"Machine Learning, Artificial Intelligence and Optimization: Opportunities for Inter-Disciplinary Innovation"
Presentation by Dr. Radhika Kulkarni Vice President (Retired), Advanced Analytics R&D, SAS Institute Inc.
Wednesday, September 23
3:30-5:00 PM CST — Reception and Seminar
About:
Machine learning tools and AI platforms have become prolific in many industries. Applications range from health care to financial applications to manufacturing industries. In the world of big data and ML / AI tools, there are numerous opportunities for application of optimization techniques. Large scale implementation of machine learning tools in artificial intelligence platforms require automation at several levels – increasing productivity along the entire analytics lifecycle as well as automated model selection to improve predictive models. In many of these problems, optimization techniques play an important role in finding solutions as well as improving performance.
This presentation will provide several examples that describe some of these innovations in various industries as well as discuss trends and upcoming challenges for future research.
Often, the mathematical model and solution are only a small part of the overall problem. It is also important to ensure the availability of the data required for the model, whether the final result is easy to interpret and sustainable in the real world and a myriad other aspects. In this talk, Kulkarni will discuss some of the practical concerns that are of equal importance: ease of implementation, acceptance of the results, safeguards needed to allow for over-rides of automatic decisioning, etc.
Bio:
Dr. Radhika Kulkarni retired as VP, Advanced Analytics R&D at SAS Institute Inc. where she was responsible for the world’s leading Analytics Software products portfolio. She spearheaded creation of the OR/AIML Center of Excellence to provide expert consulting to several Fortune 100 companies. She holds a Ph.D. in Operations Research from Cornell University. Under her leadership, OR gained recognition as a key contributor to scalability and performance of algorithms in statistics, machine learning, forecasting, data mining, econometrics, etc. She sponsored several partnerships with universities and robust internship programs for PhD students and serves on many academic advisory boards. Kulkarni is an INFORMS Fellow and WORMS Award winner, and has contributed in numerous ways to advance the careers of analytics professionals.
