2021 SIGKDD Dissertation Award Winners

2021 SIGKDD Dissertation Award Award

ACM SIGKDD dissertation awards recognize outstanding work done by graduate students in the areas of data science, machine learning and data mining. The original call for nomination is available here.

Review Criteria:

Relevance of the Dissertation to KDD
Originality of the Main Ideas in the Dissertation
Significance of Scientific Contributions
Technical Depth and Soundness of Dissertation (including experimental methodologies, theoretical results, etc.)
Overall Presentation and Readability of Dissertation (including organization, writing style and exposition, etc.)

Congratulations to all the outstanding students who were nominated and to the winners of this year.

Following are the granted awards, including one winner, one runner-up, and two honorable mentions.


Dissertation title: Learning to Represent and Reason Under Limited Supervision
Aditya Grover, Stanford University (USA)

Aditya Grover is a research scientist in the Core ML team at Facebook AI Research. He also collaborates with Pieter Abbeel at UC Berkeley as a visiting postdoctoral researcher. In Fall 2021, he will join UCLA as an assistant professor of computer science. 

Aditya’s research is centered around machine learning for probabilistic modeling, unsupervised representation learning, and sequential decision making, with applications at the intersection of physical sciences and climate change.

Previously, he completed my PhD at Stanford University (2020) and his bachelors at IIT Delhi (2015), both in computer science. During his PhD, he spent wonderful summers interning at Google Brain, Microsoft Research, and OpenAI. At Stanford, he created and taught a new course on Deep Generative Models with his advisor Stefano Ermon.


Dissertation title: Counting Cliques in Real-World Graphs
Shweta Jain, University of California, Santa Cruz (USA)

Shweta Jain is a postdoc at the University of Illinois, Urbana-Champaign, working with Prof. Hanghang Tong. She recently obtained a PhD in Computer Science from the University of California, Santa Cruz, where she was advised by Prof. Seshadhri Comandur. Shweta’s research interests are in randomized and approximation algorithms, combinatorial optimization, graph mining, and algorithms for massive data. 

Prior to joining UCSC, she completed her Master's in Computer Science at the University of Chicago.


Dissertation title: Rigorous and Efficient Algorithms for Significant and Approximate Pattern Mining 
Leonardo Pellegrain, University of Padova (Italy)

Leonardo is a Postdoctoral Researcher at the Department of Information Engineering of the University of Padova.  In his research, he works on providing novel efficient and statistically sound algorithms for knowledge and pattern discovery from data, often motivated by biological applications.

He obtained his Ph.D. in Information Engineering from the Department of Information Engineering of the University of Padova, under the supervision of Professor Fabio Vandin.  Leonardo also had an amazing opportunity of visiting the Department of Computer Science at Brown University as a Visiting Research Fellow under the supervision of Professor Eli Upfal.

DISSERTATION AWARD COMMITTEE: Tina Eliassi-Rad (Chair); Kristin P. Bennett; Christos Faloutsos; Rayid Ghani; Aristides Gionis; Yizhou Sun; Jie Tang; Evimaria Terzi

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