October 13, 2023 (Friday)
Location: Smith Memorial Student Union (SMSU), room 333
Time: 3:15pm - 4:15pm
Please Note: This talk is open to PSU students, faculty, and staff only.
Speaker: Dr. Dimitrije Kostic, US Government
Faculty Host: Dr. Steven A. Bleiler
Title: Defeating the German Enigma
Abstract: The Enigma is a message encryption device (or, more accurately, a family of similarly-designed devices) most famously used by the Nazi military during World War II. The Nazis were confident that the sophisticated and cleverly-designed Enigma devices could protect their sensitive military communications from Allied cryptanalysis. But a sequence of unlikely accidents and the ingenuity of mathematicians in Poland and England culminated in an Allied capability to decrypt this traffic. This remarkable success was among the most carefully guarded secrets of World War II, remained so until decades later, saved untold millions of lives, and was mythologized in the popular 2014 film The Imitation Game.
In this talk, we will survey the history of cryptography that informed the Enigma’s design and see why the Nazis had such confidence in it. Then we will examine how the Enigma actually encrypts messages, and discuss in some detail how the cryptanalysis against it worked. We will have an actual working Enigma machine on hand to help us! An undergraduate course in abstract algebra will be helpful to understand the cryptanalytic details, but the bulk of the talk will be generally accessible and we assume no prior familiarity with cryptography.
For remote participation, Join Zoom Meeting: https://pdx.zoom.us/j/84540759571
(Join before 3:15pm. Meeting may be locked once the talk begins.)
October 16, 2023 (Monday)
Location: Fariborz Maseeh Hall (FMH), room 462
Time: 4:00pm - 5:00pm
Speaker: Dr. Andrei Jorza, University of Notre Dame
Faculty Host: Dr. Liubomir Chiriac
Title: Non-archimedean approaches to modular forms
Abstract: Some of the most astonishing progress in number theory in the last decades, including Fermat's Last Theorem and special cases of the Birch and Swinnerton-Dyer conjecture, was based on the interactions between the classical world of arithmetic geometry and the world of archimedean and non-archimedean analysis. Much like complex analysis introduced modular forms as a fundamental tool in studying elliptic curves and other arithmetic settings, analysis over p-adic numbers has opened the way to the study of deformations and approximations of number theoretic objects in a way compatible with divisibility, culminating in the recent work of Scholze and his collaborators on p-adic geometry. In this general talk, aimed at a non-specialist audience, I will explain some instances where the interplay between complex and p-adic analysis can elucidate some classical problems about modular forms, based on papers with Liubomir Chiriac.
Biography: Andrei Jorza is an Associate Professor of the Practice and Director of Undergraduate Studies at the University of Notre Dame. He received an AB degree in mathematics from Harvard University in 2005, and a PhD in mathematics from Princeton University in 2010. He was a postdoctoral instructor at Caltech before joining Notre Dame in 2013. His interests lie at the intersection of number theory and algebraic geometry, with a focus on non-archimedean interactions.
Friday, November 17, 2023
Location: Fariborz Maseeh Hall (FMH), room 462
Time: 3:15pm - 4:15pm
Speaker: Dr. John Gallup, Associate Professor of Economics, PSU
Faculty Host: Dr. Jong Sung Kim
Title: Generalized Added-Variable Plots
Abstract: An added-variable plot shows the correlation between an outcome variable and an explanatory variable conditional on other explanatory variables. Although usually applied to OLS regression, this paper extends the method to a broad class of linear and nonlinear estimators including generalized least squares, instrumental variables, maximum likelihood and generalized methods of moments estimators. Added-variable plots show the contribution of each data observation to the estimated correlation, providing an intuitive presentation of complex estimation results whose meaning is often opaque to the uninitiated.
Biography: John Luke Gallup is an associate professor in the PSU Economics Department. He conducts research on economic growth, health, and economic geography. He received his PhD in Economics and MA in Demography from Berkeley and his BA in Economics and Politics from Swarthmore College. He previously taught at Harvard University, as a Fulbright Scholar in Vietnam, and visited at the Toulouse School of Economics. John’s recent research assesses the impact of early child development on economic growth, the theory of added-value plots, the economic impact of tropical disease, and the effect of economic development on income distribution. He has consulted for the World Bank, UNDP, EU, ADB, ILO, USAID, and the governments of Vietnam and Bolivia. John has more than 9,000 citations in Google Scholar. He has conducted economic research in Vietnam for three decades and speaks Vietnamese.
