking (ACM MobiCom; poster/short paper), Washington DC, November .Predicting the Performance of Cellular Networks: A Latent-resilient Approach (opens in a new tab)
- Proceedings of Forty- International Conference on Machine Learning (ICML), July .Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks (opens in a new tab)
20238 papers
- Proceedings of the Annual Conference on Learning Theory (COLT), Bangalore, India, July .Beyond Uniform Smoothness: A Stopped Analysis of Adaptive SGD (opens in a new tab)
- Proceedings of the International Conference on Machine Learning (ICML), Honolulu, HI July .Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits (opens in a new tab)
- SIGCOMM (T1)Darwin: Flexible Learning-based CDN Caching (opens in a new tab)
- Proceedings of the Annual Conference on Learning Theory (COLT), Bangalore, India, July .InfoNCE Loss Provably Learns Cluster-Preserving Representations (opens in a new tab)
- IEEE Transactions on Automatic Control (IEEE TAC), May .Multi-Agent Low-Dimensional Linear Bandits (opens in a new tab)
- Proceedings of the International Conference on Machine Learning (ICML), Honolulu, HI, July .PAC Generalization via Invariant Representations (opens in a new tab)
- Proceedings of the ACM Sigmetrics Conference on Measurement and Modeling of Computer Systems (ACM SIGMETRICS), Orlando, FL, June .Robust Multi-Agent Bandits Over Undirected Graphs (opens in a new tab)
- Proceedings of the Thirty- Conference on Neural Information Processing Systems (NeurIPS) , New Orleans, LA, December .Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to UT AustinFall 2027
Computer Science PhD
- Round
- Single round (Dec 15)
- Opens
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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