Webinar – Develop More Accurate Machine Learning Models with MIP

September 29th-30th, 2020

Please join us for this upcoming webinar

Join us for this upcoming webinar to learn how Interpretable AI uses MIP to develop more accurate machine learning models.

Interpretable AI is a software technology firm that brings cutting edge research in machine learning powered by modern optimization to an industrial scale. Recent research in interpretable machine learning has shown that many hard problems, such as finding the best subset of features in regression, can be solved both quickly and exactly with Mixed Integer Programming (MIP). These novel models pioneered by the founders of the company, have shown to achieve significantly better out of sample performance in real-world settings. Interpretable AI has applied its machine learning algorithms using MIP in a wide range of industries, including insurance, health care, manufacturing. 

In this webinar, you will learn:

  • How Interpretable AI uses MIP at scale to develop more accurate machine learning models. Examples include Optimal Decision Trees, Optimal Imputation, and Optimal Feature Selection 
  • Why machine learning under a modern optimization lens has an edge over traditional approaches
  • Real-world examples in health care and manufacturing where the interpretable machine learning algorithms bring value

For your convenience we have two sessions for you to choose from:

Tuesday, September 29th at 11 am EDT (GMT -4) presented by Daisy Zhuo, PhD, Co-Founding Partner at Interpretable AI.

Wednesday, September 30th at 9 am EDT (GMT -4) presented by Dimitris Bertsimas, PhD, Co-Founding Partner and Daisy Zhuo, PhD, Co-Founding Partner at Interpretable AI.

Click on the “Show in My Time zone” link at the top of the registration page for each webinar to see the start time in your local timezone.

Presenters

D Bertsimas-Interpretable AI

Professor Dimitris Bertsimas, is currently the Co-Founding Partner at Interpretable AI, Associate Dean of Business Analytics, Boeing Professor of Operations Research and faculty director of the Master of Business analytics at 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 four graduate level textbooks, with the most recent one being Machine Learning under a Modern Optimization Lens co-authored with another Co-Founding Partner Jack Dunn. 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 66 doctoral students and he is currently supervising 25 others. He is a member of the National Academy of Engineering since 2005, an INFORMS fellow, and he has received numerous research and teaching awards.

 

Daisy Zhuo, PhD - Interpretable AI

Daisy Zhuo, PhD, is a Co-Founding Partner at Interpretable AI. Supervised by Professor Bertsimas, during her PhD in Operations at MIT, she has developed a range of cutting-edge machine learning techniques such as Optimal Imputation and Robust Classifications, with publications in top machine learning and operations research journals. These algorithms have since become the core software modules of Interpretable AI. With expertise in mixed integer optimization and machine learning, she continues to research and develop new machine learning algorithms at Interpretable AI as well as applying them to solve real world industry problems. She has led the development of successful solutions in a wide range of industries including health care, insurance, finance, real estate, and manufacturing.

Interpretable AI : TURNING DATA INTO TRUSTED ACTION
With proprietary AI technologies, we build business solutions that simultaneously deliver full explainability and state-of-the-art performance.