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

Saturday, Dec. 1

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)

Sunday, Dec. 2

8:00 am Coffee and Bagels
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