In 2000, Chan co-founded and was the first Director of the Institute for Pure and Applied Mathematics (IPAM) at UCLA, one of a handful of Mathematical Sciences Institutes funded by the US National Science Foundation (NSF). He was instrumental in helping IPAM meet its mission of promoting the interaction of mathematics with science and technology and building new interdisciplinary research communities. From 2006 to 2009, he served as Assistant Director of the Mathematical and Physical Sciences Directorate (MPS) at the NSF. In this largest of NSF directorates, he was in charge of research funding for astronomy, physics, chemistry, mathematical science, and material science, and he increased both the government's and public's awarenesses of mathematics. He has served on the editorial boards of many journals in mathematics and computing, including SIAM Review, SIAM Journal on Scientific Computing, and Asian Journal of Mathematics, and is one of the three Editors-in-Chief of Numerische Mathematik. He co-wrote the proposal to start the SIAM Journal on Imaging Sciences and served on its inaugural editorial board until 2012. He provided guidance and support to generations of young applied and computational mathematicians and to the organization of numerous conferences and workshops in Asia, including SIAM Conferences on Imaging Science (2014), Applied Linear Algebra (2018), and Optimization (2020) all in Hong Kong.
Tony F. Chan assumed his role as President of KAUST in September 2018, after nearly a decade as president of The Hong Kong University of Science and Technology (HKUST), which he joined in 2009. HKUST’s global visibility and recognition significantly increased during his leadership. Before joining HKUST, he was Assistant Director of the MPS Directorate at the NSF from 2006 to 2009. Chan received his B.S. and M.S. degrees in engineering from California Institute of Technology and his Ph.D. in computer science from Stanford University, and he pursued postdoctoral research at Caltech as Research Fellow. He taught computer science at Yale University before joining UCLA as Professor of Mathematics in 1986 and was appointed Chair of the Department of Mathematics in 1997 and later Dean of Physical Sciences from 2001-2006. He was a co-founder (and director 2000-2001) of IPAM, the Institute for Pure and Applied Mathematics, a NSF funded Mathematical Sciences Institute at UCLA.
Chan is an elected member of the US National Academy of Engineering and a Fellow of SIAM, IEEE, AAAS, and the Hong Kong Academy of Engineering Sciences. He is currently a member of the board of trustees of KAUST, President’s Council of the Korea Advanced Institute of Science and Technology (KAIST), and Scientific Advisory Board of the University of Vienna. He is on the Board of Trustees/Directors of Hong Kong Academy of Science; IPAM, UCLA: King Abdullah City for Science and Technology (KACST), Saudi Arabia; Skolkovo Foundation, Russia; and Yidan Prize Foundation, Hong Kong.
Q: Why are you excited to be awarded this prize?
A: I feel particularly proud and honored to be awarded the 2020 SIAM Prize for Distinguished Service to the Profession. SIAM has been my main professional society for my whole career and I have been a member for over 40 years. I have served in multiple capacities, from conference organizer to speaker, to journal founder/editor, and to Council/Board/committee member. To have my modest contributions recognized by my peers is the highest honor that I can hope for and I’ll try my best to continue to serve SIAM.
Q: Could you tell us a bit about the accomplishments that won you the prize?
A: I have already mentioned the various capacities in which I have served SIAM, but without specifics. Perhaps as answer to this question, I can add (1) my service as a member of SIAM’s Science Policy Committee during which we visited Congressional offices to make the case for support for the mathematical sciences (that was at a time when few members of SIAM appreciated the importance of such activities) and (2) my service as Assistant Director of the Directorate of Mathematical and Physical Sciences (MPS) at NSF, which included the Division of Mathematical Sciences, which funds the research of many SIAM members. I am particularly proud of my contribution to articulating the importance of the mathematical sciences in broader NSF initiatives, such as sustainability and energy, and data science. Particularly noteworthy is my role in initiating the study that resulted in the report “Mathematical Sciences 2025” published by the Conference Board of the Mathematical Sciences (CBMS), a first for the mathematical sciences but a long tradition for other physical sciences.
Q: What does your work and service mean to the public?
A: My research work is in the interface between computational mathematics and applications in a variety of fields, including imaging, computer vision and brain mapping, and VLSI design. I hope that my research has contributed not only to both the mathematical sciences and the applications areas but also to raising the awareness that the mathematical sciences is an important component in the broad ecosystem of science and engineering.
I hope my service work through SIAM, NSF, and elsewhere has helped to advance the awareness and importance of the mathematical sciences in the broader scientific and political arena.
