Utkarsh Lal

I currently work as a Machine Learning Engineer at Deloitte, building conversational agents and fine-tuning LLMs for RAG systems. I recently completed my MS in Business Analytics at UCLA, where I won first place in the Applied Analytics Program for building a Conversational Ads agent. Prior to Deloitte, I worked at Amazon, where I built an Autonomous Anomaly Diagnostics Engine using multi-step GraphRAG, processing data across 1000+ warehouses in production.
I have previously worked at American Express and Accenture, gaining exposure to everything from credit risk modeling and time-series forecasting to large-scale data engineering and production ML systems. I've also published research in Applied ML and Computational Neuroscience, which keeps me grounded in the fundamentals while working on applied problems.
What excites me most right now is making AI systems that actually work reliably in the real world. I'm drawn to the messy, practical challenges of turning research into products people can trust. My long-term goal is to lead teams building AI-native products that solve meaningful problems and push the boundaries of what's possible.

Research Interests

I'm currently conducting research in Computational Neuroscience. I'm interested in learning more about how AI tools can be leveraged to understand the neurological characteristics of debilitating disorders like Parkinson's and Alzheimer's. My recent research in this field utilizes Electroencephalography (EEG) and combines machine learning with statistical feature extraction measures such as fractal dimensions and entropies to capture the principal differentiating characteristics of such disorders. Furthermore, my recent research also delves into Sleep Stage Classification using Polysomnography, illustrating the significance of sophisticated feature extraction as a potentially more efficient avenue for sleep staging than complex deep neural networks.

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