Qile Chen is an Associate Professor in the Department of Mathematics at Boston College . His research focuses on Algebraic Geometry , particularly in Logarithmic Geometry , Moduli Spaces , and Gromov-Witten Theory . He has made significant contributions to understanding A^1-connectedness , Stable Log Maps , and Virtual Cycles in geometric contexts. His publications include collaborations with leading mathematicians such as Dan Abramovich , Felix Janda , Yi Zhu , and Dawei Chen . Key topics span Logarithmic GLSM , Multi-scale Differentials , and Spin/Hyperelliptic Structures . Recent Articles : Punctured logarithmic maps (2025), Gorenstein contractions (2024), Campana rational connectedness (2024) Co-advised Student : Zijian Han (Ph.D. in progress at Boston College)
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.
Steven Zucker is the David & Lucile Packard Professor of Biomedical Engineering & Computer Science at Yale University, with additional appointments in Applied & Computational Mathematics. His work bridges computational vision, neurophysiology, and differential geometry to model human visual perception and cortical computation. Research Interests : Zucker's research focuses on computational vision , leveraging differential geometry to develop theories for curve detection, shading analysis, stereo vision, and 3D shape description. He also explores interdisciplinary applications in plant biology through auxin dynamics and political science via diffusion geometry. Article Trends : Recent publications emphasize 3D shape estimation from shading and texture flows curvature-driven neural computation models applications in plant venation and heart myofibril geometry psychophysical studies of color and orientation flows Scientific Contributions : Recognized as a Packard Professor, Zucker has pioneered Hamilton-Jacobi skeletons and curve indicator random fields . His work spans computer vision, neuroscience, and mathematical modeling.
Dr. Zichun Zhong is an Associate Professor and Graduate Program Director in the Department of Computer Science at Wayne State University's James and Patricia Anderson College of Engineering. He earned his Ph.D. from the University of Texas at Dallas and completed postdoctoral training at UT Southwestern Medical Center. His research focuses on geometric modeling, computer graphics, medical image processing, and visualization technologies. Research encompasses: Geometric modeling of surfaces and volumes 3D computer vision and reconstruction Medical image segmentation and visualization Virtual/augmented reality applications GPU-accelerated algorithms Awards and honors include NSF CAREER and CRII awards, Faculty Research Excellence Award, and Excellence in Teaching recognition. He serves as Technical Paper Chair for Shape Modeling International conferences and associate editor for multiple journals. Current doctoral advisees: Shiman Zhou, Hongbo Li, Haikuan Zhu, and Sikai Zhong. Notable alumni include researchers at Samsung NEON, Skoltech, and General Motors.
Martin Rajman is a Senior Scientist at École Polytechnique Fédérale de Lausanne (EPFL) with multiple affiliations across the institution. He holds positions in the School of Computer and Communication Sciences (SIN - Teaching, SCI IC MR Group, SSC - Teaching) as well as in the Vice Presidency for Strategic Development (VPS Artificial Intelligence) and the Vice Presidency for Academic Affairs (SNAI Administration). He serves as the Executive Director of Nano-tera.ch, a large Swiss Research Program funding collaborative multi-disciplinary projects in Health and the Environment. Rajman's research spans the intersection of artificial intelligence, natural language processing, and information retrieval. His work demonstrates a consistent focus on developing practical applications of computational linguistics and machine learning techniques. Early in his career, he contributed significantly to syntactic parsing, stochastic language models, and vector space representations for text. More recently, his research has expanded into deep learning applications for 3D reconstruction, empathetic conversational agents, and distributed analytics systems. His publications reveal a trajectory from foundational NLP research toward increasingly applied and interdisciplinary work connecting AI with healthcare, environmental monitoring, and human-computer interaction. Analysis of his recent publications (2015-2024) shows a clear evolution toward more applied AI research with strong interdisciplinary connections. While maintaining his core expertise in natural language processing and information retrieval, his work has expanded into computer vision, healthcare applications, and sustainable computing. The publications demonstrate increasing collaboration across disciplines, with applications in medical imaging, mental health support systems, environmental monitoring, and human-centered AI. His leadership role in the Nano-tera.ch program reflects this interdisciplinary approach, connecting computing research with real-world challenges in health and environmental contexts. Rajman has mentored several PhD students including Ailomaa Marita, Eckard Emmanuel, Melichar Miroslav, and Veselý Martin. His research has been supported through the Nano-tera.ch program, which has funded more than 100 research projects with over 95 million CHF in public funding. He has also managed more than 20 European projects during his tenure as Director of the EPFL Global Computing Center. As Executive Director of Nano-tera.ch, Rajman leads a significant research initiative connecting EPFL with national and international partners. His work bridges academic research with industry applications, notably through collaborations with eBay on product ranking technology and with Elsevier on article recommendation systems. His leadership extends to managing large-scale research programs while maintaining an active research agenda and mentoring the next generation of computer scientists.
