Debmalya panigrahi

Associate Professor

Department of Computer Science

Duke University


  • November '21: See our new paper (on arxiv) for min-cut for all vertex pairs that breaks Gomory and Hu's 60-year old bound for this problem!

  • October '21: SODA 2022 papers

      • Connectivity Augmentation and Splitting-Off: We give new algorithms for the edge connectivity augmentation and edge splitting-off problems using the isolating cuts framework that we introduced in a FOCS 2020 paper. This is the first improvement for these problems since the work of Benczur and Karger in 2000.

      • Online Graph Algorithms with ML Predictions: We give an algorithmic framework for network design problems like Steiner tree, Steiner forest, and facility location in the online algorithms with predictions model. One of the main contributions is a new model for measuring the error of a prediction for metric/graph problems that takes into account both the possibility of inaccurate predictions and complete mispredictions.

  • September '21: NeurIPS 2021 paper

      • Regression for Learning-Augmented Online Algorithms: We bring a regression approach to the area of learning-augmented online algorithms. Our mian contribution is a regression framework and the design of a new loss function that helps make predictions using a PAC learning model for a broad class of online algorithms.

  • August '21: FOCS 2021 papers

      • Minimum Cut in Directed Graphs: We give an algorithm to find a minimum cut in a directed graph using (roughly) \tilde{O}(\sqrt{m}) max-flow calls. This is the first improvement for the problem since the work of Hao and Orlin in 1992.

      • All-Pairs Minimum Cuts: We give an algorithm that finds the edge connectivity between all pairs of vertices in \tilde{O}(n^2) time for simple graphs. This is optimal if the edge connectivity of every pair is represented explicitly.

      • Online Algorithm for k-Server and Extensions: We give an algorithm for the k-server problem and its extension to time windows that has a poly-logarithmic competitive ratio. Our main technical contribution is a new "hitting set" LP formulation of the k-server problem that could be useful in resolving the randomized k-server conjecture. (Our competitive ratio currently depends on the size and aspect ratio of the metric space in addition to the number of servers k.)

  • April '21: ICALP 2021 papers

      • Sparsification of directed graphs: It is well-known that undirected graphs admit cut sparsifiers, and directed graphs do not. We give a more refined view by parametrizing directed graphs using a notion called cut balance and give tight results for sparsification as a function of this parameter. This is one of the first attempts at trying to understand cut sparsification for directed graphs.

      • Universal algorithms for clustering: We give robust clustering algorithms for standard objectives like k-median, k-means, and k-center, where a set of possible inputs is available offline, but the actual input realizes online.

  • February '21: STOC 2021 papers

      • Approximate Gomory-Hu trees: We give an algorithm that uses only polylog(n) maxflow calls instead of n-1 and finds the edge connectivity between all pairs of vertices up to an arbitrarily small constant approximation. Previously, the best known was n-1 max-flows to find exact all-pairs connectivities from the work of Gomory and Hu in 1961.

      • Vertex connectivity: We give an algorithm for computing the vertex connectivity of a graph by running polylog(n) maxflow algorithms. This is the first improvement in the running time of the vertex connectivity problem since the work of Henzinger, Rao, and Gabow in 1996.

about me

I am an Associate Professor of Computer Science at Duke University. Before coming to Duke in 2013, I spent one year as a postdoctoral research in the theory group at Microsoft Research Redmond, where I did research in algorithms. In 2012, I obtained my PhD in theoretical computer science at MIT under the supervision of Prof. David Karger by defending this. Before coming to MIT, I worked at Bell Labs in Bangalore for a year, where I did research on algorithms and networks. I obtained my bachelors' and masters' degrees in Computer Science and Engineering at Jadavpur University, Kolkata in 2004 and IISc, Bangalore in 2006 respectively. At IISc, I did research in algorithms under the supervision of Prof. Ramesh Hariharan. A long time ago, I grew up in the industrial town of Durgapur where I went to St. Xavier's School.

My CV is available here.


Research Interests

I am broadly interested in theoretical computer science, particularly in the design and analysis of algorithms. My two main research thrusts are graph algorithms and algorithms under uncertainty. In graph algorithms, I am interested in the study of network flows, graph cuts, and connectivity. Some recent highlights include the fastest deterministic algorithm to find a minimum cut of an undirected graph, the fastest (randomized) algorithms for finding a minimum vertex cut and a minimum cut of a directed graph, and the fastest algorithm for finding the connectivity of all pairs of vertices in an undirected graph. In algorithms under uncertainty, I am interested in both classical online algorithms, and also the design of algorithms that leverage machine learning to overcome worst-case performance barriers. Some recent highlights include new LP relaxations and algorithms for $k$-server and caching problems, and learning-augmented algorithms for fundamental online problems such as weighted caching and ski rental. I also work in approximation algorithms, combinatorial optimization, and algorithmic game theory.

In addition to theoretical research, I am also interested in the design of algorithms for practical problems. This includes algorithms for online search, advertising, social networks, and e-commerce, the design and management of computer networks, database management and query processing algorithms, and algorithms with applications in artificial intelligence. I have collaborated with researchers in these areas to design algorithms that are practical, implementable, and scalable. Most of this work has been published in peer-reviewed venues in the application areas, and some of it has led to patents and deployment in prototypes or products.

For a sample of my current research directions, see here.

I am part of the theory group and am also affiliated/collaborate with the CS-econ, AI/ML, and database groups at Duke.


A complete list of my publications (along with download links to papers) is available here (arranged chronologically) and here (arranged by topic). You can also download my PhD thesis here.

Research group


I currently have openings for graduate students and undergraduates. I also occasionally have opening for postdocs. Please see here for details.

Current Students

Keerti Anand (3rd year PhD student, jointly advised with Prof. Rong Ge)

Ruoxu Cen (1st year PhD student)

Kevin Sun (3rd year PhD student)

I also often work with Xiao Hu and Hanrui Zhang.

To see the list of former students and postdocs, please click here.


My research has been funded by the National Science Foundation, the Army Research Office, Google, Yahoo!, the Indo-US Science and Technology Forum, and Duke University. I gratefully acknowledge their support.

For more information about my funding, including current grants, please click here.


Current Course

Fall 2021. COMPSCI 290 Special Topics: Great Ideas in Computer Science (an introduction to computer science from the perspective of the big ideas, conflicts, and questions that computer science has brought to the fore: the course will include discussions on the historical perspective of the emergence of computer science and its meteoric rise in less than a century, how computer science relates to mathematics, engineering, and other branches of science, the role of computer science in shaping human thought and knowledge in a modern world, and some of the big futuristic questions that computer science faces today in theory and practice)

For a complete list of the courses that I have taught (including links to course material), please click here.

Contact Information

Mailing Address

Duke University, Campus Box 90129

308 Research Drive (LSRC Building), Room D203

Durham, NC 27708 USA

Tel: +1 (919) 660-6545

Fax: +1 (919) 660-6519


The fastest and preferred way to reach me is by email. My username is my first name and the domain is