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Aldo Mozzanica is a Researcher at the Paul Scherrer Institute (PSI) in Switzerland, affiliated with the Laboratory for X-ray Nanoscience and Technologies. He holds a degree in Physics from Insubria University and a Ph.D. from the University of Milan, where his doctoral work focused on scintillating fiber vertex detectors for CERN's Antiproton Decelerator facility. At PSI, he leads detector development projects for synchrotron and free-electron laser applications. His research centers on advancing X-ray detector technology, including: Developing next-generation integrating pixel/strip detectors (JUNGFRAU, GOTTHARD) Improving frame rates, noise performance, and radiation hardness Exploring novel detector concepts for XFEL/synchrotron applications Enabling new experimental capabilities in structural biology and materials science Mozzanica's 135+ publications focus on X-ray detector innovation, with recent work emphasizing: Hybrid pixel detector optimization for 4th-generation light sources On-chip digitization and charge transport modeling High-speed data acquisition systems Applications in crystallography, spectroscopy, and phase-contrast imaging As principal developer of the JUNGFRAU detector, he oversees: ASIC design, testing, and characterization Readout electronics and firmware development Module production and supply chain management Commissioning at SwissFEL endstations

• X-ray Detectors
• Synchrotron Technology
• Free Electron Lasers
• +2 more

Richard Szeliski is a Distinguished Scientist at Google DeepMind and Affiliate Professor at the University of Washington's Department of Computer Science & Engineering. He previously led the Interactive Visual Media Group at Microsoft Research and founded the Computational Photography group at Facebook. His research focuses on computer vision, computer graphics, and numerical methods, with specialties in 3D modeling from imagery, computational photography, and neural rendering. Education details are not explicitly listed, but his career trajectory indicates advanced academic training in computer science. Research interests include algorithms for 3D reconstruction, image stitching, and optimization techniques. His recent work emphasizes neural rendering, volumetric representations, and large-scale scene modeling. Key contributions include the widely cited textbook Computer Vision: Algorithms and Applications and foundational papers on multi-view stereo, panorama stitching, and energy minimization in MRFs. Publications span 40+ years, with recent focus on radiance fields (NeRF), 3D scene understanding, and real-time rendering systems. Though no explicit awards are listed, his textbook adoption and industry roles reflect significant academic and industrial impact. He advises through his academic role and has contributed to open-source projects like the Bundle Adjustment Library (BAL). Labs/teams include collaborations with Google DeepMind, prior work at Microsoft Research's Interactive Visual Media Group, and academic partnerships at UW's Graphics & Imaging Lab. His work bridges theory and application, addressing challenges in both academic research and industrial-scale systems.

• Computer Vision
• Computer Graphics
• Numerical Methods
• +8 more

Stefan Vandewalle is a full professor at the Department of Computer Science, Faculty of Engineering Sciences, KU Leuven. His research focuses on numerical analysis, applied mathematics, and computational methods for stochastic differential equations, wind energy modeling, and uncertainty quantification. Department Chair, KU Leuven Member, Subdivision Numerical Analysis and Applied Mathematics Member, iSi Health Institute Observer, Faculty Council of Sciences Chair, Department Council for Computer Science His recent work explores multiscale modeling, Monte Carlo methods, and data assimilation techniques. Projects include micro-macro Parareal algorithms, wind turbine aeroelasticity, and turbulent flow reconstruction for wind farms. He supervises PhD candidates and collaborates on interdisciplinary studies involving structural mechanics and renewable energy systems. Publications highlight advancements in parallel-in-time methods, stochastic optimization for tokamak reactors, and DNS-based control of turbulent flows. Key keywords: Multiscale numerical methods Uncertainty quantification Wind energy simulation Monte Carlo algorithms PDE-constrained optimization Stochastic differential equations He contributes to academic governance as a member of extended faculty boards and evaluation committees.

• Numerical Analysis
• Applied Mathematics
• Computational Fluid Dynamics
• +5 more

Friedrich Prinz is the Leonardo Professor in the School of Engineering at Stanford University, holding dual professorships in Mechanical Engineering and Materials Science and Engineering. He is also a Senior Fellow at the Precourt Institute for Energy and an affiliate of the Stanford Woods Institute for the Environment. Additionally, he directs the Nanoscale Prototyping Laboratory and co-directs the NPL-Affiliate Program. He earned a PhD in Physics from the University of Vienna (1975) and joined Stanford in 1994 after faculty roles at Carnegie Mellon University. His research focuses on nanoscale energy conversion and storage, employing advanced fabrication techniques like atomic layer deposition (ALD) to develop prototype fuel cells and capacitors. His lab investigates material structures using scanning tunneling microscopy, impedance spectroscopy, and atomic-scale modeling. Prinz has authored over 360 publications and advised numerous students. Notable awards include AAAS Fellowship (2007) and the AM Strickland Prize (2005). His work bridges solid-state physics, materials engineering, and renewable energy technologies. He teaches courses in manufacturing processes, quantum field theory applications, and advanced material science. Current roles include supervising doctoral and postdoctoral researchers in energy-related nanotechnologies.

