Rayadurgam Srikant is the Fredric G. and Elizabeth H. Nearing Endowed Professor of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign, affiliated with the Coordinated Science Lab. He co-directs the C3.ai Digital Transformation Institute, focusing on AI-driven solutions for global challenges. His research spans machine learning, communication networks, stochastic systems, and game theory. Srikant has authored influential textbooks including Communication Networks: An Optimization, Control and Stochastic Networks Perspective . He holds IEEE Fellow status and has received prestigious awards like the ACM SIGMETRICS Achievement Award (2021) and IEEE Koji Kobayashi Award (2019). Over 20 of his advisees hold faculty positions globally. Education: PhD (1991), MS (1988) in Electrical Engineering from UIUC; B.Tech (1985) from IIT Madras. He has taught advanced courses on optimization, stochastic systems, and game theory. His work bridges theory and practice, with contributions to congestion control, cloud computing, and reinforcement learning. Current projects include AI applications for pandemic response and digital transformation initiatives. Research highlights include foundational work on Lyapunov drift methods for network stability and distributed algorithms. He serves as Area Editor for Mathematics of Operations Research and has led editorial roles for IEEE/ACM Transactions on Networking. His lab collaborates with industry leaders like Microsoft and C3.ai, leveraging supercomputing resources for societal impact.
Ruonan Xu is an Assistant Professor in the Department of Economics at Rutgers University, specializing in Econometrics. She joined the department in Fall 2020. Her research focuses on finite population inference, spatial correlation, and causal inference methodologies. Education: Ph.D. in Economics, Michigan State University, 2020 B.A. in Mathematical Economics, Fudan University, 2015 Research Interests: Dr. Xu’s work emphasizes econometric methodologies for addressing complex data structures, including spatial correlation, clustered data, and interference effects. She has contributed to instrumental variable estimation with binary endogenous variables and developed design-based approaches for spatial analysis. Her recent focus includes robustness considerations in econometric models and multidimensional clustering techniques. Publications & Work in Progress: Her published work includes studies in The Econometrics Journal and Economics Letters . Current projects explore distributionally robust average treatment effects and difference-in-differences with interference mechanisms. A working paper on multidimensional clustering has been submitted to the Journal of Econometrics . Advising & Grants: No formal advisees or grants explicitly listed in the provided materials.
Kumar Vaibhav Srivastava is a Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur (IIT Kanpur). His research focuses on electromagnetic theory, meta-materials, and antenna design, with significant contributions to the field of computational electromagnetics and microwave engineering. Dr. Srivastava received his B.Tech from Kamla Nehru Institute of Technology, Sultanpur U.P. in 2002, followed by an M.Tech from IIT Kanpur in 2004. He completed his PhD at IIT Kanpur in 2008 with a thesis titled "Studies on Inhomogeneous Dielectric Resonators for Improved Mode Separation in MIC Environment using Efficient FDTD Algorithm and its Application to Band-pass Filter" under the supervision of Prof. Animesh Biswas. His research interests span a wide range of topics in electromagnetics, with particular expertise in Meta-Materials , Microwave Antennas , Microwave Absorbers , Microwave Cloaking , and the Finite-Difference Time-Domain (FD-TD) Technique . Dr. Srivastava's work has significant applications in wireless communications, radar systems, and electromagnetic compatibility. His innovative approaches to meta-material absorbers and antenna design have garnered attention in both academic and industrial circles. Dr. Srivastava's recent publications demonstrate a strong focus on advancing meta-material absorber technology and antenna design. His work shows consistent innovation in bandwidth enhancement techniques, polarization independence, and compact antenna designs for ultra-wideband applications. His research bridges theoretical electromagnetic principles with practical engineering solutions. Cadence Gold Medal-2005 for Best M.Tech. Thesis Best Teaching Assistantship Award (2005-06) Appreciation Letter from GE Global Research Centre Young Research Fellowship (Class of 1979) Best Paper Award at ATMS Conference (2013) IEI Young Engineer Award 2014 Dr. Srivastava has supervised numerous research projects and students in the field of electromagnetics and antenna design. His work with industry partners, including GE Global Research Centre, demonstrates his ability to translate theoretical research into practical applications. He maintains active collaborations with researchers both within IIT Kanpur and internationally. Based in the Advanced Centre for Electronic Systems (ACES) at IIT Kanpur, Dr. Srivastava works within a vibrant research ecosystem that supports cutting-edge work in electronic systems and electromagnetic applications. His laboratory focuses on computational electromagnetic simulations and experimental validation of novel meta-material structures and antenna designs.
