Pierre Baldi is a Distinguished Professor of Computer Science and Director of the Institute for Genomics and Bioinformatics at the University of California, Irvine (UCI). He is affiliated with the Donald Bren School of Information and Computer Sciences. His research spans artificial intelligence, machine learning, bioinformatics, and communication networks, with notable projects in protein structure prediction, gene expression modeling, and neutrino physics collaborations like DUNE. Baldi’s work bridges theoretical foundations (e.g., neural network theory) and applied domains, including medical imaging and fusion technology. Key research interests include AI-driven biomedical applications, neural network theory, and interdisciplinary projects such as the DUNE neutrino experiment. His contributions to neural network engineering were recognized with the 2023 INNS Dennis Gabor Award, highlighting his paradigm-changing impact on computational neuroscience and physics. Baldi’s academic leadership includes directing UCI’s Institute for Genomics and Bioinformatics, fostering collaborations in computational biology and AI. His recent work explores AI’s role in healthcare, climate modeling (e.g., ClimSim-Online), and fundamental physics challenges like neutrino oscillation studies.
Dirk Praetorius is a Professor of Numerics of Partial Differential Equations (PDEs) at the Technische Universität Wien (TU Wien) , affiliated with the Institute for Analysis and Scientific Computing (ASC) within the Faculty of Mathematics and Geoinformation . He leads the research group on Numerics of PDEs and has held various leadership roles, including Institute Director (since 2020) and head of the Numerics research area. His work focuses on numerical methods for PDEs, including Finite Element Methods (FEM), Boundary Element Methods (BEM), adaptive algorithms, and computational micromagnetics. Education and Career: Praetorius earned his Diplom in Mathematics (2000) and PhD in Applied Mathematics (2003) from TU Wien, followed by a Habilitation in Numerical Analysis (2005). He has been a faculty member at TU Wien since 2005, progressing from Assistant Professor to full Professor in 2017. He has also held visiting positions at institutions such as the University of Jyväskylä and RICAM (Linz). Research Interests: His research spans numerical analysis, adaptive FEM/BEM, a-posteriori error estimation, matrix compression, and computational micromagnetics. He has contributed to modeling spin dynamics, magnetic skyrmions, and multiscale systems. His work emphasizes efficient algorithms for large-scale problems and optimal computational complexity. Awards and Editorial Roles: Praetorius received the TU Best Teacher Award (2021) and TU Best Lecture Award (2019). He serves as Senior Editor for Computational Methods in Applied Mathematics (CMAM) and on the editorial board of Applied Numerical Mathematics (APNUM) . He co-founded the outreach initiative TUForMath to promote mathematics education. Grants and Projects: He leads or co-leads several research projects funded by the Austrian Science Fund (FWF), including the collaborative SFB "Taming Complexity in Partial Differential Systems" (2017–2025) and international collaborations with Germany. His work addresses topics like functional error estimates, nonlinear PDEs, and computational design of magnetic devices. Labs and Teams: He contributes to the ASC Institute and coordinates interdisciplinary projects involving computational physics and engineering. His team develops software tools like MooAFEM and Commics for micromagnetic simulations.
Matthias Schlottbom is an Associate Professor specializing in Mathematics of Computational Science, with a focus on numerical methods and their applications in physics, biology, and engineering. His research integrates advanced computational techniques with interdisciplinary problems, including radiative transfer, photonic crystals, and chemotaxis modeling. Research Interests: Schlottbom’s work spans numerical analysis, finite element methods, and machine learning. He develops high-order discretization schemes, iterative solvers for anisotropic transport, and mathematical frameworks for biological network formation. Publications: Recent articles highlight his contributions to accelerating radiative transfer simulations, extending component mode synthesis for Helmholtz equations, and analyzing diffusion limits in kinetic models. His work often bridges computational mathematics with practical applications in photonics and multiscale systems. Collaborations: He actively collaborates on datasets for optical simulations, radiative transfer algorithms, and photonic crystal modeling, contributing to open-access repositories like 4TU.Centre and Zenodo. Activities: Schlottbom has organized workshops such as the Kinetic Theory Workshop in the Netherlands and delivered keynotes on residual minimization and data-driven methods for transport equations. Scientific Awards: No specific awards or fellowships are mentioned in the provided materials. Advising & Grants: Details about students, advising roles, or grant funding are not included in the available data.
