Congratulations to the following 2023 prize recipients who will be recognized at SIAM conferences this month:
Yongxin Chen, Isabel Haasler, Johan Karlsson, and Axel Ringh
Yongxin Chen, Isabel Haasler, Johan Karlsson, and Axel Ringh are 2023 recipients of the SIAM Activity Group on Control and Systems Theory Best SICON Paper Prize for their article, “Multimarginal Optimal Transport with a Tree-Structured Cost and the Schrödinger Bridge Problem”, SIAM Journal on Control and Optimization, Vol. 59, No. 4, pp. 2428 - 2453, (2021), solving the Schrödinger bridge problem by means of a new efficient Sinkhorn iteration.
Dr. Haasler will present the paper at the 2023 SIAM Conference on Control and Its Applications (CT23), taking place July 24 – 26, 2023, in Philadelphia, Pennsylvania. The talk will take place on Monday, July 24 at 5:15 p.m. Eastern.
The SIAM Activity Group on Control and Systems Theory (CST) awards this prize every two years to the authors of the two most outstanding papers, as determined by the prize committee, published in SIAM Journal on Control and Optimization (SICON) in the three calendar years preceding the award year.
Yongxin Chen received his bachelor’s degree from Shanghai Jiao Tong University (2011), and his Ph.D. from the University of Minnesota (2016), both in mechanical engineering. After that, Dr. Chen spent one year at the Memorial Sloan Kettering Cancer Center as a postdoctoral fellow. He then served on the faculty at Iowa State University (2017-2018). Currently, he is an assistant professor in the School of Aerospace Engineering at Georgia Institute of Technology. He is the 2017 recipient of the George S. Axelby Best Paper Award of IEEE Transactions on Automatic Control. He also earned an NSF CAREER Award (2020), a Simons-Berkeley Research Fellowship (2021), the A. V. ‘Bal’ Balakrishnan Award (2021), and the Donald P. Eckman Award for outstanding young engineer in the field of automatic control (2022). He enjoys developing new algorithms and theoretical frameworks for real world applications. Learn more about Dr. Chen.
Isabel Haasler earned her bachelor’s degree in mathematics from the Albert Ludwig University of Freiburg (2015), and her master’s and Ph.D. degrees in applied and computational mathematics from KTH Royal Institute of Technology in 2017 and 2022, respectively. She is now a postdoctoral researcher with the Signal Processing Laboratory at Ecole Polytechnique Federale de Lausanne, Switzerland. Her current research interests are within systems theory and signal processing. In particular, she is interested in optimal transport, network data analysis, inverse problems, and machine learning. Learn more about Dr. Haasler.
Johan Karlsson received a master’s degree in engineering physics (2003) and a Ph.D. in optimization and systems theory (2008), both from KTH. Subsequently, he worked at Sirius International (2009-2011), Stockholm, then was a postdoctoral associate in the department of computer and electrical engineering at the University of Florida (2011-2013). Dr. Karlsson then joined the department of mathematics at KTH as an assistant professor (2013), and since 2017, he has held an associate professor position. He is also an Associate Director of Executive Research at Digital Futures and has been the main organizer of several workshops that establish collaborations between the academia and the industry. His current research interests include optimal transport, methods for large scale optimization, and inverse problems, for applications in control theory, network problems, and remote sensing. Learn more about Dr. Karlsson.
Axel Ringh received his master’s degree in engineering physics (2014) and a Ph.D. degree in applied and computational mathematics (2019), both from KTH Royal Institute of Technology. From 2019 to 2021, he was a postdoctoral researcher with the department of electronic and computer engineering at the Hong Kong University of Science and Technology. Currently, he is an assistant professor in the department of mathematical sciences at Chalmers University of Technology and the University of Gothenburg. His current research is in the intersection of optimization, control theory, inverse problems, and machine learning. Learn more about Dr. Ringh.
The authors collaborated on their answers to our questions.