Kathleen C. Howell is the Hsu Lo Distinguished Professor of Aeronautics and Astronautics at Purdue University's School of Aeronautics and Astronautics. Her expertise spans orbit mechanics, spacecraft trajectory optimization, and mission design in multi-body systems. She holds degrees from Iowa State University (B.S., 1973), Stanford University (M.S., 1977; Ph.D., 1983). B.S. in Aerospace Engineering, Iowa State University, 1973 M.S. in Aeronautical & Astronautical Engineering, Stanford University, 1977 Ph.D. in Aeronautical & Astronautical Sciences, Stanford University, 1983 Her research focuses on libration point orbits, solar sail trajectories, and trajectory optimization in Earth-Moon and interplanetary systems. She has pioneered methods for analyzing Lissajous trajectories, invariant manifolds, and low-thrust mission design. Recent work includes solar sail applications for lunar coverage and ARTEMIS mission trajectory analysis. Publications highlight innovations in multi-body dynamics, with contributions to journals like Acta Astronautica , Journal of Guidance, Control, and Dynamics , and AIAA/AAS Conference Proceedings . Her work emphasizes practical mission design tools and visualization techniques. Awards: Fellow, AIAA (2013) W.A. Gustafson Teaching Award (2012) Dirk Brouwer Award (2004) Presidential Young Investigator Award (1984) Multiple Elmer F. Bruhn Teaching Awards Her advising and grants include leadership in space mission design, formation flight, and solar sail technology. She has collaborated on projects like the TRIANA mission and contributed to the Cassini end-of-mission analysis. Active in professional societies, she has edited conference proceedings and delivered invited lectures globally.
Dr. Jean-Christophe Nave is an Associate Professor in the Department of Mathematics and Statistics at McGill University. He holds a PhD from the University of California, Santa Barbara (2004), under advisors Xu-Dong Liu and Sanjoy Banerjee. Prior to McGill, he served as a Lecturer and Instructor at MIT's Mathematics Department (2005-2010). His research focuses on numerical analysis, partial differential equations, fluid mechanics, and computational methods for interface problems. He has led research groups involving postdocs, PhD, and undergraduate students, collaborating on projects like the Correction Function Method for PDEs and the Characteristic Mapping Method for advection problems. Education: Ph.D. in Applied Mathematics from UCSB (2004). Affiliations include the Institut des Sciences Mathematiques Steering Committee, Centre de Recherches Mathematiques Applied Math Lab, and CNRS-UMI. Active in teaching courses like Numerical Analysis I/II and Non-Linear Dynamics at McGill, with sabbatical periods noted in recent years. Research interests span numerical methods for PDEs, fluid-structure interaction, and multi-phase flows. His work integrates computational geometry and invariant numerical techniques, addressing challenges in complex fluid dynamics and interface-driven phenomena. Over 40 peer-reviewed publications and continuous contributions to the field of computational applied mathematics. Scientific advising includes over 20 graduate and undergraduate students, with notable alumni now in academia and industry. Collaborations include projects on volcano dynamics, fiber drawing instabilities, and concentrated solar power systems. His methods have advanced numerical simulations for engineering and physical systems involving discontinuous coefficients and sharp interfaces.
