Anna-Karin Tornberg is a Professor in Numerical Analysis at the Department of Mathematics, KTH Royal Institute of Technology. She holds positions as Vice Chair of the Department of Mathematics and previously served as Head of the Numerical Analysis division (2011–2023). Her research focuses on numerical methods for PDEs, particularly boundary integral methods for fluid flows involving particles and drops. She is active in the Linne FLOW Centre and Swedish e-Science Research Center (SeRC). Key roles include membership in the Royal Swedish Academy of Engineering Sciences (IVA), Royal Academy of Sciences, and receipt of awards like the Göran Gustafsson Prize (Mathematics, 2014). She has advised numerous PhD students and postdocs, including current supervisees Anna Broms, David Krantz, and Emanuel Ström. Her work spans theoretical, computational, and applied fluid dynamics with emphasis on microfluidics and high-accuracy numerical techniques. Education includes a PhD in Numerical Analysis from KTH (2000) followed by postdoctoral positions at NYU’s Courant Institute. Promoted to Full Professor at KTH in 2012. Service roles include membership in KTH’s University Board, Faculty Council, and editorial roles at Advances in Computational Mathematics and BIT Numerical Mathematics . Active in international conferences, delivering plenary/invited lectures at ICIAM, ECM, and ICM. Research group projects include development of fast numerical methods for microfluidics and molecular dynamics simulations. Current openings for PhD candidates in numerical methods for non-elliptic PDEs in time-dependent domains. Her lab collaborates on high-performance computing and fluid-structure interaction problems.
Wenyu Pan is an Assistant Professor in the Department of Mathematics at the University of Toronto, Faculty of Arts and Science. His research lies at the intersection of dynamical systems, ergodic theory, geometry, discrete subgroups of Lie groups, and spectral theory. His primary research interests include: Dynamical Systems Ergodic Theory Geometry Discrete Subgroups of Lie Groups Spectral Theory His recent publications reveal a strong focus on geometric and dynamical aspects of hyperbolic manifolds, limit sets, mixing properties of geodesic and frame flows, and fractal measures such as Patterson-Sullivan measures. The body of work demonstrates deep engagement with homogeneous dynamics, measure rigidity, and spectral analysis in non-compact geometric settings, particularly those involving cusps and abelian covers. His collaborations with researchers like J. Li, H. Oh, and F. Naud indicate active participation in the global mathematical community. Scientific awards are not mentioned in the provided text. There is no information available on student advising, grants, or teaching responsibilities. No lab or research team is explicitly mentioned, though collaborative work is evident in publication records.
Aditi Majumder is a Professor of Computer Science at the University of California, Irvine (UCI), affiliated with the School of Engineering and Information Sciences. Her research focuses on multi-projector display systems, augmented reality (AR), and their applications in scientific and medical fields. She holds a Ph.D. from the University of North Carolina, Chapel Hill (2003). Her work addresses challenges in geometric, chromatic, and luminescent corrections for tiled displays, with applications in surgical assistance and deformable surface visualization. Recent projects include precision stencils for surgical sites and dynamic projection mapping on non-rigid surfaces. Notable achievements include the Inaugural Hasso Plattner Endowed Chair in Artificial Intelligence (2025). Her research spans AR in medicine, real-time multi-projector synchronization, and color gamut optimization. Dr. Majumder also engages in public discourse on computer science education, emphasizing its societal importance and foundational skills like programming and discrete mathematics. Her 15 most recent articles (2021–2024) highlight advancements in surgical AR, deformable surface projection systems, and medical visualization. These contributions bridge computer graphics, vision, and biomedical engineering, reflecting her interdisciplinary approach to solving complex display and interaction challenges.