Joseph D Rabinoff is an Associate Professor of Mathematics at Duke University's Trinity College of Arts & Sciences. His research focuses on non-Archimedean analytic geometry, tropical geometry, and their applications to algebraic and arithmetic geometry. He holds a Ph.D. in Mathematics from Stanford University (2009). Key research areas include non-Archimedean theta functions, Diophantine geometry, and the interplay between tropical and algebraic structures. He has led NSF-funded projects on non-Archimedean analytic geometry and number theory. His work bridges abstract algebraic geometry with computational and combinatorial methods. Rabinoff has presented at international conferences, including the Regensburg Days on non-Archimedean geometry and the Oberwolfach Tropical Geometry workshop. He serves as a referee for major journals such as the Journal of Algebra and Comptes Rendues Mathematiques.
Erin Wolf Chambers is a Professor in the Department of Computer Science and Engineering at the University of Notre Dame, with a concurrent appointment in the Department of Applied and Computational Mathematics and Statistics. She holds the Snyder Family Mission Collegiate Professorship. Previously, she was at Saint Louis University (SLU) as a Professor in Computer Science and Mathematics. Her research focuses on computational topology and geometry, combinatorial algorithms, and improving STEM education inclusivity. She earned her Ph.D. in Computer Science from the University of Illinois Urbana-Champaign in 2008. Dr. Chambers teaches courses such as Topological Data Analysis and Algorithms. She has been recognized with the Rev. Edmund P. Joyce Award for Excellence in Undergraduate Teaching and serves as an editor for the Journal of Computational Geometry and Journal of Applied and Computational Topology. She is actively involved in initiatives like SafeToC and the Society for Computational Geometry. Her research includes NSF-funded projects, with notable contributions in topological data analysis, geometric algorithms, and educational equity. Over 40 publications span topics like Reeb graphs, medial axis analysis, and plant root morphology. She advises students on cutting-edge interdisciplinary research and advocates for fostering inclusive academic environments.
Yasutoshi Makino is a researcher specializing in ultrasound-based haptics, tactile feedback systems, and human-computer interaction. He has collaborated extensively with Hiroyuki Shinoda and other colleagues, focusing on mid-air haptic displays, noncontact object manipulation, and sensory reproduction. Research Interests Makino's work explores the intersection of acoustics, neuroscience, and engineering to create immersive tactile experiences without physical contact. His innovations include ultrasound-driven actuation mechanisms, thermal sensation rendering, and real-time human motion prediction for robotic systems. Recent Publications The 15 most recent articles highlight advancements in airborne ultrasound tactile displays, texture synthesis via GANs, and applications in guide dog training analysis, virtual reality, and interactive robotics. Key themes include dynamic pressure control , multi-stimulus integration , and low-latency systems . Collaborations Co-authored with Masahiro Fujiwara (43 papers) Collaborated with Hiroyuki Shinoda (145 papers) Worked with Shun Suzuki, Takaaki Kamigaki, and Ryoya Onishi on thermal and mechanical haptic feedback systems.
Simone Barbieri is a Research Engineer (EngD) at Bournemouth University's Centre for Digital Entertainment, funded by EPSRC. He collaborates with Thud Media in Cardiff under the supervision of Dr. Xiaosong Yang and Dr. Zhidong Xiao. His academic background includes a BSc (2012) and MSc (2014) in Computer Science from the University of Cagliari, Italy, where his Master's Thesis developed a curve-skeleton editing tool and inverse-skeletonization algorithm. His research integrates computer graphics with virtual reality, focusing on sketch-based interaction for character posing and deformation. Key interests include VR content creation, animation pipelines, and human-computer interaction. Publications emphasize VR adaptation, 3D modeling, and geometric algorithms, with consistent applications in animation and gaming. Grants include the EPSRC-funded project '3D VR content creation exploiting 2D character animation' (ongoing since October 2015).
