I am a PhD candidate advised by Prof. Gerhard Weikum and Prof. Krishna P. Gummadi. I am broadly interested in the area of data mining and machine learning, and specifically in studying algorithmic fairness and bias in data-driven decision-making systems. I recently had an opportunity to do a 5 month research internship at Google Brain in Mountain View, USA.

Prior to joining MPI as a PhD student, I worked as 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. I earned my master's degree (honours) in Machine Learning and Data Mining at Aalto University (erstwhile Helsinki University of Technology) in Finland and bachelor's degree (distinction) in Computer Science at Osmania University in India.

Previously, 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.

[Google Scholar]

• 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 2020
  [pdf]
• 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]
• 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]

• (Septemeber 2019 – January 2020) Research Intern, Google Brain, U.S.A:
  Proposed an adversarial robust learning approach to learn fair and robust machine learning models; Implemented an end-to-end robust learning framework. All the code from the internship is available open-source. [code]
• (October 2015 – June 2017) Research Assistant, Aalto University, Finland:
  My research is mainly concerned with applying well founded data science techniques to study 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.
• (June 2016 - August 2016) Research Intern, Nokia Bell Labs, Ireland:
  Contributed to a text mining and ML system; proposed an efficient approach to compute exact set intersection sizes; Implemented an end-to-end framework and performed experimental analysis on large text datasets.
• (June 2012 – August 2015) Software Engineer, Microsoft, India:
  Contributed to the core ranking & relevance team responsible for shipping ranker to 40 + worldwide markets and languages; performed web log data mining and generated metadata for training pipeline.
  
• (June 2011 - August 2011) Software Development Intern, Microsoft, Hyderabad, India:
  Built an incident management tool to mine large scale backend services, analyze and summarize key statistics of data in real-time.

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.