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Bo WangView profile

Associate Professor

• Systems biology of whole-body regeneration
• Cell type evolution through single-cell multiomic sequencing
• Quantitative biology of brain regeneration
• +2 more

Bo Wang is an Associate Professor at Stanford University with dual appointments in the Department of Bioengineering and the Department of Developmental Biology. He is also a member of Bio-X and the Wu Tsai Neurosciences Institute. His research integrates developmental biology, bioengineering, and statistical physics to understand collective cell behaviors that optimize tissue regeneration, adaptation, and evolution. His research interests span systems biology of whole-body regeneration, cell type evolution through single-cell multiomic sequencing, quantitative biology of brain regeneration, and regeneration of animal-algal photosymbiotic systems. He has developed innovative tools like expansion spatial transcriptomics and quantitative organism-wide imaging approaches. Systems biology of regeneration Cell type evolution Neuroregeneration Photosymbiosis His recent publications focus on regeneration mechanisms in flatworms and acoels, cross-species single-cell mapping, and expansion microscopy techniques. The work has been featured in top journals like Cell , Nature Communications , and eLife . 2024 - Genetic toolkit for Macrostomum lignano regeneration 2024 - Animal-algal holobiont wound coordination 2023 - Expansion spatial transcriptomics 2022 - Planarian mRNA transfection Scientific awards include: SN10: Scientists to Watch (2020) Human Frontier Science Program Young Investigator (2019) Beckman Young Investigator (2017) Hellman Faculty Scholar (2017) Baxter Faculty Scholar (2016) Burroughs Wellcome Fund Career Award (2013) PhD students and postdocs in his lab study regeneration biology, cell type evolution, and bioengineering tools. His research has been supported by the Beckman Foundation, Burroughs Wellcome Fund, Bio-X, Bachrach Family Foundation, Baxter Foundation, Human Frontier Science Program, NIH, NSF, and Wu Tsai Neurosciences Institute.

Mikael BrixView profile

Researcher

• Medical Imaging
• Computed Tomography
• X-ray Detectors
• +3 more

Mikael Brix serves as a post-doctoral researcher and project manager at the Research Unit of Health Sciences and Technology within the Faculty of Medicine, University of Oulu, Finland, while concurrently working as a physicist at Oulu University Hospital's Department of Diagnostics. He leads the Medical Imaging in Diagnostics, Algorithms and Software (MIDAS) research group as vice-leader. His research centers on medical imaging physics, with primary focus on X-ray detectors, computed tomography physics, and reconstruction algorithm development. He actively bridges fundamental physical phenomena research with clinical translation through industrial partnerships and diagnostic expert collaboration, increasingly leveraging artificial intelligence to enhance imaging techniques for practical healthcare applications. Analysis of his 2024-2025 publications reveals consistent innovation in CT technology, particularly photon counting detectors, metal artifact reduction, and low-dose reconstruction algorithms. His work spans cardiology, orthopedics, and forensic science applications, with notable emphasis on cost-effectiveness analysis and real-world healthcare system integration, especially within Finnish medical infrastructure. Scientific Awards: Young Investigator Award of the Radiological Society of Finland (2023) No information regarding student advising or grant funding is provided in available sources. Dr. Brix directs the MIDAS research group at University of Oulu, which develops cutting-edge medical imaging solutions, specialized algorithms, and diagnostic software tools with strong industry-academia collaboration for clinical deployment.

