Artificial Intelligence in Management (AI)

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AI Keynotes Summer Semester 2022



1 05.05.2022, 12:00-13:30 Nicolas Banholzer, ETH Zurich Modeling the effects of non-pharmaceutical interventions during the COVID-19 pandemic
2 12.05.2022, 18:30-20:00 Prof. Tim Althoff, University of Washington
Human-AI Collaboration Enables More Empathic Conversations in Mental Health Support
3 19.05.2022, 12:00-13:30 Prof. Robert West, École Polytechnique Fédérale de Lausanne United States Politicians' Tone Became More Negative with 2016 Primary Campaigns
4 02.06.2022, 16:00-17:30 Prof. Daniel Neill, New York University Machine Learning and Event Detection for Urban Public Health
5 09.06.2022, 12:00-13:30
session cancelled and postponed to winter semester
Ioana Bica, University of Oxford From Longitudinal Patient Observational Data to Personalised Treatment Effects Using Causal Inference
6 23.06.2022, 16:00-17:30 Prof. Nathan Kallus, Cornell University Smooth Contextual Bandits: Bridging the Parametric and Nondifferentiable Regret Regimes

Post-Contextual-Bandit Inference
7 30.06.2022, 12:00-13:30 Tobias Hatt, ETH Zurich

Daniel Tschernutter, ETH Zurich
Personalized Medicine using Real-World Data: A Causal Machine Learning Approach
Advances in Data-Driven Decision-Making: A Mathematical Optimization Perspective
8 07.07.2022, 12:00-13:30 Prof. Damian Borth, Universität St. Gallen Trustworthy AI & Hyper-Representation Learning
9 14.07.2022, 18:00-19:30 Prof. Hamsa Bastani, Wharton, University of Pennsylvania Efficient and targeted COVID-19 border testing via reinforcement learning
10 21.07.2022, 12:00-13:30 Prof. Sebastian Gabel, Rotterdam School of Management Market Basket Analysis with Context Aware Product Embeddings
11 28.07.2022, 16:00-17:30 Prof. Maytal Saar-Tsechansky, McCombs, University of Texas at Austin Cost-Effective Learning from Imperfect and Biased Humans Labelers

*Please note

  • We aim to provide an overview of current trends in AI research
  • The weekly sessions, on Thursdays, consist of 45-60 minutes of presentation, followed by discussion, feedback and QA


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