Sujay Sanghavi
Professor
Studies algorithms for high-dimensional data analysis and large-scale machine learning, including robust and efficient training methods.
Sujay Sanghavi's lab at UT Austin works on learning theory & optimization, trustworthy & safe ML and efficient training & inference. 16 papers since 2023, 14 at top venues, mostly ICML and NeurIPS. 14 alumni, most often to Amazon and Google. Applications close .
At a glance
- papers, 2023–2026
- 4/ year
- at T1 venues
- 88%
- PhD students
- 0
- MS students
- 0
- alumni listed
- 14
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 9 papers2025 · T1 8
- 6 papers2024 · T1 5
- 1 papers2023 · T1 1
T1 venuesOtherVenues: ICML, NeurIPS, ICLR, NAACL
Research focuspaper titles that use each topic's words
- Learning theory & optimization4 papers
- Efficient training & inference3 papers
- Trustworthy & safe ML0 papers
- efficient training0 papers
- large-scale machine0 papers
Paper list
From the lab homepage.
20259 papers
- ICLR (T1)Enhancing Language Model Agents using Diversity of Thought (opens in a new tab)
- ICML (T1)Geometric Median (GM) Matching for Robust -Subset Selection from Noisy Data (opens in a new tab)
- ICLR (T1)Infilling Score: A Pretraining Data Detection Algorithm for Large Language Models (opens in a new tab)
- NAACL (T1)InfoPO: On Mutual Information Maximization for Large Language Model Alignment (opens in a new tab)
- ICML (T1)Learning Mixtures of Experts with EM: A Mirror Descent Perspective (opens in a new tab)
- Conference on Language Modeling (COLM)RARe: Retrieval Augmented Retrieval with In-Context Examples (opens in a new tab)
- ICML (T1)Retraining with Predicted Hard Labels Provably Increases Model Accuracy (opens in a new tab)
- NeurIPS (T1)Understanding Self-Supervised Learning via Gaussian Mixture Models (opens in a new tab)
- ICML (T1)Upweighting Easy Samples in Fine-Tuning Mitigates Forgetting (opens in a new tab)
20246 papers
- COLMEarly Weight Averaging meets High Learning Rates for LLM Pre-training (opens in a new tab)
- ICML (T1)Improving Computational Complexity in Statistical Models with Local Curvature Information (opens in a new tab)
- NeurIPS (T1)In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness (opens in a new tab)
- NeurIPS (T1)SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors (opens in a new tab)
- ICML (T1)Time Weaver: A Conditional Time Series Generation Model (opens in a new tab)
- ICML (T1)Understanding the Training Speedup from Sampling with Approximate Losses (opens in a new tab)
20231 paper
Where alumni went14 listed, 85% to industry
Industry 11Academia 2
Most often to
- Amazon
- Citadel
Also to
- Cornell University
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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- Papers
- The lab homepage
- Members
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