ICDS supports the grassroots Penn State Data Science Community, which hosts events for anyone interested in data science at Penn State. Upcoming Spring 2021 Data Science talks include:
February 25, 1:00-2:00 p.m.
“Algorithmic fairness for socially optimal decision-making”
Presented by Hadi Hosseini, Assistant Professor of Information Sciences and Technology
Abstract: The advent of distributed platforms has given rise to novel challenges in designing socially desirable algorithms in complex multi-agent systems, which call for subtle, practical, and scalable solutions. Algorithmic fairness has been the focus of study in economics, mathematics, and AI for decades. It encompasses solutions for a wide variety of real-world applications including assigning students to courses, assigning riders to drivers in ride-sharing platforms, dividing inheritance among heirs, matching donors to kidney patients, and distributing charitable food items. In this talk, I will focus on the importance of making decisions according to preference data, discuss algorithmic and theoretical advances in fair resource allocation, and argue how empirical and theoretical findings, together, can provide deep insights into designing socially desirable systems.
“Using machine learning to understand gene regulation”
Presented by Shaun Mahony, Assistant Professor of Biochemistry and Molecular Biology
Abstract: The regulation of genes within each of the cell types in our bodies is orchestrated by the activities of transcription factors and other regulatory proteins. Determining how such regulators recognize their genomic targets would enable a deeper understanding of how cellular processes are controlled and how disease states occur. However, understanding the gene regulatory code has turned out to be challenging. While many regulatory proteins recognize specific DNA patterns, the vast majority of sequences that match the pattern will not in fact be bound by the protein in a given cell type. Furthermore, a given regulatory protein can recognize different instances of its binding pattern in different cell types. Such context-dependent activities appears to be determined by the regulatory environment of the cell: interactions with other proteins, chemical modifications on the genome, and the organization of the genome within the cell all play roles in specifying a given regulatory protein’s targets. My lab applies machine learning techniques to understand how genes are regulated and how particular cellular identities are established. In this presentation, I’ll discuss how we’re using neural networks to interpret how regulatory proteins establish distinct regulatory landscapes on the genome during particular steps in development.
March 4, 11:00 a.m.-Noon
- “Studying Sharing of Political Content on Facebook,” presented by S. Shyam Sundar, James P. Jimirro Professor of Media Effects
- “Multi Modal Sensor Fusion for Data driven Process Monitoring of Additive Manufacturing Processing,” presented by Jan Petrich, research development engineer, Geospatial Intelligence Department, Applied Research Laboratory
March 18, 11:00 a.m.-Noon
- “Human movement and infectious diseases,” presented by Nita Bharti, assistant professor of biology and Lloyd Huck Early Career Professor
- “Health System Barriers Preventing Use of ‘Big Data Analytics’ to More Effectively Manage The Covid-19 Pandemic,” presented by Dennis Scanlon, Distinguished Professor of Health Policy and Administration
April 1, 11:00 a.m.-Noon
- “Expert knowledge capture for 2D materials synthesis,” presented by Wesley Reinhart, assistant professor of materials science and engineering and ICDS faculty co-hire
- “Human Science without Data Collection,” presented by Timothy Brick, assistant professor of human development and family studies and Institute for Computational and Data Sciences (ICDS) faculty co-hire