INFORMS 2026 Panel: AI in the ORMS Workforce: Threat, Tool, or Career Accelerator?
, INFORMS 2026 Annual Conference, San Francisco, CA,
Panel discussion on the role of AI in the OR/MS workforce at the INFORMS 2026 Annual Conference.
, INFORMS 2026 Annual Conference, San Francisco, CA,
Panel discussion on the role of AI in the OR/MS workforce at the INFORMS 2026 Annual Conference.
, INFORMS 2026 Annual Conference Doctoral Student Colloquium, San Francisco, CA,
Doctoral Student Colloquium Industry Career Paths Panel at the INFORMS 2026 Annual Conference.
, Alberta School of Business, University of Alberta,
This talk focuses on common management structures—centralized, decentralized, and matrix organizations—and how they impact the success of analytics projects.
, Columbia University, Department of Industrial Engineering and Operations Research (IEOR),
Guest lecture on disaster response operational planning in Operations Research for Public Policy at Columbia University IEOR.
Tutorial, INFORMS Webinar / INFORMS Analytics+ 2026,
Demonstration and discussion of generative AI workflows, common pitfalls, and best practices presented for INFORMS.
, Carnegie Mellon University, Heinz College of Information Systems and Public Policy,
Fireside chat with Heinz College at CMU Healthcare Analytics and AI Management students.
, INFORMS-Pittsburgh, Pittsburgh, PA,
Panel discussion on mentoring strategies and professional development in analytics.
, INFORMS 2025 Annual Conference, Atlanta, GA,
Panel on the integration of artificial intelligence and mathematical optimization in modern healthcare systems.
, PyCon USA 2025, Pittsburgh, PA,
This talk discusses some experiences using Generative AI as an aid in applied analytics and walks through an example that illustrates working around its weaknesses and taking advantage of its capabilities.
, INFORMS-Pittsburgh, Pittsburgh, PA / INFORMS Analytics+, Indianapolis, IN,
This talk focuses on common management structures—centralized, decentralized, and matrix organizations—and how they impact the success of analytics projects.
, INFORMS 2024 Annual Conference, Seattle, WA,
Panel exploring practical applications and challenges of generative AI in enterprise analytics practice.
, POMS 2024, Minneapolis, MN,
Working with the American Red Cross and using the FEMA Individuals and Households Program (IHP) valid registrations data set, we explore the relationship between socially vulnerable populations and the damage that occurred to their homes from Hurricane Michael.
, INFORMS Analytics 2024, Los Angeles, CA,
Career development panel at the INFORMS Analytics Conference.
, Carnegie Mellon University, Heinz College of Information Systems and Public Policy,
Guest lecture on disaster analytics and decision support tools at Carnegie Mellon University Heinz College.
, INFORMS 2023 Annual Conference, Phoenix, AZ,
Panel session discussing strategies and pathways for entering the data science and analytics profession.
, University of Pittsburgh, Department of Industrial Engineering,
Guest lecture on data for public good and Red Cross operational modeling at the University of Pittsburgh.
, ChristianaCare Institute for Research in Equity and Community Health (iREACH) / Delaware-CTR,
Invited technical seminar on epidemic modeling using two-population SIR formulations for ChristianaCare iREACH.
, INFORMS 2022 Annual Meeting, Indianapolis, IN,
Conference presentation at INFORMS 2022 on epidemic trajectory forecasting using two-population SIR modeling.
, INFORMS 2021 Annual Meeting, Anaheim, CA / Virtual,
Operations research modeling of telestroke staffing levels to support emergency department consults.
, INFORMS 2019 Annual Conference, Seattle, WA,
Panel on leveraging analytics expertise for social good and supporting nonprofit organizations.
, Pittsburgh Python Meetup at IBM Pittsburgh,
This talk on Bayesian Methods in Python given at the Pittsburgh Python Meetup on May 27, 2015. It gives examples of Bayesian Markov Chain Monte Carlo (MCMC) methods using the emcee, PyMC, and PyStan.
, Pittsburgh Python Meetup at Google Pittsburgh,
This talk on the Natural Language Toolkit and Association Rules uses the Mine Safety and Health Administration Accident Injuries Data Set as example data was given at the Pittsburgh Python Meetup on October 22, 2014.