## Peter Robinson

My research focuses on designing new distributed and parallel algorithms, the distributed processing of big data, achieving fault-tolerance in communication networks against adversarial attacks, and developing robust protocols that work in highly dynamic environments such as peer-to-peer Blockchain networks and mobile ad-hoc networks.

## News

- General Chair of ACM PODC 2019
- Program committee member of PODC 2020, SIROCCO 2020, DISC 2019

## Tags (Show all)

Asynchrony Big Data Byzantine Failures Churn Communication Complexity Distributed Agreement Distributed Storage Dynamic Network Fault-Tolerance Gossip Communication Graph Algorithm Haskell Information Complexity Leader Election Machine Learning Mobile Ad-Hoc Network Natural Language Processing P2P Secure Computation in Networks Self-Healing Symmetry Breaking Wireless Networks## Publications

2016

- DEX: Self-Healing ExpandersDOI

Gopal Pandurangan, Peter Robinson, Amitabh Trehan. Distributed Computing (DC).

AbstractWe present a fully-distributed self-healing algorithm DEX, that maintains a constant degree expander network in a dynamic setting. To the best of our knowledge, our algorithm provides the first efficient distributed construction of expanders --- whose expansion properties hold deterministically --- that works even under an all-powerful adaptive adversary that controls the dynamic changes to the network (the adversary has unlimited computational power and knowledge of the entire network state, can decide which nodes join and leave and at what time, and knows the past random choices made by the algorithm). Previous distributed expander constructions typically provide only probabilistic guarantees on the network expansion which rapidly degrade in a dynamic setting; in particular, the expansion properties can degrade even more rapidly under adversarial insertions and deletions. Our algorithm provides efficient maintenance and incurs a low overhead per insertion/deletion by an adaptive adversary: only $O(\log n)$ rounds and $O(\log n)$ messages are needed with high probability ($n$ is the number of nodes currently in the network). The algorithm requires only a constant number of topology changes. Moreover, our algorithm allows for an efficient implementation and maintenance of a distributed hash table (DHT) on top of DEX, with only a constant additional overhead. Our results are a step towards implementing efficient self-healing networks that have guaranteed properties (constant bounded degree and expansion) despite dynamic changes.

2015

- Enabling Efficient and Robust Distributed Computation in Highly Dynamic NetworksDOI

John Augustine, Gopal Pandurangan, Peter Robinson, Scott Roche, Eli Upfal. 56th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2015).

AbstractMotivated by the need for designing efficient and robust fully-distributed computation in highly dynamic networks such as Peer-to-Peer (P2P) networks, we study distributed protocols for constructing and maintaining dynamic network topologies with good expansion properties. Our goal is to maintain a sparse (bounded degree) expander topology despite heavy churn (i.e., nodes joining and leaving the network continuously over time). We assume that the churn is controlled by an adversary that has complete knowledge and control of what nodes join and leave and at what time and has unlimited computational power, but is oblivious to the random choices made by the algorithm. Our main contribution is a randomized distributed protocol that guarantees with high probability the maintenance of a constant degree graph with high expansion even under continuous high adversarial churn. Our protocol can tolerate a churn rate of up to $O(n/\text{polylog}(n))$ per round (where $n$ is the stable network size). Our protocol is efficient, lightweight, and scalable, and it incurs only $O(\text{polylog}(n))$ overhead for topology maintenance: only polylogarithmic (in $n$) bits needs to be processed and sent by each node per round and any node's computation cost per round is also polylogarithmic. The given protocol is a fundamental ingredient that is needed for the design of efficient fully-distributed algorithms for solving fundamental distributed computing problems such as agreement, leader election, search, and storage in highly dynamic P2P networks and enables fast and scalable algorithms for these problems that can tolerate a large amount of churn.

2014

- DEX: Self-Healing ExpandersPDFDOI

Gopal Pandurangan, Peter Robinson, Amitabh Trehan. 28th IEEE International Parallel Distributed Processing Symposium (IPDPS 2014).

AbstractWe present a fully-distributed self-healing algorithm DEX, that maintains a constant degree expander network in a dynamic setting. To the best of our knowledge, our algorithm provides the first efficient distributed construction of expanders --- whose expansion properties hold deterministically --- that works even under an all-powerful adaptive adversary that controls the dynamic changes to the network (the adversary has unlimited computational power and knowledge of the entire network state, can decide which nodes join and leave and at what time, and knows the past random choices made by the algorithm). Previous distributed expander constructions typically provide only probabilistic guarantees on the network expansion which rapidly degrade in a dynamic setting; in particular, the expansion properties can degrade even more rapidly under adversarial insertions and deletions. Our algorithm provides efficient maintenance and incurs a low overhead per insertion/deletion by an adaptive adversary: only $O(\log n)$ rounds and $O(\log n)$ messages are needed with high probability ($n$ is the number of nodes currently in the network). The algorithm requires only a constant number of topology changes. Moreover, our algorithm allows for an efficient implementation and maintenance of a distributed hash table (DHT) on top of DEX, with only a constant additional overhead. Our results are a step towards implementing efficient self-healing networks that have guaranteed properties (constant bounded degree and expansion) despite dynamic changes.

## Code

I'm interested in parallel and distributed programming and related technologies such as software transactional memory and the actor-model. Recently, I have been working on implementing a simulation environment for distributed algorithms in Elixir/Erlang, and implementing non-blocking data structures in Haskell suitable for multi-core machines. Below is a (non-comprehensive) list of software that I have written.

- concurrent hash table: a thread-safe hash table that scales to multicores.
- 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.
- I extended Haskell's Cabal, for using a "world" file to keep track of installed packages. (Now part of the main distribution.)

## Teaching

- Database Systems, Spring 2020.
- Computer Networks, Fall 2019.
- Distributed Computing, Spring 2019.
- Randomized Algorithms, Fall 2018: Intro slides. Part 1 on Concentration Bounds.
- Advanced Distributed Systems, Fall 2016, 2017.
- Computation with Data, Fall 2016.
- Internet and Web Technologies, Spring 2016.

## Misc

- Google scholar profile
- My profile on StackExchange