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Department of Mathematics

Colloquium Series

Upcoming Colloquia

Colloquia will resume in Fall 2024. Have a great summer!

Recent Colloquia

Friday, April 19, 2024 - Symeon Papadimitropoulos, Ph.D., and Nathan Willis, Ph.D.  (UC Merced postdocs)

At 9:00 AM in PB 103

Title: Synthetic aperture imaging using physically informed convolutional neural networks  (Dr. Papadimitropoulos)

Abstract: We propose a physically informed neural network (PINN) approach to address the problem of locating scatterers using synthetic aperture radar (SAR). The proposed approach uses the physical properties of wave propagation to aid the neural network in learning to detect and localize scatterers from SAR data. This methodology is especially efficient in super-resolution cases, i.e. when the scatterers are located close to each other, at distances smaller than the nominal resolution of the imaging system. Using the proposed approach, we are able to detect an a priorly unknown number of scatterers and localize them with high accuracy.

Short Speaker Bio: Symeon is a Postdoctoral Scholar at the University of California, Merced. Prior to that, he held postdoctoral appointments at Tel-Aviv University and The Technion. Symeon obtained his Ph.D. degree in 2018 from the University of Crete, in Greece. His research interests include absorbing boundary conditions, numerical methods for PDEs, inverse problems relating to wave propagation and imaging, and underwater acoustics. Current projects involve the incorporation of deep learning methods in these areas.

Title: Shallow-water simulations of colliding turbidity currents (Dr. Willis)

Abstract: Motivated by future deep-sea mining for polymetallic nodules, we investigate colliding turbidity currents in one and two horizontal dimensions. The standard shallow-water equations are amended to include the sediment concentration and pressure difference with the ambient fluid. Our simulations show that currents do not mix upon collision and instead develop a reflecting wave in each current. We describe the effects of the ratio of current concentrations, ratio of current heights, settling speed, and collision angle. To understand the overall impact of the mining process on the ocean floor, we present the resulting patterns of sediment deposition.

Short Speaker Bio: Nathan Willis is a Visiting Assistant Professor at the University of California, Merced. In May 2022, Nathan graduated with his PhD in Mathematics under the advisement of Christel Hohenegger. Nathan utilizes numerical approximations, asymptotic analysis, and applied analysis to research a variety of applied problems arising from fluid dynamics. Some examples of ongoing research includes surface tension in free surface sloshing, steady streaming in non-Newtonian fluids, and colliding turbidity currents arising in deep sea mining. As a member of Project NExT, Nathan is also passionate about teaching mathematics and incorporating innovative and active learning techniques in his classroom. 

Friday, April 5, 2024 - 

At 9:00 AM on Zoom (Note:  Pre-registration is required.)

Title: Using Supercomputers to Understand Our World

Friday, March 1, 2024 - Bethany Lusch (Argonne National Laboratory)

At 9:00 AM on Zoom (Note: Pre-registration is required.)

Title: Accelerating Simulations with Machine Learning

Abstract: Large-scale simulations are used in many fields to address questions such as how to design an efficient engine, how climate change will affect a particular region, or how cancer spreads in the body.  There are always more simulations that would be useful to run than available time, especially when each simulation takes weeks on a supercomputer. Can we leverage data saved from previous simulations to speed up future ones? In this talk, I will cover a couple of ways that we have used machine learning to accelerate simulations.

Short Speaker Bio: Dr. Bethany Lusch is an Assistant Computer Scientist at Argonne National Lab. She is on the data science team within Argonne's supercomputing facility. Her work focuses on machine learning for large-scale scientific problems. Her postdoctoral training was in the area of machine learning for dynamical systems at the University of Washington in Seattle. She obtained her PhD and MS in applied mathematics at the University of Washington. Dr. Lusch also holds a BS degree in mathematics from the University of Notre Dame. 

Celebrating Women in Math Series

Tuesday, March 5, 2024 - Abigayle Dirdak, PhD Candidate (University of Arizona)

At 4:00 PM in PB 032

Title: How would you describe a Mathematician? Oh, and some stuff about the Pentagram map!

Abstract: Have you ever pondered about what defines a mathematician? Join us while we discuss the topic. Additionally I will be presenting on my research involving polygons, projective spaces, and something called the pentagram map. So if polygons and/or discussions on mathematicians interest you, come to my talk.  