Dr. Arno Berger is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. He holds a Dipl.Ing. (ME) and Dipl.Ing. (MSc) in Mechanical Engineering and Applied Mathematics from TU Wien (Vienna University of Technology), followed by a Dr. techn (PhD) and Habilitation in Applied Mathematics from the same institution. His research focuses on dynamical systems, ergodic theory, Benford's Law, nonautonomous dynamics, bifurcation theory, applied probability, and dimensional analysis. He has held visiting positions at prestigious institutions including Georgia Tech, University of Warwick, Goethe University Frankfurt, and University of Canterbury. His recent work includes studies on Saint-Venant-Polya inequalities, planar curves with position-dependent curvature, and distributions of logarithmic functions. He co-authored the seminal book An Introduction to Benford's Law (2015), and maintains the Benford Online Bibliography. His teaching spans courses like Differential Equations and Real Variables. Dr. Berger’s research has explored Benford’s Law in diverse contexts, from stochastic processes to finite-time dynamics. His articles often bridge theoretical insights with practical applications, emphasizing the ubiquity of Benford’s Law in mathematical systems.
Hans Bihs is a Professor in the Department of Civil and Environmental Engineering, Faculty of Engineering. His research focuses on computational fluid dynamics (CFD), wave hydrodynamics, and wave-structure interaction using the open-source framework REEF3D. Key Research Areas: CFD simulations, wave modeling, floating body dynamics, ocean wave energy, aquaculture hydrodynamics, sediment transport, and high-performance computing. Projects: ERC Consolidator Grant PARTRES (2023-2028), EEA Grants Portugal SurfWave (2023), NFR KPN IPIRIS (2021-2025), EEA Baltic SolidShore (2021-2024), NTNU's MAPLE (2022-2025), and DigiCoast (2021-2024). Email: hans.bihs@ntnu.no His recent publications (2025-2020) analyze fluid-structure interaction, ship-induced waves, floating offshore wind turbines, submerged vegetation, and coastal structures using advanced CFD techniques. Topics include wave hydrodynamics, turbulence, and numerical modeling for marine and aquaculture systems.
Bernardo Cockburn is a Distinguished McKnight University Professor in the School of Mathematics at the University of Minnesota. He has been a faculty member since 1987, progressing from Assistant Professor to Associate Professor in 1992, and achieving full Professor status in 1997. He also held positions as an Affiliate Professor at the University of Delaware (2019-2020) and Chair Professor of Mathematics at King Fahd University of Petroleum and Minerals in Saudi Arabia (2012-2014). Education: Ph.D. from University of Chicago (1986), Doctorat de 3eme Cycle from University of Paris VI/INRIA (1983), Masters and Licenciatura from Universidad Nacional de Ingenieria in Lima, Peru Research Focus: Numerical methods for partial differential equations, particularly discontinuous Galerkin methods Cockburn's research primarily centers on the devising and analysis of efficient methods for numerically solving linear and nonlinear partial differential equations . His most significant contribution has been in the development and analysis of discontinuous Galerkin methods , particularly the hybridizable discontinuous Galerkin (HDG) methods which he pioneered. His work spans error estimation for hyperbolic problems, continuous dependence for Hamilton-Jacobi equations, and numerous applications across fluid dynamics, structural mechanics, and electromagnetics. He has developed theoretical frameworks for superconvergence properties and created practical algorithms for a wide range of engineering applications. Analysis of his recent publications reveals a strong focus on hybridizable discontinuous Galerkin methods , with significant contributions to superconvergence theory, error estimation, and applications to diverse physical problems including Stokes flow, linear elasticity, Timoshenko beams, and convection-diffusion problems. His work demonstrates a clear trajectory from theoretical foundations to practical implementation, with increasing emphasis on curved domains, adaptive methods, and coupling techniques between different numerical approaches. Doctor Honoris Causa from Universidad Nacional de Ingenieria, Lima, Peru (2013) Invited Speaker at the International Congress of Mathematicians, Numerical Analysis Section (2010) Distinguished McKnight University Professor, University of Minnesota (2007) Cockburn has supervised an impressive 23 PhD students throughout his career, many of whom have gone on to become professors at major universities worldwide including the University of Puerto Rico, Purdue University, and University of Concepcion in Chile. His advisees have produced significant research in discontinuous Galerkin methods, particularly in applications to structural mechanics, fluid dynamics, and Hamilton-Jacobi equations. His research has been supported by numerous grants from the National Science Foundation and other funding agencies, enabling extensive collaboration with researchers across the United States and internationally. Cockburn leads a vibrant research group focused on computational mathematics, with particular emphasis on developing and analyzing discontinuous Galerkin methods. His work has fostered significant collaboration between mathematicians and engineers, with applications spanning aerospace, civil engineering, and materials science. The research group maintains strong connections with institutions worldwide, including regular collaborations with researchers in Peru, Chile, and Europe, reflecting Cockburn's international background and influence.
