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Daniel Emaasit
Building The AI Assistant for Logistics & Supply Chain Managers
Daniel Emaasit is a Ph.D. Candidate of Transportation Engineering with a focus on nonparametric Bayesian methods for developing flexible statistical models for traveler-behavior analytics.
His interests lie in creating principled probabilistic models for data-scarce problems by incorporating expertise from subject-matter experts and contextual knowledge.
He is the creator of Pymc-learn, a Python library for probabilistic machine learning, and is authoring a book titled "Practical Probabilistic Machine Learning in Python."
Daniel's research spans Bayesian machine learning, nonparametric Bayesian methods, Bayesian optimization, model-based reinforcement learning, and probabilistic programming.
His educational background includes a Ph.D. in Civil and Transportation Engineering from the University of Nevada-Las Vegas, an MEng from Tennessee State University, and a BSc from the University of Dar es Salaam.
Previously, Daniel has held roles such as Co-Founder, CEO & Chief AI Officer at Logistify AI, Data Scientist at Haystax, Research Intern at IBM, Ph.D. Research Assistant at UNLV, Graduate Research Assistant at Tennessee State University, and Research Assistant at UDSM.
