Schedule

All presentations to take place in MATH 175. Coffee Breaks to be held outside MATH 175 in the lobby area.

Time	Event Information
8:30 am	Coffee and Bagels
9:00 am	Information and Opening Remarks
9:10 am	Suresh Garimella, Office of the Executive Vice President and Mechanical Engineering, Purdue University, Purdue’s Integrative Data Science Initiative: Robust interdisciplinary collaboration to harness data for the greater good
9:30 am	Dejan Slepcev, Mathematics, Carnegie Mellon University, Optimization problems on random structures and their continuum limits
10:30 am	Coffee Break
11:00 am	Jennifer Neville, Computer Science and Statistics, Purdue University, Deep Learning for Relational Networks
12:00 pm	Lunch
2:00 pm	Mark Daniel Ward, Statistics and Mathematics, Purdue University, A Game Theory Problem from a Computational View
3:00 pm	Alum talk: Jason Lucas, Metron, Inc, A Brief Explanation of Kalman Filters
3:30 pm	Alum talk: Parsa Bakhtary, YouTube, Problem Formulation in Product Analytics
4:00 pm	Coffee Break
4:30 pm	Alum talk: Yi Wang, Bloomberg, ML/AI @ Bloomberg: Practices & Practitioners
5:00 pm	Alum talk: Pete Weigel, McKinsey & Company, Ensemble Machine Learning and Churn Reduction
5:30 pm	Kaiser Fung, Founder, Principal Analytics Prep, The Data Science Boom: What is a Myth, What is Real, and Is it a Fit for You?
7:00 pm	Reception, Purdue Memorial Union, 2nd floor, West Faculty Lounge (PMU 250)

Time	Event Information
8:30 am	Patrick Wolfe, College of Science, Computer Science, and Statistics, Purdue University, Big (Network) Data: Challenges and Opportunities for Data Science
9:30 am	Michael Trosset, Statistics, Indiana University, Distances and Dissimilarities in Mathematics and Data Science
10:30 am	Coffee Break
11:00 am	Mireille Boutin, Electrical and Computer Engineering, Mathematics, and Regenstrief Center for Healthcare Engineering, Purdue University, Clustering Small Data using Random Projection
12:00 pm	Alum talk: Nicholas Wegman, Antuit, Experiences in Data Science Consulting
12:30 pm	Break (lunch provided)
1:30 pm	Huda Nassar, Computer Science, Purdue University, The Julia Programming Language for Data Science
2:00 pm	David Gleich, Computer Science and Mathematics, Purdue University, Principled higher-order and multi-way methods in data science
3:00 pm	Raghu Pasupathy, Statistics, Purdue University, Stochastic Gradient Descent