Scholar
Vinayak Gupta
Google Scholar ID: tQuRm1AAAAAJ
ML Researcher, Lawrence Livermore National Laboratory
Citations & Impact
Citations
464
H-index
11
i10-index
13
Publications
20
Co-authors
17
list available
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- Selected Publications: 'Differentiable Adversarial Attacks for Marked Temporal Point Processes' (AAAI 2025); 'Cost-Effective Biological Data Analysis via a Benchmark and Ensemble of LLMs' (OSTI 2025); 'Are Language Models Actually Useful for Time Series Forecasting?' (NeurIPS 2024); 'Language Models Still Struggle to Zero-shot Reason about Time Series' (EMNLP 2024); 'SPML: A DSL for Defending Language Models Against Prompt Attacks' (Dataset Paper, 2024); 'Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event Sequences' (AAAI 2022); 'ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences' (KDD 2022); 'Learning Temporal Point Processes with Intermittent Observations' (AISTATS 2021). Awards: Nominated for ACM SIGKDD and ACM Doctoral Dissertation Awards; Featured on IndiaAI by the Ministry of IT, Government of India; Runner-up in NASSCOM's AI Gamechangers of India; Outstanding Doctoral Paper Award at the conference on AI-ML Systems.
Research Experience
- Current: Researcher at Lawrence Livermore National Lab's AI research group; Former: Postdoctoral Scholar at the Paul G. Allen School of Computer Science, University of Washington, working with Tim Althoff; AI Scientist at IBM Research; Applied Scientist Intern at Amazon; R&D Intern at Siemens.
Education
- Ph.D. in Machine Learning, Indian Institute of Technology (IIT), Delhi; B.Tech. in Computer Science, Indian Inst. of Info. Tech. (IIIT), Jabalpur
Background
- Research Interests: AI for Time Series and Health, including algorithms and benchmarks (including language models) for time series derived from medical records, health devices, and recommender systems. Also training foundational models for sequential biological data such as amino acids and nucleic acids. About Me: A Researcher in the AI research group (AIRG) at Lawrence Livermore National Lab. Previously, a Postdoctoral Scholar at the Paul G. Allen School of Computer Science, University of Washington, where I worked with Tim Althoff, an AI Scientist at IBM Research, and also held positions as an Applied Scientist Intern at Amazon and an R&D Intern at Siemens.