Short Bio: Abigayle grew up on a small family farm in central California. After graduating high school she attended College of the Sequoias before transferring to Fresno State. Participating in research projects with Dr. Carmen Caprau, and Dr. Khang Tran inspired her to continue doing math and to attend graduate school.  Abigayle received her BA in Mathematics from Fresno State in 2018, and is now excited to be finishing her final year as a PhD student in mathematics at the University of Arizona.

Friday, March 8, 2024 - Jennifer Elder, PhD (Missouri Western State University)

At 10:00 AM in S2 207

Title: Finding Needles in Haystacks

Abstract: Picture a one way street with n parking spots. Parking preferences are tuples of numbers that correspond to n cars' preferred parking spots on the street. For example, (1,3,1) would mean there are three spots, car one wants to park in spot 1, car two wants to park in spot 3, and car three also wants to park in spot 1. A preference tuple is called a parking function if all the cars are able to park in their preferred spot, or some spot further down the street. In this talk, we will discuss a special class of parking functions called unit interval parking functions. These are the parking functions in which cars park at most one spot away from their preferred parking spot. We will discuss a variety of new bijections, recursions, and generating functions produced by working with unit interval parking functions, including solutions to previously unsolved problems from Richard Stanley.

Short Bio: Jennifer earned both a Bachelor’s and a Master’s Degree in Mathematics at Fresno State. Her Master’s Thesis was on “Generalizations of the Futurama Theorem,” a problem related to cycle decompositions of permutations. After graduating in 2016, she went on to earn a PhD in Mathematics from Arizona State University in 2021. Her Dissertation research was in Combinatorics, also on permutation properties. After her PhD, she spent two years as a Visiting Professor of Mathematics at Rockhurst University in Kansas City Missouri, and was a Postdoc TA for the 2022 Summer@ICERM Undergraduate Research Program at Brown University. As of Fall 2023, she is an Assistant Professor of Mathematics at Missouri Western State University in Saint Joseph Missouri. She is focused on teaching General Education Math classes, and is an active researcher in Combinatorics. 

Thursday, November 16, 2023 - Ayse Ozturk, PhD (The Ohio State University, Newark)

At 1:00 PM in PB 390 and Zoom

Title: Bridging Access and Equity with Mathematical Modeling and Teacher Preparation 

Abstract: Dr. Ozturk will outline her overarching research agenda, encompassing access and equity in mathematics education, mathematical modeling, and teacher preparation.  She will highlight the key findings from her last two publications and offer insights into the future direction of her ongoing research initiatives.

Short Bio: Ayse Ozturk is a lecturer of Mathematics Education in the Department of Teaching and Learning and the Department of Mathematics at The Ohio State University, Newark. Her research focuses on the learning and teaching of mathematics as a humanizing practice. As a teacher educator, she is dedicated to crafting equity-based lessons that empower students to become active and critical mathematical thinkers. 

Tuesday, November 16, 2023 - Casey Griffin, PhD Candidate (University of Delaware)

At 1:00 PM in PB 390 and Zoom

Title: Women’s Sense of Belonging in Undergraduate Calculus

Abstract: Prior research has identified low sense of belonging as a key reason why women leave undergraduate STEM majors, especially after taking Calculus. Historically, Calculus instruction has been primarily lecture-based, though recently, efforts have shifted toward incorporating instruction that provides opportunities for students to engage in active learning. Prior studies have suggested that incorporating active learning may support students’ sense of belonging, however, there lacks consensus on the particular types of active learning opportunities that best support students. In my talk, I will be presenting findings from two studies investigating women’s sense of belonging in undergraduate Calculus, and the influence of the learning opportunities they experience during class on their sense of belonging. My results suggest that women tend to report positive influences of active learning opportunities on their sense of belonging, particularly those that allow them to interact with their classmates and with their instructor. I conclude by describing directions where I hope to take this work. 

