Join faculty from Illinois Institute of Technology for an online panel discussion on data intelligence and data science, “How Data Science Impacts Industry, Education, and Society” on Friday, March 11 from 12:45–1:45 p.m.
Moderated by Lance Fortnow, College of Computing dean, the panel will cover the following topics:
- Discussion of the trustworthiness, credibility, and reliability of data science and AI algorithms and how they impact business and society
- How does data science impacts the pandemic and steer responses
- How does systems building relates to data science
- Why companies should enhance their ability to utilize data science
- How students can get involved in data science
Panelists will include Matthew Dixon, Kai Shu, Lulu Kang, and Ioan Raicu.
Panel Moderator
Lance Fortnow joined Illinois Tech in 2019 as the Dean of the College of Computing at the Illinois Institute of Technology. Previously, Fortnow was the chair of the School of Computer Science at the Georgia Institute of Technology and previously was a professor at Northwestern University and the University of Chicago, a senior research scientist at the NEC Research Institute and a one-year visitor at CWI and the University of Amsterdam. From 2007 to 2018, Fortnow held an adjoint professorship at the Toyota Technological Institute at Chicago. Fortnow’s research spans computational complexity and its applications. His work on interactive proof systems and time-space lower bounds for satisfiability have led to his election as a 2007 ACM Fellow. In addition, he was an NSF Presidential Faculty Fellow from 1992-1998 and a Fulbright Scholar to the Netherlands in 1996-97.
Panelists
Matthew Dixon, associate professor of applied mathematics, is a British applied mathematician working in the area of algorithmic finance. His research focuses on applying concepts in computational and applied mathematics to financial modeling, especially in the area of algorithmic trading and derivatives. He is a frequently invited speaker at quant and fintech events around the world in addition to be referenced as a computational finance expert in multiple reputed media outlets including the Financial Times and Bloomberg Markets.
Kai Shu, Gladwin Development Chair Assistant Professor of Computer Science, conducts research lying in machine learning, data mining, and social computing with applications in areas such as disinformation, artificial intelligence, education, and healthcare. He obtained his Ph.D. in Computer Science at Arizona State University in July 2020 under the supervision of Professor Huan Liu. He was the recipient of the 2020 ASU Engineering Dean’s Dissertation Award, 2020/2015 ASU CIDSE Doctoral Fellowship, 2018 SBP Disinformation Challenge Winner. He interned at Microsoft Research AI, Yahoo Research, and HP Labs.
Lulu Kang, associate professor of applied mathematics, obtained an M.S. in Operations Research and a Ph.D. in Industrial Engineering from the Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. She has worked on various areas in statistics and machine learning, including uncertainty quantification, statistical design and analysis of experiments, Bayesian computational statistics, etc. Kang is currently the associate editor for journals SIAM/ASA Journal on Uncertainty Quantification and TechnoKmetrics. She has been the program director of Master in Data Science at Illinois Tech and created other undergraduate programs including B.S. in Statistics.
Ioan Raicu is an associate professor in the Department of Computer Science (CS) at Illinois Institute of Technology, as well as a guest research faculty in the Math and Computer Science Division (MCS) at Argonne National Laboratory (ANL). He is also the founder and director of the Data-Intensive Distributed Systems Laboratory (DataSys) at Illinois Tech. His research focuses on distributed systems, grid computing, supercomputing, and cloud computing. He has been a strong supporter of engaging students early in their educational careers (high school and undergraduate students) to prepare them for graduate school and careers in research.