Esha Singh

Esha Singh

e3singh@ucsd.edu

I am a fourth-year Ph.D. student at the University of California, San Diego (CSE), fortunately advised by Prof. Yi-An Ma. I also work closely with Prof. Rose Yu and Prof. Yu-Xiang Wang.

I spent my recent summers interning at Amazon Alexa+ AI (2025) and Cerebras Systems (2024).

Prior to my doctoral studies, I earned my Master's at the University of Minnesota, Twin Cities, advised by Prof. Ju Sun (GLOVEX lab). I also worked as a Research Assistant with Prof. Rui Zhang from 2019–2021, and completed my undergraduate degree at BITS Pilani.

News

Jul 2026 Presenting my work at ICML 2026, Seoul!
Jun 2026 Started as Applied Scientist Intern II at AWS AGI Foundations!
Feb 2026 New preprint: Divide and Learn — multi-objective combinatorial optimization at scale, accepted at ICML 2026!

Selected Publications

My research develops principled and scalable algorithms for modern machine learning, centering on generative models, optimization theory, and large language models. I am driven by a core question: how can we design learning algorithms that are simultaneously theoretically grounded, computationally efficient, and practically impactful?

Divide and Learn
Esha Singh, Dongxia Wu, Chien-Yi Yang, Tajana Rosing, Rose Yu, Yi-An Ma
ICML 2026

Reformulates multi-objective combinatorial optimization as online learning over decomposed decision spaces, achieving 80–98% of specialized solvers with two to three orders of magnitude improvement in sample and computational efficiency.

AmbientNova
Esha Singh
Amazon Alexa+ AI, 2025 · Technical Report

Temporally aligned multi-modality video generation using latent diffusion models for ambient sounds. Work done as an Applied Scientist II Intern at Amazon Alexa+ AI, Bellevue, WA.

Unstructured sparsity
Esha Singh, S. Bergsma, N. Dey, Joel Hestness, Gavia Gray
NeurIPS 2024 · Workshop on Machine Learning & Compression
MoXCo
CPAL 2025 (PMLR) · NeurIPS 2023 (M3L Workshop)

Adaptive optimizers that expedite exploration with faster convergence while ensuring avoidance of over-exploitation, leading to convergence to good solutions.

Social determinants of health survey
A. Bompelli, Y. Wang, R. Wan, Esha Singh, Y. Zhou, L. Xu, D. Oniani, B. Singh, J. Balls-Berry, Rui Zhang
SPJ, AAAS, 2021

A scoping review of SBDH factors, the relationship between SBDH and diseases, NLP techniques to extract SBDH from clinical notes, and predictive models using SBDH factors.

Conversational agent
Esha Singh, A. Bompelli, R. Wan, J. Bian, S. Pakhomov, Rui Zhang
Springer Nature, BMC, 2022

Develops the first Conversational Agent system for dietary supplement use using the MindMeld framework and the iDISK knowledge base.

Everyday Living AI Hub
R. Finzel, Esha Singh, M. Michalowski, M. Gini, S. Pakhomov
NAACL 2021

A proof-of-concept framework for enhancing health and wellbeing via tailored wearable and Conversational Agent solutions for non-invasive monitoring and personalized interventions.

iDISK
Esha Singh, A. Bompelli, A. Yang, A. Wang, S. Pakhomov, R. Zhang
IEEE ICHI 2020 (Oral Presentation)

A prototype conversational agent system catered to resolve user queries regarding dietary supplements.

Full list on Google Scholar →

Mentorship

Student program, ERSP (2022–23)

Undergraduate students, NAB Challenge & Zhang Lab (2020–21)

Service

Reviewer

  • NeurIPS 2025–26, ICML 2026, ICLR 2025–26, SLLM 2025 & ICLR FM-Wild 2025
  • Amazon Berlin ML Workshop 2025
  • HSSCOMMS, Springer Nature 2023

Teaching