Ness B. Shroff
Engineering · The Ohio State University
Publications
679
Citations
20,336
Est. group size
~12
Recurring co-author estimate
Active years
36
Publishing since 1991
This researcher works on networking, wireless communication, and distributed computing systems, with recent work extending into the theory of machine learning, including large language model alignment, diffusion models, and federated/continual learning. The research combines mathematical analysis (e.g., convergence guarantees, optimization theory) with applied systems problems like edge computing, GPU cluster scheduling, and mobile crowdsourcing. Prospective students would likely engage with both theoretical modeling and applied systems experimentation.
Publication output has gradually declined over the last decade, from around 40 papers per year in 2017-2018 to roughly 15-20 per year in recent years, though the researcher remains active with new work appearing through 2026.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Learnable Chernoff Baselines for Inference-Time Alignment
Open MIND · 2026
- Learnable Chernoff Baselines for Inference-Time Alignment
arXiv (Cornell University) · 2026
- Constraint-Rectified Training for Efficient Chain-of-Thought
Open MIND · 2026
- Constraint-Rectified Training for Efficient Chain-of-Thought
arXiv (Cornell University) · 2026
- Provable Last-Iterate Convergence for Multi-Objective Safe LLM Alignment via Optimistic Primal-Dual
Open MIND · 2026
- Provable Last-Iterate Convergence for Multi-Objective Safe LLM Alignment via Optimistic Primal-Dual
arXiv (Cornell University) · 2026
- Sharp Convergence Rates for Masked Diffusion Models
Open MIND · 2026
- Sharp Convergence Rates for Masked Diffusion Models
arXiv (Cornell University) · 2026
- When Mobile Crowdsourcing Meets Queueing Systems: Human-in-the-Loop Learning
IEEE Transactions on Networking · 2026
- Prediction-Assisted Online Distributed Deep Learning Workload Scheduling in GPU Clusters
arXiv (Cornell University) · 2025
- BeST -- A Novel Source Selection Metric for Transfer Learning
arXiv (Cornell University) · 2025
- Discrete Diffusion Models: Novel Analysis and New Sampler Guarantees
arXiv (Cornell University) · 2025
- Unlocking the Power of Rehearsal in Continual Learning: A Theoretical Perspective
arXiv (Cornell University) · 2025
- Large Language Models Achieve Gold Medal Performance at the International Olympiad on Astronomy & Astrophysics (IOAA)
arXiv (Cornell University) · 2025
- Absorb and Converge: Provable Convergence Guarantee for Absorbing Discrete Diffusion Models
arXiv (Cornell University) · 2025
- arXiv (Cornell University)×111
- ACM SIGMETRICS Performance Evaluation Review×16
- Proceedings of the ACM on Measurement and Analysis of Computing Systems×9
- IEEE/ACM Transactions on Networking×8
- IEEE Transactions on Mobile Computing×5
- Xiaojun Lin
Engineering · Purdue University West Lafayette
- Xiaojun Lin
Engineering · Purdue University West Lafayette
- Atilla Eryılmaz
Engineering · The Ohio State University
- Mung Chiang
Engineering · Purdue University West Lafayette
- Irem Koprulu
Engineering · The Ohio State University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 19, 2026.
Claim or correct this profile