University of Colorado Denver Mathematical and Statistical Sciences

08/20/2026

Eye-opening article in today's CU Connections about two Department of Mathematical and Statistical Sciences, CU Denver's faculty: Diana White and Pamela Whitten. The article is titled "AI Sprint Grant recipients on helping math students use AI more productively" and explains how Diana White and Pamela Whitten are leveraging the AI Sprint Grant to improve student learning. The project will integrate MathGPT.ai into Calculus I at University of Colorado Denver to address high rates of unsuccessful course completion in a key gateway course for STEM and other quantitatively intensive fields.

More information at https://connections.cu.edu/stories/ai-sprint-grant-recipients-helping-math-students-use-ai-more-productively

Launched earlier this year, the CU system Artificial Intelligence (AI) Sprint Grant: AI for Teaching & Learning is focused on improving course outcomes using AI with goals of supporting and accelerating innovative teaching practices that leverage AI to enhance student learning.

08/13/2026

Graduate student orientation was today!
New students, continuing students, new faculty members, continuing faculty members.

Happy start of academic year 26-27 to all!

08/12/2026

🥳🎉Congratulations to Dr. Florian Pfender, on receiving the National Science Foundation award, "Flag Algebra and Semidefinite Programming" (Award #2554130), with total funding of $150,000. This is a collaborative award with Dr. Bernard Lidický, from Iowa State University, (Award #2554129, $150,000).

This project advances powerful mathematical and computational methods for solving complex optimization problems—problems that ask how to find the best possible arrangement or structure under a given set of constraints. Drs. Pfender and Lidický will expand a technique known as flag algebra, which combines theoretical mathematics with modern optimization software to address problems that have remained unsolved for decades.
Although the research is foundational, the methods it develops have broader significance. Combinatorial optimization and related mathematical techniques underpin many areas of modern science and technology where large numbers of possibilities must be analyzed efficiently, including computer science, data analysis, network design, and operations research. By improving the mathematical tools available for understanding complex discrete systems, this research contributes to the foundation on which future computational and technological advances can be built.

The project will also help prepare the next generation of mathematicians and researchers. Graduate students from both institutions and other universities will have opportunities to participate in the research and in annual workshops organized by the investigators, gaining experience in advanced mathematics, computational methods, optimization, and collaborative problem-solving.

Through the development of new mathematical tools, the solution of longstanding problems, and the training of graduate students, this NSF-supported collaboration strengthens both fundamental mathematical research and the broader scientific workforce.

For more information, see: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2554130

08/10/2026

📢 Please join us for an upcoming Department Seminar!

We are excited to welcome Alejandro Parada-Mayorga, Assistant Professor in the Department of Electrical Engineering at the University of Colorado Denver, for a talk on:

📊 “Sampling and Uniqueness Sets in Graphon Signal Processing”

In this seminar, Alejandro will discuss recent advances in sampling on large and complex networks through the lens of graphs and graph limits. He will explore how graphon representations extend classical sampling and uniqueness-set concepts to large-scale networks, offering practical strategies for efficient sampling and signal reconstruction.

📅 Monday, August 24, 2026
🕧 12:30–1:45 PM
📍 ACAD 4125

All are welcome! We hope you’ll join us for this engaging seminar and learn more about emerging approaches to graph signal processing and large-network data.

08/05/2026

🎉 Please join us in welcoming Lucas Quintero as Instructor in the Department of Mathematical and Statistical Sciences at CU Denver!

📚 Lucas joins the department from the Colorado School of Mines, where he has served as a Teaching Postdoctoral Fellow. He brings experience teaching and coordinating a wide range of undergraduate mathematics courses for both STEM and non-STEM students, including Calculus, Differential Equations, Linear Algebra, Probability, Statistics, and Algebra. In addition to classroom instruction, he has developed syllabi, assessments, course materials, and evidence-based teaching practices designed to support student success.

🌟 Lucas earned his Ph.D. in Applied Mathematics in 2022 and is passionate about making mathematics engaging, relevant, and accessible. His teaching philosophy blends traditional instruction with active learning, flipped classrooms, formative assessment, and real-world applications to help students build confidence and deepen their understanding of mathematics. He is committed to fostering an inclusive learning environment where students from diverse backgrounds feel supported and empowered to succeed.

💻 Lucas also enjoys incorporating modern technologies and innovative instructional approaches into his courses, including coding applications, mastery-based grading, and collaborative learning tools that enhance student engagement both inside and outside the classroom. We are excited to welcome Lucas to the department and look forward to the expertise, enthusiasm, and student-centered approach he will bring to our mathematics community. Please join us in giving Lucas a warm welcome! 👏

08/05/2026

🎉 Please join us in welcoming Meaghan Cheeke as our newest Instructor in the department of Mathematical and Statistical Sciences at CU Denver!

