Professional Overview
I am a final-year PhD Candidate in Machine Learning at IISc. My doctoral research explores the theoretical and empirical aspects of federated optimization, with a core focus on mitigating data heterogeneity and modeling rational clients. Alongside this, I have developed a working knowledge of reinforcement learning for LLM post-training and of diffusion models, positioning me to contribute to the foundations of modern generative and alignment methods. Before returning to academia, In the tech industry, I developed machine learning models for EDA tools as a Principal Engineer at Cadence and designed algorithms based on statistical estimation and optimization under noise at Qualcomm
Experience
AI Expertise
Skills
Employment Type
AI Expertise
Skills
Employment Type
Education
Field of Study: Machine Learning and federated Optimization
Field of Study: Signal Processing
Areas of Expertise
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