Short Bio: Casey Griffin is a Ph.D. candidate specializing in mathematics education at the University of Delaware’s School of Education. Casey is interested in learning more about the ways in which active learning opportunities in undergraduate mathematics courses might support underrepresented students’ sense of belonging, and further serve as a way to diversify the STEM major population. Her dissertation focuses on women’s sense of belonging in an undergraduate Calculus course that incorporates frequent opportunities for students to engage in active learning, and how students perceive the impact of these opportunities on their sense of belonging. Before attending graduate school, Casey earned a B.A. in Secondary Mathematics Education from the University of Delaware and taught high school mathematics in Long Branch, New Jersey for two years. She then went on to pursue a M.A. in Mathematics from Villanova University and returned to the University of Delaware for her Ph.D. 

Wednesday, November 8, 2023 - Ernesto Daniel Calleros, PhD Candidate (San Diego State University, UC San Diego)

At 2:00 PM in PB 390 and Zoom

Title: Investigating the Language Demands and Resources of InquiryBased Mathematics Education for Linguistically Diverse Students: The Case of Inquiry-Oriented Linear Algebra

Abstract: Prior research shows that inquiry-based instructional approaches are more effective than lectures. However, these approaches might not yield equal benefits for students from certain marginalized groups (e.g., see Reinholz et al., 2022). Given the centrality of communication in inquiry-based mathematics classrooms, one important group to consider is linguistically diverse students whose primary language may differ from the language of instruction. In this presentation, I explore an overarching question related to equity for linguistically diverse students: What language demands and resources do linguistically diverse students experience in an inquiry-oriented linear algebra course?  Grounded in a situated sociocultural theory of learning, I constructed a framework that captures language demands and resources along three interrelated dimensions: (a) lexico-grammatical – words, phrases, and grammar, (b) situational – material, activity, semiotic, and sociocultural aspects, and (c) normative – social and socio-mathematical norms. The data analyzed were classroom observations of one inquiry-oriented linear algebra course taught by an expert instructor, as well as semi-structured interviews with 5 male linguistically diverse students from the course. The 5 students spanned a diverse range of cultural backgrounds (Korean, Vietnamese, Malaysian, Hispanic, and White) and comfort levels with English. The data was analyzed using thematic analysis (Miles et al., 2020), allowing for both a priori and emergent codes. Results indicated that language demands experienced by linguistically diverse students included: unclear meanings of mathematical notation and terminology, verbal dominance, and violated expectations about a single “right” way to solve a task. Language resources included: student familiarity with problem contexts, group roles based on different language systems, and translation tools. These findings shed light on the language challenges and resources that linguistically diverse students may experience in inquiry-based mathematics classes. I conclude with recommendations for addressing the identified linguistic challenges and leveraging the identified linguistic resources. 

Short Bio: Ernesto Daniel Calleros is a Ph.D. candidate in the Mathematics and Science Education joint doctoral program at San Diego State University and the University of California San Diego. Mr. Calleros earned an M.A. in Mathematics from Rice University and a B.S. in Applied Mathematical Sciences from Texas A&M University. His research interests include undergraduate mathematics education and investigating ways to make mathematics linguistically accessible to all students, especially multilingual learners, in K-16 educational contexts. Mr. Calleros has taught mathematics in both college-level and K-12 settings. 

Friday, November 3, 2023 - Chris Barker, Ph.D. (Statistical Consulting Section of the American Statistical Association) )

At 9:00 AM in PB 390 and Zoom 

Title: Sample Size for Number of Patient Interviews when Developing a PRO by the 2009 FDA Guidance 

Abstract: Patient Reported Outcomes (PRO’s) developed according to the 2009 FDA PRO guidance require an initial step of structured patient interviews or focus groups for “item Generation”. The 2009 guidance does not provide sample size suggestions or methodology for number of interviews (sample size) for item generation. This talk defines a “Potential” Type I error, an URN model for use in simulation, simple descriptive statistics and pragmatic methodology of Kaplan Meier and “capture recapture” for estimating a sample size for the Item Generation step in the 2009 FDA PRO guidance. These methods also applicable in the more recent update to the 2002 “Patient-Focused Drug Development: Selecting, Developing, or Modifying Fit-for-Purpose Clinical Outcome Assessments “ (PFFD ) guidance. This work proposes an URN model for use in simulating the Item Generation process, and the adoption of a Kaplan Meier statistical methodology and additional Capture- Recapture sample size methodology that address this gap in the FDA PRO guidance. This work also appears to be the first to provide a definition of potential Type I error in interviews for a PRO methods for assessing sample size and a new descriptive statistics to summarize saturation based on a probability distribution to confirm whether enough interviews have been prepared. A Potential Type I error is declaring saturation when it has not been achieved during the interviews. Two worked examples applied to actual interview data and published data, are presented. Guidelines are proposed for the estimation of sample size useful for PRO experts conducting interviews for a PRO. These methods are applied in the setting of qualitative research. Future research for other methods of interviewing and determining saturation is required as well as incorporating the correlation among items during the item generation process.