Professor Pascal Fua is a distinguished faculty member at EPFL (Swiss Federal Institute of Technology) in the School of Computer and Communication Science. He joined EPFL in 1996 and currently serves as Head of the Computer Vision Laboratory (CVLAB). His extensive research spans multiple cutting-edge areas in computer vision and geometric deep learning, with applications ranging from 3D reconstruction to medical imaging and aerodynamic optimization. Dr. Fua's research interests encompass Computer Vision, 3D Reconstruction, Shape Modeling, Geometric Deep Learning, Medical Image Analysis, Augmented Reality, Motion Recovery, Surface Mesh Processing, and Aerodynamic Shape Optimization. His work demonstrates a remarkable ability to bridge theoretical computer vision with practical applications across diverse domains. His research has evolved from traditional geometric computer vision techniques to incorporating deep learning approaches for 3D modeling, with recent focus on differentiable rendering, implicit surface representations, and applications in medical imaging and engineering design. His publication record shows a consistent trajectory of high-impact research, with recent work focusing on differentiable iso-surface extraction, geometric deep learning for aerodynamic shape optimization, and novel approaches to 3D reconstruction. His work spans both theoretical advances in computer vision algorithms and practical applications in medical imaging, autonomous driving, and computational fluid dynamics. IEEE Fellow Multiple ERC Grants recipient Associate Editor of IEEE Transactions for Pattern Analysis and Machine Intelligence Throughout his career, Professor Fua has mentored numerous PhD students who have gone on to make significant contributions in computer vision and related fields. His laboratory has established collaborations across multiple disciplines, including medical imaging, aerospace engineering, and neuroscience, demonstrating the broad applicability of his research. His current work continues to push the boundaries of geometric deep learning and 3D vision, with particular emphasis on making these techniques more practical and applicable to real-world engineering and medical problems.
Nianyi Li is an Assistant Professor in the School of Computing at Clemson University. He holds a B.E. in Electronic and Information Engineering from Huazhong University of Science and Technology and a Ph.D. in Computer and Information Sciences from the University of Delaware. His research focuses on machine learning, computer vision, computational photography, and medical image processing. He has served as an Area Chair for NeurIPS 2024 and CVPR 2023/2024, demonstrating leadership in the academic community. Education: Ph.D., Computer and Information Sciences, University of Delaware (Advisor: Jingyi Yu) B.E., Electronic and Information Engineering, Huazhong University of Science and Technology Research Interests: Machine Learning Computer Vision Computational Photography Medical Image Processing His work includes contributions to atmospheric turbulence removal, microscopy video denoising, and deep learning applications in medical imaging. Key projects include the 'Turb-Seg-Res' pipeline for dynamic video restoration and the NimBLE non-rigid hand model. Recent articles focus on unsupervised methods for object segmentation, fluid surface reconstruction, and medical image analysis. Awards and grants are not explicitly listed but reflect his active research contributions.
Stephen Boyes is a Professor of Chemistry and Director in the Division of Chemistry at the National Science Foundation, affiliated with George Washington University's Columbian College of Arts & Sciences. His research focuses on polymer synthesis, organic chemistry, polymer brushes, nanomedicines, and tissue engineering, with an emphasis on biomedical applications and surface modifications. His work spans the development of novel polymer materials, including refractive index-matched polymers and rigid-rod brushes, as well as nanoparticles for medical imaging and therapy. The Boyes Research Lab, located at 800 22nd St. NW, Washington DC, drives innovations in nanomedicine and advanced materials. Recent publications highlight advancements in lithium extraction, antifouling coatings, and gold/lanthanide nanoparticle conjugates. While no awards are explicitly listed in the provided texts, his research contributions reflect significant impact in materials and biomedical fields. His lab's interdisciplinary approach integrates polymer chemistry, nanotechnology, and surface science, addressing challenges in energy storage, medical devices, and tissue engineering.
Chikako Mese is a Full Professor in the Department of Mathematics at Johns Hopkins University (JHU), part of the Krieger School of Arts & Sciences. She served as Department Chair (2008-2011) and Director of the Graduate Program (2014-2017, 2018-2019). Her research focuses on differential geometry and geometric analysis , with contributions to harmonic maps, Teichmüller theory, and CAT(k) spaces. She holds a PhD from Stanford University (1996), advised by Richard Schoen. Education: PhD in Mathematics, Stanford University, 1996 M.S., Stanford University, 1993 B.S. in Mathematics & Physics, University of Dayton, 1991 (Summa Cum Laude) Research Interests: Mese explores geometric structures in singular spaces, harmonic maps, and applications to Teichmüller theory. Her work bridges differential geometry with analysis of metric spaces, addressing questions of rigidity, regularity, and variational problems. Notable areas include CAT(k) spaces, minimal surfaces, and Higgs bundles in geometric group theory. Grants & Awards: Fellow of the American Mathematical Society (AMS) Simons Fellowship (2017-2018) Multiple NSF DMS grants (2003–2023) for geometric analysis research Woodrow Wilson Career Enhancement Fellowship (2001-2002) Advising & Mentoring: Mese has advised doctoral students including Benjamin Dees (current), Duncan Sinclair (2014), Jonathan Dahl (2010), and Patrick Zulkowski (2009). She mentors junior faculty and leads research groups in minimal submanifolds at workshops like the Women in Geometry series (Banff 2015, Oaxaca 2019). Labs/Teams: Collaborates with leading institutions globally (e.g., Brown University, CNRS, Tohoku University) on projects involving geometric rigidity, harmonic maps, and moduli spaces. Active in editorial roles, including Notices of the AMS.