Dr. Alexander Paulus serves as a Researcher at the Chair of High-Frequency Engineering within the Department of Electrical Engineering at the Technical University of Munich (TUM), School of Computation, Information and Technology. Working under Prof. Dr.-Ing. Thomas Eibert, he contributes to advanced electromagnetic research and measurement systems development at TUM's Arcisstr. 21 campus in Munich. Research Expertise His core specialization lies in near-field antenna measurement and transformation techniques, with significant contributions to phase retrieval algorithms, inverse source methods, and UAV-based electromagnetic field measurements. He addresses critical challenges including probe correction with unknown antennas, sparse sampling for directive antennas, and electromagnetic modeling of environmental effects like rain attenuation. His work bridges theoretical electromagnetics with practical antenna characterization solutions. Publication Trends From 2014-2025, Paulus has published 25+ papers focusing on near-field to far-field transformations, particularly in phaseless and multi-probe scenarios. Recent work (2023-2025) demonstrates innovation in spectral filtering, sparse reconstruction, and UAV-based systems for defect localization and wet antenna modeling. His research increasingly integrates computational techniques to solve complex inverse problems in antenna measurements. Scientific Recognition No formal awards documented in available information Academic Contributions Student Mentoring: No advisees listed in provided materials Research Funding: Grant details not specified in source text Research Environment Paulus operates within TUM's Chair of High-Frequency Engineering facilities, which include advanced near-field measurement ranges, UAV-based electromagnetic characterization systems, and laboratories for metamaterials research and electromagnetic compatibility testing. His work supports applications in 5G/6G communications, aviation navigation systems, and precision antenna diagnostics.
Yang Zhang is a Visiting Assistant Professor in the Department of Mathematics at the University of California, Irvine (UCI), working under Prof. Katya Krupchyk. His research focuses on inverse problems in imaging sciences, nonlinear hyperbolic equations, and medical imaging applications. He previously held a postdoctoral position at the University of Washington, Seattle, under Prof. Gunther Uhlmann. His work integrates microlocal analysis and partial differential equations to address challenges in wave propagation, nonlinear acoustics, and elasticity. Education & Career: PhD from Purdue University (advisor: Prof. Plamen Stefanov) Postdoc: University of Washington, Seattle (2020–2024) Research Interests: Dr. Zhang's work spans inverse problems for nonlinear hyperbolic equations, acoustic imaging, and integral transforms in medical contexts. He develops novel methodologies using multi-fold linearization, wave interactions, and advanced calculus techniques. His studies on Rayleigh and Stoneley waves in elasticity further demonstrate his expertise in microlocal analysis. Key Contributions: His research bridges theoretical mathematics and applied imaging, with notable publications on inverse scattering, damping effects in wave equations, and Compton camera imaging. He is an active member of the Inverse Problems International Association (IPIA). Grants & Collaborations: Collaborations with prominent figures like Prof. Gunther Uhlmann and Prof. Katya Krupchyk highlight his network in inverse problems. His work often involves both analytical and computational approaches, with applications in medical diagnostics and geophysics.
Qin Li is an Associate Professor in the Mathematics Department at the University of Wisconsin-Madison. She holds affiliations with the Wisconsin Institutes for Discovery and serves as a senior PI at the Institute for Foundations of Data Science. Her research focuses on numerical analysis, scientific computing, and inverse problems, with a strong emphasis on kinetic theory and multiscale PDEs. Her work spans computational methods for inverse transport and radiative transfer equations, Bayesian approaches in optical tomography, and optimization techniques for solving stochastic and deterministic PDEs. Recent publications highlight applications of diffusion models, Wasserstein gradient flow, and random sampling in inverse problems, as well as control theory for Vlasov-Poisson systems and reconstruction of chemotaxis kernels. She leads a research group within the Mathematics Department and has received funding from the National Science Foundation (NSF), the Office of Naval Research (ONR), and the Wisconsin Alumni Research Foundation (WARF). Her lab, Kinetic At Madison, explores nonlinear hyperbolic PDEs and their applications. She also contributes to teaching as a TA Supervisor.