Short Bio: Chris Barker is a Consultant Biostatistician, an Adjunct Associate Professor of Biostatistics at the University of Illinois, and in the process of retiring. Chris’ background, training and Ph.D. are in Bio-statistics from the University of Illinois Graduate School of Public Health. He has worked in the pharmaceutical industry on drug and vaccine development, medical devices and disease diagnostics. Chris has also worked in a Global Health Economics and Strategic Pricing group for a large international pharmaceutical company (Roche) collaborating with economists developing cost-benefit, cost-utility, and cost consequences for new drug development. During the pandemic, Dr. Barker has been working on new drugs for covid19 and medical diagnostics for detection of SARS-COV-2, the virus causing covid19. His work in the pharmaceutical industry has permitted him to give lectures around the world including London UK, Basel Switzerland, Suva Fiji and Havana Cuba. One of his current interests is in use of “role play” for training students and statisticians throughout their career in Statistical Consulting Skills.

Friday, October 13, 2023 - Elliott Vest, PhD Candidate (UC Riverside) (Fresno State alumnus)

At 9:00 AM in PB 390

Title: The relevance of CAT(0) cube complexes in Geometric Group Theory

Abstract: Geometric Group Theory, as the name suggests, is the study of groups from a geometric perspective. The hope is to associate a group with a metric space, then use the geometry of the metric space to find more information about the group. A very popular collection of metric spaces we use is CAT(0) cube complexes, formed by gluing collections of Euclidean cubes together via isometries of their faces. The relatively easy geometry of CAT(0) cube complexes has contributed to the resolutions of long-standing conjectures, and they have shown to be a fruitful tool in the study of mapping class groups. This talk starts with an overview of Geometric Group Theory, then pivots to how CAT(0) cube complexes are defined as well as what makes their geometry so nice. With this foundation, we will see how recently created tools in the field of Geometric Group Theory can extend arguments for CAT(0) cube complexes to a larger class of spaces; namely CAT(0) spaces, hierarchically hyperbolic spaces, or even proper geodesic metric spaces.

Short Bio: As of Fall 2023, Elliott Vest is a PhD mathematics student finishing his last year at University of California, Riverside. Born and raised in Visalia, California, he began his college journey at the College of the Sequoias in Visalia, then transferred to Fresno State in 2017 as a mathematics major. He graduated from Fresno State in 2019. It was the inspiration of many of the Fresno State faculty, such as Dr. Comlan De Souza and Dr. Katherine Kelm, that led him to apply to a PhD program in mathematics in the first place. From tutoring, to facilitating labs at Fresno State, to now teaching mathematics at UC Riverside as well as mentoring incoming graduate students, Elliott has a strong passion for teaching and hopes to obtain a teaching position after graduation. He enjoys going to his family cabin at Bass Lake, playing piano, going to the gym, destroying opponents at ping pong, and roleplaying in Dungeons and Dragons with his friends. 

Friday, October 6, 2023 - Youngsoo Choi (Lawrence Livermore National Laboratory)

At 9:00 AM by Zoom - Preregister (using QR code in the flyer) to receive the link.

Title: Physics-constrained data-driven methods for accurately accelerating simulations

Abstract: See the flyer

Friday, May 5, 2023 - Joseph Sifuentes, Ph.D. (UTRGV) 

At 3:30 PM in PB 011

Title: Why I Became a Mathematician -  A Tale of Art, Heavy Metal, Eigenvalues  and Killer Muscle Cars 

Abstract: In 2011, President Obama awarded Applied Mathematics Professor Richard Tapia the National Medal of Science for his seminal contributions to the field of optimization and the tremendous work he has done as a mentor and advocate for young mathematicians from underrepresented groups.  In this talk, I'll tell you how he did that using a customized 1970 Super Sport Chevelle named Heavy Metal and the prospect of making a music video. This was how I began my irreversible descent into a love affair with mathematics and research.