Michael Bronstein is a Professor at Università della Svizzera italiana (USI Lugano) in Switzerland and Imperial College London in the UK, where he holds the Chair in Machine Learning and Pattern Recognition. He serves as Head of Graph Learning Research at Twitter following the acquisition of his startup Fabula AI, and maintains a principal engineer position at Intel Perceptual Computing. His research focuses on the interplay between geometry, machine learning, and computer vision, with particular emphasis on non-Euclidean structured data. Professor Bronstein received his Ph.D. with distinction in Computer Science from the Technion in 2007. He has held visiting appointments at Stanford University, MIT, Harvard University (as a Radcliffe Fellow), and Tel Aviv University, and has been affiliated with multiple Institutes for Advanced Study including TUM-IAS where he was a Rudolf Diesel Industry Fellow (2017). He is a Fellow of IAPR, Senior Member of the IEEE, and a member of the Young Academy of Europe. His research program centers on theoretical and computational methods in spectral and metric geometry applied to computer vision, pattern recognition, and machine learning. He pioneered the field of geometric deep learning, developing novel neural network architectures that process non-Euclidean data structures like graphs and manifolds. His work spans from theoretical foundations to practical applications, with over 100 publications in top scientific journals and conferences, and has been featured in international media including CNN. Analysis of his recent publications reveals a strong trajectory in geometric deep learning with applications spanning computer vision, 3D shape analysis, social network analysis, and bioinformatics. His research consistently bridges theoretical innovation with real-world applications, developing novel neural architectures for processing complex data structures. The work demonstrates increasing interdisciplinary reach, connecting machine learning with fields from particle physics to molecular biology. Dalle Molle Prize (2018) Royal Society Wolfson Research Merit Award (2018) ERC Proof of Concept Grant (2018) Amazon AWS Machine Learning Research Award (2018) Fellow, International Association for Pattern Recognition (IAPR) Google Faculty Research Award (2017) Radcliffe fellowship, Harvard University (2017) Rudolf Diesel industrial fellowship, TU Munich (2017) ERC Consolidator Grant (2016) World Economic Forum Young Scientist (2014) Professor Bronstein has secured multiple ERC grants (Starting Grant 2012, Proof of Concept Grants 2016 and 2018, Consolidator Grant 2016) and has mentored numerous students who have contributed to over 30 granted patents. He has chaired more than a dozen conferences and workshops in his field and served as area chair at major computer vision conferences including ECCV 2016 and ICCV 2017. His research group at USI Lugano collaborates extensively with industry partners including Intel and Twitter. As a serial entrepreneur, Professor Bronstein co-founded Novafora (2005-2009) developing large-scale video analysis, Invision (2009-2012) which created low-cost 3D sensors and was acquired by Intel, and Fabula AI (2018-2019) focused on fake news detection which was acquired by Twitter. His work bridges theoretical research with commercial applications, with his technology contributing to Intel RealSense and Twitter's graph learning infrastructure.
Dr. Christopher Gilliam is an Assistant Professor in Applied Signal Processing at the University of Birmingham's Department of Electronic, Electrical and Systems Engineering. He holds an MEng (1st Class Hons) in Electrical & Electronic Engineering (2008) and a Ph.D. in Signal Processing (2013), both from Imperial College London. Prior to joining Birmingham in 2022, he was a Postdoctoral Fellow at The Chinese University of Hong Kong (2013–2017) and a Research Fellow at RMIT University, Australia (2017–2022). Research Interests: Sensor signal processing, radar imaging, sampling theory, motion estimation, quantum navigation, and medical imaging. Labs: Microwave Integrated Systems Laboratory (MISL). Committees: Member of IEEE Signal Processing Society and APSIPA Technical Committees. His work focuses on advancing signal processing techniques for radar systems, navigation, and medical imaging. Recent research highlights include drone-based SAR imaging, motion correction in MRI, and fusion of classical/quantum sensors for inertial navigation. He is actively supervising PhD students and contributes to projects sponsored by DSTG. Publications span radar SLAM, probabilistic navigation algorithms, and deep learning-driven medical imaging solutions. His research bridges theoretical signal processing with practical applications in autonomous systems and healthcare.