Yusuf SahillioğluView profile

Professor

• Computer Graphics
• 3D Shape Processing
• Geometric Algorithms
• +3 more

Professor Yusuf Sahillioğlu is a faculty member in the Computer Engineering Department at Middle East Technical University (METU), Faculty of Engineering, specializing in computer graphics and geometric processing. His academic journey includes a BS from Bilkent University, MS from University of Florida, and PhD from Koç University, followed by postdoctoral work at Koç University and University of Pennsylvania. His research focuses on 3D shape correspondence , deformation algorithms , deep learning for 3D data , and geometry processing . Key contributions include novel methods for isometric shape matching, topologically-stable correspondence, and real-time secondary animation. His recent work integrates deep learning with traditional geometric approaches for mesh segmentation, point cloud classification, and 3D scene extrapolation. His 15 most recent publications (2021-2025) reveal a strong trend toward real-time geometric algorithms (e.g., spring-decomposed skinning), kernel computation for mesh processing, and deep generative models for 2D-to-3D conversion. The research consistently bridges theoretical geometry with practical applications in animation, reconstruction, and medical imaging. Award highlights include: METU Academic Performance Award (2018) METU Young Scientist Awards (2015-2017) TÜBİTAK 1001 Grants (2016, 2020, 2024) for 3D content analysis, shape matching, and reconstruction TÜBİTAK 3501 Career Grant (2015) for virtual medicine applications He teaches core courses including Data Structures, Operating Systems, Computer Graphics, and Advanced Digital Geometry Processing. His lab focuses on geometric algorithms with applications in medical imaging, animation, and 3D printing, maintaining strong industry and international collaborations through projects like TÜBİTAK-funded initiatives.

• Geosciences
• Engineering and Technology
• Rock Mechanics
• +5 more

Mateusz Janiszewski is a Postdoctoral Researcher at Aalto University's Department of Civil Engineering, School of Engineering. His work focuses on rock mechanics, photogrammetry, and educational technology. He co-developed a virtual learning environment (VR) for rock engineering education, enabling flexible student training and improving learning outcomes through iterative feedback. Department of Civil Engineering, Aalto University Research Group: Mineral Based Materials and Mechanics His research spans advanced digital techniques like photogrammetry and extended reality (XR) for rock mass characterization, borehole thermal energy storage systems, and geotechnical risk management in mining. Collaborations with Lauri Uotinen and Mikael Rinne highlight his interdisciplinary approach. Recent publications emphasize photogrammetry for fracture analysis, VR-based training, and machine learning applications in rock classification. His work bridges technical innovation with pedagogical impact. Aalto University School of Engineering Award for Achievements in Teaching (2019) Janiszewski's contributions include developing smartphone LiDAR methods for tunnel mapping, remote fracture mapping, and real-time risk assessment frameworks for underground mining operations.

Álvaro MorenoView profile

Researcher

• Fluid Mechanics
• Aerodynamics
• Machine Learning
• +5 more

Álvaro Moreno is a postdoctoral researcher at Universidad Carlos III de Madrid (UC3M), following a postdoctoral position at Massachusetts Institute of Technology (MIT) and a PhD in Physics of Fluids at Universiteit Twente. His academic training includes a Bachelor’s and Master’s in Aerospace Engineering from Universidad de Sevilla and Technische Universität München, respectively.

Jacek GondzioView profile

Professor

• Large Scale Optimization
• Interior Point Methods
• Sparse Matrix Methods
• +3 more

Jacek Gondzio is a Professor in the School of Mathematics at the University of Edinburgh. He received his M.Eng. in Electronics (1983) and PhD in Automatic Control and Robotics (1989) from Warsaw University of Technology. His career includes positions at the Polish Academy of Sciences (1989–1993), University of Geneva (1993–1998), and the University of Edinburgh since 1998, where he progressed from Lecturer to Professor. Research Interests: Gondzio's work spans large-scale optimization techniques, including interior point methods, sparse matrix computations, parallel algorithms, and applications in finance and engineering. Key focus areas include: Development of efficient solvers (HOPDM, OOPS) for linear/quadratic/nonlinear programming Matrix-free methods and preconditioning for massive-scale problems Applications in quantum information, tomography, structural design, and financial planning Publication Trends: His recent articles emphasize scalable algorithms for optimization, including proximal methods for semidefinite programming, interior-point innovations, and applications in medical imaging and transport. Work frequently integrates regularization, decomposition techniques, and structure-exploiting linear algebra. Awards: EUROPT Fellow (2019) for contributions to continuous optimization Grants & Advising: Current projects include EPSRC-funded work on building structure optimization (EP/N019652/1), Google-funded LP solvers, and risk modeling with Standard Life Investments. He has supervised 16+ PhD students on topics ranging from interior point methods to machine learning optimization. Software includes HOPDM, PDCGM, and the parallel solver OOPS. Leadership: Organizes workshops on optimization (e.g., COA, Advances in Preconditioners series) and serves on editorial boards for Mathematical Programming Computation , Computational Optimization and Applications , and other leading journals.