Q: Why are you all excited to receive the award?
A: It is a great honor to receive the SIAM Activity Group on CST Best SICON Paper Prize, and we are thrilled that our work is recognized in this way. It is very encouraging to see the research community’s interest in our work, and we are enthusiastic to continue working on this topic.
Q: Could you tell us about the research that won your team the award?
A: This work deals with the optimal transport problem, a classical problem in mathematics, which finds the most efficient way to morph one distribution into another one. It is a powerful framework in which an underlying distance measure between two points can be lifted into a distance between two distributions. However, many control and estimation problems involve more than two distributions, which may stem from different time instances, or multiple measuring devices, for example. We propose a new framework based on optimal transport that takes into account the underlying structure between several given distributions; we call it graph-structured multi-marginal optimal transport. This is our first work in this direction where we deal with the case when the underlying graph-structure is a tree.
In the optimal transport community, it is well known that the optimal transport problem is related to another classical problem, called the Schrödinger bridge problem. The latter was posed by Schrödinger in the 1930s: Given the distribution of a cloud of particles at two different time points, under the assumption that the movements of the particles are given by Brownian motion, what is the most likely time-evolution between the two observations? We found out that this connection between optimal transport and Schrödinger bridges can be lifted to our framework as well.
Q: What does your team's work mean to the public?
A: The applications we had in mind when writing the paper are related to a large multi-agent system; think of traffic on a road network, movements of crowds of people, or opinion dynamics in social networks. In a system of a large number of agents, it is often not feasible to model each individual agent. The methods developed in our paper allow us to instead model the ensemble of agents as a whole, by describing it as a density function (i.e., a distribution). This can be utilized to estimate or control a very large group of agents, for example. However, optimal transport has also many other application fields such as computer vision, economics, physics, and machine learning, and we believe that our results can lead to interesting developments in these areas as well.
Q: What does being a member of SIAM mean to your team?
A: Connecting and collaborating with our peer researchers is of high importance to us. SIAM is a great forum for this, since it is a community that brings together applied mathematicians from a broad range of fields in both academia and industry. We are looking forward to presenting our work at the SIAM Conference on Control and Its Applications and having interesting discussions with members in the community.
Diego Cifuentes
Diego Cifuentes, Georgia Institute of Technology (Georgia Tech), is the recipient of the 2023 SIAM Activity Group on Algebraic Geometry Early Career Prize for several diverse and fundamental contributions to the theory of polynomial optimization with far reaching potential impact in- and outside mathematics. Dr. Cifuentes will give a talk titled, “Exact Semidefinite Relaxations for Polynomial Optimization and Applications in Euclidean Distance Problems” at the 2023 SIAM Conference on Applied Algebraic Geometry (AG23), taking place July 10 – 14, 2023, in Eindhoven, The Netherlands. The talk will be held on Tuesday, July 11 at 5:45 p.m. CEST.
The SIAM Activity Group on Algebraic Geometry (AG) awards this prize every two years to one outstanding early career researcher in the field of algebraic geometry and its applications for distinguished contributions to the field in the three calendar years prior to the year of the award. The contributions must be contained in a paper or papers in English in peer-reviewed journals.
Diego Cifuentes earned his master’s degree and Ph.D. in the department of electrical engineering and computer science at MIT, under the supervision of Professor Pablo Parrilo. Following that, he became a postdoctoral researcher in the Max Planck Institute for Mathematics in the Sciences, and then an applied math instructor in the mathematics department of MIT. Currently, he is an assistant professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. His research centers around the development of mathematical optimization methods, and the application of these methods in engineering areas such as machine learning, statistics, robotics, power systems, and computer vision. He also works in the theoretical analysis of optimization methods, leveraging algebraic, geometric, and combinatorial information to improve efficiency and robustness. Learn more about Dr. Cifuentes.
Q: Why are you excited to receive the award?
