Devavrat Shah 2025 ACM SIGMETRICS Achievement Award
ACM SIGMETRICS is pleased to announce the selection of Professor Devavrat Shah of MIT as the recipient of the 2025 ACM SIGMETRICS Achievement Award in recognition of his outstanding contributions to the performance analysis and design of computer and communication networks.
Prof. Shah is the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT. His research focuses on statistical inference and stochastic networks. His contributions span a variety of areas including distributed algorithms for computer communications networks, inference and learning on graphical models, algorithms for social data processing including ranking, recommendations and crowdsourcing and, more recently, causal inference.
In distributed algorithms he has made four key contributions: Medium Access Protocols, where he developed a network-agnostic algorithm that delivers maximum throughput; Gossip Algorithms, where he analyzed the convergence time for evaluating global and separable functions through local node communications; Belief Propagation, in which he connected algorithmic performance to classical network flow optimization; and he has developed a PubSub-based architecture for message-passing algorithms. Additionally, his work on Patient-Zero identification has established statistical methods for detecting the origin of a contagion. He has introduced time series algorithms through connecting the statistical methods with low-rank matrix and tensor estimation.
In the areas of social choice and ranking systems he has three main contributions: Developing the Rank Centrality Algorithm for efficiently creating global rankings from sparse pairwise comparisons, establishing frameworks for learning high-dimensional distributions over rankings from partial preferences, and providing theoretical foundations for Collaborative Filtering in recommendation systems.
More recently, he has advanced causal inference methodologies, particularly for data-rich environments. He has bridged structural causal models with latent factor models to address unobserved confounding and to enable consistent estimation of causal effects. This has enabled unit-level counterfactual inference for personalized therapeutics in Alzheimer’s Disease and Oncology, societal policy evaluation, and trace-driven simulation for communication network protocols. Viewed together, these contributions demonstrate that Shah has developed a robust and practical toolkit for drawing causal conclusions in a variety of complex, real-world scenarios.
These contributions have been recognized with several awards, including The ACM Sigmetrics Test of Time Awards (2019 and 2020), and best paper and student paper awards at ACM Sigmetrics (2006, 2009), NeurIPS (2008), IEEE INFOCOM (2004), INFORMS Applied Probability Society (2012), the INFORMS Revenue Management and Pricing (2015) and the INFORMS Management Science and Operations Management (2016). He has received career recognition awards; notably, the 2008 ACM Sigmetrics Rising Star Award, the 2010 Erlang Prize from INFORMS Applied Probability Society and he delivered the 2024 INFORMS Applied Probability Society Markov Lecture. He is an IEEE Fellow and a Kavli Fellow of the U.S. National Academy of Science. He is a distinguished alumnus of his alma mater IIT Bombay.
In addition to academic significance, these contributions have had substantial industry impact leading to the founding of Celect (later acquired by Nike) which helped retailers optimize inventory, and founding of Ikigai Labs to enable large enterprises transform their forecasting and planning.
Further information can be found at https://devavrat.mit.edu/