Bonnie Berger - AWM-SIAM Sonia Kovalevsky Lecture
Bonnie Berger of Massachusetts Institute of Technology is the 2020 AWM-SIAM Sonia Kovalevsky Lecturer. Her AWM-SIAM Kovalevsky Lecture is entitled “Compressive Genomics: Leveraging the Geometry of Biological Data.”
The AWM-SIAM Sonia Kovalevsky Lecture is awarded every year at the SIAM Annual Meeting by the Association for Women in Mathematics (AWM) and SIAM. The lectureship is awarded to a member of the scientific or engineering community whose work highlights the achievements of women in applied or computational mathematics.
Bonnie Berger has an outstanding record of research contributions in the area of computational biology which have furthered our understanding of the structure of proteins and the genome. Berger's work is characterized by its successful interdisciplinarity and mathematical depth. In addition to theoretical and algorithmic contributions, she has contributed significantly to the rapid advancement of the fields of systems biology and genomics by her numerous software developments, which are widely used by researchers in other disciplines. Her work has received, and continues to receive, numerous accolades and national and international awards. In addition to being a highly accomplished research leader in bioinformatics and computational biology, Berger has an enviable record of mentorship of young investigators and service to the profession. Her intellectual impact is multifaceted and far-reaching.
Bonnie Berger is the Simons Professor of Mathematics and Professor of Electrical Engineering and Computer Science at Massachusetts Institute of Technology (MIT) and Faculty of Harvard-MIT Health Science and Technology. She is also an Associate Member of the Broad Institute of MIT and Harvard, and Affiliated Faculty of Harvard Medical School.
Berger received her PhD in computer science from MIT in 1990. After beginning her career working in algorithms at MIT, she has become one of the pioneer researchers in computational biology. Together with the many students she has mentored, she has been instrumental in defining the field. She continues to lead efforts to design algorithms to gain biological insights from recent advances in automated data collection and the subsequent large data sets drawn from them.
In recent years, Berger has served as Vice President of the International Society for Computational Biology (ISCB), as Head of the Steering Committee for RECOMB (Annual International Conference on Research in Computational Molecular Biology), as AAAS Member-at-Large for the Mathematics Section, on the Advisory Councils of the National Institute of General Medical Sciences (NIGMS) and National Center for Biotechnology Information (NCBI) of the National Institutes of Health (NIH) and as Interim Head of Applied Mathematics at MIT. She won the 2019 ISCB Senior Scientist Award and is a Fellow of ACM, AMS, and ISCB.
Q: Why are you excited to be awarded the AWM-SIAM Sonia Kovalevsky Lecture?
A: It’s a tremendous honor to join such a distinguished and accomplished group of scientists in celebration of the work of Sonia Kovalevsky!
Q: Could you tell us a bit about the research that won you the prize?
A: The mission of computational biology is to answer biological and biomedical questions by using computation in support of or in place of laboratory procedures, with a goal being to get more accurate answers at a greatly reduced cost. We are currently generating massive datasets that often contain sensitive information. However, their sheer size creates challenges that I have solved through transforming and creating techniques from algorithmic thinking to design computational platforms that for the first time allow scalable and secure sharing of this data.
Q: What does your work mean to the public?
A: Combining genomic and health-related data from millions of patients will empower unprecedented insights into human health and disease risk. We provide novel computational methods and software to enable biomedical data sharing and analysis at scale.
Q: What does being a SIAM member mean to you?
A: It’s a privilege to belong to a society where applications of mathematics to the sciences and engineering are valued.
Anna Seigal - Richard C. DiPrima Prize
Anna Seigal of the University of Oxford is the recipient of the 2020 Richard C. DiPrima Prize. The award recognizes Seigal for her dissertation, “Structured Tensors and the Geometry of Data” and for her exemplary work in multilinear algebra that advances theoretical and applied knowledge, and that serves as a model of mathematical communication.
The Richard C. DiPrima Prize is awarded every two years at the SIAM Annual Meeting to one early career researcher who has done outstanding research in applied mathematics and who has completed their doctoral dissertation and all other requirements for their doctorate or equivalent degree during the period running from three to one years prior to the award date. Selection is based on the dissertations of the candidates.
Anna Seigal earned her undergraduate and master's degrees in mathematics at the University of Cambridge. She attended the University of California, Berkeley, for her PhD, where her thesis was awarded the Bernard Friedman Memorial Prize in Applied Mathematics from UC Berkeley. She is now based at the University of Oxford, as a Hooke Research Fellow at the Mathematical Institute and a Junior Research Fellow at The Queen's College.
Q: Why are you excited to receive the Richard C. DiPrima Prize?
A: It is an honor to receive the Richard C. DiPrima Prize, and very motivating for me as I pursue the research directions from my PhD, now as a postdoc. I am grateful to my collaborators for the fun projects we are working on together, for the support of my mentors who nominated me, and for the encouragement of the research community.