Richard Canary is a Professor in the Department of Mathematics at the University of Michigan. His research focuses on hyperbolic geometry, geometric group theory, and low-dimensional topology, with notable contributions to the study of hyperbolic 3-manifolds, Kleinian groups, and Anosov representations. He has delivered lectures such as 'Non-Euclidean Sports and the Geometry of Surfaces' in 2008, illustrating the intersection of geometry and physics. His recent work explores themes like Patterson-Sullivan measures, pressure metrics, and entropy rigidity in contexts involving Hitchin components and deformation spaces. He actively participates in academic conferences and workshops, contributing to the advancement of geometric structures and dynamics.
Harold S. Park is a Professor of Mechanical Engineering and Materials Science & Engineering at Boston University. He leads research in computational nanomechanics, focusing on nanowire mechanics, soft materials, and coupled electromechanical phenomena. Park earned his PhD from Northwestern University in 2004 and has held faculty positions since 2007. His work bridges atomistic simulations and continuum mechanics, addressing challenges in nanoscale material behavior and energy conversion. Education: PhD, Northwestern University (2004) MS, Northwestern University (2001) BS, Northwestern University (1999) Research Interests: Park’s lab explores topological mechanics, phononic metamaterials, and machine learning-driven materials design. Key areas include surface-dominated plasticity in nanomaterials, flexoelectricity in 2D materials, and energy harvesting via crumpling mechanics. His team develops multiscale models to predict mechanical behavior across length scales. Publications: Over 150 peer-reviewed articles span topics like nanowire deformation mechanisms, nanoresonator dynamics, and topology-optimized metamaterials. Recent work highlights include enhancing nanowire Q-factors via strain tuning and designing non-reciprocal elastic systems. Awards: NSF CAREER Award (2007) DARPA Young Faculty Award (2008) ASME Fellow (2016) John Argyris Award (2016) Advising & Teams: Park mentors graduate students and postdocs in experimental and computational mechanics. Notable advisees include Dr. Penghui Cao (UC-Irvine faculty) and Dr. Saman Seifi. His lab collaborates with industry partners on nanomaterials for sensors and energy systems. Labs: Affiliated with the Materials by Design Center at BU, focusing on computational and experimental nanomechanics. Active in editorial roles for journals like Journal of Applied Mechanics and Nano Letters .
Dr. Shideh Kabiri Ameri serves as Associate Professor in the Department of Electrical and Computer Engineering at Queen's University, where she joined in September 2018 after completing postdoctoral research at the University of Texas at Austin. Her interdisciplinary expertise bridges nanomaterials engineering and biomedical applications, with particular focus on developing imperceptible wearable sensors for continuous health monitoring. Her educational foundation includes: PhD in Electrical Engineering (2015) from Tufts University Master's and Bachelor's degrees in Physics (solid state) AS degree in Medical Laboratory Sciences Dr. Ameri's research program centers on 2D material-based electronic devices for wearable bioelectronics, human-machine interfaces (HMI), and mobile healthcare systems . Her lab pioneered graphene electronic tattoos (GETs) that achieve unprecedented skin conformity while recording high-fidelity physiological signals. Current work emphasizes ultrasoft hydrogel-based sensors that eliminate motion artifacts and enable months-long wear without skin irritation, representing a paradigm shift from conventional rigid medical devices toward truly imperceptible health monitors. Analysis of her 40+ publications reveals a strategic evolution from fundamental nanomaterial characterization toward clinically viable systems. Recent work (2021-2025) demonstrates increasing sophistication in multimodal sensing (simultaneous ECG/EEG/temperature), reusable sensor architectures , and wireless power integration . The trajectory shows clear progression from lab prototypes to FDA-pipeline devices, particularly in cardiac and neurological monitoring applications. Her scientific recognition includes: Rising Star in EECE 2017 award Dr. Ameri leads the Ameri Nano Research Group which operates advanced nanofabrication facilities for developing next-generation bioelectronic interfaces. Her research has attracted significant media attention from BBC, IEEE Spectrum, and Phys.Org, highlighting real-world impact in remote patient monitoring. The group actively collaborates with medical institutions to translate innovations into point-of-care diagnostics, with current projects focusing on in-ear physiological monitors and strain-neutralized neural recording systems. The research team maintains strong industry partnerships for commercializing soft bioelectronics, with particular emphasis on creating accessible health monitoring solutions for underserved communities through low-cost manufacturing approaches.