Endre Süli is a Professor of Numerical Analysis at the University of Oxford, affiliated with Worcester College and Linacre College. He has held various academic roles since 1985, including Fellowships and Tutorships in Mathematics. University Education: B.Sc. in Mathematics, University of Belgrade (1974-1978) M.Sc. in Mathematics, University of Belgrade (1978-1980) Ph.D. in Mathematics, University of Belgrade (1985) M.A., University of Oxford (1985) British Council Visiting Student, Reading University and University of Oxford (1983/84) Süli's research focuses on numerical methods for partial differential equations (PDEs), with expertise in finite element methods, adaptive algorithms, error control, and computational modeling of fractures and non-Newtonian fluids. His work bridges mathematical theory and practical applications in fluid dynamics and material science. His recent publications emphasize finite element approximations, nonlinear PDEs, and stochastic models for polymer dynamics. Themes include multiscale methods, tensor-sparsity for high-dimensional problems, and compressible flow simulations. Scientific Awards: Fellow of the Royal Society (2021) London Mathematical Society Naylor Prize and Lectureship (2021) Pro Urbe Prize, City of Subotica (2021) SIAM Fellow (2016) Member, Academia Europaea (2020) Foreign Member, Serbian National Academy of Sciences and Arts (2009) IMA Service Award (2011) Fellow, European Academy of Sciences (EurASc) (2010) Fellow, Institute of Mathematics and its Applications (2007) London Mathematical Society/New Zealand Mathematical Society Forder Lecturer (2015) Professor Hospitus, Charles University, Prague (2012) Distinguished Visiting Chair Professor, Shanghai Jiao Tong University (2013) Invited Speaker, International Congress of Mathematicians, Madrid (2006) Süli has supervised numerous research projects and held visiting appointments globally. His contributions to numerical analysis span foundational work on error estimation, nonlinear stability, and advanced computational frameworks for complex physical systems.
Martin Huber is Professor of Applied Econometrics and Policy Evaluation at the University of Fribourg, Switzerland, within the Faculty of Management, Economics and Social Sciences, Department of Economics. He leads the Chair of Applied Econometrics and maintains an active research profile with numerous publications in top economics and statistics journals. His work bridges theoretical econometrics with practical policy applications across multiple domains including labor, health, and education economics. Professor Huber earned his Ph.D. in Economics and Finance in 2010 and served as Assistant Professor at the University of St. Gallen until 2014. He has conducted research stays at Harvard University (2011/2012) and the University of Sydney (2014 and 2019), establishing an international research network. His academic affiliations include the Committee for Econometrics of the Verein für Socialpolitik, Global Labor Organization, Soda Labs (Monash Business School), and Centre for European Economic Research (ZEW) Mannheim. Huber's research focuses on data-based causal analysis , machine learning applications in economics , and policy evaluation methods . He specializes in developing and applying statistical and econometric methods for measuring causal effects, with particular emphasis on semi- and nonparametric microeconometrics. His work spans labor economics (gender occupational segregation, maternal labor supply), health economics, education policy, and competition policy (bid-rigging cartels detection). His recent publications (2023-2025) demonstrate a clear trajectory toward integrating machine learning techniques with traditional econometric methods for causal inference. This includes developing frameworks for causal discovery, improving difference-in-differences methods with machine learning, and creating novel approaches for detecting collusion in markets. His 2023 book "Causal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R" (MIT Press) has become a key reference in the field. As an active researcher, Professor Huber directs several research projects including experimental evaluations of gender occupational segregation in the Swiss apprenticeship market. His work combines theoretical rigor with practical policy relevance, often employing experimental and quasi-experimental methods to address questions of causal mechanisms in social and economic phenomena. Through his Chair of Applied Econometrics, Huber supervises Ph.D. students and maintains an active research group focused on advancing causal inference methodologies. His work has significant implications for evidence-based policymaking across multiple sectors, particularly in evaluating the effectiveness of social programs and economic policies.
