Winter 2024 Line Up
Thursday January 18, 12 to 1 pm
Renaissance, data, and Wall Street with Gregory Zuckerman, Wall Street Journal
Gregory Zuckerman is a special writer at The Wall Street Journal and non-fiction author. His non-fiction books include “The Greatest Trade Ever: The Behind-the-Scenes Story of How John Paulson Defied Wall Street and Made Financial History”, “The Frackers: The Outrageous Inside Story of the New Billionaire Wildcatters”, two books for children, “The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution” and “A Shot to Save the World: The Inside Story of the Life-or-Death Race for a COVID-19 Vaccine”. He has been awarded the Gerald Loeb Award, the highest honor in business journalism, three times.
Thursday January 25, 12 to 1 pm
In-class Data Analysis Replications: Teaching Students while Testing Science with Kristina Gligorić, Stanford University
Kristina Gligorić is a Postdoctoral Scholar at Stanford University Computer Science Department, advised by Dan Jurafsky at the NLP group. Previously she obtained her Ph.D. in Computer Science at EPFL, where she was advised by Robert West. Her research focuses on developing computational approaches to solve burning societal issues, understand and improve human well-being, and promote social good. She leverages large-scale textual data and digital behavioral traces and tailors computational methods drawn from AI, NLP, and causal inference. Her work has been published in top computer science conferences (such as ACM CSCW, AAAI ICWSM, and TheWebConf) and broad audience journals (Nature Communications and Nature Medicine). She is a Swiss National Science Foundation Fellow and University of Chicago Rising star in Data Science. She received awards for her work, including EPFL Thesis Distinction, CSCW 2021 Best Paper Honorable Mention Award, ICWSM 2021 and 2023 Best Reviewer Award, and EPFL Best Teaching Assistant Award.
Thursday February 1, 12 to 1 pm
AI at the Frontiers of Economic Research with Oliver Giesecke, Stanford University
Oliver Giesecke is a research fellow at the Hoover Institution at Stanford University. Giesecke works on topics related to asset pricing and public finance. His recent work studies the finances of state and local governments across the United States. This includes the capital structure of state governments, the book and market equity position of city governments, and the status quo and trend of public pension obligations. For his work on city governments’ finances, he was awarded the NASDAQ OMX Award for the best paper on asset pricing. His work on pension obligations was instrumental to shaping state legislation. In addition, Giesecke has conducted a large-scale survey that elicits the retirement plan preferences of public sector employees across the United States. He is the author of the Stanford municipal finances dashboard which provides, for the first time, credit spreads and fiscal fundamentals for many state and local governments in the United States. The dashboard has received national media coverage in The Bond Buyer. Prior to his academic career, he has worked for Germany’s Federal Agency for Financial Market Stabilization (FMSA) and as a senior quantitative finance consultant. Giesecke received a Ph.D. in finance and economics from Columbia University, a Master’s in economics from the Graduate Institute in Geneva, Switzerland, and a BA from Frankfurt University, Germany.
Tuesday February 6, 12 to 1 pm
Introduction to NFL Analytics with R with Bradley Congelio, Kutztown University of Pennsylvania
Bradley Congelio is an Assistant Professor in the College of Business at Kutztown University of Pennsylvania. His main area of instruction & research is in Data Analytics and Sport Analytics. He is the author of Introduction to NFL Analytics with R, which was published by CRC Press in December 2023. His research focuses on using big data, the R programming language, and analytics to explore the impact of professional stadiums on neighboring communities. He uses the proprietary Zillow ZTRAX database as well as the U.S. Census and other forms of data to create robust, applied, and useful insight into how best to protect those livings in areas where stadiums are proposed for construction. His work in sport analytics, specifically the NFL, has been featured on numerous media outlets, including the USA Today and Sports Illustrated.
Thursday February 8, 12 to 1 pm
LLM Interactive Optimization of Open Source Python Libraries – Case Studies and Generalization presented by Andreas Florath, Deutsche Telekom
Thursday February 15, 12 to 1 pm
Using Great Tables to Make Presentable Tables in Python
Richard Iannone is a software engineer at Posit, PBC (formerly RStudio). He focuses on making useful R and (more lately) Python packages for data analysis and presentation workflows.
Thursday February 22, 12 to 1 pm
By the Numbers: Numeracy, Religion, and the Quantitative Transformation of Early Modern England with Jessica Otis, George Mason University
Dr. Jessica Marie Otis is Assistant Professor of History and Director of Public Projects at the Roy Rosenzweig Center for History and New Media at George Mason University. She is the author of the new book “By the Numbers: Numeracy, Religion, and the Quantitative Transformation of Early Modern England” published by Oxford University Press.
Thursday February 29, 12 to 1 pm
Sky CH-Wang, Columbia University
Thursday March 7, 12 to 1 pm
Matheus Facure, Nubank
Matheus has a background in Economics and is a Staff Data Scientist at Nubank. He works mostly with credit underwriting and causal inference. He is the author of Causal Inference in Python (O’Reilly) and Causal Inference for the Brave and True (Online, Open Source).
Thursday March 14, 12 to 1 pm
Tom Davidson, Rutgers University
Thursday, March 28, 12 to 1 pm
Laura Plein, University of Luxembourg
Thursday, March 28, 12 to 1 pm
The philosophy of Large Language Models with Cameron Buckner, University of Houston
Cameron Buckner is an Associate Professor of Philosophy at University of Houston, Author of From Deep Learning to Rational Machines.
Thursday, April 4, 12 to 1 pm
Election Modeling at The Washington Post with Lenny Bronner, The Washington Post
Lenny Bronner is a data scientist at The Washington Post, specializing in elections.
