Frederik Mallmann-Trenn

Senior Lecturer (Associate Professor) at King's College London. Previously, I did a postdoc at MIT with Nancy Lynch. At King's, I'm the head of the group Algorithms and Data Analysis and I direct the Random Lab focusing on understanding stochastic processes that surround us in our daily lives.

e-mail: <full name as above with a dot after the first name>@kcl.ac.uk

Office Hours: Monday 16:00 - 17:00 (send email before)

Research Interests: Sparsification of neural networks, Stochastic processes, biological distributed computing, theory of NNs, brain, computing with noise.

Overview

Teaching

Team / Random Lab Members

Prospective Students

Publications / Lay summary

CV

Academic Career:

(Joint-)PhD from École normale supérieure (with Claire Mathieu) and Simon Fraser University (with Petra Berenbrink) [PhD Thesis]

Postdoc at MIT with Nancy Lynch

Program Committees: BDA 2024 (co-chair), Sirocco 2024, BDA 2023 (co-chair), UAI 2022, UAI 2021, AAAI 2020, Sirocco 2020, BDA 2020, UAI 2019

Grants: EPSRC New Investigator Award 01/22 - 03/25

Teaching

2019-20 Artificial Intelligence Reasoning and Decision Making
2020-21 Artificial Intelligence Reasoning and Decision Making
2021-22 Artificial Intelligence Reasoning and Decision Making
2022-23 Artificial Intelligence Reasoning and Decision Making
2023-24 Artificial Intelligence Reasoning and Decision Making
2024-25 Artificial Intelligence Reasoning and Decision Making
2024-25 Machine Learning

Team / Random Lab Members

Hind Almahmoud (PhD student)
Kristijan Atanasov (PhD student)
Mattia Villiani (PhD student, co-supervised with Prof. McBurney)

Graduated/Former Members:

Dr Cristina Gava
Dr Giordano Giambartolomei (postdoc)

See here if you are interested in doing a funded PhD under my supervision.

Prospective Students

Undergraduates:

I currently don't offer any internships for undergraduates.

Prospective PhD Students:

Project 1: Multi-agent systems to treat cancer – towards nanorobotics

Project 2: Detecting fAIke news

Publications / Lay summary

Lay summary of some of my research:

Publications [last updated Dec'24] see dblp or Google Scholar for an up-to-date version

Community Consensus: Converging Locally Despite Adversaries and Heterogeneous Connectivity Cristina Gava, Áron Vékássy, Matthew Cavorsi, Stephanie Gil, Frederik Mallmann-Trenn
ACC 2024
Sorting in One and Two Rounds Using t-Comparators Ran Gelles, Zvi Lotker, Frederik Mallmann-Trenn
DISC 2024
Dynamic Crowd Vetting: Collaborative Detection of Malicious Robots in Dynamic Communication Networks Matthew Cavorsi, Frederik Mallmann-Trenn, David Saldaña, Stephanie Gil
CDC 2023
Distributed Averaging in Opinion Dynamics Petra Berenbrink, Colin Cooper, Cristina Gava, David Kohan Marzagão, Frederik Mallmann-Trenn, Tomasz Radzik, Nicolas Rivera
PODC 2023
Learning Hierarchically-Structured Concepts II: Overlapping Concepts, and Networks with Feedback Nancy A. Lynch, Frederik Mallmann-Trenn
SIROCCO 2023
Crowd Vetting: Rejecting Adversaries via Collaboration With Application to Multirobot Flocking Frederik Mallmann-Trenn, Matthew Cavorsi, Stephanie Gil
IEEE Trans. Robotics
Community Recovery in the Degree-Heterogeneous Stochastic Block Model Vincent Cohen-Addad, Frederik Mallmann-Trenn, David Saulpic
COLT 2022
A Massively Parallel Modularity-Maximizing Algorithm with Provable Guarantees Vincent Cohen-Addad, Frederik Mallmann-Trenn, David Saulpic
PODC
On early extinction and the effect of travelling in the SIR model Petra Berenbrink, Colin Cooper, Cristina Gava, David Kohan Marzagão, Frederik Mallmann-Trenn, Tomasz Radzik
UAI 2022
Online Page Migration with ML Advice Piotr Indyk, M.-T., Slobodan Mitrovic, Ronitt Rubinfeld:

AISTATS 2022 (to appear), talk (use Chrome)

A Massively Parallel Modularity-Maximizing Algorithm With Provable Guarantees Vincent Cohen-Addad, M.T., David Saulpic:

PODC 2022

Diversity, Fairness, and Sustainability in Population Protocols Nan Kang, M.-T., Nicolás Rivera:

PODC 21

Learning Hierarchically Structured Concepts Nancy Lynch and M.-T.

