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About me

Hello! I’m an AI/ML researcher and a Master of Science student at Brown University, where my research focuses on building safe and efficient AI systems.


My graduate studies are a deep dive into Reinforcement Learning (covering MDPs and POMDPs for planning), Human-AI Interaction (including RLHF and Imitation Learning), and AI safety.


This academic work is backed by two years of professional experience supporting enterprise-grade solutions powered by ML models at Nice Actimize and a published paper on anomaly detection.

Master’s project:

Currently working on “Affordable AI for Everyone: Building Efficient Models with Minimal Training Samples” : Investigating efficient generative modeling (Small Language Models/SLMs) based on the Transformer architecture, focusing on optimization techniques to reduce training data and computational costs.

Undergrad Project 1: Smart Hospital Management System (Team Lead, 4 members) 

Designed and implemented a cloud-integrated system to monitor patient vitals in real-time. Developed system modules using Python and IoT device integration.

Undergrad Project 2: Meteor- (IoT) Bot making Project (Team Lead, 4 members)

Designed and built a physical robotic agent, implementing embedded Arduino code for real-time control and design.

Skills

AI & ML: PyTorch, TensorFlow 2, Hugging Face, JAX, Scikit-Learn, Reinforcement Learning

Programming & Data: Python, C++, Pandas, NumPy, Matplotlib

Engineering & Concepts: DSA, Object-Oriented Design, Test Automation, Full Project Lifecycle

Industry Experience

Associate Support Engineer | Nice Interactive Solutions (Actimize) (July 2023 – July 2025)
• Provided technical support for enterprise-grade platforms, troubleshooting systems powered by machine learning (ML) and AI models.
• Awarded the Individual Recognition Award (Q2 2024) for contributions to client success.


Business & Data Analysis

Associate Business Analyst | Merkle Sokrati (A Dentsu Inc. Company) (Nov 2022 – May 2023)
• Managed client accounts and supported data-driven strategies to optimize outcomes, using tools like Google Analytics.

Publications

"Real-Time Anomaly Detection in IoT Networks: Building ML Models to Identify Anomalies in IoT Device Behavior"
• Authored paper on detecting irregular device behavior by training and evaluating time-series (LSTM) and unsupervised (Isolation Forest) models; the LSTM model successfully learned typical behavior patterns and achieved an F1-score of 0.92 on a public dataset.