• Combinatorial optimization
• Statistical physics
• Epidemic inference
• +7 more

Alfredo Braunstein is an Associate Professor at the Department of Applied Science and Technology (DISAT), Politecnico di Torino, and a member of SmartData@PoliTO laboratory. His research spans combinatorial optimization, statistical physics of complex systems, epidemic inference, and computational biology . He leads the Simultaneous Inference from Multiscale Biological Data (SIMBAD) project (2023-2027) and previously led Statistical Inference via Belief Propagation for Epidemics (SIBYL) (2015-2017). He has supervised multiple PhD students in Physics of Complex Systems . Research Interests : Statistical physics, machine learning, network science, epidemic modeling, and optimization. Key Collaborations : Collegio Carlo Alberto, Human Genetics Foundation, and interdisciplinary teams. His recent publications focus on epidemic risk estimation , network dismantling , and message-passing algorithms . He received the La Ricerca dei Talenti award from Fondazione CRT (2015).

Wu-chun FengView profile

Professor

• High-Performance Computing
• Data Analytics Systems
• Computational Biology
• +7 more

Wu-chun Feng is a Professor of Computer Science and Electrical & Computer Engineering at Virginia Tech, holding the Elizabeth & James E. Turner Jr. '56 Faculty Fellowship. He directs the SyNeRGy Laboratory and is affiliated with the School of Biomedical Engineering & Sciences, CHREC, VBI, and Wireless @ VT. His research focuses on high-performance computing, energy-efficient systems, bioinformatics, and heterogeneous architectures. Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (1996) M.S. in Computer Engineering, Penn State University (1990) B.S. in Electrical & Computer Engineering and Music (Honors), Penn State University (1988) Research Interests: His work spans high-performance computing systems, green supercomputing (e.g., Green500 List, Green Destiny), computational biology (mpiBLAST), and parallel programming models. He emphasizes energy-efficient architectures and interdisciplinary applications in biomedicine and data science. Key Contributions: Green500 List: Ranks energy-efficient supercomputers globally mpiBLAST: Parallel implementation of NCBI BLAST Supercomputing in Small Spaces initiative Awards: Three R&D 100 Awards (Green Supercomputing, High-Speed Networking, Bioinformatics) Over 15 Best & Outstanding Paper Awards Service & Leadership: Editorial roles in journals like IEEE/ACM Transactions on Computational Biology Conference leadership (SC, ICPP, etc.) Director of SyNeRGy Lab and CUDA Research Center

• Machine Learning
• Operations Research
• Optimization
• +3 more

Silvia Villa is an Associate Professor in the Department of Mathematics (DIMA) at the University of Genoa (UniGe). Her teaching responsibilities include courses such as Game Theory, Machine Learning, Optimization and Operations Research, and Operations Research at both undergraduate and master's levels. Her research focuses on optimization theory and its applications in machine learning, inverse problems, and regularization methods. She has contributed to advancements in iterative regularization techniques, stochastic optimization algorithms, and convex optimization frameworks. Her work often bridges theoretical foundations with practical applications in signal processing and data science. Her research interests emphasize structured optimization problems, including low-complexity regularizers, bilevel optimization, and sparse recovery. Key methodologies in her studies include primal-dual dynamics, proximal algorithms, and variance reduction techniques. Recent trends in her publications highlight advancements in unrolled deep networks for sparse signal restoration, adaptive optimization strategies, and convergence analysis of stochastic methods. Villa's collaborative efforts span interdisciplinary projects involving applied mathematics, computer science, and engineering. While no specific awards are listed, her prolific publication record reflects her active role in the optimization and machine learning communities. She has advised multiple research projects but no formal student names are provided in the available data.

