I'm interested in designing new distributed and parallel algorithms, the distributed processing of big data, achieving fault-tolerance in networks, and secure distributed computing in dynamic environments such as peer-to-peer networks and mobile ad-hoc networks.

## News

• General Chair of ACM PODC 2019
• Program committee member of BGP 2017, SPAA 2016, SIROCCO 2016
• Giving a talk at a workshop on Dynamic Graphs in Distributed Computing (co-located with DISC 2016)
• Co-chairing the program committee of ICDCN 2016
• Giving a talk at ADGA 2015, (4th Workshop on Advances in Distributed Graph Algorithms, co-located with DISC 2015)

## Publications

2017
• Breaking the $\tilde \Omega(\sqrt{n})$ Barrier: Fast Consensus under a Late Adversary
Peter Robinson, Christian Scheideler, Alexander Setzer. (under review)
Abstract
We study the consensus problem in a synchronous distributed system of $n$ nodes under an adaptive adversary that has a slightly outdated view of the system and can block all incoming and outgoing communication of a constant fraction of the nodes in each round. Motivated by a result of Ben-Or and Bar-Joseph (1998), showing that any consensus algorithm that is resilient against a linear number of crash faults requires $\tilde \Omega(\sqrt{n})$ rounds in an $n$-node network against an adaptive adversary, we consider a late adaptive adversary, who has full knowledge of the network state at the beginning of the previous round and unlimited computational power, but is oblivious to the current state of the nodes. Our main contributions are randomized distributed algorithms that achieve consensus with high probability among all except a small constant fraction of the nodes (i.e., almost-everywhere'') against a late adaptive adversary who can block up to $\epsilon n$ nodes in each round, for a small constant $\epsilon >0$. Our first protocol achieves binary almost-everywhere consensus and also guarantees a decision on the majority input value, thus ensuring plurality consensus. We also present an algorithm that achieves the same time complexity for multi-value consensus. Both of our algorithms succeed in $O(\log n)$ rounds with high probability, thus breaking the known $\tilde\Omega(\sqrt{n})$ lower bound for fully adaptive adversaries. Our algorithms are scalable to large systems as each node contacts only an (amortized) constant number of peers in each communication round. We show that our algorithms are optimal up to constant (resp. sub-logarithmic) factors by proving that every almost-everywhere consensus protocol takes $\Omega(\log_d n)$ rounds in the worst case, where $d$ is an upper bound on the number of communication requests initiated per node in each round.
• Gossiping with Latencies
Seth Gilbert, Peter Robinson, Suman Sourav. (under review)
Abstract
Consider the classical problem of information dissemination: one (or more) nodes in a network have some information that they want to distribute to the remainder of the network. In this paper, we study the cost of information dissemination in networks where edges have latencies, i.e., sending a message from one node to another takes some amount of time. We first generalize the idea of conductance to weighted graphs, defining $\phi_*$ to be the weighted conductance'' and $\ell_*$ to be the critical latency.'' One goal of this paper is to argue that $\phi_*$ characterizes the connectivity of a weighted graph with latencies in much the same way that conductance characterizes the connectivity of unweighted graphs. We give near tight upper and lower bounds on the problem of information dissemination, up to polylogarithmic factors. Specifically, we show that in a graph with (weighted) diameter $D$ (with latencies as weights), maximum degree $\Delta$, weighted conductance $\phi_*$ and critical latency $\ell_*$, any information dissemination algorithm requires at least $\Omega(\min(D+\Delta, \ell_*/\phi_*))$ time. We show several variants of the lower bound (e.g., for graphs with small diameter, graphs with small max-degree, etc.) by reduction to a simple combinatorial game. We then give nearly matching algorithms, showing that information dissemination can be solved in $O(\min((D + \Delta)\log^3{n}), (\ell_*/\phi_*)\log(n))$ time. % $O(\min(D\log^3(n), \ell_*\log(n)/\phi_*))$. The algorithm consists of two sub-algorithms: This is achieved by combining two cases. When nodes do not know the latency of the adjacent edges, we show that the classical push-pull algorithm is (near) optimal when the diameter or maximum degree is large. For the case where the diameter and maximum degree are small, we give an alternative strategy in which we first discover the latencies and then use an algorithm for known latencies based on a weighted spanner construction. (Our algorithms are within polylogarithmic factors of being tight both for known and unknown latencies.)

## Code

I'm interested in parallel and distributed programming and related technologies such as software transactional memory. Below is a (non-comprehensive) list of software that I have written.
• I extended Haskell's Cabal, for using a "world" file to keep track of installed packages. (Now part of the main distribution.)
• data dispersal: an implementation of an (m,n)-threshold information dispersal scheme that is space-optimal.
• secret sharing: an implementation of a secret sharing scheme that provides information-theoretic security.
• tskiplist: a data structure with range-query support for software transactional memory.
• stm-io-hooks: An extension of Haskell's Software Transactional Memory (STM) monad with commit and retry IO hooks.
• Mathgenealogy: Visualize your (academic) genealogy! A program for extracting data from the Mathematics Genealogy project.
• In my master thesis I developed a system for automatically constructing events out of log files produced by various system programs. One of the core components of my work was a part-of-speech (POS) tagger, which assigns word classes (e.g. noun, verb) to the previously parsed tokens of the log file. To cope with noisy input data, I modeled the POS tagger as a hidden Markov model. I developed (and proved the correctness of) a variant of the maximum likelihood estimation algorithm for training the Markov model and smoothing the state transition distributions.

## Misc

• Program committee membership: BGP 2017, ICDCN 2016, SPAA 2016, SIROCCO 2016, ICDCN 2015, SIROCCO 2014, FOMC 2014.
• DBLP entry (Shows a subset of my publications.)
• Google scholar profile
• My profile on StackExchange