A: I am deeply honored to receive the SIAM Activity Group on AG Early Career Prize. Algebraic geometry is a mature mathematical field, which many members in the optimization community are not aware of. I firmly believe in its potential to advance optimization and to contribute to applied mathematics as a whole. I am immensely grateful for the guidance provided by my mentors, such as Pablo Parrilo, Rekha Thomas, Bernd Sturmfels, and Sameer Agarwal, who have helped me enter in this field. On a personal note, the past few years have been quite challenging due to the pandemic and various family issues. This award serves as a tremendous source of motivation and reinvigorates my energy.
Q: Could you tell us about the research that won you the award?
A: My work regards polynomial optimization problems, in which the goal is to optimize polynomial functions subject to polynomial constraints. These problems are ubiquitous in several applications but are notoriously difficult to solve. A possible strategy to tackle these hard problems is to approximate them with semidefinite relaxations, which are significantly easier to solve. My work provides tools that allow us to analyze the quality of these relaxations. In particular, we identify conditions that under which the relaxation solves the original polynomial optimization problem exactly. A notable class of problems we study are denoising problems from statistics, computer science, and engineering. We show that semidefinite relaxations often solve them exactly in the low noise regime.
Q: What does your work mean to the public?
A: Optimization methods underlay many technological tools we use today, touching areas such as artificial intelligence, robotics, or electrical power systems. Semidefinite relaxations can be used in several of those areas, and my work provides tools to understand the behavior of such techniques.
Q: What does being a member of SIAM mean to you?
A: Research is only impactful if there is a community around you, and SIAM provides an excellent community of applied mathematicians. In particular, the SIAM conferences have been my main avenue of disseminating my work so far, and I am very thankful for that.
Anthony Coache
Anthony Coache, University of Toronto, is one of the 2023 SIAM Activity Group on Financial Mathematics and Engineering Conference Paper Prize recipients. Coache presented his paper, “Conditionally Elicitable Dynamic Risk Measures for Deep Reinforcement Learning,” at the 2023 SIAM Conference on Financial Mathematics and Engineering (FM23), held June 6 – 9, 2023, in Philadelphia, Pennsylvania. Finalists presented their papers for the consideration of the prize selection committee, who selected two recipients after the session.
The SIAM Activity Group on Financial Mathematics and Engineering awards the prize every two years to recognize outstanding research presented by students and postdocs at the SIAM Conference on Financial Mathematics and Engineering. Up to six finalists will be selected to present their work and up to two awards will be made at the conference. Each award has equal merit.
Anthony Coache obtained his bachelor’s and master’s degrees, both in mathematics with a concentration in statistics, from the Université du Québec à Montréal in 2017 and 2019, respectively. He is currently a 4th year Ph.D. candidate in statistics at the University of Toronto under the supervision of Sebastian Jaimungal. His research focuses on reinforcement learning with dynamic risk measures. Learn more about Anthony Coache.
Q: Why are you excited to receive the award?
A: It obviously means a lot to me! While doing research at the beginning of my Ph.D., especially with online work during the COVID-19 lockdown, I often felt the impostor syndrome; for instance, I had thought that my work was too niche. However, being awarded this prize by experts in the field is extremely rewarding and motivates me to pursue new challenges.
Q: Could you tell us about the research that won you the award?
A: In the paper titled "Conditionally Elicitable Dynamic Risk Measures for Deep Reinforcement Learning", Sebastian Jaimungal, Álvaro Cartea, and I devised an actor-critic algorithm to solve reinforcement learning (RL) problems with a time-consistent risk-aware agent. It uses the notion of conditional elicitability for efficiently estimating dynamic spectral risk measures with neural networks to any arbitrary accuracy. We illustrated the performance of the algorithm on a statistical arbitrage and portfolio allocation setting with both simulated and real data. The paper is currently under review by SIAM Journal on Financial Mathematics.
