Dean’s Seminar Series

This series feature prominent external speakers with expertise in one of the topics related to the Rensselaer signature research thrusts, including financial technology, supply chain analytics, and digitalization and transformation. These high-profile speakers add value to both management and technology researchers, both within Lally and across the Institute, and provide opportunities for deepening collaboration.

Jan. 21, 2022
Brad Greenwood, "No News is Bad News: Political Corruption and the Fourth Estate" 

Jan. 18, 2022
Debabrata Dey, "'Extortionality' in Ransomware Attacks: A Microeconomic Study of Extortion and Externality"

Dec. 20, 2021
Heng Xu, "The Future of Privacy Research: Lessons from Artificial Intelligence and Machine Learning"

Dec. 10, 2021
Miguel Lejeune, "Medical Evacuation Network Design: Chance-Constrained Model With Endogenous Uncertainty"

Oct. 20, 2021
Agostino Capponi, "The Adoption of Blockchain-Based Decentralized Exchanges"

Lally Research Seminar Series

These seminars will attract prominent and emerging scholars working in research domains aligned with Lally research directions to engage Lally faculty and doctoral students. Due to the interdisciplinary nature of the research seminars, the topics would be of interest to a broader audience across the Institute toward integrating management research and technological advances for great impact to business, industry, and society.

March 20, 2023
Dursun Delen, "Demystifying Term Confusion in Data Science with Real-World Projects"

Feb. 23, 2022
Foster Provost, "Causal Targeting - It's Not Causal Effect Estimation (And Why It Matters)"

Dec. 1, 2021
Roger White, “Helpful Tool or Costly Distraction: Does Mobile Internet Technology Improve Market Quality?”

Nov. 10, 2021
Yuheng Hu, “Customer Engagement Prediction on Social Media: A Graph Neural Network Method with Attention Mechanism”

Oct. 27, 2021
Anjana Susarla, “Harnessing User-Generated Content for Patient Education”

Oct. 13, 2021
Heng Xu, “Implications of Data Anonymization on the Statistical Evidence of Disparity”

Sept. 29, 2021
Louis Hickman, “Machine Learning for Personnel Assessment: Reliability, Validity, and Fairness Investigations of Automated Interviews and Beyond”