James Caverlee

James Caverlee is a Professor in the Department of Computer Science and Engineering at Texas A&M University, and a Visiting Researcher at Google DeepMind. He holds a Ph.D. from Georgia Institute of Technology (2007), and degrees from Stanford University and Duke University. His research focuses on trustworthy, efficient, and personalized machine learning systems, including Recommender Systems, Large Language Models (LLMs), and bias mitigation. Recent work emphasizes fairness, efficiency, and transparency in AI, with applications in conversational systems, speech, and medical QA. He has been recognized with an NSF CAREER Award, AFOSR Young Investigator Award, and DARPA Young Faculty Award. His group has published extensively at top venues like KDD, EMNLP, WWW, SIGIR, and WSDM.

• Education:
  • Ph.D., Computer Science, Georgia Tech, 2007
  • M.S., Computer Science & Engineering-Economic Systems, Stanford University, 2001–2000
  • B.A., Economics, Duke University, 1996
• Research Interests:
  • Machine Learning, Recommender Systems, NLP, LLMs
  • Bias Mitigation, Efficiency, Personalization
  • Social Computing, Information Retrieval
• Awards:
  • NSF CAREER Award (2012)
  • AFOSR Young Investigator (2012)
  • SIGIR Test of Time Honorable Mention (2022)
• Grants: Supported by NSF, AFOSR, DARPA, and industry (Google, Amazon).
• Labs/Teams: InfLab research group at Texas A&M, collaborating with Google DeepMind.

His publications address topics like adversarial attacks on LMs, gender bias in podcasts, and efficient recommendation algorithms. Recent trends show focus on generative AI for personalization and ethical AI challenges.