Initially, I had received an invitation from the Oxford-Man Institute of Quantitative Finance (OMI) at the University of Oxford for a six month research visit, during which time this preprint was produced in collaboration with Cartea and Jaimungal. I had a wonderful experience collaborating with hard-working and motivated researchers from both institutions on problems at the intersection of quantitative finance and machine learning. I am very thankful to Cartea, Jaimungal, everyone who welcomed me during my visit in Oxford, everyone back home for their continued support, and the OMI for the very educational Victoria seminars!
Q: What does your work mean to the public?
A: Advancements with neural network structures paved the way to deep learning, which has shown a lot of success recently. In most real-life applications, especially in mathematical finance, there exists inherent uncertainty in the environment, and the agent must adapt its strategy to avoid potentially catastrophic consequences. One can think of, for instance, a trader concerned by the risks associated with financial assets, or an autonomous vehicle which must pay attention to weather and road conditions. I am proud to contribute to this field by developing algorithms that account for risk in a time-consistent manner, which hopefully brings us one step closer to autonomous AI entities taking optimal decisions in multiperiodic problems.
Q: What does being a member of SIAM mean to you?
A: I am proud to be a member of the SIAM, which actively organizes worldwide conferences. Such meetings are crucial for the developments of novel research work. I believe that discussing new research directions with both academia and industry scholars and learning about the most recent statistical advances facilitated my growth as a statistician and influenced my research ideas.
Heeyoung Kwon
Heeyoung Kwon, Ulsan National Institute of Science & Technology, is one of the 2023 SIAM Activity Group on Financial Mathematics Engineering Conference Paper Prize recipients. She presented her paper at the 2023 SIAM Conference on Financial Mathematics and Engineering (FM23) held June 6 – 9, 2023, in Philadelphia, Pennsylvania. At the conference, finalists presented their papers for the consideration of the prize selection committee and two recipients were selected. Kwon received the award for her paper, “Trading Constraints in Continuous-Time Kyle Models.”
The SIAM Activity Group on Financial Mathematics and Engineering awards the prize every two years to recognize outstanding research presented by students and postdocs at the SIAM Conference on Financial Mathematics and Engineering. Up to six finalists will be selected to present their work and up to two awards will be made at the conference. Each award has equal merit.
Heeyoung Kwon is a Ph.D. student in the mathematics department at Ulsan National Institute of Science and Technology. Her research interests include mathematical finance and stochastic control. In particular, she is interested in characterizing equilibria created by agents trading for their own purposes in the context of market microstructure.
Q: Why are you excited to receive the award?
A: It is a great honor to receive this award. Presenting my research in just 10 minutes was a significant challenge. The fact that I was able to capture the attention of others through this brief introduction makes me even more motivated to study harder.
Q: Could you tell us about the research that won you the award?
A: I studied a global existence of an equilibrium when the insider has a trading constraint. The main theorem established equilibrium existence by proving existence of solutions to an autonomous system of first-order nonlinear ODEs and then provides the equilibrium stock price and holding processes in terms of these solutions. Also, the equilibrium is consistent with some empirical findings: an autocorrelated aggregate holdings, decreasing price impacts, and U-shaped trading patterns.
Q: What does your work mean to the public?
A: The work is about how market participants choose their trading strategy in a financial market. In the market, we assume that there is information asymmetry, and some agents have a trading constraint. I believe that these assumptions are quite realistic and applicable in the real world. Therefore, I think that this research will help people better understand market structures, and furthermore, provide assistances to governments.
Q: What does being a member of SIAM mean to you?
A: I think collaboration and cooperation are very important in research and becoming a member of SIAM is especially important for me. This was my first time attending a SIAM conference and it provided me with an opportunity to connect and engage with great people.