Q: Could you tell us a bit about the research that won you the prize?
A: My research is in applied algebra: I am interested in algebraic structures arising in applied contexts such as statistics and biological data analysis. Linear algebra is the foundation to algorithms, dating back one hundred years, for extracting structure from data. Modern technologies provide an abundance of multi-dimensional data, in which multiple variables or factors can be compared simultaneously. To organize and analyze such datasets we can use a tensor, the higher order analogue of a matrix. Many theoretical and practical challenges arise in extending linear algebra to the setting of tensors. My research studies the algebraic theory of tensors, and uses this theory to design algorithms for tensor data.
Q: What does your work mean to the public?
A: We analyze data to build a quantitative understanding of the world. Drawing insights from data means simplifying the information to reveal important structure. For multi-dimensional data, we seek interpretable ways to compare multiple different variables. A geometric understanding of different models can determine optimal ways to approximate data in different contexts. I am especially interested in statistical and biological contexts where we can hope to use multi-dimensional insights to, for example, understand different types of cancer and to predict patient survival in a clinical trial.
Q: What does being a SIAM member mean to you?
A: I really enjoy being part of the SIAM community, and especially attending SIAM conferences. As well as annual meetings, I have been involved in SIAM conferences on applied algebraic geometry and applied linear algebra, giving talks and organizing minisymposia. The conferences are a great opportunity to hear exciting new progress from diverse perspectives.
Roland Glowinski - W. T. and Idalia Reid Prize
Roland Glowinski of the University of Houston is the recipient of the 2020 W. T. and Idalia Reid Prize in Mathematics. His Reid Prize Lecture, “Investigating Numerically the Exact Boundary Controllability of the Wave Equation: A Historical Perspective,” is scheduled for virtual presentation as part of The Second Joint SIAM/CAIMS Annual Meeting (AN20).
SIAM awards the Reid Prize annually to recognize a member of the scientific community for outstanding work in, or other contributions to, the broadly defined areas of differential equations and control theory. The 2020 award recognizes Glowinski for seminal and fundamental contributions to the controllability for hyperbolic problems, indicating the need for developing efficient filtering methods to attenuate the effect of high frequency spurious components, and explaining how this could be done using a Tychonoff regularization procedure of the discrete models and a two-grid approach in the finite element setting.
Roland Glowinski received his BS in Mathematics, Physics, and Chemistry from École Polytechnique, Paris; his MS in Electrical Engineering from École Nationale Supérieure des Télécommunications, Paris, and his PhD in Mathematics from Pierre and Marie Curie University (UPMC), Paris VI. He was Professor of Applied Mathematics and Chair of the Department of Mathematics (1981-85) at UMPC and continues this affiliation there as Emeritus Professor. His positions in industry have included Scientific Director, INRIA (1970-85) and Director, CERFACS (1992-94). In 1980 he joined the faculty of the University of Houston, where he is Cullen Professor of Mathematics and Mechanical Engineering. He has been affiliated as Professor to Hong-Kong Baptist University, Sorbonne Université, and Fudan University. He is a member of the French National Academy of Sciences, the French Academy of Technologies, and Academia Europaea. He is a Fellow of SIAM and AMS. He received SIAM’s Theodore von Kármán Prize in 2004.
Q: Why are you excited to receive the W. T. and Idalia Reid Prize?
A: The first reason is obvious; it’s a prestigious SIAM award for those contributions at the interface of control and differential equations. The other reason is more personal; in 1998 my PhD advisor and mentor, Jacques-Louis Lions, received the W. T. and Idalia Reid Prize at the SIAM Annual Meeting in Toronto, three years before his premature death. This 2020 W. T. and Idalia Reid Prize rewards the results of investigations associated with topics J. L. Lions addressed in his 1986 John von Neumann Lecture at the SIAM Annual Meeting in Boston. As you can guess, I am very excited and honored by the W. T. and Idalia Reid Prize and Lecture.
Q: Could you tell us a bit about the research that won you the prize?
A: In the mid-eighties, motivated by the control and stabilization of flexible space structures, J. L. Lions developed the Hilbert Uniqueness Method (HUM), a rather general method to investigate the controllability properties of systems modelled by partial differential equations. In January 1986, I had the chance to attend the lecture J. L. Lions gave at ICASE on the application of HUM to the finite time boundary controllability of the linear wave equations, a fascinating problem that I decided to attack numerically. This problem was more complicated than I thought and my first attempt at solving it numerically was a failure. A spectral analysis of the failing numerical method showed that the spurious oscillations we observed were caused by the damping of the high frequency components of the solution of the approximate problem. A Tychonoff regularization took care of the above pathological behavior, leading to very good numerical results, all this taking place in 1986-1987. Then, taking advantage of some formal analogy with problems in computational fluid dynamics, we developed in 1990 a simple, accurate, and robust double grid method, able to filter without regularization the above mentioned spurious oscillations.