Friday, February 9, 2024
Location: Fariborz Maseeh Hall (FMH), room 462
Time: 3:15pm - 4:15pm
Speaker: Dr. Chen Greif, University of British Columbia
Faculty Host: Dr. Jeffrey Ovall
Title: Numerical Solution of Double Saddle-Point Systems
Abstract: Double saddle-point systems are drawing increasing attention in the past few years, due to the importance of relevant applications and the challenge in developing efficient numerical solvers. In this talk we describe some of their numerical properties. We derive eigenvalue bounds, expressed in terms of extremal eigenvalues and singular values of block submatrices. We also analyze the spectrum of preconditioned matrices based on block diagonal preconditioners using Schur complements, and it is shown that in this case the eigenvalues are clustered within a few intervals bounded away from zero, giving rise to rapid convergence of Krylov subspace solvers. A few numerical experiments illustrate our findings.
Friday, March 1, 2024
Location: Fariborz Maseeh Hall (FMH), room 462
Time: 3:15pm - 4:15pm
Speaker: Dr. Long Bui
Faculty Host: Dr. Jong-Sung Kim
Title: Mathematical Modeling and Developing Platforms for Environmental Assessment Scenarios
Abstract: The problem of environmental protection in practice requires studying mathematical models and building automated platforms that calculate the impact of different development scenarios on the environment and people's health. Faced with environmental degradation, several concepts are proposed, such as marine environmental carrying capacity (MECC) and river water environmental capacity (RWEC), which affirms the need to pay attention to waste issues and biochemical processes. In research, we have proposed a mathematical solution to these problems, which proposed a system that integrates mathematical models with databases considering chemical and biochemical reactions. Environmental pollution, especially PM2.5, is one of the leading causes of premature death. In this context, studies that have evaluated strategies to control and reduce air pollution are needed. Mathematical models are the primary tools to help make reasonable policies from an economic perspective. The damage caused by pollution to health and crops in the area has been quantified. The results of building an integrated system between mathematical models and databases by our group named HeBIS are introduced.
Biography: Dr. Long Bui is an Associate Professor at Ho Chi Minh City University of Technology, Vietnam National University of Ho Chi Minh City (VNU-HCM). He received a BS, MS degree (1985) and PhD in mathematics (1989) from Lomonosov Moscow State University in Mathematics. He interned at the Russian Academy of Sciences from 1997 to 1998 and defended a doctoral dissertation on mathematical modelling and experiments. His research fields are mathematical modelling, developing information platforms for environmental assessment scenarios for sustainable development, and the impact of climate change.
Friday, March 8, 2024
Location: Fariborz Maseeh Hall (FMH), room 462
Time: 3:15pm - 4:15pm
Speaker: Dr. Moysey Brio, University of Arizona
Faculty Host: Dr. Hannah Kravitz
Title: A Coupled Spatial-Network Model for Epidemiology
Abstract: There is extensive evidence that network structure (e.g. air transport, rivers, or roads) may significantly enhance the spread of epidemics into the surrounding geographical area, yet models coupling a network structure with a 2D area have largely not been studied. We present our work on coupling the Susceptible-Infected-Removed (SIR) system of differential equations at the population centers, with 1D-travel routes, and a 2D continuum containing the bulk of the population. We describe numerical discretization utilizing finite difference and finite element methods. Numerical examples of the impact of the network structure on the spread of an epidemic, localized solutions and comparison of various edge centrality measures for metric graphs are discussed.
Biography: Moysey Brio is Professor of Mathematics at the University of Arizona, specializing in numerical algorithms for partial differential equations (PDEs). He received his PhD in 1984 from UCLA, and held research fellowships at UCLA, Rice University, NYU, IMPA (Brazil), DTU (Denmark), and INSA de Rouen (France). Besides his recent involvement with the topic of modeling partial differential equations (PDEs), he has extensive research experience in designing novel numerical algorithms with applications to aerospace, optics, and astrophysics, as well as developing algorithms for numerical inversion of the Laplace transform. He is an author of a graduate level textbook, “Numerical Time-Dependent Partial Differential Equations for Scientists and Engineers.”
