Matthew Ding

Matthew Ding

he/him

Undergraduate CS Researcher at UC Berkeley

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

BeeGees: Stayin’ Alive in Chained BFT

Published in ACM Symposium on Principles of Distributed Computing (PODC), 2023

Modern chained Byzantine Fault Tolerant (BFT) systems leverage a combination of pipelining and leader rotation to obtain both efficiency and fairness. These protocols, however, require a sequence of three or four consecutive honest leaders to commit operations. Therefore, even simple leader failures such as crashes can weaken liveness, resulting in high commit latency or lack of commit all together. We show that, unfortunately, this vulnerability is inherent to all existing BFT protocols that rotate leaders with pipelined agreement. To resolve this liveness shortcoming we present BeeGees1, a novel chained BFT protocol that successfully commits blocks even with non-consecutive honest leaders. It does this while also maintaining quadratic word complexity with threshold signatures, linear word complexity with SNARKs, and responsiveness between consecutive honest leaders. BeeGees reduces the expected commit latency of HotStuff by a factor of three under failures, and the worst-case latency by a factor of seven.

Recommended citation: Neil Giridharan, Florian Suri-Payer, Matthew Ding, Heidi Howard, Ittai Abraham, and Natacha Crooks. ACM Symposium on Principles of Distributed Computing (PODC 2023).
Paper Link

Deterministic Minimum Steiner Cut in Maximum Flow Time

Published in European Symposium on Algorithms (ESA), 2024

We devise a deterministic algorithm for minimum Steiner cut which uses polylogarithmic maximum flow calls and near-linear time outside of these maximum flow calls. This improves on Li and Panigrahi’s (FOCS 2020) algorithm which takes O(m^{1+ϵ}) time outside of maximum flow calls. Our algorithm thus shows that deterministic minimum Steiner cut can be solved in maximum flow time up to polylogarithmic factors, given any black-box deterministic maximum flow algorithm. Our main technical contribution is a novel deterministic graph decomposition method for terminal vertices which generalizes all existing s-strong partitioning methods and may have future applications.

Recommended citation: Matthew Ding and Jason Li. European Symposium on Algorithms (ESA 2024).
Paper Link | Download Slides

Fast and Robust State Estimation and Tracking via Hierarchical Learning

Published in IEEE Transactions on Automatic Control (TAC), 2024

Fully distributed estimation and tracking solutions to large-scale multi-agent networks suffer slow convergence and are vulnerable to network failures. In this paper, we aim to speed up the convergence and enhance the resilience of state estimation and tracking using a simple hierarchical system architecture wherein agents are clusters into smaller networks, and a parameter server exists to aid the information exchanges among networks. The information exchange among networks is expensive and occurs only once in a while. We propose two consensus + innovation algorithms for the state estimation and tracking problems, respectively. In both algorithms, we use a novel hierarchical push-sum consensus component. For the state estimation, we use dual averaging as the local innovation component. State tracking is much harder to tackle in the presence of dropping-link failures and the standard integration of the consensus and innovation approaches are no longer applicable. Moreover, dual averaging is no longer feasible. Our algorithm introduces a pair of additional variables per link and ensure the relevant local variables evolve according to the state dynamics, and use projected local gradient descent as the local innovation component. We also characterize the convergence rates of both of the algorithms under linear local observation model and minimal technical assumptions. We numerically validate our algorithm through simulation of both state estimation and tracking problems.

Recommended citation: Connor Mclaughlin, Matthew Ding, Denis Erdogmus, Lili Su. IEEE Transactions on Automatic Control (TAC, Conditional Acceptance).
Paper Link

Network Fault-tolerant and Byzantine-resilient Social Learning via Collaborative Hierarchical Non-Bayesian Learning

Published in 58th Asilomar Conference on Signals, Systems, and Computers, 2024

As the network scale increases, existing fully distributed solutions start to lag behind the real-world challenges such as (1) slow information propagation, (2) network communication failures, and (3) external adversarial attacks. In this paper, we focus on hierarchical system architecture and address the problem of non-Bayesian learning over networks that are vulnerable to communication failures and adversarial attacks. On network communication, we consider packet-dropping link failures. We first propose a hierarchical robust push-sum algorithm that can achieve average consensus despite frequent packet-dropping link failures. We provide a sparse information fusion rule between the parameter server and arbitrarily selected network representatives. Then, interleaving the consensus update step with a dual averaging update with Kullback-Leibler (KL) divergence as the proximal function, we obtain a packet-dropping fault-tolerant non-Bayesian learning algorithm with provable convergence guarantees. On external adversarial attacks, we consider Byzantine attacks in which the compromised agents can send maliciously calibrated messages to others (including both the agents and the parameter server). To avoid the curse of dimensionality of Byzantine consensus, we solve the non-Bayesian learning problem via running multiple dynamics, each of which only involves Byzantine consensus with scalar inputs. To facilitate resilient information propagation across sub-networks, we use a novel Byzantine-resilient gossiping-type rule at the parameter server.date: 2024-10-01

Recommended citation: Connor Mclaughlin, Matthew Ding, Denis Erdogmus, Lili Su. Asilomar Conference on Signals, Systems, and Computers (ACSSC 2024).
Paper Link

talks

teaching

Reader

CS 70 (Discrete Mathematics and Probability Theory), University of California, Berkeley, 2023

Spring 2023 - Spring 2024

Teaching Assistant

CS 70 (Discrete Mathematics and Probability Theory), University of California, Berkeley, 2024

Fall 2024 - Present