Q: What does your work mean to the public?
A: A better understanding via mathematical modelling and simulation of the behavior and control of a physical system may have technological consequences, with impact on the general public. We know, for example, that some of our work on the controllability of wave models was used some years ago by a Finnish numerical software company to develop a wave propagation simulator used in applications. More recently, in collaboration with physicists at Oak Ridge National Laboratory, we found applications to Josephson junction based cryogenic memories.
Q: What does being a SIAM member mean to you?
A: Being a SIAM member means a lot to me, and being a SIAM Fellow (of the 1st Class in 2009) and a SIAM author means even more. Indeed, SIAM is by far the world’s most prestigious organization dedicated to computation and applied mathematics via its publications, conferences, educational programs and so on. It has been a model for sister organizations located all around the world. I have published several articles in various SIAM journals and two books in SIAM series, been associate editor of a SIAM journal, and attended many SIAM conferences and workshops. I am very proud to have been occasionally a plenary speaker at some SIAM meetings, in particular the SIAM Annual Meeting in Portland, Oregon, in 2004, when I gave the Theodore von Kármán Lecture. Again, I am honored by the W. T. and Idalia Reid Prize and Lecture.
Kaushik Bhattacharya - Theodore von Kármán Prize
Kaushik Bhattacharya of California Institute of Technology is the recipient of the 2020 Theodore von Kármán Prize. The prize is awarded to him in recognition of his major mathematical and computational contributions to materials science. Bhattacharya’s Theodore von Kármán Lecture, “Mathematics, Mechanics and Materials: The Case Study of Liquid Crystal Elastomers,” is scheduled to be presented virtually on Thursday, July 9, 2020 as part of The Second Joint SIAM/CAIMS Annual Meeting (AN20).
The Theodore von Kármán Prize is awarded every five years for a notable application of mathematics to mechanics and/or the engineering sciences made during the five to ten years preceding the award. The award may be given for a significant achievement by one individual or a significant body of work that might have been produced by multiple contributors.
Kaushik Bhattacharya is Howell N. Tyson, Sr., Professor of Mechanics and Professor of Materials Science as well as Vice-Provost at the California Institute of Technology. He received his B.Tech degree from the Indian Institute of Technology, Madras, India in 1986, his Ph.D from the University of Minnesota in 1991, and his postdoctoral training at the Courant Institute for Mathematical Sciences, New York University, during 1991-1993. He joined Caltech in 1993. Bhattacharya is a Fellow of SIAM. His research concerns the mathematical modeling and theoretical analysis of problems motivated by solid mechanics and materials science.
Q: Why are you excited to receive the Theodore von Kármán Prize?
A: I have participated in SIAM activities since I was a graduate student, and it is gratifying to be recognized by peers and colleagues. I look at the list of past recipients, and it is humbling to join my mentors as well as colleagues whom I have admired over the years. I also have had the privilege of spending my career at Caltech where von Kármán is a living legend, and it is nice to be recognized in his name. Most importantly, I have had the enormous good fortune of working with an extraordinary group of students and postdoctoral scholars as well as collaborators over the years, and I see this as a recognition of their work as well.
Q: Could you tell us a bit about the research that won you the prize?
A: I am broadly interested in understanding why materials have the mechanical properties that they do: why do some materials break easily while others do not, what makes some materials light but strong, how do some materials come to possess unusual properties like the shape-memory effect (the ability to remember a given shape) or photomechanical coupling (the ability to deform when illuminated), etc. I am also interested in understanding how the mechanical properties change when one changes configuration and dimension. Most materials have structures, patterns, and phenomena at multiple scales (electronic, atomistic, grains, defects, etc.), and the behavior that we observe is the result of an interaction between all these scales. It is a great challenge, and a fascinating one, to understand these interactions. It is also intriguing to exploit this understanding to improve existing materials, to suggest new phenomena, and to see their adaptation in new applications. For example, I have worked on shape-memory alloys where subtle features in the crystal structure are critical to their ability to display the phenomenon, and where computational methods have been extremely useful in designing medical devices. Another example is fracture, where a group of us conceived and designed what we call fracture diodes where cracks can propagate right to left but not left to right. Importantly, these issues give rise to interesting mathematical problems, and mathematical and computational analysis can provide new insight.
Q: What does your work mean to the public?
