How to gain access to the ML Machine

Antonio Possolo
Chief Statistician, NIST, Gaithersburg, MD.
https://www.nist.gov/people/antonio-possolo
Time: 9:00-10:30am

Rick Mueller
USDA/NASS

Time: 10:45-11:45am

Title: An Update on the Annual National Cropland Data Layer Program
Abstract: The Cropland Data Layer (CDL) is a national land cover product produced by the US Department of Agriculture (USDA), National Agricultural Statistics Service (NASS) to assess planted crop acreage annually. The 2018 CDL product, released in February 2019 serves as the eleventh consecutive national mapping of conterminous US agriculture. The CDL is a 30-meter agricultural-specifc national land cover product that is released into the public domain upon completion of the growing season via the CropScape portal at https://nassgeodata.gmu.edu/CropScape. The annualized CDL product provides users the opportunity to study cultivation practices, crop rotations and intensifcation, and the changing trends and localities in production agriculture. The CDL is a supervised land-cover classifcation utilizing a decision tree machine learning approach using optical satellites while leveraging ground reference data from the USDA/Farm Service Agency and the National Landcover Database (NLCD) from the US Geological Survey, and multiple cooperative industry partnerships. Medium resolution satellites such as Landsat 8, Disaster Monitoring Constellation Deimos-1 and UK2, Resourcesat-2 LISS-III, and Sentinel-2 were used to monitor agricultural production throughout the North American 2018 growing season. This talk focuses on the continuous CDL product improvement process, including leveraging cooperative industry partnerships, obtaining improved remote sensing data, improving the methods used in classifcation, and leveraging historical data to improve identifcation of specialty and locally grown crops.

Bio: Rick Mueller is Head of the USDA NASS Research and Development Division/Spatial Analysis Research Section in Washington, DC. He is responsible for agricultural monitoring of domestic crop area, yield, crop condition, and disaster monitoring programs using remote sensing and geospatial analysis methods for public dissemination. His group developed the online geospatial visualization portals called CropScape that depicts the national land cover dataset called the Cropland Data Layer, VegScape showing crop vegetative condition, and is developing a soil moisture monitoring portal. Rick received a B.S. degree in Geography from the University of Maryland and an M.S. in Business from Johns Hopkins University.

Victor De Oliveira
The University of Texas at San Antonio

Time: 1:00-2:00pm

Title: Gaussian Copula Models for Geostatistical Count Data
Abstract: We describe a class of random field models for geostatistical count data based on Gaussian copulas. Unlike hierarchical Poisson models often used to describe this type of data, Gaussian copula models allow a more direct modeling of the marginal distributions and association structure of the count data. We study in detail the correlation structure of these random fields when the family of marginal distributions is either negative binomial or zero-inflated Poisson; these represent two types of overdispersion often encountered in geostatistical count data. We also contrast the correlation structure of one of these Gaussian copula models with that of a hierarchical Poisson model having the same family of marginal distributions. We also describe the computation of maximum like-lihood estimators which is a computationally challenging task. Finally, a data analysis of Lansing Woods tree counts is used to illustrate the methods.

Bio: Victor De Oliveira is a professor in the Department of Management Science and Statistics, College of Business, University of Texas at San Antonio. He joined the UTSA faculty in 2006 and previously worked at the University of Arkansas and Simon Bolivar University. He holds a Ph.D. in statistics from the University of Maryland, and a master's in water resources and a bachelor's
in mathematics from the Universidad Simon Bolivar. He teaches a variety of undergraduate and graduate courses in Statistics and Applied Probability.

Claire Boryan
USDA/NASS

Time: 2:15-3:15pm

Title: Operational Agricultural Flood Monitoing with Copernicus Sentiel-1 Synthetic Aperture Radar
Abstract: Agricultural Flood monitoring is important for food security and economic stability and is of signifcant interest for agricultural policy makers and decision support. In agricultural remote sensing applications, optical sensor data are traditionally used for acreage, yield, and crop condition assessments. However, optical data are affected by clouds, rain, and darkness, which limit their utility to monitor and estimate the extent of flooding during disaster in a timely manner. Synthetic Aperture Radar, however, can penetrate cloud cover and acquire imagery day or night, which makes it particularly useful for flood disaster monitoring in near-real time. A flood detection method was implemented in 2017 using freely available Copernicus Sentinel-1 Synthetic Aperture Radar data for operational agricultural flood monitoring in the United States. The data were used operationally in near-real time to identify and map flooding of agriculture during major hurricanes in 2017, 2018 and 2019. This presentation describes 1) the agricultural flood monitoring method that utilizes Copernicus Sentinel-1 Synthetic Aperture Radar and the United States Department of Agriculture National Agricultural Statistics Service 2018 Cultivated Layer and 2018 Cropland Data Layers and 2) inundated cropland and pasture maps and acreage estimates. This flood monitoring method based on Synthetic Aperture Radar data and National Agricultural Statistics Service geospatial data is effective, effcient, and affordable for operational disaster assessment. Further, flood assessment maps,flood inundation raster data, and a method paper are disseminated to the public on the NASS Disaster Analysis website at https://www.nass.usda.gov/ResearchandScience/DisasterAnalysis/index.php

