I am a PhD candidate advised by Prof. Gerhard Weikum and Prof. Krishna P. Gummadi. My PhD thesis is about building responsible machine learning systems, which are fair and trustworthy. Prior to joining MPI as a PhD student, I was a research assistant in Data Mining Group at Aalto University headed by Prof. Aristides Gionis, where my research was mainly concerned with knowledge discovery in large graphs - as well as investigate how these results can be used in a wide range of applications, including finding experts, recommendations, social network analysis. During my PhD and M.Sc. I had the opportunity to do research internships at Google Brain (Mountain View, U.S.A), People + AI Research (PAIR) at Google Research (Zurich, Switzerland) and Bell Labs (Dublin, Ireland).

I earned my master's degree (honours) in Machine Learning and Data Mining at Aalto University (Finland) and bachelor's degree (distinction) in Computer Science at Osmania University (India). Before that, I was a software engineer at Microsoft (India). In the 3 exciting years (2012-2015) at Microsoft, I worked in various roles revolving around data, including data mining, data analytics, business intelligence, search engine technology and have learnt various aspects of building large and scalable systems.

I recently started a new position as a Research Scientist at Google Research. This page is no longer updated. Please visit my new homepage.

[Google Scholar]

• Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learning
  Preethi Lahoti, Krishna P. Gummadi, and Gerhard Weikum
  Proceedings of the IEEE International Conference on Data Mining ICDM 2021
  [pdf]
• Accounting for Model Uncertainty in Algorithmic Discrimination
  Junaid Ali, Preethi Lahoti, and Krishna P. Gummadi
  Proceedings of the AAAI/ACM conference on Artificial Intelligence, Ethics, and Society AIES 2021
  [pdf]
• Fairness without Demographics through Adversarially Reweighted Learning
  Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, and Ed H. Chi
  Advances in Neural Information Processing Systems NeurIPS 2020
  [pdf]|[code]
• Operationalizing Individual Fairness with Pairwise Fair Representations
  Preethi Lahoti, Krishna P. Gummadi, and Gerhard Weikum
  Proceedings of the VLDB Endowment Vol. 13 No. 4 PVLDB 2019
  [pdf]|[code]
• An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervision
  Hanchen Wang, Nina Grgic-Hlaca, Preethi Lahoti, Krishna P. Gummadi, Adrian Weller
  Workshop on Human-Centric Machine Learning at NeurIPS 2019 NeurIPS 2019
  [pdf]
• iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making
  Preethi Lahoti, Krishna P. Gummadi, and Gerhard Weikum
  Proceedings of the 35th IEEE International Conference on Data Engineering ICDE 2019
  [pdf]|[ppt]|[code]
• Joint Non-negative Matrix Factorization for Learning Ideological Leaning on Twitter
  Preethi Lahoti, Kiran garimella, and Aristides Gionis
  Proceedings of the 11th ACM International Conference on Web Search and Data Mining WSDM 2018
  [pdf][software]
• Finding Topical Experts in Twitter via Query-Dependent Personalized PageRank
  Preethi Lahoti, Gianmarco De Francisci Morales, and Aristides Gionis
  Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ASONAM 2017
  [pdf]
• Efficient Set Intersection Counting Algorithm for Text Similarity Measures
  Preethi Lahoti, Patrick K. Nicholson, and Bilyana Taneva
  Proceedings of the 19th Workshop on Algorithm Engineering and Experiments ALENEX 2017
  [pdf]

• Wearable Device for Facilitating Interaction between Individuals. Preethi Lahoti (Main Inventor), Patrick K. Nicholson, Deepak Ajwani, and Alessandra Sala. European Patent Filed 16306640.0. [patent pending]

• Invited Talks:
  • Fairness in Machine Learning: Challenges and Approaches
    MSR Seminar Series @ Microsoft Research, Montreal, Canada, Dec 2020
  • ML Fairness in Practice: Challenges and Approaches
    Search Engine Meetup, Amsterdam, Netherlands, Nov 2020
  • Operationalizing Individual Fairness for Algorithmic Decision Making
    FACTS-IR @ SIGIR 2019, Paris, France, July 2019
  • Fairness in Algorithmic Decision Making (Best Poster Award)
    Women in Big Data 2019, Zurich, Switzerland, June 2019
• Program Committees:
  • Area Chair, RAI @ ICLR 2021: Workshop on Responsible AI
  • PC Member, WCDMDE @ NeurIPS 2020: Workshop on Consequential Decision Making in Dynamic Environments
  • PC Member, FAccTRec @ RecSys 2020: Workshop on Fairness, Accountability and Transparency in Recommender Systems

• Ayan Majumdar, M. Sc. Thesis (M.Sc. student, Saarland University)
• Anubrata Das, Research Internship (Ph.D. student, University of Texas at Austin)

I love cooking, travelling and hiking! I like to dream about the day when I will have a travelling food truck - worlds best moving street food restaurant, you never know where I might pop up! Until then, I work on my passions in increments. My next travel goal is 30/30 (visit 30 countries by the time I am 30).
Current statistics: visited 24 countries, lived and worked in 5 of them.