• Energy Conversion
• Nanotechnology
• Materials Science
• +3 more

John Wawrzynek is a Professor of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He is affiliated with the Department of Electrical Engineering and Computer Sciences in the College of Engineering and serves as Co-Director of the Berkeley Wireless Research Center and Co-PI of the CONIX Research Center, one of the six centers in the Joint University Microelectronics Program sponsored by DARPA. Dr. Wawrzynek received his B.S. in Electrical Engineering from SUNY, Buffalo (1977), M.S. in EE from the University of Illinois, Urbana/Champaign (1979), and Ph.D. in Computer Science from Caltech (1987). Before joining the Berkeley faculty in 1988, he worked as a consultant at Schlumberger Palo Alto Research. His research focuses on Computer Architecture, Reconfigurable Computing, Wireless Systems, and Integrated Circuit and System Design . His work spans both theoretical foundations and practical implementations, with particular emphasis on FPGA-based computing systems, reconfigurable architectures, and wireless communication systems. His research group has made significant contributions to the field of reconfigurable computing, including the development of the Garp architecture and various tools for reconfigurable computing systems. Analysis of his recent publications (2022-2025) reveals continued focus on reconfigurable computing, FPGA design, wireless networking, and formal methods for hardware verification. His work shows an evolution from traditional computer architecture towards specialized hardware acceleration, machine learning for EDA, and wireless systems research, with particular emphasis on SAT sampling, differentiable computing, and efficient FPGA implementation of neural networks. DAC's Most Influential Paper Award (2025) NSF Presidential Young Investigator (PYI) (1989) Charles Lee Powell Fellowship (1985) NASA Certificate of Recognition (1983) Rensselaer Engineering and Science Medal (1975) Professor Wawrzynek has advised numerous graduate students throughout his career, many of whom have gone on to prominent positions in both industry and academia including Google, Xilinx, and MIT Lincoln Laboratory. His research has been supported by various grants from NSF, DARPA, and industry partners. He leads the Berkeley Wireless Research Center, which focuses on next-generation wireless communication systems and technologies, and is actively involved in the CONIX Research Center which explores connected intelligence at the network's edge.

• Computer Architecture
• Reconfigurable Computing
• Wireless Systems
• +3 more

Yen-Chi Chen is an Associate Professor in the Department of Statistics at the University of Washington. He also holds positions as a Data Science Fellow at the UW eScience Institute and as a co-investigator and statistician at the National Alzheimer's Coordinating Center. His academic career spans multiple interdisciplinary fields including statistics, data science, and astrostatistics. Chen's educational background includes a Ph.D. from Carnegie Mellon University, where he received prestigious awards including the Umesh K. Gavasakar Thesis Award (2017) and the William S. Dietrich II Presidential Ph.D. Fellowship Award (2015). His research focuses on nonparametric statistics, topological data analysis, missing data methodologies, cluster analysis, manifold learning, and applications in large-scale structure analysis and astrostatistics. Chen has made significant contributions to the development of statistical methods for analyzing cosmic web structures, GPS data, and causal inference with continuous treatments. His work bridges theoretical statistics with practical applications in astronomy, neuroscience, and public health. Analysis of his recent publications reveals a strong emphasis on developing novel statistical frameworks for complex data structures, particularly focusing on density-based methods, manifold learning, and approaches that address challenges in missing data and causal inference without standard assumptions. ASA Noether Early Career Scholar Award, American Statistical Association (2022) CAREER Award, National Science Foundation (2022-2027) Umesh K. Gavasakar Thesis Award, Carnegie Mellon University (2017) William S. Dietrich II Presidential Ph.D. Fellowship Award, Carnegie Mellon University (2015) Chen has advised numerous graduate students across multiple publications, with a focus on developing new statistical methodologies. His research has been supported by major funding agencies including the National Science Foundation and the National Institutes of Health. He is actively involved in several research groups including the UW Geometric Data Analysis Group, the UW Center for Statistics and the Social Sciences, and the National Alzheimer's Coordinating Center.

