Thorsten Joachims

About

Thorsten Joachims is the Jacob Gould Schurman Professor of computer science and information science at Cornell University. He is also the Vice Provost for Artificial Intelligence Strategy at Cornell, where he leads the Cornell AI Initiative and the AI Radical Collaboration. Joachims joined Cornell in 2001 after completing his Ph.D. as a student of Prof. Morik at the University of Dortmund, where he also received a Diplom in computer science in 1997. His research interests center on a synthesis of theory and system building in machine learning from human interaction, with applications in information access, generative AI, and recommendation. His work focuses on counterfactual and causal inference, policy learning, learning to rank, structured output prediction, and learning from implicit feedback. Joachims has served as program chair of prominent conferences such as ICML, KDD, and RecSys, and has held leadership roles including interim dean for Cornell Bowers, associate dean for research for Cornell Bowers, and chair of the Department of Information Science. He is recognized as an ACM Fellow, AAAI Fellow, and Humboldt Fellow, reflecting his outstanding accomplishments in the fields of computing and information technology.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Mathematics
• Information Retrieval
• Political Science
• Statistics
• Algorithm
• Knowledge management
• Mathematical optimization

Selected publications

• Controlling Fairness and Bias in Dynamic Learning-to-Rank

  2020 · 200 citations

  Senior authorCorresponding

  Rankings are the primary interface through which many online platforms match users to items (e.g. news, products, music, video). In these two-sided markets, not only the users draw utility from the rankings, but the rankings also determine the utility (e.g. exposure, revenue) for the item providers (e.g. publishers, sellers, artists, studios). It has already been noted that myopically optimizing utility to the users -- as done by virtually all learning-to-rank algorithms -- can be unfair to the…

  DOI

• MOReL : Model-Based Offline Reinforcement Learning

  arXiv (Cornell University) · 2020 · 158 citations

  Senior authorCorresponding

  In offline reinforcement learning (RL), the goal is to learn a highly rewarding policy based solely on a dataset of historical interactions with the environment. The ability to train RL policies offline can greatly expand the applicability of RL, its data efficiency, and its experimental velocity. Prior work in offline RL has been confined almost exclusively to model-free RL approaches. In this work, we present MOReL, an algorithmic framework for model-based offline RL. This framework consists o…

  DOI

• User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets

  2021 · 54 citations

  Senior authorCorresponding

  Ranking items by their probability of relevance has long been the goal of conventional ranking systems. While this maximizes traditional criteria of ranking performance, there is a growing understanding that it is an oversimplification in online platforms that serve not only a diverse user population, but also the producers of the items. In particular, ranking algorithms are expected to be fair in how they serve all groups of users --- not just the majority group --- and they also need to be fai…

  DOI

• Large language models, social demography, and hegemony: comparing authorship in human and synthetic text

  Journal Of Big Data · 2024-09-27 · 29 citations

  articleOpen access

  Large language models have become popular over a short period of time because they can generate text that resembles human writing across various domains and tasks. The popularity and breadth of use also put this technology in the position to fundamentally reshape how written language is perceived and evaluated. It is also the case that spoken language has long played a role in maintaining power and hegemony in society, especially through ideas of social identity and “correct” forms of language.…

  PublisherOA PDFDOI

• Algorithms for College Admissions Decision Support: Impacts of Policy Change and Inherent Variability

  2024-06-20 · 3 citations

  preprintOpen access

  Each year, selective American colleges sort through tens of thousands of applications to identify a first-year class that displays both academic merit and diversity. In the 2023-2024 admissions cycle, these colleges faced unprecedented challenges to doing so. First, the number of applications has been steadily growing year-over-year. Second, test-optional policies that have remained in place since the COVID-19 pandemic limit access to key information that has historically been predictive of acad…

  PublisherOA PDFDOI

Recent grants

• BIGDATA: Mid-Scale: ESCE: Collaborative Research: Discovery and Social Analytics for Large-Scale Scientific Literature

  NSF · $1.3M · 2013–2016

• III: Medium: Machine Learning with Humans in the Loop

  NSF · $1.0M · 2015–2020

• III-COR:Small: Information Genealogy

  NSF · $450k · 2008–2012

Frequent coauthors

• Adith Swaminathan

  25 shared

• Tobias Schnabel

  Microsoft (United States)

  22 shared

• Filip Radlinski

  17 shared

• Lequn Wang

  Netflix (United States)

  13 shared

• Karthik Raman

  Indian Institute of Technology Madras

  12 shared

• Ashudeep Singh

  11 shared

• Pannaga Shivaswamy

  11 shared

• Yisong Yue

  California Institute of Technology

  10 shared

Awards & honors

• NSF Faculty Early Career Development Award (CAREER)
• ACM Fellow
• AAAI Fellow
• Humboldt Fellow

Similar researchers at Cornell University

• Jon Kleinbergh-122
• Deborah Estrinh-120
• Wen Sunh-102