Sanmi Koyejo is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign and a research scientist at Google AI in Accra. Koyejo's research interests are in developing the principles and practice of adaptive and robust machine learning. Additionally, Koyejo focuses on applications to biomedical imaging and neuroscience. Koyejo co-founded the Black in AI organization and currently serves on its board.
Shakir Mohamed is a senior staff scientist at DeepMind in London. Shakir's main interests lie at the intersection of approximate Bayesian inference, deep learning and reinforcement learning, and the role that machine learning systems at this intersection have in the development of more intelligent and general-purpose learning systems. Before moving to London, Shakir held a Junior Research Fellowship from the Canadian Institute for Advanced Research (CIFAR), based in Vancouver at the University of British Columbia with Nando de Freitas. Shakir completed his PhD with Zoubin Ghahramani at the University of Cambridge, where he was a Commonwealth Scholar to the United Kingdom. Shakir is from South Africa and completed his previous degrees in Electrical and Information Engineering at the University of the Witwatersrand, Johannesburg.
Kyunghyun Cho is an associate professor of computer science and data science at New York University and a research scientist at Facebook AI Research. He was a postdoctoral fellow at the Université de Montréal until summer 2015 under the supervision of Prof. Yoshua Bengio, and received PhD and MSc degrees from Aalto University early 2014 under the supervision of Prof. Juha Karhunen, Dr. Tapani Raiko and Dr. Alexander Ilin. He tries his best to find a balance among machine learning, natural language processing, and life, but almost always fails to do so.
I am a senior research scientist at Google Brain, where I lead the “Deep Phenomena” team. My approach is to bond theory and practice in large-scale machine learning by designing algorithms with theoretical guarantees that also work efficiently in practice. Over the recent years, I have been working on understanding and improving deep learning.
Prior to Google, I was a Research Scientist at Allen Institute for Artificial Intelligence and before that, a postdoctoral fellow at UC Irvine. I received my PhD from University of Southern California with a minor in mathematics in 2015.
Joaquin Vanschoren is an Assistant Professor in Machine Learning at the Eindhoven University of Technology. He holds a PhD from the Katholieke Universiteit Leuven, Belgium. His research focuses on meta-learning and understanding and automating machine learning. He founded and leads OpenML.org, a popular open science platform that facilitates the sharing and reuse of reproducible empirical machine learning data. He obtained several demo and application awards and has been invited speaker at ECDA, StatComp, IDA, AutoML@ICML, CiML@NIPS, AutoML@PRICAI, MLOSS@NIPS, and many other occasions, as well as tutorial speaker at NIPS and ECMLPKDD. He was general chair at LION 2016, program chair of Discovery Science 2018, demo chair at ECMLPKDD 2013, and co-organizes the AutoML and meta-learning workshop series at NIPS 2018, ICML 2016-2018, ECMLPKDD 2012-2015, and ECAI 2012-2014. He is also editor and contributor to the book 'Automatic Machine Learning: Methods, Systems, Challenges'.
Dr Ignatius Ezeani A Senior Teaching/Research Associate with the Data Science Group at Lancaster University. I'm interested in the application of NLP techniques in building resources for low-resource languages especially African languages, but my interests span other related areas like corpus linguistics, distributional semantics, machine learning, deep neural models and general AI.
Computing Innovation Fellow 2020, Research Assistant at University of Utah, Postdoctoral Fellow at UCLA starting Jan 2020. Research interests are Responsible and Interpretable AI, NLP and Algorithmic Fairness.
Data Engineer, Machine Learning Engineer, Cloud Engineer. Completing graduate CS work on classification of non-canonical, temporally variable, high-dimensional data.
I am a CIFAR AI Chair Assistant Professor of Computer Science at the University of Toronto, a Faculty Member at the Vector Institute, and Sr. Researcher at Nvidia. My current research focuses on machine learning for perception and control in robotics.
Freddie is a part-time Senior Research Fellow, and Theme Lead of ML for Earth Observation and Remote Sensing, in the Oxford Applied and Theoretical Machine Learning lab (led by Yarin Gal) of Oxford University. He's also an ML & Project Lead at NASA's Frontier Development Lab (FDL), and the (part-time) ML Lead of Trillium Technologies , the R&D production company behind FDL.
Since FDL US 2020, Freddie has been a ML & Project Lead for project Waters Of The United States (WOTUS), in partnership with the USGS, Planet, Maxar, Google Cloud and NVIDIA, towards the ultimate vision for mapping all flowing water on Earth , at near real-time, by fusing LiDAR sensors and daily very high resolution (VHR) satellite imagery.
He started his journey with FDL 2019 as a mentor , helping teams super-resolve solar magnetograms and predict GPS disruptions induced by solar weather .
Until April 2020, he was an Applied Research Scientist in the AI for Good lab (led by Julien Cornebise) of Element AI in London, focusing on applications of ML and statistics that enable NGOs and nonprofits.
During this work, he led the Multi-Frame Super-Resolution research collaboration with Mila Montréal , which was awarded by ESA …
PhD student at McGill University / Mila, advised by Dr Joelle Pineau & William L Hamilton. Research Assistant at Facebook AI Research (FAIR), Montreal.
I primarily work on logical language understanding, systematic generalization, logical graphs and dialog systems.