I am a Master’s student in Computer Science at the University of Illinois Urbana-Champaign (UIUC), focusing on Distributed Systems, Machine Learning Systems, and Natural Language Processing. My work centers on building scalable, production-grade ML systems, where algorithmic performance, system efficiency, and real-world constraints matter equally.

I hold a Bachelor’s degree in Computer Science and Engineering, an AMIE in Electronics & Communication Engineering (with a focus on Mechatronics, Robotics, and Automation), and a Gold Medal in Advanced Diploma in Mechatronics & Industrial Automation (CSIO-ISTC). This multidisciplinary training grounds my work across systems, ML, and physical-world deployment.

Software Engineer — JPMorgan Chase & Co. I worked on data-intensive distributed systems for trade analytics, designing pipelines that processed large volumes of financial data with strong correctness and latency requirements. I also built NLP-driven chatbots used internally, and contributed to AWS-based cloud migrations, modernizing legacy on-prem systems into scalable, fault-tolerant architectures. This role shaped my interest in ML deployment, reliability, and systems-level performance.

Robotics Engineer — FANUC (Industrial Automation) Prior to software and ML, I spent ~4 years designing and deploying industrial robotic systems across large-scale manufacturing environments. I worked on real-time control, automation workflows, and system integration, where failures had physical consequences. This experience deeply informs how I think about correctness, latency, and robustness, which now translates directly into my work on distributed systems and ML infrastructure.

I have worked on Machine Learning, NLP, and Computer Vision projects, and authored research papers in applied deep learning. My current focus is on NLP systems—not just model accuracy, but efficient inference, data pipelines, and end-to-end system behavior. I am particularly interested in problems that sit at the boundary of ML and systems, such as scalable training, model serving, and resource-aware inference.

• Distributed Systems & ML Systems
• Natural Language Processing
• Computer Vision
• Robotics & Embodied AI

My long-term goal is to build intelligent systems with visual and conversational perception, capable of understanding their environment and interacting with humans in meaningful, reliable ways—bridging machine learning, systems engineering, and real-world deployment.

📌 Open to opportunities in Distributed Systems, Machine Learning, NLP, Deep Learning, and Python / KDB+/Q roles.

Long-term goal: to build machines with visual and conversational perception, enabling them to understand, reason about, and respond intelligently to their environments.