Robert J. McCann
Robert J. McCann, University of Toronto, is the recipient of the 2023 W. T. and Idalia Reid Prize for pioneering and fundamental results in the field of optimal transport theory with deep applications to analysis and geometry. Dr. McCann will give a talk titled, “Duality and Free Boundaries for Optimal Nonlinear Pricing,” at the 2023 SIAM Conference on Control and Its Applications (CT23), taking place July 24 – 26, 2023, in Philadelphia, Pennsylvania. The talk will be held on Wednesday, July 26 at 5:15 p.m. Eastern.
SIAM awards the W. T. and Idalia Reid Prize annually to one individual for research in, or other contributions to, the broadly defined areas of differential equations and control theory.
Robert McCann holds the Canada Research Chair in Mathematics, Economics, and Physics at the University of Toronto. He studied engineering and physics at Queen's University, before completing a degree in mathematics and then a doctorate at Princeton University. Prior to moving to Toronto in 1998, he spent four years as a Tamarkin Assistant Professor at Brown University and has held a number of visiting positions in the U.S. and Europe. Other prizes he has received include the Jeffery-Williams (2017) and Coxeter-James (2006) Prizes of the Canadian Mathematical Society, and the Monroe H. Martin Prize in Applied Mathematics (2002). He was an invited speaker at the International Congress of Mathematicians in Seoul (2014). Additionally, he is a fellow of the Canadian Mathematical Society, the Fields Institute, the Royal Society of Canada, and the American Mathematical Society. Learn more about Dr. McCann.
Q: Why are you excited to receive the award?
A: It's a great honor to have my work celebrated by my peers and to see my name added to the long list of distinguished previous winners, many of whom served as role models for me and have achievements I respect and admire. Despite older roots, the theory of optimal transport has enjoyed a renaissance from the time I was an undergraduate student until today. It's exciting to see these new developments embraced and exploited across different fields in both pure and applied mathematics as well as adjacent disciplines. It's especially nice to have them welcomed by the differential equations and control communities represented by this prize.
Q: Could you tell us about the research that won you the award?
A: The prize committee cited my “pioneering and fundamental results in the field of optimal transport theory with deep applications to analysis and geometry.” One of the themes that has permeated my work is the interplay between optimal transport and various notions of curvature – sectional, Ricci, and mean. As a Ph.D. student in the 1990s, I started working on variational models for rotating stars and for crystalline interfaces. In that context, I found a new way of interpolating between probability densities based on optimal transport and established the convexity of various energies and entropies along the interpolation. Research with collaborators and others a decade later showed these displacement convexity inequalities could be used to characterize lower Ricci curvature bounds on a smooth manifold.
This led to Lott-Villani and Sturm's metric-measure theory of lower Ricci bounds, which provides a powerful setting to complete the space of Riemannian manifolds and to study the limiting and more general objects. Over the last five years, I have begun to explore a non-smooth theory of gravity based on analogous ideas in the Lorentzian setting. With some of my students and postdocs at the University of Toronto, I've explored the smoothness of optimal map. I also discovered that the cost function one chooses to optimize endows the product space of sources cross targets with a geometry, whose sectional curvatures control this smoothness. The sign of the same sectional curvature turns out to control the concavity of various strategic games which arise in microeconomics. And the optimal maps/strategies turn out to form volume-maximizing submanifolds of a conformally equivalent geometry on the source, target, or space, yielding an unexpected connection of the optimal transport problem to more classical questions of geometric measure theory reset into pseudo-Riemannian signature. I continue to be fascinated by and actively exploring these themes.
Q: What does your work mean to the public?
A: Uber is constantly tasked with solving a dynamical version of the optimal transport problem of assigning drivers to requests so as to minimize average waiting time. Unexpectedly, the same mathematics which describes this transportation problem turns out to be relevant to applications as diverse as: matching medical school graduates to residency opportunities, students to high schools, organ donors to patients, weather modelling, decision theory, image processing in medical among other applications, information geometry, and adversarial approaches to classification problems in machine learning. So, although the public may not be aware of the mathematics happening in the background, it has a very real and increasing impact on many aspects of our daily lives. One of the great virtues of mathematics is its universality: any progress we make understanding one of these problems inevitably leads to progress in our understanding of the others. But as with all science, we can only hope such progress is used wisely for the benefit of many rather than just a few.