Jim W. PortegiesView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Applied Mathematics
• +5 more

Jim W. Portegies is an Assistant Professor in the Applied Analysis group at the Eindhoven University of Technology (TU/e). He holds additional roles as EAISI Foundational Assistant Professor and EAISI Mobility Assistant Professor within the TU/e's interdisciplinary initiatives. His research focuses on applying mathematical techniques from analysis, geometry, and measure theory to problems in machine learning and artificial intelligence, with a particular interest in designing algorithms that emulate human learning mechanisms. He obtained his MSc in Industrial and Applied Mathematics and Applied Physics from TU/e before completing his PhD in Mathematics at the Courant Institute of Mathematical Sciences, New York. After postdoctoral work at the Max Planck Institute in Leipzig, he returned to TU/e in 2016. Education: MSc in Industrial and Applied Mathematics & Applied Physics (TU/e, 2009) PhD in Mathematics (Courant Institute, NYU, 2014) Research Interests: Jim's work bridges pure mathematics and applied AI. Key areas include: Geometric and spectral methods for machine learning Universal learning algorithms inspired by human cognition Analysis of dimensionality reduction techniques Stochastic processes and control systems His recent work explores neural stochastic differential equations and interpretable AI mechanisms. Awards: Member of TU/e Young Academy of Engineering (2016–present) Collaborations & Grants: Jim collaborates extensively in interdisciplinary projects, including EU-funded initiatives on AI mobility systems. His research has been supported by grants from NWO (Netherlands Organization for Scientific Research) and industry partnerships with semiconductor and control systems companies. Labs & Teams: He contributes to the TU/e's Center for Analysis, Scientific Computing and Applications (CASA) and leads the Applied Analysis & AI research group, focusing on theoretical foundations of AI and their practical implementations.

Tristan van LeeuwenView profile

Professor

• Imaging Reconstruction
• Inverse Problems
• Scientific Computing
• +4 more

Tristan van Leeuwen is a Professor of Computational Inverse Problems at Utrecht University's Faculty of Science, within the Mathematical Institute's Department of Mathematical Modeling. He holds a MSc in Computational Science (2006) and a PhD in Geophysics (2010). His career includes postdoctoral roles at the University of British Columbia and Centrum Wiskunde & Informatica (CWI), followed by faculty positions at Utrecht University and group leadership at CWI. His research focuses on inverse problems, scientific computing, imaging reconstruction, and computational methods for geophysics and medical imaging. Key areas include wave-equation inversion, tomographic reconstruction, and uncertainty quantification. Notable contributions span seismic inversion techniques, convex optimization frameworks for tomography, and deep learning applications in imaging. His publications emphasize methodologies like wavefield reconstruction inversion, convex programming for shape sensing, and Bayesian approaches for uncertainty analysis. He collaborates with institutions like CWI and the University of British Columbia, contributing to open-source tools like Tomosipo for tomography. His work bridges theoretical mathematics with practical applications in geophysics, medical diagnostics, and industrial inspection.

• Mathematical Biology
• Dynamical Systems
• Medical Signal Analysis
• +5 more

Philip James Aston is a Professor of Mathematics at the University of Surrey, affiliated with the School of Mathematics and Physics and the Department of Mathematics. His research focuses on dynamical systems, mathematical biology, and medical signal analysis. He holds a BSc (First Class Honours) and PhD from Brunel University, followed by a postdoc at the University of Bath. Key research interests include bifurcation theory, Lyapunov exponents, pharmacokinetics, and machine learning applications in healthcare. Notable contributions include the Symmetric Projection Attractor Reconstruction (SPAR) method for waveform analysis, used in ECG and PPG signal processing. He has secured grants, including £50,000 for wearable sensor development. His work bridges mathematics and medicine, addressing challenges in cardiovascular monitoring, genetic mutation detection, and drug efficacy modeling. He collaborates with clinicians and engineers, contributing to datasets like PTB-XL+ and advancing AI-driven diagnostics.