Short Bio: Dr. Josef Sifuentes is a boot-loving Texan who majored in Math, Computational and Applied Mathematics, and Art as an undergraduate at Rice University and stayed to complete a Ph. D. in Computational and Applied Mathematics there. He has worked as a research scientist at the Courant Institute at NYU and a visiting professor at Texas A&M before joining the faculty in the School of Mathematical and Statistical Sciences at UTRGV. His research interests are in numerical linear algebra and wave scattering problems. Dr. Sifuentes has served as a mentor to talented students as director of the UTRGV Pathways to Math Graduate School, co-director of the NSF-LSAMP summer research academy and faculty mentor for the Gulf States Math Alliance, amongst other programs aimed at promoting undergraduate research involvement. And he really likes BBQ and coffee.

Friday, April 28, 2023 - Alex Kontorovich, Ph.D. (Rutgers University)

At 11 AM by Zoom only

Title: (Potential) Applications of Machine Learning to Pure Mathematics

Abstract: Recent developments both in Machine Learning and Interactive Theorem Prover technology lead to interesting and important questions about the future of research mathematics. We will discuss some of these advances and speculate on potential changes and challenges in the medium and perhaps even short term horizon.

Short Bio: Kontorovich is a Professor of Mathematics at Rutgers University. He received his BA in Mathematics from Princeton, followed by a PhD in Mathematics from Columbia. Before moving to Rutgers, Kontorovich taught at Brown, Stony Brook, and Yale. Kontorovich’s research interests span number theory, dynamical systems, ergodic theory, geometry, and representation theory. He is a Fellow of the American Mathematical Society, a 2017 Kavli Fellow of the National Academy of Sciences, and during the 2020-21 year, he was also Distinguished Visiting Professor for the Public Dissemination of Mathematics at the National Museum of Mathematics (MoMath) in New York.

Friday, April 21, 2023 - Dr. Nicolle Gonzalez (UC Berkeley)

At 12 PM in PB 013

Title: Crystals!

Abstract: A basic question in linear algebra is finding the coefficients of a vector in terms of a given basis. A fundamental goal in combinatorics is finding formulas for these coefficients when our vector spaces are certain polynomial rings. It turns out questions like these can be encoded and answered using certain directed graphs known as crystals. However, these crystals contain much more information than the original polynomials themselves. Algebraic properties like symmetries of the polynomials and multiplication rules are all encoded as symmetries and tensor product structures on these graphs. Hence, the polynomials can be seen as "shadows" of the crystals. In fact, the crystals themselves are also shadows of more complicated algebraic objects known as representations. In this talk we will introduce crystals from scratch and with lots of examples. We will show how the product of symmetric polynomials follows from tensoring certain symmetric crystal graphs. We will also discuss how certain bases in the full polynomial ring correspond to truncations of these symmetric crystals and how multiplying these truncated graphs turns out to be unexpectedly mysterious.  

Short Bio: Nicolle was born and raised in Venezuela. After some time in Hawaii and Oregon for undergrad, she moved to LA to pursue grad school at USC. She has had postdocs at UCLA and MSRI and is currently a Morrey visiting professor at UC Berkeley. 

Thursday, March 30, 2023 - Dr. Melissa Zhang (postdoc at UC Davis)

At 12 PM in PB 390

Title: Using Algebra to Study the 4D Behavior of Knots

Abstract: A knot is a closed loop of string in 3-space. When subject to gentle wiggling and deforming, a knot does not lose its most inherent properties, making it a foundational object in many areas of topology. The simultaneous visual intuitiveness and rich diversity of knots allows them to also serve as bookkeeping tools for many other fields, including statistical mechanics, algebraic combinatorics, quantum computing, and many more.

Given two random pictures of complicated knots, it would be near impossible for a human to immediately determine whether the knots are different. For tasks like this, we use knot invariants, which assign diagrams of knots to some other mathematical object that we understand better. For instance, a famous knot invariant called the Jones polynomial assigns to each knot diagram a (Laurent) polynomial, and if given two different diagrams of the same knot, it will produce the same polynomial.