Q: What does being a member of SIAM mean to you?
A: As someone whose research crosses disciplinary boundaries, the transdisciplinary nature of SIAM appeals to me a lot. The internationality of SIAM also appeals to me, as someone based outside the U.S. and outside Europe. I admire the way in which SIAM celebrates the breadth of applied mathematics through the richness of its meetings and its line of journals, many of which I have watched percolate to the tops of their fields over the course of my career. I proudly serve on the editorial board of SIAM Journal on Mathematical Analysis, and have been a lifetime member of SIAM since 2007.
Martin Skrodzki
Martin Skrodzki, Delft University of Technology (TU Delft), is the recipient of the 2023 SIAM Activity Group on Geometric Design Early Career Prize for his excellent contributions on the acquisition of point sets via 3D-scanning or higher-dimensional forms of data collection as well as denoising, cluster algorithms, and visualization. Dr. Skrodzki will give a talk at the 2023 SIAM Conference on Computational Geometric Design (GD23), taking place July 3 – 7, 2023, in Genoa, Italy.
The SIAM Activity Group on Geometric Design awards this prize every two years to an early career researcher who has made outstanding, influential, and potentially long-lasting contributions within five years of receiving the Ph.D. or equivalent degree as of January 1 of the award year. At least one of the papers containing this work must be published in English in a peer-reviewed journal or conference proceedings.
Martin Skrodzki studied mathematics and computer science in Dortmund, Germany; Laredo, Texas, U.S.; and Berlin, Germany, funded by the German Academic Scholarship Foundation. He earned a Dr. rer. nat. from the Freie Universität Berlin (2019). Afterwards, he was a postdoctoral researcher at the Institute for Computational and Experimental Mathematics at Brown University, Rikagaku Kenkyusho (RIKEN), and TU Delft. Dr. Skrodzki is currently an assistant professor at TU Delft in the group of Computer Graphics and Visualization. His research interests include the visualization of high-dimensional data, discrete geometry processing, as well as interactions between mathematics and arts. He is an associate editor of the Journal of Mathematics and the Arts and an editor of the online journal w/k - Between Science & Art. Furthermore, he is a member of the Solid Modeling Association and Delft Young Academy. Learn more about Dr. Skrodzki.
Q: Why are you excited to receive the award?
A: There are three things that really excite me about the reception of this award. First, I see this award as an opportunity for professional growth. It will open up new opportunities for me to continuously develop myself as a researcher and further my career. Second, the award helps bring attention to my research and increase its visibility within the academic community and beyond. Third and finally, receiving an award is a tangible recognition of the hard work and dedication my collaborators and I have put into this research. It is a validation of our efforts, and we are both humbled and excited about this distinction.
Q: Could you tell us about the research that won you the award?
A: Generally, surface geometries are represented via collections of triangles. However, there is also a less coherent representation that comes from, e.g., scanning objects for rapid prototyping workflows or cultural heritage. This representation consists only of points placed on or near the scanned surface. Often, a file for a scanned object contains millions of points, thus an efficient method is needed to show only relevant parts of the object without taking into account the large number of points. For the triangle representation, such a method is known, going by the name of Variational Shape Approximation (VSA). In this work, we translate the VSA method to the point representation setting. Furthermore, we enriched it in two aspects.
First, we describe an explicit example for the conjectured non-convergence of VSA. It is the first explicit example for this method to be caught in a loop, not providing any answers on an input geometry. To overcome this problem, we introduce an alternative version of VSA based on a new operation, for which we show that it circumvents the non-convergence problem. That is, our version always provides an answer. Second, we add two more operations to the method that make it more versatile. In its original form, the algorithm always finds a number of user-prescribed primitives to represent the geometry. By letting the method split or merge these primitives where needed, the algorithm can attain a certain quality of the representation, which is mostly more important than a fixed size. Finally, our research shows that from a set of such primitives, a triangle representation of the input can be obtained, thereby connecting the two main representations.
