Interpretable AI & Women in Data Science

The ORSNZ is sponsoring an event next Monday (10 February 2020) with two international experts in the field of data science and machine learning. This is being hosted at the University of Auckland, as a NZ Data Science + Analytics Forum event.

The event will be held from 3:45pm-6:00pm in the Neon Lecture Theatre in Engineering (401.439) at 20 Symonds Street. There will be pizza and drinks provided after the presentations.

Parking is available in the Owen G Glenn Building (OGGB) carpark accessed via Grafton Road, opposite Stanley Street.

The event is free of charge and kindly sponsored by the University of Auckland and the Operations Research Society of New Zealand.

Speakers

Professor Margot Gerritsen from Stanford University is the co-director and co-founder of the global Women in Data Science (WIDS) phenomenum. Started as a conference at Stanford in November 2015, WiDS now includes a global conference with approximately 150+ regional events worldwide and a datathon, that encourages participants to hone their skills using a social impact challenge. Professor Gerritsen is also the host of the very popular WIDS podcasts featuring leaders in data science talking about their work, their journeys, and lessons learned.

Professor Dimitris Bertsimas has consulted widely in a variety of industries and has co-founded several very successful Analytics/AI startups. These include Dynamic Ideas (subsequently sold to American Express) which developed machine learning methods for asset management, D2 Hawkeye, a data mining health care company specialising in machine learning, and most recently P2 Analytics LLC, a consulting company and Interpretable AI, a machine learning company. Professor Bertsimas’ talk will focus on this latter work where he is building AI solutions that are human explainable. The benefits of using models, such as decision trees, that humans can interpret are well recognised. However, these models have often given poor performance when compared with black-box approaches. The dominant approaches for generating interpretable models were developed in the 80s, when computing power was limited. Bertsimas’ work is leveraging advances in modern optimization to revisit these approaches, delivering models that are both high performing and interpretable.

Come along and hear from these two international leaders in data science and artificial intelligence.

Biographies

Professor Margot Gerritsen, Stanford University, USA

Professor Gerritsen was born and raised in the Netherlands. After receiving her MS degree in Applied Mathematics at the University of Delft, she moved to the U.S. in search of hillier and sunnier places. In 1996 she received her Ph.D. in Scientific Computing and Computational Mathematics at Stanford University. Before returning to Stanford in 2001, she spent nearly five years in Auckland, New Zealand as a faculty member in the Department of Engineering Science.

Professor Gerritsen is a professor in the Department of Energy Resources Engineering at Stanford, interested in computer simulation and mathematical analysis of engineering and natural processes. From 2010 to 2018, she directed the Institute for Computational and Mathematical Engineering (http://icme.stanford.edu). Since 2015, she has been the Senior Associate Dean for Educational Affairs in the School of Earth, Energy and Environmental Sciences, as well as the co-director of Women in Data Science (WiDS, widsconference.org) and the host of the WiDS podcasts.

Professor Dimitris Bertsimas, MIT, USA

Professor Dimitris Bertsimas is currently the Boeing Professor of Operations Research, the Associate Dean of Business Analytics at the Sloan School of Management, MIT. He received his SM and PhD in Applied Mathematics and Operations Research from MIT in 1987 and 1988 respectively. He has been with the MIT faculty since 1988. His research interests include optimization, machine learning and applied probability and their applications in health care, finance, operations management and transportation. He has co-authored more than 200 scientific papers and five graduate level textbooks. He is the editor in Chief of INFORMS Journal of Optimization and former department editor in Optimization for Management Science and in Financial Engineering in Operations Research. He has supervised 72 doctoral students and he is currently supervising 25 others.

Professor Bertsimas is a member of the National Academy of Engineering since 2005, an INFORMS fellow, and he has received numerous research and teaching awards including the John von Neumann theory prize for fundamental, sustained contributions to the theory of operations research and the management sciences and the president’s award of INFORMS recognizing important contributions to the welfare of society, both in 2019, the Morse prize (2013), the Pierskalla award for best paper in health care (2013), the best paper award in Transportation (2013), the Farkas prize (2008), the Erlang prize (1996), the SIAM prize in optimization (1996), the Bodossaki prize (1998) and the Presidential Young Investigator award (1991-1996).

Professor Bertsimas has consulted widely in a variety of industries and has cofounded several very successful companies. In 1999, he co-founded Dynamic Ideas, LLC, which developed machine learning methods for asset management. In 2002, the assets of Dynamic Ideas were sold to American Express. From 2002-2010, he was the head of the quantitative asset management group of Ameriprise Financial, responsible for $12 billion of assets. In 2001, Bertsimas cofounded D2 Hawkeye, a data mining health care company and responsible for its machine learning capabilities. The company was sold to Verisk Health in 2009. In 2011 he cofounded Benefits Science Technologies LLC, a company that designs health care benefits, Savvi Financial LLC, a financial advice company and Alpha Dynamics LLC, an asset management company. In 2015 he cofounded P2 Analytics LLC, a consulting company and in 2018 Interpretable AI, a machine learning company.

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