• Nonparametric statistics
• Topological data analysis
• Missing data
• +7 more

Dr Peter Horak is an Associate Professor at the Optoelectronics Research Centre (ORC) of the University of Southampton. With expertise in theoretical and computational photonics, his research spans nonlinear optics, quantum technology, and fiber optics. He obtained his MSc in Theoretical Physics (1993) and PhD in Theoretical Quantum Optics (1997) from the University of Innsbruck, followed by postdoctoral positions in Paris and Glasgow before joining the ORC in 2001. Current roles: ORC Exam Officer Admissions Tutor, EPSRC CDT in Quantum Technology Engineering Dr Horak leads the Computational Nonlinear Optics group, focusing on theoretical and numerical modeling of photonics systems across scales—from single photons to gigawatt laser pulses. His work addresses fundamental physics and device optimization in hollow-core fibers, quantum devices, and high-harmonic generation. Recent publications highlight hollow-core fiber gas dynamics, nonlinear optical sensing, and microresonator alignment challenges. Collaborations include the UK National Quantum Technology Programme and EPSRC-funded projects like QCI3 and HiPPo. External roles: Editorial Board, Physical Review A (2017–2022) Peer reviewer for EPSRC (since 2012) and ESF (since 2017) He supervises PhD students in nonlinear and quantum optics, and teaches optical fiber and computational modeling topics.

• Nonlinear Optics
• Quantum Optics
• Photonics
• +4 more

Bruce MacDowell Maggs is a Professor in the Department of Computer Science at Duke University and serves as Vice President of Research at Akamai Technologies. His career bridges academic research and industrial innovation in computer science, particularly in distributed systems and networking. Research Interests: His work spans computer networks , distributed systems , parallel algorithms , content delivery , and fault-tolerant computing . He has made significant contributions to network routing, load balancing, scalability of web applications, and energy efficiency in large-scale systems. His research often combines theoretical rigor with practical system design. The recent publications highlight a consistent focus on scalability , network performance , and security in internet-scale applications. Key themes include query caching , traffic modeling , resilient routing , and energy optimization , reflecting his deep involvement in the infrastructure of modern web services. No scientific awards are mentioned in the provided text. Advising and Teaching: He has advised numerous Ph.D. students, many of whom are now faculty or researchers at top institutions. His former students include Ramesh Sitaraman, Anja Feldmann, and Andrea Richa. He currently advises Anat Talmy at Duke. He has taught a wide range of courses at Duke, Carnegie Mellon, and MIT, including Computer Networks, Operating Systems, Algorithms, and Discrete Mathematics. Labs and Teams: While not explicitly named, his research is closely tied to systems and networking groups at Duke and his industrial work at Akamai, a leader in content delivery networks. His collaborations with Tom Leighton and others at Akamai suggest leadership in research teams developing foundational internet technologies.

• Computer Networks
• Distributed Systems
• Algorithms
• +7 more

Xiaowen Zhang is a Professor of Computer Science at the College of Staten Island (CSI), City University of New York (CUNY), and a Doctoral Faculty Member at the CUNY Graduate Center. His academic work bridges theoretical and applied research in cybersecurity, information systems, and network technologies. Dr. Zhang holds a Ph.D. in Computer Science from the CUNY Graduate Center (2007) and a Ph.D. in Electrical Engineering from Northern Jiaotong University (1999), along with an M.A. from CUNY Queens College, an M.S. from Northern Jiaotong University, and a B.S. from Shanxi University. His research focuses on Cryptography, Information Security, Cybersecurity, Secure Biometrics, RFID Security & Privacy, Information Retrieval, and Wireless Sensor Networks . He explores both foundational cryptographic methods—such as secret sharing schemes and hash functions—and their practical implementations in secure systems, including RFID authentication protocols and data visualization platforms for sensor networks. The analysis of his recent publications reveals a consistent focus on security mechanisms in distributed and wireless environments . His work frequently combines cryptographic theory with system-level implementations, particularly in RFID and sensor networks. There is a strong trend toward privacy-preserving protocols, efficient data retrieval, and secure information sharing , often leveraging mathematical structures like Latin squares and Bloom filters. Dr. Zhang has been actively involved in mentoring students, as evidenced by numerous co-authored publications with graduate and undergraduate researchers. His contributions span journals such as Security and Communication Networks , Journal of Applied Security Research , and International Journal of Security and Networks , as well as major conferences including IEEE LISAT, ACM CODASPY, and IEEE Sarnoff Symposium.

