Kannan Ramchandran
Professor
Studies distributed machine learning, coding theory and signal processing.
Kannan Ramchandran's lab at UC Berkeley works on signal processing & information theory, learning theory & optimization and speech enhancement & signal processing. 25 papers since 2023, 16 at top venues, mostly NeurIPS and ICLR. 3 PhD students. 49 alumni. Applications close .
At a glance
- papers, 2023–2026
- 7/ year
- at T1 venues
- 64%
- PhD students
- 3
- MS students
- 0
- alumni listed
- 49
Papers by yearfrom DBLP, 2023–2026
- 2 papers2026 · T1 1 · collecting
- 7 papers2025 · T1 7
- 6 papers2024 · T1 3
- 10 papers2023 · T1 5
T1 venuesOtherVenues: NeurIPS, ICLR, AISTATS, ICML, ISIT
Research focuspaper titles that use each topic's words
- Learning theory & optimization7 papers
- Signal processing & information theory1 paper
- Speech enhancement & signal processing0 papers
- studies distributed0 papers
- coding theory0 papers
Paper list
From DBLP.
20262 papers
20257 papers
- ICLR (T1)EmbedLLM: Learning Compact Representations of Large Language Models (opens in a new tab)
- ICLR (T1)Looped Transformers for Length Generalization (opens in a new tab)
- AISTATS (T1)Online Assortment and Price Optimization Under Contextual Choice Models (opens in a new tab)
- NeurIPS (T1)ProxySPEX: Inference-Efficient Interpretability via Sparse Feature Interactions in LLMs (opens in a new tab)
- ICML (T1)SPEX: Scaling Feature Interaction Explanations for LLMs (opens in a new tab)
- ICML (T1)VersaPRM: Multi-Domain Process Reward Model via Synthetic Reasoning Data (opens in a new tab)
- NeurIPS (T1)Why Do Multi-Agent LLM Systems Fail? (opens in a new tab)
20246 papers
- NeurIPS (T1)An Analysis of Tokenization: Transformers under Markov Data (opens in a new tab)
- IEEE Trans. Inf. TheoryCompeting Bandits in Non-Stationary Matching Markets (opens in a new tab)
- NeurIPS (T1)Learning to Understand: Identifying Interactions via the Möbius Transform (opens in a new tab)
- IEEE Trans. Inf. TheoryModel Selection for Generic Contextual Bandits (opens in a new tab)
- Trans. Mach. Learn. Res.The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning (opens in a new tab)
- NeurIPS (T1)Transformers on Markov data: Constant depth suffices (opens in a new tab)
202310 papers
- ISITEfficiently Computing Sparse Fourier Transforms of q-ary Functions (opens in a new tab)
- NeurIPS (T1)Greedy Pruning with Group Lasso Provably Generalizes for Matrix Sensing (opens in a new tab)
- AISTATS (T1)Interactive Learning with Pricing for Optimal and Stable Allocations in Markets (opens in a new tab)
- NeurIPS (T1)Learning a 1-layer conditional generative model in total variation (opens in a new tab)
- IEEE Trans. Signal Process.Minimum-Rate Spectrum-Blind Sampling Based on Sparse-Graph Codes (opens in a new tab)
- ACSSCMRI Reconstruction with Side Information using Diffusion Models (opens in a new tab)
- NeurIPS (T1)Online Pricing for Multi-User Multi-Item Markets (opens in a new tab)
- Trans. Mach. Learn. Res.Spectral Regularization Allows Data-frugal Learning over Combinatorial Spaces (opens in a new tab)
- KDD (T1)Test Accuracy vs. Generalization Gap: Model Selection in NLP without Accessing Training or Testing Data (opens in a new tab)
- Manag. Sci.The Square Root Agreement Rule for Incentivizing Truthful Feedback on Online Platforms (opens in a new tab)
Where alumni went49 listed
Apply to UC BerkeleyFall 2027
UC Berkeley EECS Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- not required
- English
- TOEFL/IELTS (non-English-country degrees)
- Fee
- See program page
Confirmed on the program page.
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