Erin Moriarty Wolf Chambers is the Snyder Family Mission Collegiate Professor of Computer Science at the University of Notre Dame's Department of Computer Science and Engineering, with a concurrent appointment in Applied and Computational Mathematics and Statistics. Her research is supported by the National Science Foundation, and she serves as editor for the Journal of Computational Geometry and Journal of Applied and Computational Topology. She is also active in committee work for SafeToC and the Society for Computational Geometry. Her research focuses on computational topology and geometry, combinatorics, and combinatorial algorithms, with additional work in improving STEM education and academic culture. Recent publications demonstrate strong emphasis on topological data analysis methods including Reeb graphs, interleaving distances, and mapper graphs, alongside geometric graph comparisons and curve algorithms on 3-manifolds. She maintains active NSF funding for her research program and teaches undergraduate algorithms courses. While no specific research lab is mentioned, she collaborates widely as evidenced by her service roles and multi-author publications.
Dr. Susanne Wenzel is a postdoctoral researcher and teaching assistant at the Photogrammetry group (IGG - Institute of Geodesy and Geoinformation) at the University of Bonn, and a scientific coordinator at Forschungszentrum Jülich since February 2018. Her academic journey began with studies in Geodesy at the Technical University of Berlin and University of Bonn, following professional training as a surveying technician at the Berlin Senate of Urban Development. Her research focuses on pattern recognition and image interpretation, particularly applying machine learning and deep learning techniques to photogrammetry and remote sensing problems. Wenzel's work prominently features Markov Marked Point Processes and the analysis of symmetries and repeated structures in images, with applications ranging from facade interpretation to oceanographic analysis. Her interdisciplinary approach bridges computer vision, geospatial analysis, and machine learning. The analysis of her 15 most recent publications reveals a strong trend toward applying advanced machine learning techniques, particularly deep learning and self-taught learning approaches, to photogrammetric and remote sensing problems. Her research spans multiple domains including urban modeling (facade interpretation), environmental monitoring (ocean eddies, sea level anomalies), and forensic applications (latent trace detection), demonstrating remarkable versatility while maintaining a core focus on image interpretation methodologies. Faculty Teaching Award 2014 Faculty Award for the best student in 2007 in Geodesy and Geoinformation Turbo-Preis 2007 of Society for Geodesy, Geoinformation and Land Management (DVW) Dr. Wenzel has supervised numerous Master's and Bachelor's students on diverse topics including neural network applications for ocean eddy tracking, hyperspectral imaging for latent trace detection, and deep learning for remote sensing image classification. Her teaching portfolio includes Photogrammetrie I and II courses since 2009, and she managed the development of the Geodetic Engineering Master's program at IGG from 2015-2018. Her research has been supported through positions at both the University of Bonn and Forschungszentrum Jülich, where she contributes to interdisciplinary projects bridging geospatial analysis and machine learning.
Mirko Mauri is a Junior CNRS Professor at the Institute of Mathematics of Jussieu-Paris Rive Gauche, University of Paris Cite. Previously, he held positions as Professor Monge at École polytechnique, group leader of the Junior Trimester Term on Algebraic geometry at the Hausdorff Institute for Mathematics in Bonn, IST-BRIDGE and Marie Skłodowska-Curie postdoctoral fellow at ISTA in Hausel group, Postdoctoral Assistant Professor at University of Michigan, and Postdoctoral Fellow at Max Planck Institute for Mathematics in Bonn. Mauri obtained his PhD in Mathematics at Imperial College London as a member of the London School of Geometry and Number Theory, under the supervision of Paolo Cascini. His doctoral thesis was titled "The geometry of dual complexes." Mirko Mauri's research focuses on several interconnected areas of algebraic geometry and related fields. His primary interests include birational geometry , where he investigates properties of algebraic varieties through birational transformations; non-abelian Hodge theory , particularly the P=W conjecture which connects topology of character varieties with Hodge theory of moduli spaces; and the study of holomorphic symplectic varieties including hyperkähler manifolds and their Lagrangian fibrations. He also works on dual complexes associated with log Calabi-Yau pairs, non-archimedean geometry with applications to essential skeletons, and symplectic topology focusing on Lagrangian fibrations and mirror symmetry phenomena. An analysis of Mauri's recent publications reveals a strong focus on the interplay between birational geometry, Hodge theory, and symplectic structures. His work frequently centers on the P=W conjecture and its geometric implications, the structure of dual complexes in birational geometry, and the study of hyperkähler manifolds and their degenerations. Many of his papers bridge multiple areas, demonstrating how techniques from one field can illuminate problems in another, particularly the connections between algebraic geometry, topology, and non-archimedean geometry. Mauri has organized several significant workshops and reading groups, including the "Higher-dimensional log Calabi–Yau pairs" workshop at the American Institute of Mathematics (2024), the "New advances on the Dolbeault moduli spaces" workshop at École polytechnique (2024), and the "Birational workshop" at the Hausdorff Institute for Mathematics in Bonn (2023). He has also organized reading groups on topics such as the Tate-Shafarevich group, degeneration of hyperkähler varieties, and the geometric Langlands conjecture.
