Suhas Kotha

Hello! I am a PhD student at Stanford, currently researching how to do deep learning when compute is infinite relative to data. I am fortunate to be advised by Percy Liang and Tatsunori Hashimoto. I previously studied CS at CMU, fortunate to be advised by Aditi Raghunathan.

In Fall 2026, Tatsu and I are teaching Deep Learning Alchemy, a new introduction to deep learning where students design experiments to probe empirical phenomena and test their understanding by predicting experiment outcomes. Consider taking it or following along at home!

Papers

Content

Fresh work/ideas

• Perturbation Invariant Gradients: Permutation Case Study
• 💜 Self-Improvement via Synthetic Data Augmentation
• Optimal Experimental Design with Compute Constraints
• Highest Likelihood Decoding
• 💜 Lower Diversity Accelerates Convergence for Regression
• Synthesizing Optimal Parallel Collectives via SAT
• The Type Theory of Transformers

Paper Analyses

• From Entropy to Epiplexity (Interestingness Part 2)
• Prediction with a Short Memory
• Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo
• The Optimization Trajectory of Two-Layer Linear Neural Networks
• Convex Optimization Theory Agrees with Learning Rate Scheduling
• Transformers are RNNs
• Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss
• A Universal Law of Robustness via Isoperimetry

Introductions

• Algorithmic Information Theory (Interestingness Part 1)
• Asymptotic Efficiency of Kernel Density Estimation
• 💜 Pre-training and RL as minimizing KL
• 💜 Off-policy RL
• Some Intuition Behind Double Descent
• Linear Policies are Optimal (for simple dynamics and costs)
• Second Law of Thermodynamics
• Elegance of Linear Regression
• Understanding Reinforcement Learning from Human Feedback
• Convex Geometry for Computers

Challenges

• Language Data Ordering and Filtering
• Emergence as Prediction Across Scale

Teaching

• Intructor/co-creator for Deep Learning Alchemy (Fall 2026)
• Instructor for Hype for Types (Spring 2022, Fall 2022)
• TA for Functional Programming (Spring 2021, Spring 2022)
• TA for Algorithm Design/Analysis (Fall 2022)
• TA for Mathematical Foundations for CS (Fall 2021)