Xiaoping Lu is an Associate Professor at the School of Mathematics and Applied Statistics, University of Wollongong, Australia. She has served as Academic Program Director for the Bachelor of Mathematics (Advanced) program since 2008 and holds an ORCID identifier (0000-0003-1090-8437). Her research focuses on applied mathematics and financial mathematics, particularly in option pricing, stochastic volatility models, and computational finance. Research Themes: Transaction cost modeling, regime-switching financial markets, numerical methods for PDEs, utility-indifference valuation, and stochastic optimization algorithms. Awards: 2024 AustMS-WIMSIG Anne Penfold Street Award 2024 Cheryl E. Praeger Travel Award Leadership: President of the Asia Pacific Consortium of Mathematics for Industry (APCMfI) since 2024; leadership roles in ANZIAM and WIMSIG committees. Teaching: Coordinated courses like MATH142, MATH141, and MATH283; currently available for PhD supervision in topics including financial derivatives and stochastic liquidity risk. Funding: Contributed to grants like 'The AI Tutor' (2024) and industry partnerships for advanced mathematics education.
Jake M. Yang is a Lecturer in Physical Chemistry at the School of Chemistry, University of Leicester, where he leads an interdisciplinary research group focused on electrochemistry and sustainable material processing. He holds a DPhil and MChem from the University of Oxford and was awarded an EPSRC Doctoral Prize in 2020 for developing electrochemical sensors to monitor oceanic 'blue carbon'. His research integrates operando electrochemistry with spectroscopic and fluorescent imaging to investigate chemical reactions at electrode interfaces and their environmental applications. He is particularly known for pioneering green recycling methods for lithium-ion batteries and fuel cell membranes. Electroanalysis and Sensor Instrumentation Operando opto/spectro-electrochemical instrumentation Recycling of Technological Critical Materials Monitoring Microplastics and Ocean Ecosystems Fundamental electrochemistry Finite difference simulations The recent publications highlight a strong trend toward sustainability-driven electrochemistry, with a focus on recycling technologies using ultrasound and vegetable oil nanoemulsions. These works bridge fundamental science with industrial applications, particularly in the circular economy of electronics and energy systems. Award Highlights: EPSRC Doctoral Prize Award RSC Horizon Prize 2024 (Faraday Institute ReLIB project) University of Leicester Chemistry Image of Research Competition, 1st Prize Jake actively mentors students and offers funded PhD opportunities. His work is supported by institutional and industry-aligned grants, particularly in sustainable battery and fuel cell recycling. He collaborates across disciplines, including Earth Sciences and engineering, and promotes knowledge transfer through public engagement and media outreach. He is a key member of the Centre for Sustainable Material Processing and leads research on techno-economic analysis of recycling processes, ensuring scientific innovation meets real-world industrial and environmental needs.