Neural Networks, Arxiv, Interview with Grainstone Lee

On the Power of Louvain in the Stochastic Block Model. Vincent Cohen-Addad, Adrian Kosowski, M.T., David Saulpic:

Neurips 20

Bayes Bots: Collective Bayesian Decision-Making in Decentralized Robot Swarms Julia Ebert, Melvin Gauci, M.-T., and Radhika Nagpal

ICRA 20, Talk (ICRA 20)

Self-Stabilizing Task Allocation In Spite of Noise. Anna Dornhaus, Nancy Lynch, M.-T., Dominik Pajak and Tsvetomira Radeva

arXiv (SPAA 20) , Talk at BDA

Skyline Computation with Noisy Comparisons. Benoît Groz, M.-T., Claire Mathieu, Victor Verdugo

arXiv (IWOCA 20)

Instance-Optimality in the Noisy Value-and Comparison-Model Vincent Cohen-Addad, M.-T., Claire Mathieu

SODA 20, arXiv (full version), Slides, Talk (Video)

How to Spread a Rumor: Call Your Neighbors or Take a Walk? George Giakkoupis, M.T., Hayk Saribekyan

PODC 19, Talk

Remember the Past and Forget Thresholds. Anna Dorhnhaus, Nicole Leitner, Nancy Lynch, M.T., Dominik Pajak

BDA 19, Talk

How to Color a French Flag--Biologically Inspired Algorithms for Scale-Invariant Patterning Alberto Ancona, Ayesha Bajwa, Nancy Lynch, and M. T.

arXiv, BDA 19, Talk

Hierarchical Clustering: Objective Functions and Algorithms. Vincent Cohen-Addad, Varun Kanade, M.T., Claire Mathieu

JACM (merge of two papers), Talk

Noidy Conmunixatipn: On the Convergence of the Averaging Population Protocol M.T., Yannic Maus, Dominik Pajak

ICALP, arXiv, Talk,

On coalescence time in graphs–When is coalescing as fast as meeting? Varun Kanade, M.T., Thomas Sauerwald

arXiv, Talk, SODA 19

Clustering Redemption–Beyond the Impossibility of Kleinberg’s Axioms. Vincent Cohen-Addad, Varun Kanade, M.T.,

NeurIPS 18

Eigenvector Computation and Community Detection in Asynchronous Gossip Models. M.T., Cameron Musco, Christopher Musco

ICALP 18, arXiv, Talk

Hierarchical Clustering: Objective Functions and Algorithms. Vincent Cohen-Addad, Varun Kanade, M.T., Claire Mathieu

SODA 18, arXiv, Talk, (see superseding JACM version)

Hierarchical Clustering Beyond the Worst-Case. Vincent Cohen-Addad, Varun Kanade, M.T.

NIPS 17, Poster

Ignore or Comply? On Breaking Symmetry in Consensus. Petra Berenbrink, Andrea Clementi, Robert Elsässer, Peter Kling, M.T., Emanuele Natale

PODC 17, arXiv

How large is your graph? Varun Kanade, M.T., Victor Verdugo.

DISC 2017, BA at PODC 2016, arXiv, Talk

Bounds on the Voter Model in Dynamic Networks. Petra Berenbrink, George Giakkoupis, Anne-Marie Kermarrec, M.T.

ICALP 2016, arXiv, Master’s Thesis SFU, Talk

Self-stabilizing Balls & Bins in Batches. Petra Berenbrink, Tom Friedetzky, Peter Kling, M.T., Lars Nagel, Chris Wastell

Algorithmica, PODC 2016, arXiv, Talk

Plurality Consensus in Arbitrary Graphs: Lessons Learned from Load Balancing. Petra Berenbrink, Tom Friedetzky, Peter Kling, M.-T., Chris Wastell

ESA 2016, arXiv, Talk

Distance in the Forest Fire Model. How far are you from Eve? Varun Kanade, Reut Levi, Zvi Lotker, M.-T., Claire Mathieu

SODA 2016, Talk

On the Voting Time of the Deterministic Majority Process. Dominik Kaaser, M.-T., Emanuele Natale

MFCS 2016, BA at DISC 2015, arXiv

Improved Analysis of Deterministic Load-Balancing Schemes. Petra Berenbrink, Ralf Klasing, Adrian Kosowski, M.-T., Przemysław Uznański

PODC 2015, arXiv, Talk, (to appear in TALG)

Threshold Load Balancing With Weighted Tasks. Petra Berenbrink, Tom Friedetzky, M.-T., Sepehr Meshkinfam, Chris Wastell

JPDC, IPDPS 2015, Talk

Palindrome Recognition In The Streaming Model. Petra Berenbrink, Funda Ergün, M.-T., Erfan Sadeqi Azer

STACS 2014, arXiv (newer), Talk

Slow Down and Sleep for Profit in Online Deadline Scheduling. Peter Kling, Andreas Cord-Landwehr, M.-T.

MedAlg2012

Estimating The Number Of Connected Components In Sublinear Time. Petra Berenbrink, Bruce Krayenhoff, M.-T.

Information Processing Letters

Manuscripts

Rapid asynchronous plurality consensus. Robert Elsässer, Tom Friedetzky, Dominik Kaaser, M.-T., Horst Trinker

BA at PODC 2016, arXiv, Talk

Todo Video

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