Manash Goswami

Manash Goswami is a product management leader with a passion to solve problems and build products that meet end-user needs. Grown from development – coding, test & verification, systems design & architecture, to product management and business development. Delivered consumer electronics products from smartwatch to tablets. Deep understanding of the Android, Chrome & Windows eco-system. Demonstrated leadership by managing through influence across a matrixed org structure. Capable of driving new initiative/agenda internally or with customers, enable collaboration across functional teams and drive for financial results. Excel at dealing with ambiguity and managing complex, cross-group interactions. Can successfully tie long-term strategic thinking with near-term execution.

Ml Inference on Mobile Device With Onnx Runtime(Tutorial)

Ari Zitin

Ari Zitin holds bachelor’s degrees in both physics and mathematics from UNC-Chapel Hill. His research focused on collecting and analyzing low energy physics data to better understand the neutrino. Ari taught introductory and advanced physics and scientific programming courses at UC-Berkeley while working on a master’s in physics with a focus on nonlinear dynamics. While at SAS, Ari has worked to develop courses that teach how to use Python code to control SAS analytical procedures, including machine learning and optimization.

End to End Modeling and Machine Learning (Workshop)

Joaquin Vanschoren, PhD

Joaquin Vanschoren is Assistant Professor in Machine Learning at the Eindhoven University of Technology. His research focuses on machine learning, meta-learning, and understanding and automating learning. He founded and leads OpenML.org, an open science platform for machine learning. He received several demo and open data awards, has been tutorial speaker at NeurIPS and ECMLPKDD, and invited speaker at ECDA, StatComp, AutoML@ICML, CiML@NIPS, DEEM@SIGMOD, AutoML@PRICAI, MLOSS@NIPS, and many other occasions. He was general chair at LION 2016, program chair of Discovery Science 2018, demo chair at ECMLPKDD 2013, and he co-organizes the AutoML and meta-learning workshop series at NIPS and ICML. He is also co-editor of the book ’Automatic Machine Learning: Methods, Systems, Challenges’.

Tutorial on Automated Machine Learning(Workshop)

Emma Ning

Emma Ning is a senior Product manager in AI Framework team under Microsoft Cloud + AI Group, focusing on AI model operationalization and acceleration with ONNX/ONNX Runtime for open and interoperable AI. She has more than five years of product experience in search engine taking advantage of machine learning techniques and spent more than three years exploring AI adoption among various businesses. She is passionate about bringing AI solutions to solve business problems as well as enhancing product experience.

ML Inference on Mobile Device With Onnx Runtime(Tutorial)

Robert Osazuwa Ness, PhD

Robert didn’t start in machine learning. He started his career by becoming fluent in Mandarin Chinese and moving to Tibet to do developmental economics fieldwork. He later obtained a graduate degree from Johns Hopkins School of Advanced International Studies.

After switching to the tech industry, Robert’s interests shifted to modeling data. He attained his Ph.D. in mathematical statistics from Purdue University, and then he worked as a research engineer in various AI startups (he is currently a ML research scientist at Gamalon). He has published in journals and venues across these spaces, including RECOMB and NeurIPS, on topics including causal inference, probabilistic modeling, sequential decision processes, and dynamic models of complex systems. In addition to startup work, he is a machine learning professor at Northeastern University.

Causal Machine Learning Blitz(Workshop)