About Me

I build multimodal AI that understands long-form video: what happens, when, who is speaking, and what matters to the viewer.

I am a Senior Researcher in the Multimodal Experiences Lab of the Advanced Technology Group at Dolby Laboratories in San Francisco. I received my PhD in Electrical Engineering from New York University in 2022, advised by Chinmay Hegde. Before that I studied at Iowa State University and BITS Pilani, Goa, where I earned degrees in Electrical Engineering and Physics.

At Dolby, I work on video moment retrieval, long-form video question answering, event-level scene graphs, temporal action localization, active speaker detection, and agentic workflows for personalized video curation.

During my PhD I worked on inverse imaging problems, including compressed sensing, phase retrieval and HDR imaging with deep generative and untrained network priors, with provable guarantees. I also developed algorithms for training adversarially robust networks and studied new attacks on vision transformers.

Multimodal learning Video understanding Vision-language models Adversarial robustness Computational imaging

News

  • Sep 2026Semantic Action Graph (SportSAGE) accepted at the IEEE VIS 2026 Workshop on GenAI, Agents, and the Future of VIS. arXiv
  • Jul 2026Two papers accepted at ECCV 2026: OnPoint on point-supervised online action localization, and Video2Reaction on predicting audience reactions to video.
  • Apr 2026Our work on mitigating hallucinations in vision-language models was accepted to Findings of ACL 2026. Paper
  • Jun 2025Presented Moment Sampling in Video LLMs for Long-Form VQA at the CVPR 2025 Workshop on Video LLMs.
  • 2022Completed my PhD at NYU and joined Dolby Laboratories as a Senior Researcher.

Selected publications

ECCV 2026

OnPoint: Offline-to-Online Multi-Level Distillation for Point-Supervised Online Temporal Action Localization

S. Reza, G. Jagatap, M. Moghaddam, O. Camps, A. Fanelli

European Conference on Computer Vision, 2026

ECCV 2026

Video2Reaction: Training Foundation Video Models to Predict Audience Reaction

T. Nguyen, S. Zhang, S. Shankar, G. Jagatap, D. Chandran, A. Fanelli, M. Fiterau

European Conference on Computer Vision, 2026Also at the CVPR 2026 Workshop on Emerging Directions in Data for Multimodal Foundation Models

ACL 2026

Towards Mitigating Hallucinations in Large Vision-Language Models by Refining Textual Embeddings

A. Agrawal, G. KV, R. Aralikatti, G. Jagatap, J. Yuan, S. Baskar, V. Kamarshi, A. Fanelli, F. Huang

Findings of the Association for Computational Linguistics, 2026Also at the ICLR 2026 Workshop on Multimodal Intelligence

NeurIPS 2019

Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors

G. Jagatap, C. Hegde

Advances in Neural Information Processing Systems, 2019Acceptance rate: 21.18%

IEEE T-IT

Sample Efficient Algorithms for Recovering Structured Signals from Magnitude-Only Measurements

G. Jagatap, C. Hegde

IEEE Transactions on Information Theory, 2019

NeurIPS 2017

Fast, Sample Efficient Algorithms for Structured Phase Retrieval

G. Jagatap, C. Hegde

Advances in Neural Information Processing Systems, 2017Acceptance rate: 20.93%

All publications