Alistarh Group

Distributed Algorithms and Systems

Distribution has been a major trend in computing over the last decade, which affects the way we compute in several ways: microprocessor architectures are now multi-core, offering several parallel threads of computation, while large-scale systems distribute storage and computation across several processors, machines, or data centers. The Alistarh group works to create algorithms that take advantage of these developments, by creating software that scales – in other words, it improves its performance when more computation is available.

This fundamental change in the way computation is performed puts forward exciting open questions. How do we design algorithms to extract every last bit of performance from the current generation of architectures? How do we design future architectures to support more scalable algorithms? Are there clean abstractions to render high-performance distribution accessible to programmers? The Alistarh group’s research is focused on answering these questions. In particular, they are interested in designing efficient, practical algorithms for fundamental problems in distributed computing, in understanding the inherent limitations of distributed systems, and in developing new ways to overcome these limitations.

Group Leader

On this site:


Current Projects

Distributed machine learning | Concurrent data structures and applications | Molecular computation


Koval N, Alistarh D-A, Elizarov R. 2019. Lock-free channels for programming via communicating sequential processes, ACM Press,p. View

Chatterjee B, Peri S, Sa M, Singhal N. 2019. A simple and practical concurrent non-blocking unbounded graph with linearizable reachability queries. ACM International Conference Proceeding Series. ICDCN: Conference on Distributed Computing and Networking 168–177. View

Alistarh D-A, Aspnes J, King V, Saia J. 2018. Communication-efficient randomized consensus. Distributed Computing. 31(6), 489–501. View

Rybicki J, Kisdi E, Anttila J. 2018. Model of bacterial toxin-dependent pathogenesis explains infective dose. PNAS. 115(42), 10690–10695. View

Lenzen C, Rybicki J. 2018. Near-optimal self-stabilising counting and firing squads. Distributed Computing. View

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since 2017 Assistant Professor, IST Austria
2016 – 2017 Visiting Researcher, Computer Science Department, ETH Zurich
2014 – 2016 Researcher, Microsoft Research, Cambridge, UK
2014 – 2016 Morgan Fellow, Downing College, University of Cambridge, UK
2012 – 2013 Postdoc, Massachusetts Institute of Technology, Cambridge, USA
2012 PhD, EPFL, Lausanne, Switzerland

Selected Distinctions

2018 ERC Starting Grant
2015 Awarded Swiss National Foundation “Ambizione” Fellowship
2014 Elected Morgan Fellow at Downing College, University of Cambridge
2012 Postdoctoral Fellowship of the Swiss National Foundation
2011 Best Paper Award at the International Conference on Distributed Computing and Networking

Additional Information

Dan Alistarh’s website

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