Bio: Claire Boryan is a Senior Geographer with the Research and Development Division of the USDA/National Agricultural Statistics Service (NASS). She received a BA from the University of Virginia, a MS in Geographic and Cartographic Sciences and PhD in Earth Systems and Geoformation Sciences from George Mason University. She has extensive experience in agricultural geospatial analysis and remote sensing research. Her research interests include: using Synthetic Aperture Radar for agricultural applications, agricultural disaster analysis, remote sensing methods, geographic information science and applied research using geospatial data to improve agricultural statistics.

Luca Sartore
USDA/NASS

Time: 3:30-4:30

Title: Predicting Crop Yield Using Spatio-Temporal Functional Covariates
Abstract: The USDA's National Agricultural Statistics Service (NASS) produces annual yield estimates for major crops at national, state, agricultural district, and county levels. Several surveys are conducted to produce reliable estimates that are further enhanced by incorporating remote sensing measurements at the county level. These measurements consist of Moderate Resolution Imaging Spectroradiometer (MODIS) data based on multispectral composites and Land Surface Temperature (LST) based on thermal composites. The data are summarized by empirical density functions for crop regions throughout the growing season. Corn-yield predictions at the county level are then produced using non-parametric models that combine spatial coordinates with satellite data. Machine learning algorithms are compared when processing additional information based on Kullback-Leibler distances.

This program is funded by the department and honors Distinguished University Professor Emeritus Serguei Novikov. The Novikov Postdoctoral program supports mathematicians who have recently completed or will soon complete a doctorate in mathematics or a closely related field, and whose work shows remarkable promise in mathematical research. The appointments are for one to three years, with a light teaching requirement of two to three courses per academic year.

Current Novikov Postdoctoral Fellows:

• James Hanson works in continuous logic and applications of logic to topology and analysis. He also has background in theoretical physics. James is a student of Uri Andrews at Wisconsin. His mentor is Chris Laskowski.
• Hussain Ibdah is interested in theoretically analyzing nonlinear, nonlocal PDEs, in particular, those of fluid mechanics and transport-diffusion systems. He is a student of Edriss Titi at Texas A&M. His mentor is Eitan Tadmor.
• Fushuai Jiang works in aspects of multivariate interpolation and extension theory as well as harmonicanalysis with applications to data science. He is a doctoral student of Garving Kevin Luli (UC Davis). His mentor is Radu Balan.
• Jaime Paradela Diaz works on celestial mechanics and dynamical systems.  His PhD is from the Universitat Politècnica de Catalunya.
• Shalin Parekh works in aspects of probability with emphasis on interacting particle systems and theirconvergence to stochastic partial differential equations, random walks in random environments and polymermodels. He is a student of Ivan Corwin (Columbia). Shalin's mentor is Yu Gu.
• C. Alex Safsten works on mathematical biology and machine learning.  His PhD is from Penn State.
• Matthias Wellershoff works in aspects of data science, particularly problems of phase retrieval from time-frequency and time-scale structured data. Matthias is a student of Rima Alaifari (ETH). Matthias' mentor is Wojtek Czaja.
• Rigoberto Zelada works in ergodic theory. He is a student of Vitaly Bergelson at Ohio State. His mentor is Adam Kanigowski.
• Lutian Zhao works in enumerative algebraic geometry with applications to mathematical physics. He is a student of Sheldon Katz at Illinois. His mentor is Amin Gholampour.
• Jenna Zomback works in ergodic theory, descriptive set theory, and dynamics.  She was a student of Anush Tserunyan at Illinois.

