Industry
2025 – Present
Machine Learning Engineer
Nirva Labs · Contract via Gradient Works LLC
Building AI-powered audio understanding systems: end-to-end ML pipelines for speaker diarization, voiceprint matching, and LLM-driven content extraction. Full-stack contributions spanning Python backend (FastAPI), iOS app (SwiftUI), and cloud infrastructure (AWS).
Publications
Two-Level Actor-Critic Using Multiple Teachers
Transactions on Machine Learning Research (TMLR) & AAMAS 2023
Enhanced Learning from Multiple Demonstrations with a Two-level Structured Approach
ALA Workshop at AAMAS 2018
Maintenance for Case Streams: A Streaming Approach to Competence-Based Deletion
ICCBR 2017
Case-Base Maintenance: A Streaming Approach
ICCBR Workshops 2016
Selected Research
Multiagent Coordination via Option Value Decomposition
2023 – 2024
Multi-agent option-critic framework (CTDE); addressed sparse reward with optimistic heuristic integration.
Two-Level Actor-Critic Using Multiple Teachers
2021 – 2023
Hierarchical RL framework for learning from heterogeneous-quality demonstrations via teacher selection and low-level policy optimization. Published in TMLR and AAMAS.
Efficient Exploration with Probability Map
2019 – 2021
Leveraged prior probability distributions as exploration heuristics to accelerate RL agent training in robotic search tasks.