Applied ML Engineer
& Neuro-Tech Researcher

I’m a Machine Learning Engineer with hands-on industry experience in applied AI and a research background in computational neuroscience. My work connects scientific insight with real-world deployment—specializing in natural language processing (NLP), speech-driven interfaces, and end-to-end ML systems.

In industry, as an AI Robotics Engineer, I developed and deployed an embedded voice-to-motion robotics system showcased to 10K+ exhibition visitors. I implemented the full AI pipeline: speech recognition, real-time command mapping using a fine-tuned NLP model, and robotic motion execution—all running on embedded hardware, with integrated speech synthesis.

In academia, at NYU Grossman School of Medicine, I contributed to neuroscience research under Prof. Robert C. Froemke, analyzing hypothalamic neural activity using Tensor Component Analysis (TCA). My work focused on dimensionality reduction, temporal precision, and oxytocin-linked plasticity—revealing population-level neural dynamics underlying maternal learning.

As a technical initiative, I built a Voice AI Workout Assistant: a production-grade voice interface that transcribes, interprets, and recommends workouts in real time—featuring real-time speech recognition (Whisper ASR), intent and entity extraction (fine-tuned transformer classifier + hybrid NER with spaCy), and query-time boosted search (OpenSearch)—deployed end-to-end with FastAPI and React.