Professor Ali Yapar is a faculty member at Istanbul Technical University in the Electronics and Communication Engineering department. His research focuses on Electromagnetics , Microwave Engineering , and Antenna Technologies , with a particular emphasis on inverse scattering problems and microwave imaging for biomedical applications. He has supervised numerous graduate students and led projects related to breast cancer treatment and rough surface imaging. PhD in Electronics and Communication Engineering from Istanbul Technical University (1997) MSc in Electronics and Communication Engineering (1995) His recent publications analyze advanced techniques for microwave hyperthermia systems, reverse time migration methods, and Newton-based solutions for electromagnetic inverse scattering. Key projects include TUBITAK-funded initiatives on microwave tomography and brain stroke imaging. He serves as a project investigator and executive for electromagnetic research programs. Research areas span Electromagnetic Wave Propagation , Green's Function Applications , and Dielectric Material Analysis . Collaborations include IEEE members and international researchers in computational electromagnetics.
Daniele Venturi is a Professor of Applied Mathematics at the University of California, Santa Cruz, where he has been faculty since 2015, rising from Assistant Professor to full Professor by 2021. Previously, he was a Research Assistant Professor at Brown University from 2010-2015. His academic journey began at the University of Bologna, where he earned both his combined B.S./Sc.M. in Mechanical Engineering (2002) and Ph.D. in Applied Physics with a focus on thermo-fluid dynamics (2006). University of Bologna: B.S./Sc.M. Mechanical Engineering (2002), Ph.D. Applied Physics (2006) Brown University: Research Assistant Professor (2010-2015) UC Santa Cruz: Assistant to Associate to Full Professor (2015-present) Professor Venturi's research spans multiple cutting-edge areas in computational mathematics. His primary interests include stochastic modeling and uncertainty quantification, numerical tensor methods for high-dimensional PDEs, data-driven modeling approaches, approximation of functional-differential equations, and theoretical/computational fluid dynamics. His work bridges theoretical mathematical frameworks with practical computational implementations, particularly focusing on overcoming the curse of dimensionality in complex systems. His recent research has been heavily focused on hierarchical tensor methods for solving high-dimensional partial differential equations. The analysis of his publication record reveals a strong emphasis on developing computational frameworks that address high-dimensional challenges in uncertainty quantification and model reduction. His work frequently intersects machine learning techniques with traditional numerical methods, particularly in developing physics-informed neural networks and multifidelity modeling approaches. A consistent theme across his publications is the development of mathematical frameworks that maintain computational tractability while preserving physical fidelity in complex systems. Professor Venturi has secured substantial research funding from major agencies including the Air Force Office of Scientific Research (AFOSR), Department of Energy (DoE), National Science Foundation (NSF), Army Research Office (ARO), and Defense Advanced Research Projects Agency (DARPA). His most significant current grant is a 2024-2029 AFOSR MURI award totaling $7.5M as co-PI for 'Tensor Network for simulating kinetic systems.' 2024-2029: AFOSR MURI, $7.5M (co-PI) 2023-2027: DoE, $3.8M (co-PI) 2023-2026: AFOSR, $2.5M (co-PI) 2020-2025: NSF TRIPODS, $2.3M (co-PI) At UC Santa Cruz, Venturi teaches a range of courses including Fundamentals of Uncertainty Quantification, Applied Dynamical Systems, Nonlinear Dynamical Systems, and Numerical Methods for Differential Equations. His teaching spans both undergraduate and graduate levels, reflecting his expertise across theoretical and computational mathematics. His lecture notes for these courses are publicly available and demonstrate his commitment to pedagogical excellence in complex mathematical subjects.