 Former Novikov Postdoctoral Fellows:

• Xiaoqi Huang works in harmonic and geometric analysis and partial differential equations. He is a student of Chris Sogge at Hopkins. His mentor was Manos Grillakis. Placement: Assistant Professor, Lousiana State University.
• Yordanka Kovacheva is an algebraic geometer, a student of Madhav Nori from the University of Chicago. She has one published paper in Experimental Mathematics coming out of an REU while she was an undergraduate at Amherst College.  Her research mostly concerns algebraic cycles, Chow groups, and K-theory of algebraic varieties.  Her mentor was Patrick Brosnan.
• Jakob Hultgren. Jakob is a student of Robert Berman and David Witt Nyström from Chalmers University of Technology in Gothenburg, Sweden. His research field is complex differential geometry. He is interested in canonical metrics in Kähler geometry and their relation to optimal transport, statistical mechanics and stability notions in algebraic geometry. His mentor was Yanir Rubinstein. Placement: Umeå University.
• Xiumin Du. A student of Xiaochun Li, Xiumin received her PhD from the University of Illinois at Urbana-Champaign. She works in Harmonic Analysis. Her thesis was on Schrödinger maximal estimates.  Her mentor was Wojtek Czaja. (2018-2020). Placement: Assistant Professor, Northwestern University.
• Ruiwen Shu. A student of Shi Jin from the University of Wisconsin at Madison, Ruiwen works on uncertainty quantification for kinetic equations with random inputs.  His mentor was Eitan Tadmor. (2018-2021). Placement: Assistant Professor, University of Georgia.
• Zehua Zhao is a student of Benjamin Dodson at Johns Hopkins and works in dispersive PDE, especially nonlinear Schrödinger equations.  His mentor was Matei Machedon. (2019-2021). Placement: Beijing Institute of Technology.
• Guangyu Xi. A student of Zhongmin Qian and Gui-Qiang Chen from Oxford, Guangyu works in stochastic analysis, PDEs and fluid mechanics.  His mentor was Sandra Cerrai. (2018-2021). Placement: Quant Researcher, Akuna Capital.
• Jingren Chi. A student of Bảo Châu Ngô from the University of Chicago, Chi works at the junction of algebraic geometry, number theory and representation theory. He is interested in applying methods in geometric representation theory to problems in p-adic harmonic analysis.  His mentor was Tom Haines. (2019-2021). Placement: Chienese Academy of Sciences.
• Mickaël Latocca works in partial differential equations, particularly equations arising in fluid mechanics and Schrödinger dynamics.  He was a doctoral student of Isabelle Gallagher and Nicolas Burq at École Normale Supérieure, Paris. His mentor was Matei Machedon.  Placement: Université d'Évry in France.
  
• Eoin Mackall works in algebraic geometry and algebraic K-theory.  He was a student of Nikita Karpenko at the University of Alberta.  His mentor was Niranjan Ramachandran. Placement: UC San Diego.

Geometry Week

March 12 – 16, 2018

Department of Mathematics, University of Maryland

Graduate students, undergraduate students, and postdocs are especially encouraged to participate.

Contact: Yanir A. Rubinstein, Scott A. Wolpert

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The AWM Distinguished Colloquium series is being established in Spring 2021 in celebration of the 50th anniversary of the founding of the Association for Women in Mathematics. The series will comprise three colloquium talks this spring and will continue thereafter with one colloquium per semester.

Fall 2024

Speaker: Başak Gürel (UCF)
When: Friday, November 1, 2024 at 3:15 p.m.
Where: Kirwan Hall 3206
Abstract: Topological Entropy of Hamiltonian Systems and Persistence Modules

Topological entropy is a fundamental invariant of a dynamical system, measuring its complexity. In this talk, we discuss connections between the topological entropy of a Hamiltonian system, e.g., a geodesic flow, and the underlying filtered Morse or Floer homology viewed as a persistence module in the spirit of Topological Data Analysis. We introduce barcode entropy — a Morse/Floer theoretic counterpart of topological entropy — and show that barcode entropy is closely related to topological entropy and that, in low dimensions, these invariants agree. For instance, for a geodesic flow on any closed surface, the barcode entropy is equal to the topological entropy. The talk is based on joint work with Erman Cineli, Viktor Ginzburg, and Marco Mazzucchelli.