Knot invariants like the Jones polynomial are useful not only for telling knots apart but also for classifying them based on their similarities. A more recent enhancement of the Jones polynomial called Khovanov homology assigns a more complex algebraic object to a knot diagram. By studying knots through their Khovanov homology, one can now capture relationships between knots, or more precisely, the evolution of knots throughout 4D spacetime.

In this talk, we will get a taste of how Khovanov homology extends the Jones polynomial to the fourth dimension, and explore several ways the 4D behavior of knots makes them even more interesting.

Short Bio: Melissa Zhang is currently a Krener Assistant Professor at UC Davis. Previously, she was a postdoc at MSRI/SLMath, and the University of Georgia. She earned her PhD at Boston College under the supervision of her advisors Eli Grigsby and David Treumann. She is a low-dimensional topologist who studies visualizations of physical objects like knots, 3D spaces with portals, as well as 4D analogues of knots. In practice, if you ever watch her doing research, she is probably doing one of the following activities: (1) drawing pictures, (2) collaborating with friends, (3) trying to write down her proofs carefully, or (4) thinking through arguments using linear algebra. 

She's always loved science and math growing up, but her pure math career really began when she took her first proof-based course in college and loved it! She also enjoys singing, knitting, hiking, and playing with her pet rats.

Friday, March 24, 2023 - Dr. Scott Mitchell (Sandia Lab)

At 9 AM via Zoom (Register in advance at

Title: Computing geometry as a mathematician in an engineering laboratory

Abstract: Geometry is all around us. Sandia creates physical objects with physical shape, and we need to explore the abstract high-dimensional spaces that describe the variety of environmental conditions these objects might encounter. They
must always "work" when we want them to, and never "work" when we don't! I study computational geometry, which is the math and algorithms for calculating geometric quantities of objects, or creating new objects with prescribed geometry. My favorite work is joining up with people with a different background from me, such as mechanical engineering, optimization, uncertainty quantification, geophysics, or data sciences. As a mathematician, they often ask me *how* to solve some problem they are stuck on. Instead, I ask them *why* they want to solve it and usually end up redefining their problem so it is easy to solve. My favorite problems involve the interplay between continuous shapes and the discrete connections binding them together. I study triangular and cubical meshes, and how to optimize them for increased simulation accuracy. I also study point-sampling of abstract spaces, and computational topology to characterize the structure of fragments. I'll describe my journey from my family to the labs, what it's like to work there, and work with the international research community. I'll mention a few research highlights from my nearly 30 years at Sandia and 75 publications. The talk is intended to be accessible to STEM undergraduates.

Short Bio: Scott A. Mitchell received a B.S in Applied Math, Engineering & Physics from the University of Wisconsin-Madison in 1988. He received an M.S. (1991) and Ph.D. (1993) in Applied Math from Cornell University. Since Oct 1992 he has been at Sandia National Laboratories. He researched triangular and tetrahedral meshing algorithms via a computational geometry approach from 1992-1993. He was part of the Cubit project, doing mesh generation R& D from 1993-2000, and project leadership from 2000-2002. He managed the Optimization and Uncertainty Estimation department from 2002-2007. He served on Sandia's LDRD (internal research program) team and NNSA's ASC program. He moved on to technical work in 2007. He published about 75 papers, and served on the program committee for the International Meshing Roundtable and Symposium on Computational Geometry He is a member of SIAM and ACM.

Friday, February 24, 2023 - Dr. Kelly Moran (Los Alamos National Lab)

At 9 AM via Zoom (Register at

Title: Understanding the universe on large scales

Abstract: In this seminar, I introduce myself and my background and talk about a cosmology project I've been working on for a little over a year. You'll hear about how when I first started working at Los Alamos National Lab (LANL) I knew nothing about statistics, compared to now when I have a PhD in it! In a similar vein, you'll hear that when I first started working on this project I knew nothing about cosmology, but now I know enough to be useful. Cosmology aims to understand the universe on large scales. In order to maximally extract information from modern cosmological surveys, matching theoretical predictions are 39 needed. At low redshifts cosmological simulations are the main way of getting predictions, but their computational cost makes it impossible to run very large ensembles. We built an emulator for the matter power spectrum based on the Mira-Titan Universe simulation suite that is designed to be useful for
cosmological inference studies on a range of cosmological probes. It is more accurate (at the 2-3% level) than competing methods, and covers a larger range of cosmological parameters.