• Cryptography
• Information Security
• Cybersecurity
• +5 more

Filippo Maria Bianchi is an Associate Professor in the Department of Mathematics and Statistics at UiT The Arctic University of Norway, where he conducts research at the intersection of machine learning, dynamical systems, and complex networks. He is also a Senior Researcher at NORCE Norwegian Research Centre and actively contributes to the IEEE Task Force on Learning for Structured Data and the ELLIS Society. Department: Department of Mathematics and Statistics School: Faculty of Science and Technology University: UiT The Arctic University of Norway Adjunct Position: Senior Researcher, NORCE Education: Bachelor’s in Computer Engineering, Sapienza University of Rome Master’s in Artificial Intelligence & Robotics, Sapienza University of Rome (cum laude, 2012) PhD in Machine Learning, Sapienza University of Rome His research focuses on graph machine learning, time series analysis, reservoir computing, and probabilistic forecasting , with applications in energy analytics and remote sensing. He has led and contributed to numerous projects involving Arctic power grids, satellite-based environmental monitoring, and deep learning for sustainability. The recent publications reflect a strong trend in graph neural networks —particularly pooling mechanisms, spatiotemporal modeling, and explainability—alongside applications in energy forecasting, avalanche detection, and remote sensing . His work combines theoretical innovation with real-world impact, especially in Arctic and remote environments. Scientific Affiliations and Leadership: Vice-Chair, IEEE Task Force on Learning for Structured Data Member, ELLIS Society Co-founder, Northernmost Graph Machine Learning group Member, IEEE Task Force on Reservoir Computing Visiting Professor, Politecnico di Milano (2024–2025) He actively mentors students and collaborates on interdisciplinary research. He has led projects in power grid reliability, solar fault detection, and unsupervised change detection in satellite imagery . His work is supported by open-source implementations and reproducible research practices. Laboratories and Research Groups: Northernmost Graph Machine Learning group (co-founder) ARC Research Group, UiT Graph Machine Learning Group, Lugano

• Machine Learning
• Graph Neural Networks
• Time Series Analysis
• +7 more

Sanjeev Baskiyar is a Professor in the Department of Computer Science and Software Engineering at Auburn University's College of Engineering. He has been actively involved in research, teaching, and academic leadership, with a strong focus on computer systems, real-time and embedded computing, scheduling, cloud and fog computing, and energy-aware architectures. Education: Ph.D., Electrical and Computer Engineering, University of Minnesota M.S., Electrical and Computer Engineering, University of Minnesota B.S., Electronics and Communications, Indian Institute of Science, Bangalore B.S., Physics (with honors), and distinction in Mathematics Dr. Baskiyar’s research interests span scheduling, real-time and embedded systems, computer architecture, fog/cloud computing, thermal/energy-aware computing, and STEM education. His recent work explores machine learning applications in scheduling and quantum computing for fake news detection. He has supervised over 25 graduate students, many of whom now hold academic and industry positions. His recent publications emphasize fog computing simulation, service placement, quantum-inspired fake news detection, and adaptive scheduling using machine learning. These works reflect a trend towards intelligent, scalable, and energy-efficient computing systems, particularly in distributed and edge environments. Scientific Awards and Honors: Walker Teaching Excellence Award, Auburn University, 2020 Summer Faculty Fellow, Air Force Research Labs, 2020 Nominated Best Teaching Assistant, University of Minnesota, 1992 Multiples Merit and State-merit Scholarships Honors in Physics and Distinction in Mathematics Dr. Baskiyar has successfully advised numerous MS and PhD students and secured over $2 million in research funding as Principal Investigator from the National Science Foundation, DARPA, NASA, and industry partners like Wind River Systems and Mentor Graphics. His grants focus on parallel computing education, real-time micro-architectures, and embedded systems. He has also served on editorial boards, program committees, and as a reviewer for NSF and IEEE journals. He has held leadership roles including Senator in the University Faculty Senate and Chair of the E-day Committee. Labs and Research Groups: While not explicitly named, Dr. Baskiyar leads a research group focused on computer systems, scheduling, and embedded computing, as evidenced by his long list of graduate student supervision and funded projects in fog, cloud, and real-time systems.

• Scheduling and Cloud Computing
• Computer Architecture
• Real-time and Embedded Computing
• +11 more

Javier Fernandez Blanco is an Associate Professor and Chair of the Department of Economics and Economic History at the University Autonomous of Barcelona (UAB), affiliated with the Barcelona School of Economics (BSE). He holds a PhD in Economics from the University of Minnesota (2008) and degrees in Economics and Mathematics from the Universitat de Barcelona. His research focuses on labor markets, public insurance design, and macroeconomic policy, with a particular interest in unemployment dynamics, household insurance mechanisms, and aging populations. He has held visiting positions at the University of Toronto (2018-19 as Visiting Associate Professor), UNSW Sydney (2012), and Carlos III University of Madrid (2008-12). His work has been published in top journals like the International Economic Review, Journal of Economic Theory, and European Economic Review. He has secured research grants, including two BSE Seed grants as Principal Investigator, and contributed to national research initiatives. He currently co-directs research projects on labor markets and education, and has advised numerous PhD and master’s students. His awards include fellowships from the Generalitat, Ramon Areces Foundation, and University of Minnesota. He actively participates in academic service, including organizing conferences, serving on editorial boards, and holding leadership roles in academic governance at UAB and BSE.

