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Machine Learning & Reinforcement Learning
ML and RL built on the math underneath, not just the API.
Classical ML (regression, classification, clustering, anomaly detection), deep learning for vision and NLP, single-agent RL (DQN, PPO, policy gradients), and Multi-Agent RL for coordination problems. Quantization for mobile. Mentored academic foundation in MDPs, Bellman equations, and optimization theory.
Right call when…
- →Your problem has structure deep learning won't solve alone.
- →You need multi-agent coordination, not just a single predictor.
- →Quantizing a research model to ship on a phone is part of the brief.
Questions people ask about Machine Learning & Reinforcement Learning
When is classical ML the right tool over deep learning?
When you have structured, tabular, or domain-shaped problems with limited data, deep learning often overfits. Classical ML (regression, gradient boosting, anomaly detection) shines there. We pick based on the actual data, not the trend.
Have you shipped RL outside research papers?
Yes — single-agent DQN/PPO, multi-agent RL for coordination problems, and quantization for mobile deployment. We have academic foundation in MDPs and optimization theory.
Do you handle data infrastructure too?
Through partnerships with Backend & Infrastructure work. Data pipelines, labeling workflows, retraining cadence — all part of the ML engagement when needed.