Short Bio: Dr. Moran works on developing, analyzing, and visualizing Bayesian statistical models across varied applications such as global and environmental health, cosmology, and space physics. She has experience in developing novel methods for probabilistic dimension reduction and fast Gaussian process approximation. She first joined Los Alamos National Laboratory as a post-baccalaureate student with the Information Systems and Modeling (A-1) group, returned throughout graduate school to work with the
Statistical Sciences (CCS-6) group, and hired on with CCS-6 as a staff scientist in 2021. She enjoys being outside, blues and fusion dancing, playing the oboe, and hanging out with her two large orange cats.

Celebrating Women’s History Month

Friday, March 3, 2023 - Dr. Catherine Cavagnaro (Sewanee: The University of the South)

Dr. Cavagnaro photo

At 11 AM in SII 206

Title: Mathematical Models for Aircraft Longitudinal Motion

Abstract: Longitudinal motion for an aircraft refers to forward and vertical translational motion as well as rotation about the lateral axis.  We will present and explore mathematical models for such motion and extract techniques pilots can use to more safely fly their aircraft.

Short Bio: In addition to teaching mathematics at Sewanee, Catherine Cavagnaro holds FAA Flight Instructor and Airline Transport Pilot certificates. She teaches aerobatics at the airport on campus and is a monthly columnist for AOPA Pilot Magazine.  In 2018 Catherine was inducted into the Tennessee Aviation Hall of Fame and in 2022, she was inducted into the National Flight Instructor Hall of Fame.  She enjoys flying with her two sons around the United States and delights in finding mathematical applications within aviation.

Friday, February 3, 2023 - Dr. Gi-Sang Cheon, Sungkyunkwan University

At 10 AM in PB 138

Title: Introduction to Riordan arrays and applications

Abstract: Enumeration problems in combinatorics arise in many areas of mathematics, notably in algebra, probability theory, topology, and geometry, as well as in its many application areas. Although primarily concerned with finite systems, some combinatorial questions and techniques can be extended to an infinite (specifically, countable) but discrete setting. A Riordan array (or Riordan matrix) is an infinite lower triangular matrix encoding the coefficients of certain sequences of power series. Such an array can be regarded as a vast generalization of Pascal’s triangle. These concepts lead to an elegant, unified, and fruitful approach for dealing with a host of enumerative problems.

Short Bio: Gi-Sang Cheon is a Korean mathematician working in combinatorial matrix theory, enumerative combinatorics, and Riordan group theory. He is currently the director of the Applied algebra and Optimization Research Center (AORC) , SRC-NRF of Korea since 2016. He is also a professor of the Department of Mathematics at Sungkyunkwan University. He received his Ph.D. from Sungkyunkwan University in Feb 1991. His research interests include linear and multilinear algebra, graph theory, and their applications.

Thursday, December 1, 2022 - William Godoy, PhD (Oak Ridge National Laboratory (ORNL))

To attend the talk you will first need to register in advance at

Title: Pursuing graduate school and career paths for PhDs

Abstract: In this presentation we will talk about career paths for those planning to pursue a graduate school degree. We will provide exposure to the audience from my experiences on different career paths environments: academia, national labs and industry. In particular, we would point out the importance of having a diverse network for collaboration, being part of a scientific community, and the role of professional societies. We will also discuss the different kind of expectations as people progress in their professional career levels and uptake on early, mid and senior roles. Overall, we hope students will get exposure to these topics to get a grasp of the landscape in science and help them navigate these environments.

Short Bio: Dr. William F Godoy joined Oak Ridge National Laboratory in 2016, currently a Senior Computer Scientist in the Computational Science and Mathematics Division. A native of Peru, his background is in Mechanical Engineering with a strong research and development focus on the computational aspects of large-scale scientific modeling and simulation using high performance
computing (HPC) He obtained his PhD in 2009 from the University at Buffalo, The State University of New York. Prior experiences include a Senior Software Engineer position at Intel Corporation, 2012-2016, and a postdoctoral fellow at NASA Langley Research Center, 2009-2012.