Dana Cobzas is an Associate Professor at the MacEwan University in the Department of Computer Science , with adjunct appointments at the University of Alberta. Her academic journey includes a PhD in Computer Science (University of Alberta, 2004) MSc in Mathematics (Babes-Bolyai University, 1998) BSc in Mathematics (Babes-Bolyai University, 1997) . Her research focuses on imaging and computer vision , particularly mathematical models for medical image processing . Key areas include Medical image segmentation and registration 3D modeling from uncalibrated images Sparse classification for population studies Dynamic vision (tracking and modeling) Medical applications in neuroimaging and oncology . She has developed advanced techniques like deep learning-integrated level set methods and FEM-based segmentation. Scientific recognition includes: NSERC Discovery Grant (2015, 2010) Best Vision Paper at IEEE ICRA 2005 Best Student Paper at Vision Interface 2003 . She is actively involved in teaching and mentoring , with experience supervising senior students’ independent studies and contributing to collaborative projects in robotics and biomedical engineering .
Dr. Arie Levit is a Senior Lecturer (tenure track) in the Department of Theoretical Mathematics at Tel Aviv University's School of Mathematics, a position he has held since 2021. Previously, he served as a Gibbs Assistant Professor at Yale University from 2017. His academic career centers on pure mathematics with emphasis on structural properties of discrete groups and dynamical systems. His educational background includes: B.A in Mathematics from the Hebrew University of Jerusalem (2004) M.A in Mathematics from the Hebrew University of Jerusalem (2012) Ph.D. in Mathematics from the Weizmann Institute of Science (2017) under Prof. Tsachik Gelander Levit's research spans discrete groups, geometric and analytic group theory, and ergodic theory, with significant contributions to lattice theory, invariant random subgroups, character rigidity, and group stability. His work integrates algebraic, geometric, and probabilistic frameworks to solve fundamental problems in classification and rigidity of group actions, particularly in non-Archimedean and hyperbolic settings. Analysis of his 14 publications (2014-2024) reveals evolving focus from foundational lattice theory toward contemporary stability phenomena and character theory, with 60% of recent work (2022-2024) addressing permutation stability, Hilbert-Schmidt representations, and ergodic properties of group actions. Key methodological threads include the application of ergodic theory to group-theoretic classification and the development of analytical tools for stability problems. His scholarly recognition includes: Klein Prize (2017) ISF-BSF research grant (2020) As principal investigator of the ISF-BSF grant, Levit leads research on group stability and ergodic theory. His extensive collaborations with Gelander, Lubotzky, and Lazarovich demonstrate active mentorship within the global mathematics community. His work is conducted within Tel Aviv University's Theoretical Mathematics department, which maintains strong international partnerships in geometric group theory and dynamics.
Dr. John Haslegrave is a Lecturer in Probability at Lancaster University's School of Mathematical Sciences, where he is an active member of both the Probability and Combinatorics research groups. Previously, he held positions at the University of Oxford (working with Peter Keevash), University of Warwick (with Agelos Georgakopoulos), and University of Sheffield (with Hong Liu and Chris Cannings). He completed his PhD at Trinity College, Cambridge under Béla Bollobás. His research focuses on: Random graphs and evolving network models (especially preferential attachment) Interacting particle systems and random walks on graphs Extremal problems in graph/hypergraph theory Percolation theory and geometric probability Combinatorial optimization and graph invariants Analysis of recent publications reveals strong emphasis on probabilistic combinatorics, with frequent exploration of: structural graph properties, asymptotic behavior of stochastic processes, geometric embeddings, and optimization problems. His work consistently bridges discrete mathematics with statistical physics concepts. Dr. Haslegrave welcomes PhD students interested in discrete probability and graph theory, with current supervision interests including preferential attachment models, interacting particle systems, planar percolation, and extremal problems. He teaches undergraduate courses in Graph Theory (MATH326) and Probability (MATH103), and organizes Lancaster's Pure Mathematics Seminar series.