📚 Meaghan brings more than 15 years of experience teaching mathematics at the University of Colorado Denver, where she has served as both a full-time instructor and a part-time lecturer since 2010. Throughout her career, she has taught a wide range of undergraduate mathematics courses in both online and face-to-face formats, developed instructional materials and Canvas course content, coordinated multiple courses, and served on numerous departmental committees.

🌟 A dedicated and student-centered educator, Meaghan is committed to creating an inclusive learning environment that encourages curiosity, persistence, and a growth mindset. She is known for designing engaging lessons that incorporate technology, collaborative learning, and real-world applications while fostering meaningful connections with her students. Her thoughtful and reflective approach to teaching was recognized with the CU Denver Department of Mathematical and Statistical Sciences Spring 2021 Award for Teaching Excellence by a Lecturer.

🎓 Meaghan holds a Bachelor of Science in Mathematics Education from Southern Connecticut State University and a Master of Arts in Mathematics with a teaching emphasis from the University of Northern Colorado. We are excited to welcome her back to a full-time role and look forward to the experience, enthusiasm, and dedication she will bring to our students and department. Please join us in giving Meaghan a warm welcome! 👏

08/05/2026

Congratulations to Dr. Carlos Martínez Mori, on receiving the National Science Foundation award, "Collaborative Research: Designing Transit Systems with Public Approval through Cooperative Linear Production Games" (Award #2620205), with total funding of $349,614. CU Denver is the lead of this collaborative proposal that also includes Dr. Alejandro Toriello, from GA Institute of Technology, (Award #2620206, $84,088).

This prestigious award recognizes the significance of Dr. Martínez Mori's research and his leadership in advancing innovative methods for public-sector decision-making. His work exemplifies the high-impact, interdisciplinary research that funding agency seeks to support, and this award is a well-deserved acknowledgment of Dr. Martínez Mori's contributions to optimization, operations research, and public policy.

This research has the potential to make a meaningful impact on society by helping public agencies make investment decisions that are not only efficient but also equitable and broadly supported by the communities they serve. By developing mathematical models that explicitly incorporate stakeholder preferences into public planning, Dr. Martínez Mori's work will enable better decisions for transit systems and other public infrastructure investments where competing priorities often make consensus difficult. The resulting analytical tools can help governments allocate resources more effectively, improve access to essential public services, strengthen public trust in decision-making, and ensure that investments better reflect the needs and values of the communities they serve. Congratulations to Dr. Carlos Martínez Mori again on this outstanding achievement and on securing NSF support for research that promises lasting benefits for both the scientific community and society.

For more information, see: https://www.nsf.gov/awardsearch/show-award?AWD_ID=2620205

08/03/2026

Join us for Lillian Makhoul’s PhD in Applied Mathematics Research Proposal on Monday, August 10th from 10:00am-12:00pm in Student Commons Building Room 4113 or over Zoom. Please email [email protected] to request the Zoom link!

Title: Theory and Applications in Mixed Integer Non-Linear Programming

Abstract: Mixed-integer nonlinear programs (MINLPs) are a broad class of optimization problems arising across numerous domains of science, engineering, and economics. The general framework combines the modeling power of nonlinear and potentially nonconvex functions with discrete decision variables, enabling formulations that can effectively capture many real-world complexities.
This proposal will focus on two different problems in MINLP. The first project focuses more on solutions methods of MINLPS. The project itself, which is very theoretical, analyzes certain mathematical objects of interest when implementing a spatial branch and bound algorithm. The second project addresses an applied problem: placing polling location for the 2028 general election to maximize voter accessibility.

07/24/2026

🎉Congratulations to Dr. Colin Furey! 🎉

Please join us in congratulating Dr. Colin Furey on the successful defense of his Ph.D. dissertation!

We also extend our congratulations to Dr. Farhad Pourkamali Anaraki, Colin's Ph.D. advisor, for his mentorship and guidance throughout this journey.

A sincere thank you to the committee for their time, expertise, and support:
Dr. Erin Austin (Chair)
Dr. Farhad Pourkamali Anaraki (Advisor)
Dr. Yaning Liu
Dr. Joshua French
Dr. Farnoush Banaei-Kashani

Congratulations again, Dr. Furey! We wish you continued success in your future endeavors. 👏🎓

07/13/2026

🎉 Congratulations to Farhad Pourkamali-Anaraki and Courtney Franzen on the acceptance of their research paper at the SIAM International Conference on Data Mining (SDM 2026), to be held in Salt Lake City, Utah, this November.

A special congratulations to Courtney, a recent Spring 2026 PhD graduate from the Department of Mathematical and Statistical Sciences, on this exciting milestone.

Their paper, "Ensemble-Based Dirichlet Modeling for Predictive Uncertainty and Selective Classification", introduces a new approach for estimating predictive uncertainty in neural network classifiers. By leveraging ensembles of deep learning models, the research provides more stable and reliable uncertainty estimates, improving confidence scoring and selective classification for real-world machine learning applications.

Congratulations to Farhad and Courtney on this outstanding accomplishment!🎉