Q: What does your work mean to the public?
A: Our research has an impact on all workflows that incorporate handling of surface geometries. This includes improved 3D scanning and printing. Point cloud representations are commonly used in 3D scanning and printing applications, which have a wide range of uses in industries such as manufacturing, architecture, and healthcare. The development of efficient methods for processing point clouds helps making these technologies more accessible and user-friendly. Furthermore, it helps preservation of cultural heritage. Cultural heritage objects such as statues, buildings, and artifacts are often scanned using point cloud representations in order to preserve them digitally for future generations. By improving the handling of these scans, researchers can help ensure that these important cultural artifacts are preserved in a way that is faithful to their original form. Finally, our research contributes to advancements in computer graphics. Point cloud representations are a common input format for computer graphics applications such as video games, virtual reality, and special effects in movies. By developing more efficient and versatile algorithms for processing point clouds, our research helps advance the state of the art in these fields, leading to more realistic and immersive virtual environments.
Q: What does being a member of SIAM mean to you?
A: As a member of SIAM, I am part of a vibrant community of researchers, practitioners, and educators who share an interest in the application of mathematics to real-world problems. I value the variety of conferences, workshops, and other events throughout the year, which provide opportunities to connect with other researchers in your field and learn about the latest developments in applied mathematics. These events are also a great way to meet potential collaborators or mentors. As a young researcher, the leadership opportunities through SIAM’s various committees and councils are also very important to me. Being involved in these activities helps me build my reputation as a leader in my field and contribute to the broader community of applied mathematicians. As an enthusiastic science communicator, I enjoy SIAM's outreach efforts aimed at promoting the use of mathematics to solve real-world problems and increasing diversity and inclusion in the mathematical sciences. I am glad to have the opportunity to contribute to these efforts and make a positive impact on the broader scientific community.
Jingrui Sun
Jingrui Sun, Southern University of Science and Technology, is one of the recipients of the 2023 SIAM Activity Group on Control and Systems Theory Best SICON Paper Prize for his article, “Two-Person Zero-Sum Stochastic Linear-Quadratic Differential Games”, SIAM Journal on Control and Optimization, Vol. 59, No. 3, pp. 1804 - 1829, (2021), discovering fundamental properties of this class of games and illustrating the differences between stochastic and deterministic cases.
The SIAM Activity Group on Control and Systems Theory (CST) awards this prize every two years to the authors of the two most outstanding papers, as determined by the prize committee, published in SICON in the three calendar years preceding the award year.
Jingrui Sun received his Ph.D. in mathematics from the University of Science and Technology of China in 2015. He then was a postdoctoral fellow at the Hong Kong Polytechnic University (2015-2017), a research fellow at the National University of Singapore, and a visiting assistant professor at the University of Central Florida (2017-2018). Since the spring of 2019, Dr. Sun has been an assistant professor at the Southern University of Science and Technology, China. He also has co-authored two books on stochastic linear-quadratic optimal control theory with Jiongmin Yong. His main research interests include stochastic control and differential games, stochastic differential equations, and stochastic filtering theory. Yong. His current research interests focus on stochastic turnpike theory. Learn more about Dr. Sun.
Q: Why are you excited to receive the award?
A: I am very honored to have been selected by the committee as a recipient of the 2023 SIAM Activity Group on CST Best SICON Paper Prize. This award is a recognition of my continuous effort in studying stochastic differential games and will encourage me to continue doing my best work in the future. It builds my confidence in solving more important problems and finding more meaningful results in my future career.
Q: Could you tell us about the research that won you the award?