Friday, May 24, 2024
Location: Fariborz Maseeh Hall (FMH), room 462
Time: 3:15pm - 4:15pm
Speaker: Dr. Amy Wiebe, University of British Columbia
Host: Dr. Isabelle Shankar
Title: General Certificates of Polytope Non-realizability
Abstract: A classical question in polytope theory is whether an abstract polytope can be realized as a concrete convex object. Beyond dimension 3, there seems to be no concise answer to this question in general. In specific instances, a negative answer can be certified via “final polynomials” introduced by Bokowski and Sturmfels. This method involves finding a polynomial which, based on the structure of a polytope if realizable, must be simultaneously zero and positive, a clear contradiction. The search space for these polynomials is ideal of Grassmann-Plücker relations, which quickly becomes too large to efficiently search, and in most instances where this technique is used, additional assumptions on the structure of the desired polynomial are necessary.
In this talk, I will describe how by changing the search space, we are able to use linear programming to exhaustively search for similar polynomial certificates of non-realizability without any assumed structure. We will see that, perhaps surprisingly, this elementary strategy yields results that are competitive with more elaborate alternatives and allows us to prove non-realizability of several interesting polytopes.
Friday, May 31, 2024
Location: Fariborz Maseeh Hall (FMH), room 462
Time: 3:15pm - 4:15pm
Speaker: Dr. Bryce Chriestenson, ZoomInfo Technologies Inc.
Host: Dr. Derek Garton
Title: From Academia to Industry: Navigating a Career in Data Science
Abstract: Transitioning from academia to a career in data science can be both exciting and challenging. This talk aims to demystify the journey by comparing the day-to-day experiences of academics and industry data scientists. We'll explore the essential skills and tools required for success in the tech industry, highlight the differences in work environments, and provide actionable strategies for making the switch. Whether you're a student, researcher, or professor, this talk will equip you with the insights needed to leverage your academic background and thrive in a data science role. Join me to learn how to effectively translate your academic expertise into industry impact and navigate the job market with confidence.
Biography: Dr. Bryce Chriestenson is an accomplished professional with extensive experience in both academia and the tech industry. He holds a Master's degree from Portland State University, where he studied low dimensional topology under the direction of Prof. F. Rudolf Beyl, and a PhD in Mathematics from the University of Colorado Boulder, focusing on the rational homotopy type of stratified spaces under the guidance of Prof. Markus Pflaum. Dr. Chriestenson has three years of further academic experience, including a postdoctoral position at the University of Heidelberg with Prof. Markus Banagl, exploring topological invariants of stratified spaces, and a role as a Visiting Assistant Professor at Western Oregon University. In the industry, he has spent more than 6 years contributing his expertise to companies such as Ascend Analytics, Unsupervised, Chubb Insurance, and Zoominfo in data science, engineering and product development roles. His unique blend of theoretical knowledge and practical experience positions him as an insightful speaker on the journey from academia to a career in data science.
Friday, June 7, 2024
Location: Fariborz Maseeh Hall (FMH), room 462
Time: 3:15pm - 4:15pm
Speaker: Dr. Pedram Hassanzadeh, University of Chicago, Department of Geophysical Sciences
Host: Dr. Safa Mote
Title: Artificial Intelligence and the 2nd Revolution in Weather and Climate Prediction
Abstract: Accurate weather and climate predictions are critical for many applications, from early warnings for extreme events to improving resiliency and planning adaptation and mitigation. The current state of the art of weather and climate prediction relies on numerical solutions of the governing equations of the atmosphere, ocean, and other components of the Earth system, and is a result of a slow 50-year scientific revolution. However, the enormous computational cost of the current numerical weather and climate models hinders efforts on reducing the uncertainties in these predictions. In recent years, artificial intelligence (AI) techniques have received significant attention as tools that can help with improving weather and climate prediction and reducing these uncertainties. In fact, for 1-15 day weather forecasting, the AI-based models have shown substantial success in outperforming the numerical models at a fraction of the computational cost. Dubbed the second revolution in weather forecasting, this success suggests that AI can potentially transform the state of the art of climate prediction too, once a number of major challenges are addressed. I will discuss these challenges, and particularly how integrating fundamental concepts and tools from math, climate physics, and computer science need to be integrated to make progress.
Biography: Pedram Hassanzadeh leads the University of Chicago’s Climate Extreme Theory and Data Group and is an Associate Professor at the Department of Geophysical Sciences, Committee on Computational and Applied Math, and Data Science Institute. He received his MA (in applied math) and PhD (working on geophysical turbulence) from UC Berkeley in 2013. He was a Ziff Environmental Fellow at Harvard University before joining Rice University in 2016 and moving to the University of Chicago in 2024. His research is at the intersection of climate change, scientific machine learning, computational and applied math, extreme weather and turbulence physics. He has received an NSF CAREER Award, ONR Young Investigator Award, and Early Career Fellowship from the National Academies Gulf Research Program.