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My general area of interest is Algorithms and Complexity. In particular, I have worked in the areas of approximation algorithms for NP-hard problems, distributed algorithms, randomized algorithms, and, more recently, algorithms for machine learning. Please visit the web site of our group, ARC
Currently my research is supported by:
a Google Focused Grant (ALL4AI: Algorithms and Learning for AI) - coPI
Flavio Chierichetti's ERC Starting Grant DMAP
Some other recent grants:
a Google Focused Grant (Web Algorithmics for Large-scale Data Analysis) - coPI
a Google Faculty Research Award, PI
a project PRIN (projects of relevant national interest) called ARS TechnoMedia (national coordinator)
PhD Students: Matteo Almanza, Giuseppe Re
Former PhD students: Mauro Sozio, Alessandro Tiberi, Flavio Chierichetti, Silvio Lattanzi, Alessandro Epasto, Erisa Terolli
PostDocs: none at the moment
Former PostDocs: Jochen Konemann, Alex Kesselman, Carlos "Chato" Castillo (all jointly with prof. Stefano Leonardi), Stefan Dziembowski, Fabrizio Grandoni, Francesco Pasquale, Stefano Leucci, Marco Bressan
A book I co-authored with Devdatt Dubhashi
A book I co-authored with Devdatt Dubhashi
Book's errata (both 1st and 2nd edition)
Some praise for the book:
"Concentration bounds are at the core of probabilistic analysis of algorithms. This excellent text provides a comprehensive treatment of this important subject, [..] from the very basic to the more advanced tools. The presentation is clear and includes numerous examples, demonstrating applications of the bounds in analysis of algorithms. This book is a valuable resource for both researches and students in the field." Eli Upfal, Professor of Computer Science, Brown University, author of "Probability and Computing"
"It is beautifully written, contains all the major concentration results, and is a must to have on your desk." Rich Lipton, Georgia Tech
"The book does a superb job of describing a collection of powerful methodologies in a unified manner; what is even more striking is that basic combinatorial and probabilistic language is used in bringing out the power of such approaches. To summarize, the book has done a great job of synthesizing diverse and important material in a very accessible manner. Any student, researcher, or practitioner of computer science, electrical engineering, mathematics, operations research, and related fields, could benefit from this wonderful book. The book would also make for fruitful classes at the undergraduate- and graduate- levels. I highly recommend it." Aravind Srinivasan, SIGACT News
"The strength of this book is that it is appropriate for both the beginner as well as the experienced researcher in the field of randomized algorithms. I highly recommend this book both as an advanced as well as an introductory textbook, which can also serve the needs of an experienced researcher in algorithmics." Yannis C. Stamatiou, Mathematical Reviews
"This timely book brings together in a comprehensive and accessible form a sophisticated toolkit of powerful techniques for the analysis of randomized algorithms, illustrating their use with a wide array of insightful examples. This book is an invaluable resource for people venturing into this exciting field of contemporary computer science research." Prabhakar Ragahavan, Google
"Concentration inequalities are an essential tool for the analysis of algorithms in any probabilistic setting. There have been many recent developments on this subject, and this excellent text brings them together in a highly accessible form." Alan Frieze, Carnegie Mellon University
"I have seen a draft of this book earlier (following a link from geomblog) and I love it! It does really make concentration seem a lot easier." Anonymous
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