Sergey Fomel is a Professor of Geophysics at the University of Texas at Austin, holding the Wallace E. Pratt Professorship and serving as Director of the Texas Consortium for Computational Seismology (TCCS). He is affiliated with the Jackson School of Geosciences, Bureau of Economic Geology, and the Oden Institute for Computational Engineering and Sciences. His research focuses on seismic data analysis, computational seismology, and machine learning applications in geophysics. He leads the Madagascar software project for open-source geophysical data analysis. Dr. Fomel earned his Ph.D. in Geophysics from Stanford University in 2001. He has held leadership roles in the Society of Exploration Geophysicists (SEG), including Vice President, Publications (2017–2019) and Distinguished Lecturer (2020). His awards include honorary memberships in SEG and the Geophysical Society of Houston (GSH). Recent research emphasizes deep learning for seismic inversion, noise reduction, and fault segmentation. His work addresses challenges in geophysical data processing, including adaptive algorithms, wave propagation modeling, and CO2 monitoring. Fomel's contributions span both theoretical and applied domains, bridging computational methods with practical geoscience applications. Education: Ph.D. in Geophysics, Stanford University (2001) Affiliations: Jackson School of Geosciences, Bureau of Economic Geology, Oden Institute Labs/Teams: Texas Consortium for Computational Seismology (TCCS), Madagascar Project
Matti Selg is an Associate Professor at the Institute of Physics within the Faculty of Science and Technology at the University of Tartu, Estonia. He has been actively teaching graduate courses in Quantum Mechanics, Analytical Mechanics, and Mathematical Physics since 2009, with increasing responsibility over the years. Currently, he oversees the entire teaching of three mandatory courses: Master's Course in Quantum Mechanics, Analytical Mechanics, and Theory of Complex Variables. Dr. Selg completed his education at the University of Tartu, earning a diploma in physics. He received his Doctor's Degree in 1981 from the University of Tartu with a dissertation titled "Relaxation and hot luminescence of self-trapped excitons in rare gas crystals," supervised by Vladimir Hižnjakov and Rein Kink. His additional qualification includes a PhD in solid state physics from the Institute of Physics of the Estonian Academy of Sciences. Professor Selg's research spans several interconnected areas of theoretical physics. His primary interests include quantum mechanics, particularly scattering theory and inverse problems, as evidenced by his numerous publications and textbooks on quantum scattering. He has made significant contributions to the understanding of reflectionless potentials and the Marchenko equation. More recently, he has focused on classical mechanics problems, particularly exploring Binet's equation and its connections to Newtonian and Einsteinian gravity theories, as well as revisiting historical problems like Galileo's swiftest descent problem. His work bridges mathematical physics with practical applications in molecular and solid-state physics. Analysis of his recent publications reveals a clear trajectory from quantum scattering theory toward classical mechanics and historical physics problems. While maintaining his expertise in quantum systems, particularly with hydrogen molecules and diatomic systems, he has expanded into historical and mathematical analyses of foundational physics concepts. His 2023-2025 publications show a particular focus on exact solutions to classical mechanics problems and their connections to modern gravitational theory. Dr. Selg has served in several administrative roles, including as a member of the Science Council of the Institute of Physics at the University of Tartu since 2001 and as a member of the Expert Commission for Exact Sciences of the Estonian Science Foundation (2003-2006). He has successfully led research projects funded by the Estonian Science Foundation, including studies on excimers in rare gases and their crystals. As an educator, Professor Selg has developed and taught advanced courses that integrate deep theoretical concepts with practical applications. His textbooks on quantum scattering theory demonstrate his commitment to making complex topics accessible to students. His recent work on the mathematical and physical perspectives of foundational problems suggests an evolving research program that connects historical scientific developments with contemporary theoretical challenges.
Giovanna Turvani is an Associate Professor at the Department of Electronics and Telecommunications (DET) at Politecnico di Torino, with affiliations in both the College of Electronic, Telecommunications and Physics Engineering and the College of Computer, Film, and Mechatronics Engineering. Scientific Branch: IINF-01/A - Electronics ERC Sectors: PE7_4, PE7_11, PE6_1, PE6_14, PE7_3 SDG Goals: Quality Education, Gender Equality, Affordable Energy, Industry Innovation Her research focuses on advanced electronics and quantum technologies, including: Logic-in-memory computing Quantum computing architectures Microwave imaging for medical and agricultural applications CAD tools for emerging nanotechnologies Embedded systems for bee health monitoring IoT solutions for bio-waste valorization Publications show strong expertise in quantum computing, nanocomputing, and microwave imaging, with recent trends emphasizing quantum optimization frameworks, in-memory architectures, and IoT-based agricultural technologies. She supervises PhD students in areas like quantum machine learning algorithms, predictive on-board systems, and quantum hardware design. Collaborations span multiple disciplines, including medical device development and agricultural electronics. Patents include innovations in microwave imaging, racetrack memory logic functions, and in-memory computing devices.