Vincenzo Sciacca is a Full Professor in the Department of Mathematics and Computer Science at the University of Palermo, Italy. His academic position is listed under classification code MATH-04/A, which typically refers to Mathematical Analysis in the Italian academic system. He maintains regular office hours on Thursdays from 3:00 PM to 6:00 PM at the Department of Mathematics and Computer Science, Via Archirafi 34, Office No. 216 (2nd floor). Professor Sciacca's research spans several areas of mathematical physics and fluid dynamics. His primary interests include: Fluid dynamics and vortex theory Partial differential equations, particularly Navier-Stokes and Euler equations Singularity formation in boundary layer theory Numerical analysis of complex fluid systems Mathematical modeling in geophysical fluid dynamics Complex singularity analysis for nonlinear systems Analysis of his recent publications reveals a strong focus on the mathematical aspects of fluid dynamics, with particular attention to singularity formation, vortex dynamics, and the behavior of solutions to fundamental equations in fluid mechanics. His work combines rigorous mathematical analysis with computational approaches to understand complex phenomena in fluid systems. Over the years, his research has evolved from fundamental studies of singularity formation to more applied problems in geophysical fluid dynamics and mathematical biology as evidenced by his 2024 paper on Multiple Sclerosis. Professor Sciacca maintains an active research profile with publications spanning from 1994 to the present, demonstrating sustained scholarly contribution to his fields of expertise. His work shows interdisciplinary reach, connecting pure mathematical analysis with applications in physics, geophysics, and biomedical modeling. He can be contacted at vincenzo.sciacca@unipa.it and maintains a personal web page at http://math.unipa.it/~sciacca/ where additional information about his teaching and research is available.
Dr. Fengyan Li is a Professor in the Department of Mathematical Sciences at Rensselaer Polytechnic Institute (RPI). She holds a PhD in Applied Mathematics from Brown University (2004) and previously held a postdoc at the University of South Carolina. Her research focuses on numerical analysis and scientific computing, particularly discontinuous Galerkin methods for applications in wave propagation, fluid dynamics, plasma physics, and nonlinear optics. She has received prestigious awards including the NSF-CAREER Award (2009) and Alfred P. Sloan Fellowship (2008). Dr. Li serves on editorial boards of journals like SIAM Journal of Numerical Analysis and IMA Journal of Numerical Analysis. Education: PhD in Applied Mathematics (Brown University, 2004); MS & BS in Computational Mathematics (Peking University, 2000 & 1997). Research interests emphasize multi-scale simulations, reduced-order modeling, and high-order methods. Her work addresses challenges in kinetic transport, nonlinear optics, and plasma dynamics. She has delivered plenary talks at major conferences, including ICOSAHOM (2018) and NAHOMCon (2022). Professional service includes leadership roles in the Association for Women in Mathematics (AWM), co-organizing symposiums, and mentoring. She is a 2025 AWM Fellow and advises RPI's AWM Student Chapter.
Dr. Sudhir R. Paul is a Professor in the Department of Mathematics and Statistics at the University of Windsor, Faculty of Science. He holds a Ph.D. from Wales and has received prestigious awards including Fellowships from the American Statistical Association (2006) and the Royal Statistical Society (1982). His research focuses on Biostatistics and Statistical Inference, with expertise in areas such as Generalized Linear Models, Clustered/Longitudinal Data Analysis, and Categorical Data Analysis. He has supervised numerous graduate students and maintains an active research program addressing topics like risk difference estimation, bias correction in statistical models, and applications in environmental and medical contexts. Education: Ph.D. (Wales). Research interests span advanced statistical methodologies, including zero-inflated models, measurement error correction, and dose-response modeling. His work bridges theoretical development and practical applications in epidemiology, clinical trials, and environmental studies. His publications reflect contributions to clustered data analysis, interval estimation, and generalized estimating equations. Awards highlight his impact in advancing statistical science through teaching, research, and service. Advising: Over 30 M.Sc. and Ph.D. students have been supervised, with current students engaged in doctoral and master’s research. Postdoctoral fellows include experts in statistical theory and applications.
