Department of Mathematical Sciences

Stevens is collaborating with Nebius Academy, the learning and research initiative of leading AI infrastructure provider Nebius, to launch the Laboratory for AI in Mathematics Education. Nebius contributed $175,691 toward the lab.

With a broad mandate to explore and promote innovative uses of AI in math research and education, the lab will investigate how large language models (LLMs) and other AI technologies can be used to promote mathematical reasoning skills and transform teaching practices. This collaboration aims to improve student performance, apply AI to solve open math problems, discover new hypotheses and test LLM technologies in math education.

Improving mathematics education is a fundamental issue for modern educators and advanced AI technologies such as LLMs offer potentially transformative solutions. The team behind the lab, which opened March 12, is currently developing a specialized course designed to introduce Stevens math researchers to a variety of AI technologies to enhance their research processes. Through practical projects in fields such as graph theory and group theory, students will learn to accelerate hypothesis formulation, optimize the search for examples and counterexamples and validate the results obtained through these techniques.

"Our lab is focused on developing intelligent tutoring technologies and preparing future mathematicians to incorporate AI and machine learning into their work," said Alexei Miasnikov, director of the AI in Mathematics Education Lab and Distinguished Professor at Stevens. "By combining academic research with strong industry ties, we aim to accelerate innovation and bring AI-driven tools into the classroom to transform math education."

Darinka Dentcheva, professor in the Department of Mathematical Sciences at Stevens Institute of Technology, has received a $300,270 grant from the Air Force Office of Scientific Research for her latest project, “Optimization and Learning with Contextual Risk Models.” Rutgers University is also part of the total $655,000 grant.

The goal of Dentcheva's research is to improve decision-making processes by using mathematical models that handle risk and uncertainty in a clear and logical way. Dentcheva's research uses math to leverage available information to help avoid inherent risks in such situations and make choices that have the best chance of success.

This research is particularly useful in machine learning and artificial intelligence, where such models are essential for making accurate predictions in the face of variations and uncertainties. Dentcheva’s current research builds her substantial work refining the theory and methods behind risk-averse optimization. Over the past 12 years, she has received five grants from the National Science Foundation and a grant from the Office of Naval Research for her contributions in this area.

With the Air Force Office of Scientific Research grant, Dentcheva aims to make substantial strides in the field of risk-averse decision-making, equipping researchers and industry leaders with the tools to make smarter, data-informed choices in a world filled with uncertainty.

From June 14-16, 2023, the Department of Mathematical Sciences at Stevens organized a conference on group theory and its applications in honor of the 80th birthday of Professor Robert Gilman.

Gilman is the director and a cofounder of the Algebra and Cryptology Center who has spent his professional career at Stevens and established himself as a leading expert in multiple subfields of algebra.

The three-day conference, titled Groups and Computation: Interactions Between Geometric Group Theory, Computability and Computer Science, featured talks by internationally renowned experts and long-time collaborators of Gilman, including several members of the Department of Mathematical Sciences. Some talks were held on the Stevens campus; others were held online.

Kathrin Smetana, assistant professor of mathematics at Stevens Institute of Technology, was recently awarded a five-year, $461,998 grant through the National Science Foundation (NSF)’s prestigious CAREER program to develop “Randomized Multiscale Methods for Heterogeneous Nonlinear Partial Differential Equations.” In her project, Smetana and her students will focus on designing multiscale methods via randomization techniques that have originally been proposed in the context of data science. They will use these randomization techniques to detect hidden structures in the local numerical simulation data, such as the simulations of the deformation of portions of wind turbines, and, in particular, to detect the functions that are relevant for the approximation of the solution of the PDE.