A: It would be presumptuous of me to say that my work in itself has meaning to the public. However, the collective work of the community interested in similar questions is very important. We live in a world where new mechanical devices and new materials are constantly being invented, developed, and discovered. While serendipity and genius play a role, the methods and tools we develop, the fundamental discoveries that we make as well as the students we train is critical for such rapid progress. However, even with all this progress, I am convinced that the class of materials that we know is only a small subset of all the possible materials that can be, and we have a long way to go in mechanical devices. The best mechanical and robotic devices today are similar to the ENIAC — a frame to which components are added by hand and consequently limited in complexity and function and hungry for energy. So I see an infinite horizon as we learn to integrate more function into materials and integrate multiple materials together. We also use materials and machines largely in a linear fashion from synthesis to waste, and we will eventually learn to develop materials and machines that can be used in a circular loop. So stay tuned.
Q: What does being a SIAM member mean to you?
A: I have participated in SIAM since I was a graduate student. The SIAM community is remarkably interdisciplinary in the best sense of the word. As someone who does not quite fit into any traditional department, I find myself at home in SIAM. I also appreciate the organization. It is extremely focused —through its conferences, through its journals, and through its advocacy — on the community that it serves. The leadership and staff of SIAM deserve a lot of credit in making it so.
Vasileios Kalantzis – SIAM Student Paper Prize
Vasileios Kalantzis of IBM Research was awarded the 2020 SIAM Student Paper Prize for his paper, “Beyond Automated Multilevel Substructuring: Domain Decomposition with Rational Filtering.” Kalantzis co-authored the paper with Yuanzhe Xi and Yousef Saad while he was a student at the University of Minnesota, and the paper was published in SIAM Journal on Scientific Computing in 2018.
The SIAM Student Paper Prize recognizes outstanding scholarship by students in applied mathematics and computing as evidenced in a paper accepted for publication in a SIAM journal. The prize is awarded annually to the student authors of the most outstanding papers accepted by a SIAM journal within the three years preceding the nomination deadline. The award is based solely on the merit and content of the candidate’s contribution to the paper. Up to three prizes are awarded.
Vasileios (Vassilis) Kalantzis is a Research Staff Member at IBM Research, Thomas J. Watson Research Center. He received his PhD (2018) and MSc (2016) in computer science and engineering from the University of Minnesota, Twin Cities, USA; both under the direction of Yousef Saad. Prior to that, he received a MSc (2014) and a MEng (2011) in computer engineering and informatics from the University of Patras, Greece. His current research interests are primarily in trusted artificial intelligence, numerical methods for quantum computing, and in-memory computing.
Q: Why are you excited to receive the SIAM Student Paper Prize?
A: It's a big honor and I am deeply grateful to SIAM for encouraging and promoting the contribution of students in our community. On a personal level this award serves as an additional level of motivation to keep exploring and testing my ideas, and I am thankful to Yuanzhe Xi and Yousef Saad for their collaboration.
Q: Could you tell us a bit about the research that won you the prize?
A: The paper features a parallel numerical algorithm for the partial solution of large and sparse algebraic symmetric generalized eigenvalue problems. The keyword "partial" implies that we are interested in computing only those eigenvalues (and, optionally, associated eigenvectors) located inside some given interval.
The proposed algorithm starts by partitioning the discretized domain into a number of non-overlapping subdomains with the help of an algebraic graph partitioner. The original eigenvalue problem is then decoupled into two separate subproblems, one defined locally in the interior of each subdomain, and one defined on the interface region connecting neighboring subdomains. Once the solution associated with the interface variables is computed, the solution associated with the interior of each subdomain is computed independently of the rest of the subdomains.
The main bottleneck of domain decomposition eigenvalue solvers is the computation of the solution associated with the interface variables. This is a challenging task as the interface eigenvalue problem is non-linear. One of the novelties of the proposed algorithm is that it doesn't attempt to solve this eigenvalue problem. In contrast, it applies a rational filter to damp the unwanted interface eigenvector components followed by an application of the Lanczos algorithm to capture the range space of the interface filtered matrix. As a positive side-effect, no estimation of the number of eigenvalues located inside the interval of interest is necessary. The solution of the original eigenvalue problem associated with the interior variables of each subdomain can be then approximated independently in each subdomain exploiting only real arithmetic. Moreover, being a combination of domain decomposition and rational filtering techniques, the proposed algorithm can take advantage of different levels of parallelism, making itself appealing for execution in high-end computers.
Q: What does your work mean to the public?
A: Large-scale eigenvalue computations can be found in many scientific disciplines, e.g., data mining, structural mechanics, and quantum mechanics. Creating faster and more efficient parallel algorithms allows us to run our simulations in less time thus accelerating scientific discovery. This work is a first step towards parallel algorithms which retain the benefits of domain decomposition algebraic eigenvalue solvers, and, at the same time, provide higher accuracy.