• Medical Imaging
• Biomedical Engineering
• Computational Biology
• +5 more

Sang Hoon Ahn is a Clinical Associate Professor of Medicine at the University of Southern California (USC) and serves as Director of Keck Medicine of USC in Buena Park and Koreatown. His academic affiliation lies within the Department of Medicine at the School of Medicine. Research Interests: His work focuses on advanced medical imaging techniques, including: Fluorescence Molecular Tomography Time-of-Flight PET Imaging Iterative Image Reconstruction Algorithms Fourier Rebinning for Medical Imaging Optimization of Illumination Patterns in Biomedical Imaging Computational Modeling for Radiology Applications Publications: His research spans biomedical imaging, computational modeling, and medical physics, with a focus on optimizing imaging techniques and algorithm development. Contact: sanghoon.ahn@med.usc.edu

Mary VaughanView profile

Researcher

• Analysis
• Partial Differential Equations
• Nonlocal Equations
• +3 more

Mary Vaughan is a Researcher and Research Associate at the University of Western Australia (UWA), affiliated with the School of Physics, Maths and Computing and the Department of Mathematics and Statistics. Previously, she held an RTG Postdoctoral Research position at the University of Texas at Austin under Professors Luis Caffarelli and Stefania Patrizi. Education: She earned a Ph.D. in Mathematics from Iowa State University in 2020, supported by awards including the Robert J. Labert Graduate Assistantship and the Brown Graduate Research Fellowship. She also served as an NSF-MSGI Summer Intern at Oak Ridge National Laboratory. Research Interests: Her work focuses on analysis and partial differential equations, particularly nonlocal equations, fractional elliptic equations, and Harnack inequalities. She explores topics such as symmetric functions, viscosity solutions, and fractional calculus, contributing to both theoretical and applied mathematical frameworks. Publications: Her recent work includes studies on continuous symmetrizations, fractional elliptic equations, and the dual Kaczmarz algorithm, reflecting expertise in nonlocal problems and numerical methods. These publications highlight her contributions to advancing techniques like Harnack inequalities and Schauder estimates in fractional calculus. Awards: Robert J. Labert Graduate Assistantship Brown Graduate Research Fellowship NSF-MSGI Summer Internship Advising & Grants: Her postdoctoral research was supported by the RTG grant at UT Austin. No formal advisees are listed. Labs/Teams: Collaborations include work with Professors Caffarelli and Patrizi, and contributions to projects at Oak Ridge National Laboratory.

• Computational Imaging
• Inverse Problems
• Numerical Analysis
• +2 more

Greg Buzzard is Professor of Mathematics and Director of the Center for Computational & Applied Mathematics (CCAM) at Purdue University's College of Science. His research develops mathematical foundations for computational imaging systems through collaborative work with electrical engineering and materials science researchers. Dr. Buzzard leads a substantial research group focusing on inverse problems in imaging science. His work combines theoretical advances in optimization with practical applications in electron microscopy, neutron tomography, and medical imaging. Key research areas include: Consensus Equilibrium frameworks for model integration Dynamic sampling algorithms for computational imaging Plug-and-play methods for image reconstruction Multi-agent systems for large-scale inverse problems Recent publications demonstrate leadership in computational tomography innovations, particularly neutron imaging techniques for materials science and spectral CT reconstruction. His team's methods enable faster, higher-resolution imaging across scientific domains from biomedical applications to aerospace materials characterization. The 2020 SIAM Imaging Sciences Best Paper Prize recognized fundamental contributions to Plug-and-Play reconstruction methods. Professor Buzzard maintains an active mentoring program with 9 current graduate students and postdocs. His interdisciplinary collaborations include sustained partnerships with the Purdue School of Electrical and Computer Engineering and materials science research groups.