Priyatham Kattakinda

Head of Engineering, RELAI

I am the Head of Engineering at RELAI, where I lead the team building agent optimization systems that help AI agents improve through simulation, evaluation, and iterative optimization while staying deeply hands-on with architecture and execution.

My background combines frontier AI research, with publications at ICLR, ICML, and NeurIPS, and practical engineering leadership to ship systems that are robust, measurable, and trusted in the real world.

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About

I build and lead AI engineering focused on agent optimization, helping teams turn unreliable agents into systems that improve through evaluation, simulation, deployment, observability, and continuous iteration.

My edge is operating at both levels: setting technical direction and execution standards for the team while staying close to the core architecture, failure modes, and implementation details that determine product quality.

I focus on agent optimization, LLM and agent evaluation, and production architecture for AI products, with technical leadership that compounds impact by aligning research, engineering, and product execution.

Timeline

2024 - Present
Head of Engineering at RELAI
- Leading engineering strategy and execution for agentic AI platforms focused on evaluation, robustness, and production readiness.
- Driving architecture, delivery, and team direction across reliability infrastructure, multi-agent workflows, optimization pipelines, and platform foundations.
2020 - 2024
PhD in Electrical and Computer Engineering, University of Maryland, College Park
- Worked on reliability of machine learning systems and generative models with a focus on robust behavior under distribution shift.
2018 - 2020
M.Tech in Electrical Engineering, IIT Madras
- Master's thesis on image denoising with A. N. Rajagopalan. Completed a summer internship at KLA focused on super-resolution of SEM images.
2016 - 2018
Co-Founder, Tessact
- Built software for scalable frame-level video annotation to support contextual advertising and media compliance workflows.
2012 - 2016
B.Tech in Electrical Engineering, IIT Bombay
- First exposure to computer vision through courses taught by Ajit Rajwade.

Publications

Rethinking Artistic Copyright Infringements in the Era of Text-to-Image Generative Models

Mazda Moayeri, Samyadeep Basu, Sriram Balasubramanian, Priyatham Kattakinda, Atoosa Chegini, Soheil Feizi

International Conference on Learning Representations (ICLR), 2025

Paper

On mechanistic knowledge localization in text-to-image generative models

Samyadeep Basu, Keivan Rezaei, Priyatham Kattakinda, Vlad I. Morariu, Niranjan Zhao, Ryan A. Rossi, Soheil Feizi

Forty-first International Conference on Machine Learning (ICML), 2024

Paper

Fast Adversarial Attacks on Language Models In One GPU Minute

Vinu Sankar Sadasivan, Shoumik Saha, Gaurang Sriramanan, Priyatham Kattakinda, Atoosa Chegini, Soheil Feizi

International Conference on Learning Representations (ICLR), 2024

Paper

LLM-Check: Investigating Detection of Hallucinations in Large Language Models

Gaurang Sriramanan, Siddhant Bharti, Vinu Sankar Sadasivan, Shoumik Saha, Priyatham Kattakinda, Soheil Feizi

Advances in Neural Information Processing Systems (NeurIPS), 2024

Paper

FOCUS: Familiar Objects in Common and Uncommon Settings

Priyatham Kattakinda, Soheil Feizi

Proceedings of the 39th International Conference on Machine Learning, 2022

Paper Code Dataset

Unpaired Image Denoising

Priyatham Kattakinda, A. N. Rajagopalan

IEEE International Conference on Image Processing (ICIP), UAE (Virtual), October 2020

Paper Code Presentation Project Page

Preprints

Understanding and Mitigating Compositional Issues in Text-to-Image Generative Models

Arman Zarei, Keivan Rezaei, Samyadeep Basu, Mehrdad Saberi, Mazda Moayeri, Priyatham Kattakinda, Soheil Feizi

Paper

Understanding the Effect of using Semantically Meaningful Tokens for Visual Representation Learning

Neha Kalibhat, Priyatham Kattakinda, Arman Zarei, Nikita V Seleznev, Samuel Sharpe, Sanjiv Kumar, Soheil Feizi

Paper

Invariant Learning via Diffusion Dreamed Distribution Shifts

Priyatham Kattakinda, Alexander Levine, Soheil Feizi

Paper