Q: What does being a SIAM member mean to you?
A: I have been a SIAM member for about eight years. Thanks to SIAM I have had many opportunities to meet experts in the field of applied mathematics, all of whom were more than eager to teach me and to share their wisdom with me. There is no other match for this benefit.
Jonas Latz – SIAM Student Paper Prize
Jonas Latz of the University of Cambridge is a recipient of the 2020 SIAM Student Paper Prize for his paper, “On the Well-Posedness of Bayesian Inverse Problems.” He is the sole author of the paper, which he wrote while he was a student at the Technical University of Munich. The paper was published in SIAM/ASA Journal on Uncertainty Quantification in 2020.
The SIAM Student Paper Prize recognizes outstanding scholarship by students in applied mathematics and computing as evidenced in a paper accepted for publication in a SIAM journal. The prize is awarded annually to the student authors of the most outstanding papers accepted by a SIAM journal within the three years preceding the nomination deadline. The award is based solely on the merit and content of the candidate’s contribution to the paper. Up to three prizes are awarded.
Jonas Latz studied mathematics and scientific computing at Trier University and at the University of Warwick. From 2016 to 2019, he was a doctoral student of Elisabeth Ullmann at the Technical University of Munich. He graduated with a doctoral thesis on aspects of hierarchies in Bayesian inverse problems. Since January 2020, he has been a research associate in the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge. He works with Carola-Bibiane Schönlieb in the Cambridge Image Analysis Group and the EPSRC-funded project PET++. At Cambridge, he has been studying stochastic optimization and Bayesian approaches to medical imaging.
Q: Why are you excited to receive the SIAM Student Paper Prize?
A: Thanks to SIAM, I have met a lot of great people and gained many new insights. Winning this prize makes me proud and even more grateful.
Q: Could you tell us a bit about the research that won you the prize?
A: In parameter estimation, we usually require that the object of interest exists, is unique, and is stable with respect to small perturbations in the data. When estimating parameters in a Bayesian way, I showed that this is the case for the posterior distribution in a very large class of problems.
Q: What does your work mean to the public?
A: I hope that my work contributes to the use of randomness to model uncertainties in physical models. After all, precisely accounting for uncertainties in models leads to more accurate engineering and science.
Q: What does being a SIAM member mean to you?
A: SIAM is an amazing community uniting mathematicians all over the world. SIAM organizes an outstanding series of conferences and it publishes an unprecedented series of journals and books. Knowing that I am a part of this society makes me very happy.
Elizabeth Qian – SIAM Student Paper Prize
Elizabeth Qian of Massachusetts Institute of Technology was awarded the 2020 SIAM Student Paper Prize for her paper, “Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices.” Qian co-authored the paper with Benjamin Peherstorfer, Dan O’Malley, Velimir Vesselinov, and Karen Willcox while she was a student at MIT, and the paper was published in SIAM/ASA Journal on Uncertainty Quantification in 2018.
The SIAM Student Paper Prize recognizes outstanding scholarship by students in applied mathematics and computing as evidenced in a paper accepted for publication in a SIAM journal. The prize is awarded annually to the student authors of the most outstanding papers accepted by a SIAM journal within the three years preceding the nomination deadline. The award is based solely on the merit and content of the candidate’s contribution to the paper. Up to three prizes are awarded.
Elizabeth Qian is currently a PhD student at Massachusetts Institute of Technology (MIT) in the Center for Computational Science and Engineering and in the Department of Aeronautics & Astronautics. She holds SB and SM degrees from MIT in aerospace engineering and has been awarded a US Fulbright Student Grant, a Fannie and John Hertz Foundation Fellowship, and a National Science Foundation Graduate Research Fellowship. Starting in 2021, Qian will be a von Kármán Postdoctoral Instructor at California Institute of Technology in the Department of Computing and Mathematical Sciences. Her research interests are in the development of reduced models and multifidelity methods to support decision-making in engineering and scientific applications.
Q: Why are you excited to receive the SIAM Student Paper Prize?
A: Seeing what previous student paper awardees have gone on to achieve, I’m delighted to be recognized. I’m grateful to SIAM for sponsoring the prize and to my co-authors for their support and mentorship.
Q: Could you tell us a bit about the research that won you the prize?
A: Global sensitivity analysis is one tool that engineers can use to understand the relative impact of different uncertainties or randomness on the performance of a new design. However, this type of analysis involves simulating the system being designed hundreds or even hundreds of thousands of times, leading to an unacceptable draw on computational resources when advanced high-fidelity simulations are used. My work introduces a way to compute these global sensitivity indices using a combination of a few expensive simulations and many cheap approximations that makes this important calculation computationally affordable.