Katya Krupchyk is a Professor in the Department of Mathematics at the University of California, Irvine (UCI). Her research focuses on inverse problems, partial differential equations (PDEs), microlocal analysis, and spectral theory. She holds a position in the Analysis and Partial Differential Equations group at UCI. Her work often involves collaborations with leading institutions and researchers globally, addressing challenges in mathematical physics, geometric inverse problems, and nonlinear analysis. Dr. Krupchyk teaches advanced courses in real analysis, functional analysis, and partial differential equations. She has contributed to editorial boards for journals such as Journal of Spectral Theory , SIAM Journal on Mathematical Analysis , and Inverse Problems and Imaging . Her research spans theoretical and applied aspects of inverse problems, including studies on fractional operators, magnetic Schrödinger equations, and anisotropic media. Recent work emphasizes high-frequency analysis, nonlinear perturbations, and reconstruction algorithms for geometric inverse problems.
Hongyu Liu is a Chair Professor of Applied Mathematics at the Department of Mathematics, City University of Hong Kong. His research spans inverse problems, wave propagation, mathematical materials science, and game theory. He has authored books on spectral theory, inverse scattering, and numerical methods. Education: Not explicitly stated in text but inferred through academic rank and publications. Research Interests include: Inverse Problems and Imaging, Wave Propagation, Analysis and PDEs, Mathematical Materials Science, Scattering Theory, Game Theory, and Mathematical Biology. His work frequently addresses plasmon resonances, cloaking, and scattering theory applications. Publications focus on inverse scattering, mean field games, and elastic systems, with contributions to journals like Inverse Problems and SIAM Journal on Applied Mathematics. He is also an editor for Communications on Analysis and Computation . Advising and grants are not detailed here, but his research group's activities imply significant contributions to collaborative projects.
Dr. Wan Renjie is an Assistant Professor in the Department of Computer Science at the Faculty of Science, Hong Kong Baptist University (HKBU). He holds a BEng in Network Engineering from the University of Electronic Science and Technology of China and a PhD from Nanyang Technological University (NTU), Singapore. Prior to joining HKBU, he was a Wallenberg-NTU Presidential Postdoctoral Fellow (2020–2022) and a guest researcher at Peking University (2019–2020). His research focuses on computational photography, 3D vision, AI security, digital watermarking, and neural representations . He explores robustness and security in vision models, especially concerning NeRFs and 3D Gaussian Splatting, and develops methods for low-light enhancement, reflection removal, and domain adaptation. Dr. Wan has published in top-tier venues including TPAMI, IJCV, CVPR, ICCV, NeurIPS, AAAI, and ECCV . His recent work emphasizes copyright protection for neural 3D models , adversarial attacks in multimodal and event-based systems, and medical image reconstruction. He is actively mentoring PhD students and research assistants. VCIP 2020 Best Paper Award Outstanding Reviewer, ICCV 2019 He teaches courses such as Introduction to AI and ML (COMP3057) , AI Application Development (COMP3065) , and Python for Data Analysis and Machine Intelligence (COMP7035) . Dr. Wan leads a dynamic research group with ongoing projects on watermarking, 3D reconstruction, and AI security, and he is currently recruiting new PhD students and research assistants.
Peter Miller is a Professor in the Department of Mathematics at the University of Michigan, Ann Arbor. He holds a Ph.D. in Applied Mathematics from the University of Arizona (1994) and has held academic positions at institutions such as Monash University (Australia) and SAMSI (USA). His research focuses on applied analysis, integrable systems, nonlinear waves, and asymptotic methods. Key areas include the study of Painlevé equations, dispersive shock waves, and Riemann-Hilbert problems. Miller has led the Applied and Interdisciplinary Mathematics graduate program and is an active mentor, overseeing numerous Ph.D. students and postdoctoral researchers. Education: Ph.D., University of Arizona (1994); M.S., University of Arizona (1991); B.S. (Summa Cum Laude), Southern Methodist University (Mathematical Sciences and Computer Science, 1989). Research interests span asymptotics of nonlinear waves, random matrix theory, and special functions. His work emphasizes multiscale phenomena and modulation theory in integrable systems. Notable contributions include studies on the semiclassical limit of the nonlinear Schrödinger equation and universality in dispersive hydrodynamics. Awards include NSF grants (2022, 2018, 2015), a Simons Fellowship (2013), and a Sloan Fellowship (2002). He has delivered plenary lectures at international conferences and authored the textbook Applied Asymptotic Analysis (AMS, 2006). Teaching responsibilities include graduate courses in applied analysis, integrable systems, and partial differential equations. He has coordinated large undergraduate programs and mentored over 30 students and postdocs. Miller serves on editorial boards for journals like Journal of Nonlinear Science and organizes conferences on dispersive equations and inverse scattering.