A: The linear-quadratic differential game constitutes a very important class of differential games, which has common interest in control theory and is widely encountered in many fields. It has long been known that for deterministic linear-quadratic two-person zero-sum differential games, the existence of a solution to a Riccati equation is a sufficient condition for the existence of a saddle point, and a beautiful connection exists between the value function and the saddle point: Finiteness of the value function is equivalent to the existence of a saddle point. However, the situation is very complicated in the stochastic case. A number of substantial questions arise; for example, under what conditions does the Riccati equation have a solution? Is the solvability of the Riccati equation still a sufficient or necessary condition for the existence of a saddle point? Does the connection mentioned earlier still hold? How do you construct a saddle point in general? This research illustrates the differences between stochastic and deterministic cases and establishes a sequence of results which gives a rather complete answer to the above questions.
Q: What does your work mean to the public?
A: This work presents a number of interesting findings that reveal the deep feature of the stochastic linear-quadratic game, which corrects the impression that the stochastic game is just a parallel extension of the deterministic case. The results make it possible to develop an ε-approximation scheme of constructing saddle points in general case. Moreover, I believe the idea and the method of this work can serve as an inspiration for the study of more general stochastic games.
Q: What does being a member of SIAM mean to you?
A: SIAM constitutes the best platform for an applied mathematician to make connections in the scientific community. It provides us opportunity to cooperate with brilliant researchers, make new friends, and exchange ideas, and it is a constant source of my inspiration. It is always a great pleasure and honor to publish with SIAM, since its journals have high standards for articles, so I am very proud to be a member.
Renyuan Xu
Renyuan Xu, University of Southern California, is the 2023 recipient of the SIAM Activity Group on Financial Mathematics and Engineering Early Career Prize for her remarkable contributions to the theory of machine learning and dynamic games, together with their applications in mathematical finance. Dr. Xu was recognized at the 2023 SIAM Conference on Financial Mathematics and Engineering (FM23), held June 6 – 9, 2023 in Philadelphia, Pennsylvania.
The SIAM Activity Group on Financial Mathematics and Engineering (FME) awards the prize every two years to one individual in their early career for distinguished contributions to mathematical modeling in finance in the three calendar years preceding the award year.
Renyuan Xu completed her Ph.D. in the industrial engineering and operations research department at the University of California, Berkeley. Then, she spent two years as a Hooke Research Fellow in the Mathematical Institute at the University of Oxford. Dr. Xu is currently a WiSE Gabilan Assistant Professor in the department of industrial and systems engineering at the University of Southern California. In 2022, she received a J.P. Morgan AI Research Award. Her research interests lie broadly in the span of stochastic analysis, mathematical finance, game theory, and machine learning theory. Learn more about Dr. Xu.
Q: Why are you excited to receive the award?
A: I am incredibly honored to receive the SIAM Activity Group on FME Early Career Prize. This recognition is an encouragement for me to continue working on interdisciplinary methods that are broadly connected to applied mathematics and to address more challenging problems in financial systems and large-scale stochastic systems in general.
Q: Could you tell us about the research that won you the award?
A: I work in the interdisciplinary area of using stochastic analysis, control theory, machine learning, and stochastic games to address complex, real-world problems in large stochastic systems such as financial markets. Most of my research involves the mathematical modeling of real-world problems mostly in financial markets, analyzing the theoretical properties of optimal strategies or game equilibrium solutions, and designing machine learning algorithms with provable convergence guarantees.
Q: What does your work mean to the public?
A: Real-world problems in practice are far more complex than one could imagine. Having an interdisciplinary approach by combing the wisdom from different areas could possibly improve the efficiency and effectiveness of some practically challenging problems. I hope some of my work could serve as a small building block toward that direction.
Q: What does being a member of SIAM mean to you?
A: SIAM provides a great platform for applied mathematicians to exchange and discuss innovative ideas, and it plays a crucial role in fostering those ideas into scientific results. Personally, I consider SIAM as one of the most important communities that drives my scientific endeavors, including publishing in SIAM journals and attending SIAM conferences.