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12:15 PM - 1:30 PM
Lauren Lu
Generative AI in Action: Field Experimental Evidence on Worker Performance in E-Commerce Customer Service Operations
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Generative AI in Action: Field Experimental Evidence on Worker Performance in E-Commerce Customer Service Operations
Speaker: Lauren Lu
Time: 12:15 PM - 1:30 PM
Location:
12:15 PM - 1:30 PM
Michael Hamilton
Semi-Personalized Pricing: Algorithms and Implications
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Semi-Personalized Pricing: Algorithms and Implications
Speaker: Michael Hamilton
Time: 12:15 PM - 1:30 PM
Location:
12:00 PM - 1:45 PM
Bijan H. Mazaheri
Synthetic Potential Outcomes and Causal Mixture Identifiability
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Synthetic Potential Outcomes and Causal Mixture Identifiability
Speaker: Bijan H. Mazaheri
Time: 12:00 PM - 1:45 PM
Location:
12:15 PM - 1:45 PM
Galit Yom-Tov
Operationalizing Emotional Load: The Human Side of Queueing Systems
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Operationalizing Emotional Load: The Human Side of Queueing Systems
Speaker: Galit Yom-Tov
Time: 12:15 PM - 1:45 PM
Location:
12:15 PM - 1:30 PM
Daniel Guetta
Teaching Business School Students in the 21st Century: data, coding, AI, and more
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Teaching Business School Students in the 21st Century: data, coding, AI, and more
Speaker: Daniel Guetta
Time: 12:15 PM - 1:30 PM
Location:
The world is changing exponentially faster than it once was, and topics that used to be the preserve of engineers alone are now central to many businesses' core strategy. How should we change the way we teach technical topics in business schools in response? In this talk, I will begin by briefly sharing some lessons I have learned in my seven years at Columbia, during which I have had the privilege of teaching 10 distinct classes, with just under 7,000 students enrolled from Columbia Business School's MBA and EMBA programs, and from our joint programs with the engineering school. The bulk of my talk will then focus on five case studies, time permitting, in which I will discuss the tools and cases I use to teach five specific topics: the Lasso, fundamentals of deep learning, coding in Python, large language model embeddings, and market design. I will end with a discussion of what comes next for us as educators in business schools.
12:15 PM - 1:30 PM
Rad Niazadeh
Dynamic Matching for Refugee Resettlement: A Case Study
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Dynamic Matching for Refugee Resettlement: A Case Study
Speaker: Rad Niazadeh
Time: 12:15 PM - 1:30 PM
Location:
Refugee resettlement is an international effort that aims to provide a durable solution for the current global refugee crisis. The goal is to help refugee families to find a new home in a host country and eventually find a new job to get “resettled”. In this seminar, I will talk about a recent paper in partnership with a major national agency working on refugee resettlement in the United States. In this work, we re-design the core dynamic matching algorithm used by our partner, for sequential yearly assignment of refugee cases to our partner's affiliate locations. These localities should be thought of as service centers providing vocational services or assistance with job search, and many times are short in staff. I discuss various operational intricacies in this dynamic matching problem, such as lack of reliable arrival prior data, predicting employment outcomes of each match, and controlling backlogs in those service centers. I also discuss regulatory constraints imposed on the problem, such as family re-unification ties for refugees and their implications on our algorithm. Then I will introduce a new algorithmic framework to study this problem, through which I show how to design and analyze near-optimal learning-based primal-dual algorithms that aim to maximize employment outcomes while respecting operational and regulatory constraints in this problem. Time permits, I'll discuss a case study for evaluating the empirical performance of our algorithms using our partner's data and discuss some details of our collaboration.
12:15 PM - 1:30 PM
Canan Gunes Corlu
Uncertainty Quantification in Digital Twin Simulations
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Uncertainty Quantification in Digital Twin Simulations
Speaker: Canan Gunes Corlu
Time: 12:15 PM - 1:30 PM
Location:
One of the challenges of developing digital twin simulations stems from lacking full information about business process flows and characterizations of their input distributions. This chapter describes how this challenge arises in different phases of digital twin development. We present practitioners an overview of solutions to use for correctly quantifying the overall uncertainty in digital twin simulation development. We accompany the presentation with a supply chain use case.
12:00 PM - 5:00 PM
TBD
Operations Spring Conference
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Operations Spring Conference
Speaker: TBD
Time: 12:00 PM - 5:00 PM
Location:
8:00 AM - 1:00 PM
TBD
Operations Seminar Conference
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Operations Seminar Conference
Speaker: TBD
Time: 8:00 AM - 1:00 PM
Location: