The Mathematics Colloquium in the Department of Mathematics & Computer Science at Rutgers-Newark takes place on Wednesdays 4-5pm, either in person at 204 Smith Hall, 101 Warren St., or via Zoom. All are welcome!

For more information or to be added to our mailing list, please email Kyle Hayden (kyle.hayden@rutgers.edu).

Schedule — Spring 2026

January 28
Shira Wein (Amherst)
Lost in Translation, and Found: Detecting and Interpreting Translation Effects in Large Language Models

Large language models are able to generate highly fluent text, in large part because they are trained on massive amounts of data. This data may contain “translationese”: hallmarks which distinguish translated texts from texts originating in the language. Though individual translated texts are often fluent and preserve meaning, at a large scale, the presence of translated texts in training data negatively impacts performance and in test data inflates evaluation. In this work, I investigate (1) whether humans are able to distinguish texts originally written in English from texts translated into English, (2) how the surface-level features of translationese can be mitigated using Abstract Meaning Representation, and (3) why neural classifiers are able to distinguish original and translated English texts much more accurately than humans.

February 4
Isaiah King (GWU)
Cyber Threat Hunting with Graph Deep Learning

Modern computer networks generate massive volumes of high-dimensional, time-evolving data, making the detection and response to cyber-attacks increasingly challenging. This challenge is especially acute for novel or zero-day attacks, where predefined signatures or heuristics are ineffective. This talk presents a framework for modeling large-scale computer networks as temporal graphs, enabling scalable, precise, and generalizable approaches to intrusion detection and incident response. Using deep graph deep learning techniques, including temporal link prediction and graph representation learning, anomalous activity can be identified in complex network environments. If an attacker is detected on the network, the same graph-based abstraction supports decision-making for active defense. Framing network defense as a multi-agent Markov game, graph-based reinforcement learning can be used to reason about adversarial behavior and select actions that contain and remove attackers while minimizing disruption to normal operations. This work lies at the intersection of cybersecurity and data science, advancing the state of the art in attack detection, attribution, and automated response to sophisticated adversaries operating in real-world networked systems.