Q: What does your work mean to the public?
A: Designing any engineering system, whether a new engine or a new airplane or a whole transport system, is always a task plagued by uncertainty and randomness. For example, this uncertainty can come from variation within manufacturing tolerances, randomness in operating conditions (like weather), or fluctuations in peak demand. By making global sensitivity analysis possible at lower cost, my work helps engineers to better understand uncertainties earlier in the design process, leading to better designs.
Q: What does being a SIAM member mean to you?
A: With my background in aerospace engineering, I was initially drawn to computational modeling as a powerful toolbox for analyzing aerospace systems. As I learned more, I was really struck by the wide range of disciplines that can benefit from computational tools, ranging from engineering to life sciences and even social sciences. The potential for a common set of mathematical and algorithmic principles to have an impact on such a diverse array of applied sciences is what excites me most about computational research, and no organization captures this cross-disciplinary potential more than SIAM.
Yaniv Romano – SIAG/IS Early Career Prize
Yaniv Romano of Stanford University is the 2020 recipient of the SIAM Activity Group on Imaging Science Early Career Prize. The award recognizes Romano for his major contributions to several areas of imaging, in particular, in combination with statistics and data science. His talk, “Regularization by Denoising (RED),”is scheduled to be presented virtually on Friday, July 10, 2020 as part of SIAM Conference on Imaging Science (IS20).
The SIAM Activity Group on Imaging Science (SIAG/IS) awards the SIAG/IS Early Career Prize every two years to one individual in their early career for distinguished contributions to the field of imaging science. The candidate’s work must contain significant research contributions to the field, and a key paper representing this work must have been published in English in a peer-reviewed journal in the three calendar years prior to the award year. The candidate must be a graduate student or have obtained their PhD or equivalent degree within the five calendar years prior to the award year. The committee may consider exceptions to the five years-from PhD rule, for career interruptions or delays occurring, e.g., for child bearing, child rearing, or elder care.
Yaniv Romano is a postdoctoral scholar in the Department of Statistics at Stanford University, advised by Professor Emmanuel Candès. He earned his PhD and MSc degrees in 2017 from the Department of Electrical Engineering at the Technion - Israel Institute of Technology, under the supervision of Professor Michael Elad. He received his BSc in 2012 from the same department.
Romano’s research spans the theory and practice of selective inference, sparse approximation, machine learning, data science, and signal and image processing. His goal is to advance the theory and practice of modern machine learning, as well as to develop statistical tools that can be wrapped around any data-driven algorithm to provide valid inferential results.
Romano is also interested in image recovery problems: the super-resolution technology he invented with Dr. Peyman Milanfar is being used in Google's flagship products, increasing the quality of billions of images and bringing significant bandwidth savings. In 2017, he constructed with Professor Michael Elad a MOOC on the theory and practice of sparse representations, under the edX platform.
Q: Why are you excited to receive the SIAG/IS Early Career Prize?
A: I am deeply honored to receive this prize. It is gratifying that the great SIAM community recognizes the importance of my research. I am certain that the SIAG/IS Early Career Prize not only motivates my colleagues to continue working on challenging problems in imaging but also encourages others to learn more about our exciting community and field.
Q: Could you tell us a bit about the research that won you the prize?
A: The heart of image processing is the formulation and deployment of image priors. Indeed, a progressive advancement of better modeling the image statistics has led to improved performance in many tasks in image processing. Together with my collaborators, we developed highly effective methodologies that build upon the impressive success of image denoising algorithms to address more challenging inverse problems, such as image deblurring, super-resolution, and phase retrieval, to name a few. Inspired by the Plug-and-Play framework, we showed how state-of-the-art denoising functions can be used to formulate powerful priors for images, and we offered flexible optimization techniques to recover the unknown image without the need to compute the derivative of the denoising function, treating it as a "black-box".
Q: What does your research mean to the public?
A: Our work provides new and better ways to improve the quality of images and videos. This technology may assist doctors to effectively explore medical images, enhance the quality and reliability of surveillance systems, provide significant bandwidth savings, or simply allow us to enjoy better pictures of our family and friends.
Q: What does being a SIAM member mean to you?
A: Beyond the opportunity to learn more about cutting edge research at first-hand, participating in SIAM provides a priceless opportunity to build personal relationships with the top researchers from all over the world. This is the best way to communicate knowledge, advance science, and shape the future of our field.