• Labor Markets
• Public Insurance Design
• Macroeconomics
• +4 more

Martin Berggren is a Professor at the Department of Computing Science , Umeå University , Sweden. His work focuses on Computational Design Optimization , combining computer simulations and numerical optimization to enhance engineering designs for devices like antennas, microwave components, and loudspeakers. Berggren is also active in mathematical modeling of physical phenomena, particularly wave propagation and fluid mechanics, with a strong emphasis on finite-element methods . His research addresses large-scale conceptual design problems using thousands to millions of design variables, relying on gradient-based algorithms and adjoint-based computations of design sensitivities—similar to back-propagation in deep learning. Key application areas include acoustic and electromagnetic devices, where he investigates damping mechanisms, boundary conditions, and material distribution. Other interests, though less active, involve flow control and unsteady fluid–structure interaction . Berggren collaborates extensively on projects such as Structured Regularization , Topology Optimization of Acoustic Black Holes , and Design of Microstrip-to-Waveguide Transitions . His publications span journals like Journal of Computational Physics , Pattern Analysis and Applications , and IEEE Transactions on Antennas and Propagation , often co-authored with researchers like Linus Hägg , Eddie Wadbro , and Disi Lin .

• Computational Design Optimization
• Numerical Optimization Algorithms
• Gradient-Based Algorithms
• +8 more

Prof. Dr. Daniel Hug is a Professor at the Karlsruhe Institute of Technology (KIT) , affiliated with the Department of Mathematics and the Institute of Stochastics . His research focuses on Probability , Geometry , Convex geometric analysis , Stochastic geometry , and Educational mathematics . He contributes to the DFG Priority Program Random Geometric Systems and previously participated in the DFG research unit Geometry and Physics of Spatial Random Systems . Prof. Hug has co-authored two influential monographs: Poisson Hyperplane Tessellations (Springer Monographs in Mathematics) and Lectures on Convex Geometry (Springer Graduate Texts in Mathematics, 2020). His work spans theoretical advancements in convex and stochastic geometry , including integral geometry, random mosaics, and tensor valuations, alongside applied studies in digital microstructures and Minkowski tensors . His publications from 2025–2024 emphasize hyperbolic space , Boolean models , and Minkowski tensor estimators , reflecting ongoing collaborations with researchers like M. Klatt, C. Thäle, and R. Schneider. Prof. Hug leads courses such as Stochastic Geometry and seminars on Random graphs and tessellations , and is actively involved in working groups like AG Stochastische Geometrie and AG Stochastik .

• Probability
• Geometry
• (Convex) geometric analysis
• +3 more

Michio Sugeno is a distinguished Professor at Tokyo Institute of Technology's Graduate School of Information Science and Engineering, Department of Computational Intelligence. With a career spanning over four decades, he has established himself as a leading figure in fuzzy systems and computational intelligence. His research interests encompass Fuzzy Systems, Computational Intelligence, Nonlinear Control, Choquet Integral theory, Brain-Computer Interfaces, and Linguistic Computing. Sugeno's work has fundamentally shaped modern fuzzy control theory, particularly through his development of the Takagi-Sugeno fuzzy model which has become a standard approach in industrial applications. Analysis of his recent publications reveals a continued focus on piecewise nonlinear modeling, stability analysis of fuzzy systems, and the application of Choquet calculus to various computational problems. His work demonstrates a consistent trajectory from theoretical foundations to practical implementations in control systems and intelligent computing. IEEE Pioneer Award in Fuzzy Systems IFSA Fellow Emanuel R. Piore Award Sugeno has mentored numerous researchers who have become prominent in their own right, including Tadanari Taniguchi, Luka Eciolaza, and Anh-Tu Nguyen. His laboratory has been instrumental in developing novel approaches to nonlinear control systems using piecewise bilinear models and fuzzy logic. Current research directions include brain-computer interfaces using EEG analysis and the development of everyday language computing systems that enable more natural human-computer interaction.

• Fuzzy Systems
• Computational Intelligence
• Nonlinear Control
• +4 more