<aside> 💡 🏠 Home. 📖 Call for Submissions. 🕰️ Schedule. 🤓 Speakers. 💯Mentorship Session 😎Team. 🌐Program Committee. 🎉Accepted Papers
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The mentorship session at the 2021 ML4D revolves around providing mentorship and guidance to the teams that submitted to the "Problem Pitches" track.
This is the debut ML4D mentorship session and we are very happy that it is in collaboration with the Deep Learning Indaba mentorship program.
Daniel B. Neill is Associate Professor of Computer Science and Public Service at NYU’s Courant Institute Department of Computer Science and Wagner School of Public Service, and Associate Professor of Urban Analytics at NYU’s Center for Urban Science and Progress, where he directs the Machine Learning for Good Laboratory. Dr. Neill's research focuses on developing new methods for machine learning and event detection in massive and complex datasets, with applications ranging from medicine and public health to law enforcement and urban analytics. He was the recipient of an NSF CAREER award and was named one of the "ten AI researchers to watch" by IEEE Intelligent Systems. He received his M.Phil. from Cambridge University and his M.S. and Ph.D. in Computer Science from Carnegie Mellon University
Jerzy Wieczorek is an Assistant Professor of Statistics at Colby College in Waterville, Maine. His research focuses on model selection and assessment, complex survey data analysis, and visualization of estimates with uncertainty. Before his PhD work in Statistics at Carnegie Mellon University, Jerzy spent several years as a statistician at the U.S. Census Bureau. As a volunteer, Jerzy has also provided data analysis and visualization assistance for DataKind, Statistics Without Borders, and StatAid.
Giles Hooker is Professor of Statistics at the University of California, Berkeley. His work has focussed on statistical methods using dynamical systems models, inference with machine learning models, functional data analysis and robust statistics. He is the author of "Dynamic Data Analysis: Modeling Data with Differential Equations" and "Functional Data Analysis in R and Matlab". Much of his work has been inspired by collaborations particularly in ecology, human movement, and citizen science data
I am a DPhil student at Oxford University, working in collaboration with Instadeep Ltd. I completed my undergraduate and masters degree in theoretical physics at Stellenbosch University (South Africa). My main focus area is machine learning for dynamical systems. I am particularly interested in stochastic/partial differential equations, dynamics on graphs and robust uncertainty quantification in such models.