SIAM Activity Group on Imaging Science Best Paper Prize
The SIAM Activity Group on Imaging Science Best Paper Prize was awarded in 2020 to Gregery T. Buzzard (Purdue University, pictured left), Charles A. Bouman (Purdue University, pictured right), Suhas Sreehari (Wells Fargo), Singanallur V. Venkatakrishnan (Oak Ridge National Laboratory), Brendt Wohlberg (Los Alamos National Laboratory), Lawrence F. Drummy (Air Force Research Laboratory), and Jeffrey P. Simmons (Air Force Research Laboratory). The award recognizes the seven authors for their paper, “Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation,” published in IEEE Transactions on Computational Imaging in 2016.
Charles A Bouman’s talk, “Plug and Play: A General Approach to AI and Sensor Model Fusion,” is scheduled to be presented virtually on Friday, July 10, 2020 as part of IS20.
The SIAG/IS Best Paper Prize is awarded every two years to the author or authors of the most outstanding paper, as determined by the prize committee, on mathematical and computational aspects of imaging published within the four calendar years preceding the year prior to the award year. The 2020 award recognizes the authors for their paper, in which for the first time plug-and-play priors have been used for solving a challenging inverse problem such as electron tomography, alongside a profound convergence analysis. This publication has inspired various follow-up works, either using the approach in combination with other methods or providing high quality reconstructions for diverse imaging applications.
Gregery T. Buzzard is Professor and Head of the Department of Mathematics at Purdue University, where he joined the faculty in 2002. He holds a PhD in Mathematics from the University of Michigan, as well as degrees in Mathematics, Computer Science, and Violin Performance from Michigan State University. He also held an NSF Postdoctoral Fellowship at Cornell University. In conjunction with a number of collaborators, Buzzard’s work has led to theoretical advances in the construction of surrogate functions and the use of such functions for sensitivity analysis and experiment design, to extensions of the classical theory of optimal design of experiment, and to new algorithms for supervised classification. These theoretical advances have fueled a wide variety of applications, including novel experiment design and control methods for cellular-level control of immune cell response, a Raman spectroscopy system that significantly outperforms previous methods in the high-speed regime, and an adaptive approach for sampling images that forms the basis for algorithms for electron microscopy and other imaging modalities. The unifying ideas in his work are iterative methods for image reconstruction and reduction of uncertainty through appropriate measurement schemes.
Charles A. Bouman is the Showalter Professor of Electrical and Computer Engineering and Biomedical Engineering at Purdue University. He received his BSEE degree from the University of Pennsylvania, MS degree from the University of California at Berkeley, and PhD from Princeton University in 1989. Bouman’s research is in the fields of computational imaging in applications ranging from medical to scientific imaging. His research resulted in the first commercial model-based iterative reconstruction (MBIR) system for medical X-ray computed tomography (CT), and he is co-inventor on over 50 issued patents that have been licensed and used in millions of consumer imaging products. Bouman has served as the IEEE Signal Processing Society’s Vice President of Technical Directions, Editor-in-Chief of IEEE Transactions on Image Processing, Vice President of Publications for the Society for Imaging Science and Technology (IS&T), and he led the creation of IEEE Transactions on Computational Imaging. He is a member of the National Academy of Inventors, a Fellow of the IEEE, AIMBE, IS&T, and SPIE, and was the 2014 recipient of IS&T’s Electronic Imaging Scientist of the Year award.
Gregery Buzzard and Charles Bouman answer our questions below.
Q: Why are you excited to receive the SIAG/IS Best Paper Prize?
A: The SIAG on Imaging Science community sits at an important intersection between theory and applications. This work is the first of a body of work that has influenced many algorithms and has also led to theory that explains this approach in terms of equilibrium conditions. We’re really excited that many people in the community have recognized the value of this approach and have been inspired to use variants of it in many different directions to produce a wide variety of excellent results.
Q: Could you tell us a bit about the research that won you the prize?
A: The ideas and methods of regularized inversion have been developed and applied for decades to a variety of algorithms, leading to dramatic improvements in CT imaging for medical and security applications, MRI imaging, and other applications. This foundational work relied on a Bayesian formulation, which requires that both data fitting and prior information be encoded using functions to be minimized. However, modern denoisers (such as BM3D and convolutional neural networks) are incredibly effective at encoding prior information about images, but do this in an algorithmic, implicit way rather than via a loss function. The work in this paper provides a natural way to extend regularized inversion to incorporate these algorithmic priors.
Q: What does your work mean to the public?
A: The methods in this work have already found applications in medical imaging devices and security scanning devices, and have been included in a chipset for use by a major cellphone manufacturer.
Q: What does being a SIAM member mean to you?
A: SIAM serves as a key link between mathematics in theory and mathematics in practice. As collaborators, we each have our roots in one of those sides of the field, but we greatly appreciate the contributions and value to be had from the other side. By exploring those connections, we’ve made some really nice discoveries and had a huge amount of fun!