 About the Speaker

Başak Gürel is a professor in the department of mathematics at the University of Central Florida, Orlando. Her research lies at the interface of symplectic topology and Hamiltonian dynamical systems, with particular focus on the investigation of various dynamics phenomena using symplectic techniques. She graduated from the Middle East Technical University, Türkiye, in 1998 and earned her Ph.D. from the University of California, Santa Cruz, in 2003. She was a Simons postdoc at Stony Brook and a CRM postdoc at the University of Montréal. She received an NSF CAREER award in 2015 and a UCF Rising Star award in 2017. Her work was also recognized with a Concours Annuel Prize from the Royal Academy of Belgium in 2016.

Spring 2023

Speaker: Anna Wienhard (Maz Planck Institute)
When: Wednesday, March  15 2023 at 3:15 p.m.
Where: Kirwan Hall 3206
Abstract: Hight Rank Teichmüller Spaces

Classical Teichmüller space describes the space of conformal structures on a given topological surface. It plays an important role in several areas of mathematics as well as in theoretical physics. Due to the uniformization theorem, Teichmüller space can be realized as the space of hyperbolic structures, and is closely related to discrete and faithful representations of the fundamental group of the surface into PSL(2,R), the group of isometries of the hyperbolic plane. Higher rank Teichmüller spaces generalize many aspects of this classical theory when PSL(2,R) is replaced by other Lie groups of higher rank, for example the symplectic group PSp(2n, R) or the special linear group PSL(n, R). In this talk I will give an introduction to higher rank Teichmüller spaces and their properties. I will also highlight connections to other areas in geometry, dynamics and algebra.

 About the Speaker

Anna Wienhard is the Direcot of the Max Planck Institute for Mathematics in Leipzig and currently a member of the Institute for Advanced Study in Princeton. She earned her Ph.D. from the Rheinische Friedrich-Wilhelms University of Bonn in 2004 and has since held positions at the University of Basel, the University of Chicago, Princeton University, and Ruprecht-Karls University of Heidelberg. Her research covers Lie theory, representation vanities, and geometric structures on manifolds, as well as applications of geometry and topology to data science and natural sciences. Her work has been recognized with a Sloan Fellowship, and ICM invitation, and prestigious Consolidator and Advanced grants from the European Research Council. She is a Fellow of the American Mathematical Society and Scientific Chair of the Heidelberg Laureate Forum Foundation.

Fall 2023

Speaker: Emily Riehl (Johns Hopkins)
When: Friday, October  6, 2023 at 3:15 p.m.
Where: Kirwan Hall 3206
Abstract: Path Induction and the Indiscernibility of Identicals

Mathematics students learn a powerful technique for proving theorems about an arbitrary natural number: the principle of mathematical induction. This talk introduces a closely related proof technique called "path induction," which can be thought of as an expression of Leibniz's "indiscernibility of identicals": if x and y are identified, then they must have the same properties, and conversely. What makes this interesting is that the notion of identification referenced here is given by Per Martin-Löf's intensional identity types, which encode a more flexible notion of sameness than the traditional equality predicate in that an identification can carry data, for instance of an explicit isomorphism or equivalence. The nickname "path induction" for the elimination rule for identity types derives from a new homotopical interpretation of type theory, in which the terms of a type define the points of a space and identifications correspond to paths. In this homotopical context, indiscernibility of identicals is a consequence of the path lifting property of fibrations. Path induction is then justified by the fact that based path spaces are contractible.

 About the Speaker

Emily Riehl is a professor in the department of mathematics at Johns Hopkins University. She earned her Ph.D. from the University of Chicago in 2011. Before Johns Hopkins, she was a Benjamin Peirce Postdoctoral Fellow at Harvard until 2015. She is a category theorist, and her research covers higher homotopy theory and homotopy type theory. She has published four influential textbooks in her research area. Her work has been recognized with AWM-Birman Research Prize in Topology and Geometry. She is a Fellow of the American Mathematical Society and she is a Simons Fellow in 2022.

Spring 2022

Speaker: Lillian Peirce (Duke University)
When: Wednesday, April 20, 2022 at 3:15 p.m.
Where: Online Zoom
Abstract: Counterexamples for Generalizatons of the Schrödinger Maximal Operator

In 1980 Carleson posed a question: how “well-behaved” must an initial data function be, to guarantee pointwise convergence of the solution of the linear Schrödinger equation? After progress by many authors, this was recently resolved (up to the endpoint) by a combination of two celebrated results: one by Bourgain, whose counterexample construction for the Schrödinger maximal operator proved a necessary condition, and a complementary result of Du and Zhang, who proved a sufficient condition. In this talk we describe a study of Bourgain’s counterexamples, from first principles. Then we describe a new flexible number-theoretic method for constructing counterexamples, which opens the door to studying convergence questions for many more dispersive PDE’s. Along the way we’ll see why no mathematics we learn is ever wasted, and how the boundary from one mathematical area to another is not always clear. 

