Tracking Urban Activity Growth Globally with Big Location Data. - Matthew L. Daggitt; Anastasios Noulas; Blake Shaw; Cecilia Mascolo (CoRR, 2015)

Rate of Convergence of Increasing Path-Vector Routing Protocols. - Matthew L. Daggitt; Timothy G. Griffin (ICNP, 2018)

An Agda Formalization of Üresin and Dubois' Asynchronous Fixed-Point Theory. - Ran Zmigrod; Matthew L. Daggitt; Timothy G. Griffin (ITP, 2018)

Asynchronous convergence of policy-rich distributed bellman-ford routing protocols. - Matthew L. Daggitt; Alexander J. T. Gurney; Timothy G. Griffin (SIGCOMM, 2018)

An algebraic perspective on the convergence of vector-based routing protocols - Matthew L. Daggitt (2019)

A Relaxation of Üresin and Dubois' Asynchronous Fixed-Point Theory in Agda. - Matthew L. Daggitt; Ran Zmigrod; Timothy G. Griffin (J. Autom. Reason., 2020)

Actions you can handle: dependent types for AI plans. - Alasdair Hill; Ekaterina Komendantskaya; Matthew L. Daggitt; Ronald P. A. Petrick (TyDe@ICFP, 2021)

Property-driven Training: All You (N)Ever Wanted to Know About. - Marco Casadio; Matthew L. Daggitt; Ekaterina Komendantskaya; Wen Kokke; Daniel Kienitz; Rob Stewart 001 (CoRR, 2021)

Neural Network Robustness as a Verification Property: A Principled Case Study. - Marco Casadio; Ekaterina Komendantskaya; Matthew L. Daggitt; Wen Kokke; Guy Katz; Guy Amir; Idan Refaeli (CAV (1), 2022)

Differentiable Logics for Neural Network Training and Verification. - Natalia Slusarz; Ekaterina Komendantskaya; Matthew L. Daggitt; Robert J. Stewart 001 (NSV/FoMLAS@CAV, 2022)

CheckINN: Wide Range Neural Network Verification in Imandra. - Remi Desmartin; Grant O. Passmore; Ekaterina Komendantskaya; Matthew L. Daggitt (PPDP, 2022)

Vehicle: Interfacing Neural Network Verifiers with Interactive Theorem Provers. - Matthew L. Daggitt; Wen Kokke; Robert Atkey; Luca Arnaboldi 001; Ekaterina Komendantskaya (CoRR, 2022)

Why Robust Natural Language Understanding is a Challenge. - Marco Casadio; Ekaterina Komendantskaya; Verena Rieser; Matthew L. Daggitt; Daniel Kienitz; Luca Arnaboldi 001; Wen Kokke (CoRR, 2022)

CheckINN: Wide Range Neural Network Verification in Imandra (Extended). - Remi Desmartin; Grant O. Passmore; Ekaterina Komendantskaya; Matthew L. Daggitt (CoRR, 2022)

A new approach to SHRIMP II zircon U-Th disequilibrium dating. - Ruby C. Marsden; Christopher L. Kirkland; Martin Danisík; Matthew L. Daggitt; Ung-San Ahn; Bjarne Friedrichs; Noreen J. Evans (Comput. Geosci., 2022)

AnalyZr: A Python application for zircon grain image segmentation and shape analysis. - Taryn Scharf; Christopher L. Kirkland; Matthew L. Daggitt; Milo Barham; Vladimir Puzyrev (Comput. Geosci., 2022)

Dynamic asynchronous iterations. - Matthew L. Daggitt; Timothy G. Griffin (J. Parallel Distributed Comput., 2022)

ANTONIO: Towards a Systematic Method of Generating NLP Benchmarks for Verification. - Marco Casadio; Luca Arnaboldi 001; Matthew L. Daggitt; Omri Isac; Tanvi Dinkar; Daniel Kienitz; Verena Rieser; Ekaterina Komendantskaya (FoMLAS@CAV, 2023)

The Vehicle Tutorial: Neural Network Verification with Vehicle. - Matthew L. Daggitt; Wen Kokke; Ekaterina Komendantskaya; Robert Atkey; Luca Arnaboldi 001; Natalia Slusarz; Marco Casadio; Ben Coke; Jeonghyeon Lee (FoMLAS@CAV, 2023)

Compiling Higher-Order Specifications to SMT Solvers: How to Deal with Rejection Constructively. - Matthew L. Daggitt; Robert Atkey; Wen Kokke; Ekaterina Komendantskaya; Luca Arnaboldi 001 (CPP, 2023)

Logic of Differentiable Logics: Towards a Uniform Semantics of DL. - Natalia Slusarz; Ekaterina Komendantskaya; Matthew L. Daggitt; Robert J. Stewart 001; Kathrin Stark (LPAR, 2023)

Marabou 2.0: A Versatile Formal Analyzer of Neural Networks. - Haoze Wu 001; Omri Isac; Aleksandar Zeljic; Teruhiro Tagomori; Matthew L. Daggitt; Wen Kokke; Idan Refaeli; Guy Amir; Kyle Julian; Shahaf Bassan; Pei Huang 002; Ori Lahav 002; et al. (CAV (2), 2024)

Vehicle: Bridging the Embedding Gap in the Verification of Neuro-Symbolic Programs. - Matthew L. Daggitt; Wen Kokke; Robert Atkey; Natalia Slusarz; Luca Arnaboldi 001; Ekaterina Komendantskaya (CoRR, 2024)

Efficient compilation of expressive problem space specifications to neural network solvers. - Matthew L. Daggitt; Wen Kokke; Robert Atkey (CoRR, 2024)

NLP Verification: Towards a General Methodology for Certifying Robustness. - Marco Casadio; Tanvi Dinkar; Ekaterina Komendantskaya; Luca Arnaboldi 001; Omri Isac; Matthew L. Daggitt; Guy Katz; Verena Rieser; Oliver Lemon (CoRR, 2024)

Formally Verified Convergence of Policy-Rich DBF Routing Protocols. - Matthew L. Daggitt; Timothy G. Griffin (IEEE/ACM Trans. Netw., 2024)

Neural Network Verification is a Programming Language Challenge. - Lucas C. Cordeiro; Matthew L. Daggitt; Julien Girard-Satabin; Omri Isac; Taylor T. Johnson; Guy Katz; Ekaterina Komendantskaya; Augustin Lemesle; Edoardo Manino; Artjoms Sinkarovs; Haoze Wu 001 (ESOP (1), 2025)

Vehicle: Bridging the Embedding Gap in the Verification of Neuro-Symbolic Programs (Invited Talk). - Matthew L. Daggitt; Wen Kokke; Robert Atkey; Ekaterina Komendantskaya; Natalia Slusarz; Luca Arnaboldi 001 (FSCD, 2025)