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Talk Title- Chaos and Machine Learning
Talk Description- This is a talk on chaos and its interaction with machine learning. Since most systems that we model are dynamical, it is important that we think about their attractors, and possible chaotic nature. And if so, how do we address them in machine learning systems? Contrariwise is it possible to model a chaotic system using machine learning systems?
Bio- I (Vikram Mullachery) am a Sr. AI/ML engineer with deep expertise in social media algorithms, ranking and recommendation, natural language processing etc. My work in Bayesian neural networks, causal inference and reinforcement learning techniques have been practically applied at Meta and a few startups in the NYC area. Previously, I have led teams of varying sizes in distributed work setups. Currently, I am working on a proprietary machine learning system for cybersecurity.