 About the Speaker

Lillian Pierce is Leonardy Professor of Mathematics at Duke University. She graduated as valedictorian from Princeton University in 2002 and won a Rhodes scholarship to study at Oxford, where she earned her Master’s degree. Pierce earned her Ph.D. from Princeton in 2009. Her research combines harmonic analysis and number theory. Pierce has been awarded a Presidential Early Career Award in Science and Engineering, a Sloan Research Fellowship, the AWM Sadosky Prize, and the Birman Fellowship for Women Scientists; she was named a Fellow of the American Mathematical Society in 2021. Pierce is the co-founder and Editor-in-Chief of the new journal “Essential Number Theory.”

Fall 2022

Speaker: Melanie Wood (Harvard University)
When: Canceled Wednesday, November 2, 2022 at 3:15 p.m.
Where: Kirwan Hall 3206
Abstract: Finite Quotients of 3-Manifold Groups

It is well-known that for any finite group G, there exists a closed 3-manifold M with G as a quotient of the fundamental group of M. However, we can ask more detailed questions about the possible finite quotients of 3-manifold groups, e.g. for G and H_1,...,H_n finite groups, does there exist a 3-manifold group with G as a quotient but no H_i as a quotient?  We answer all such questions. To prove non-existence, we prove new parity properties of the fundamental groups of 3-manifolds. To prove existence of 3-manifolds with certain finite quotients but not others, we use a probabilistic method, by first proving a formula for the distribution of the fundamental group of a random 3-manifold, in the sense of Dunfield-Thurston. This is joint work with Will Sawin.

 About the Speaker

Melanie Matchett Wood is Radcliffe Alumnae Professor at Harvard University.  Her research in number theory centers on the distribution of number fields and the probabilistic features of their fundamental structures.  As a high school student in Indiana and undergraduate at Duke University, she broke through barriers in the realm of mathematics competitions by becoming the first woman named to the US International Mathematics Olympiad team, on which she won silver medals in 1998 and 1999, and the first woman to be named a Putnam Fellow.  She won the Alice T. Schafer Prize, a Gates Cambridge Scholarship, and the Morgan Prize, among other honors.  She earned her Ph.D. at Princeton University in 2009 with advsior M. Bhargava.  She has since held positions at Stanford, the University of Wisconsin, and UC Berkeley, and has been distinguished with a Clay Liftoff Fellowship, a Sloan Fellowship, the AWM-Microsoft Research Prize in Algebra and Number Theory, an ICM Special Lecture invitation, and the NSF Waterman Award, among many other honors.

Spring 2021

Click To ZoomSpeaker: Gigiliola Staffilani (MIT)
When: Wednesday, February 17, 2021 at 3:00 p.m.
Where: Online Zoom
Abstract: Waves: Building Blocks inWaves: Building Blocks inNature and in Mathematics

In this talk I will first give a few examples of wave phenomena in nature. Then I willIn this talk I will first give a few examples of wave phenomena in nature. Then I willexplain how, in order to understand these phenomena, mathematicians use toolsfrom many different areas of mathematics, such as Fourier analysis, harmonicanalysis, dynamical systems, number theory, and probability. I will also giveexamples of the beautiful interaction between the “concrete" and the “abstract,” andhow these interactions constantly advance the boundaries of research.

 About the Speaker

Gigliola Staffilani is Abby Rockefeller Mauze Professor of Mathematics at MIT.Gigliola Staffilani is Abby Rockefeller Mauze Professor of Mathematics at MIT.She has previously held positions at the Institute for Advanced Study, Stanford,Harvard, and Brown Universities. She graduated from the Universitá di Bolognain 1989 and obtained her Ph.D. from the University of Chicago in 1995.Staffilani is a Fellow of the American Academy of Arts and Sciences, theMassachusetts Academy of Sciences, and the American Mathematical Society.She has held fellowhips from the Sloan, Guggenheim, and Simons foundations.Her research concerns harmonic analysis and partial differential equations,including the Korteweg–de Vries equation and the Schrödinger equation.