Thursday, November 3, 2022 - Earvin Balderama, Ph.D. (Fresno State)

By Zoom at 12 PM

Title: Statistical Models for Count Data

Abstract:  Count data are a type of data that takes on non-negative integer values {0, 1, 2, ...}. The Poisson and negative binomial distributions are commonly used as the basis in a generalized linear model for such data. The negative binomial is used in situations where the Poisson fit is inadequate due to over-dispersion. When there is an excess amount of zeros in the data, zero-inflated models and hurdle models are used, again typically specified by Poisson and negative binomial. What if negative binomial is still inadequate? In this talk, I will discuss the double-hurdle model, an extension of the single-hurdle model, to describe count data that contain both an excessive amount of zeros and some extremely large counts that cause so much over-dispersion that even a negative binomial  specification is inadequate. The double-hurdle model was applied to counts of marine birds observed along the US Atlantic coast. A Bayesian MCMC framework was used for estimation and validation. An R package, hurdlr, was created to implement hurdle and double hurdle models to zero-inflated data.

Friday, October 28, 2022 - Nathan Urban, Ph.D. (Brookhaven National Laboratory (BNL))

By Zoom at 9 AM


Abstract:  As carbon dioxide emissions to the atmosphere continue from fossil fuel consumption, the Earth's climate will change considerably over the course of this century due to the resulting greenhouse effect. Societies and ecosystems will feel these global changes in the form of extreme weather, recurrent flooding and droughts, and other regional impacts. But how certain are we in projections about the distant future? Every prediction of a physical theory comes with error bars arising from limitations in data and approximations that are made for the sake of computational  tractability. The scientific question is not whether the climate will change, but how likely it is to change by a given amount. This talk will discuss at a conceptual level the general mathematical formalism of statistical uncertainty quantification, and how it is being applied within the U.S. Department of Energy and other research institutions to estimate the plausible range of possible climate futures. The mathematical methods discussed may include Bayesian parameter estimation for nonlinear regression, Monte Carlo sampling, sensitivity analysis, model averaging, and surrogate modeling or emulation of expensive computer simulations. Applications may include probabilistic predictions of global warming and climate feedbacks, sea level rise from Antarctic ice sheet disintegration, and coastal flooding, among others. As time permits, I will also discuss educational training and career paths into this research field at the forefront of interdisciplinary science.

Short Bio:  Dr. Nathan Urban leads the Applied Mathematics group in the Computational Science Initiative at Brookhaven National Laboratory, on Long Island, New York. He received undergraduate degrees in physics, computer science, and mathematics from Virginia Tech, and a Ph.D. and M.Ed. in physics (computational statistical mechanics) from Penn State. After graduating, he moved into climate uncertainty quantification with postdoctoral appointments at Penn State and Princeton, and a staff position at Los Alamos National Laboratory. He received a DOE Office of Science Early Career Research award for multi-model climate uncertainties, and has led major projects involving coastal resilience planning under uncertainty and "in-situ" methods for embedding scalable statistical inference algorithms within exascale simulations. At Brookhaven he develops methodologies for uncertainty quantification, decision making under uncertainty and optimal experimental design, model reduction, scientific machine learning, and integrated computational frameworks for decision support, applied to problems in climate science, biomedicine, materials science, and others.

Friday, October 7, 2022 - Dr. Mario Bencomo

In PB 013 at 3 PM

Title: Topics in inverse problems

Abstract:  Inverse problems are a classification of mathematical problems in engineering and science that involve estimating model parameters/inputs relative to a “forward/direct” problem. In this talk I will present several examples of inverse problems to elucidate the definition and highlight typical mathematical challenges. I will also discuss in depth applications in the field of exploration seismology where such inverse problems require mathematical tools from numerical optimization and differential equations.

Short Bio: Mario J. Bencomo joined the Department of Mathematics at Fresno State as an Assistant Professor this Fall semester. Prior to coming to California, he was a Pfeiffer Postdoctoral instructor at Rice University, in the Department of Computational Applied Mathematics and Operations Research (CMOR), where he received his Ph.D. in 2017. His research interests include numerical methods for wave propagation problems, inverse  problems, optimal control with a focus on nonlinear conservation laws.

If you need a disability-related accommodation or wheelchair access information, please contact the Mathematics Department  at 559.278.2992 or e-mail Requests should be made at least one week in advance of the event.

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