Click To ZoomSpeaker: Sommer Gentry (US Naval Academy) 
When: Wednesday, March 24, 2021 at 3:00 p.m.
Where: Online Zoom
Abstract:  People who volunteer as living kidney donors are often incompatible with their intended recipients. Kidney paired donation matches one patient and his or her incompatible donor with another pair in the same situation for an exchange. The lifespan of a transplant depends on the immunologic concordance of donor and recipient. We represent the patient-donor pairs with an undirected, edge-weighted graph and formulate the problem in terms of integer programming. I will propose an edge weighting of G which guarantees that every matching with maximum weight also has maximum cardinality, and also maximizes the number of transplants for an exceptional subset of recipients, while favoring immunologic concordance.

About the Speaker

Sommer Gentry is a Professor of Mathematics at the United States Naval Academy, and is also on the faculty of the Johns Hopkins University School of Medicine. She has a B.S. in Mathematical and Computational Science and an M.S. in Operations Research, both from Stanford University, and a Ph.D. in Electrical Engineering and Computer Science from MIT. She designed matching optimization methods used for nationwide kidney paired donation registries in both the United States and Canada, and is now redistricting liver sharing boundaries to help reduce geographic disparities in transplantation. Her work has attracted the attention of major media outlets including Time Magazine, Reader’s Digest, Science, the Discovery Channel, and National Public Radio. Gentry has received the MAA’s Henry L. Alder award for distinguished teaching and was named the Naval Academy’s 2021 recipient of the Class of 1951 Civilian Faculty Excellence in Research award.

Speaker: Kathryn Mann (Cornell University)
When: Wednesday, April 21, 2021 at 3:15 p.m.
Where: Kirwan Hall 3206
Abstract: Dynamics in dimensions 1 and 3

Suppose you have a group of transformations of a space. If you know something about individual transformations, can you extrapolate to say something global about the whole system? The paradigm example of this is an old theorem of Hölder: if you have a group of homeomorphisms of the real line and none of them fixes a point, then the group is abelian and the whole system is conjugate to an action by translations. My talk will be an illustrated introduction to this family of problems, including some recent joint work with Thomas Barthelmé that gives a new such result about groups acting on the line. As an application, we use this to prove rigidity results for a different, fascinating family of dynamical systems, Anosov flows in dimension 3.

 About the Speaker

Kathryn Mann is an Assistant Professor of Mathematics at Cornell University. She has previously held positions at UC Berkeley and Brown University. She graduated from the University of Toronto in 2008 with degrees in Mathematics and Philosopy and obtained her Ph.D. from the University of Chicago in 2014. Her research has been recognized with the Mary Ellen Rudin Young Researcher Award, the AWM's Joan and Joseph Birman Research Prize in Geometry and Topology, and the Wroclaw Mathematical Foundation's Kamil Duszenko Award. She has held a CAREER grant from the NSF and a Sloan Fellowship. She studies fundamental questions about groups actions on manifolds, including rigidity of homeomorphism and diffeomorphism groups of manifolds.

Fall 2021

Speaker: Maria Emelianenko (George Mason University)
When: Wednesday, December 8, 2021 at 3:15 p.m.
Where: Kirwan Hall 3206
Abstract: Entropy and random walks in materials, biology and quantum information science 

What do mathematics, materials science, biology and quantum information science have in common?  It turns out there are many connections worth exploring.  In this talk, I will focus on graphs and entropy, starting from the classical mathematical constructs and moving on to applications. We will see how the notions of graph entropy and KL divergence appear in the context of characterizing polycrystalline material microstructures and predicting their performance under mechanical deformation, while also allowing to measure adaptation in cancer networks and entanglement of quantum states. We will discover unified conditions under which master equations for classical random walks exhibit nonlocal and non-diffusive behavior and discuss how quantum walks may allow to realize the coveted exponential speedup.

 About the Speaker

Maria Emelianenko is Professor and Chair of Mathematics at George Mason University. She earned her B.S. in Computer Science and Mathematics from Moscow State University in 1999 and her Ph.D. in 2005 from Pennsylvania State University. She subsequently held a postdoctoral position at the Center for Nonlinear Analysis at Carnegie Mellon University.  For her work in numerical computation and scientific computing, she was awarded an NSF CAREER grant in 2011, a Mason Emerging Researcher Award in 2013, and the Penn State Alumni Society Early Career Award in 2014.  She is a member of the US National Committee for Theoretical and Applied Mechanics. Her research specialties include the study of grain growth and Voronai tesselations.