Dr. Christoph von Tycowicz serves as Head of the Research Group "Geometric Data Analysis and Processing" at the Zuse Institute Berlin (ZIB), within the "Visual and data-centric computing" department of the "Mathematics of Complex Systems" division. His research bridges advanced mathematical theory with practical applications in medical imaging, biomechanics, and cultural heritage analysis. He leads multiple interdisciplinary projects connecting mathematics, computer science, and biomedical engineering, with funding from major research initiatives. Dr. von Tycowicz earned his doctoral degree from Freie Universität Berlin in 2014 with a dissertation titled "Concepts and Algorithms for the Deformation, Analysis, and Compression of Digital Shapes" under the supervision of Konrad Polthier. His educational background established the foundation for his current work in geometric data analysis and computational shape modeling. His primary research interests center on Geometric Data Analysis , Shape Analysis , and Manifold-valued Data Processing . He develops mathematical frameworks for analyzing complex shapes in medical imaging, biomechanics, and cultural heritage applications. His work bridges differential geometry with machine learning to create robust methods for shape comparison, classification, and prediction. Dr. von Tycowicz has made significant contributions to Riemannian statistical shape modeling and geometric deep learning, with applications spanning knee osteoarthritis assessment, Alzheimer's disease progression analysis, and archaeological artifact analysis. Analysis of his publication trajectory reveals a sophisticated evolution from foundational geometric methods toward integrated approaches combining differential geometry with deep learning. His recent work increasingly focuses on manifold-valued graph neural networks, shape-based disease grading systems, and longitudinal analysis of anatomical changes. There's a clear trend toward clinical translation, with growing emphasis on applying these methods to specific medical problems like osteoarthritis assessment using data from the Osteoarthritis Initiative and Alzheimer's disease progression modeling. Dr. von Tycowicz has received significant recognition for his contributions: Best Paper Honorable Mention Award @ Eurographics (2016) Best Paper Award (2020) Student Travel Award (2020) Special Mention @ ICLR Computational Geometry & Topology Challenge (2022) As a mentor, Dr. von Tycowicz has supervised doctoral and master's students including Felix Ambellan (doctoral thesis on Efficient Riemannian Statistical Shape Analysis with Applications in Disease Assessment) and Martha Paskin (master's thesis on Estimating 3D Shape of the Head Skeleton of Basking Sharks). He currently leads multiple substantial research projects including WEAR (mathematical solutions for analyzing ancient tools), Model-Regularized Learning of Complex Dynamical Behavior, and Geometric Learning for Single-Cell RNA Velocity Modeling, demonstrating strong grant acquisition capabilities across interdisciplinary domains. The Geometric Data Analysis and Processing research group, which Dr. von Tycowicz heads, develops the open-source Morphomatics library (v4.0) for statistical shape analysis. This Python library implements intrinsic manifold-based methods that maintain geometric consistency while avoiding bias from arbitrary coordinate choices. The group participates in major research networks including MATH+ and BIFOLD, and collaborates extensively with medical researchers at Charité - Universitätsmedizin Berlin and other institutions. Their work spans medical imaging (particularly knee osteoarthritis analysis), biomechanics, archaeology, and machine learning, with a unifying focus on creating geometrically principled methods for analyzing complex shape data.
Dr Marissa Betts is a Senior Lecturer in Earth Sciences at the University of New England (UNE), Australia, specialising in early Cambrian palaeontology and stratigraphy. She is a geologist and invertebrate palaeontologist renowned for integrating shelly fossil biostratigraphy with chemostratigraphy and geochronology to date and correlate lower Cambrian successions across South Australia, Antarctica, China, Mongolia and Canada. Education & Qualifications: PhD (topic and institution inferred as UNE; exact title not stated) Research Interests: Her work centres on the early Cambrian (ca. 538–509 Ma) radiation of complex skeletons, employing both traditional palaeontological description and cutting-edge multiproxy chronostratigraphy. Key themes include: Early Cambrian chronostratigraphy & timescale refinement Shelly fossil biostratigraphy and biozonation Carbon isotope chemostratigraphy for global correlation Bradoriid arthropod palaeobiology, functional morphology and systematics Carbonate sedimentology and microbial-metazoan buildups Acid-leaching methodologies for small carbonaceous fossils Field areas span the Flinders Ranges, Stansbury & Arrowie basins, Transantarctic Mountains, Mongolian Bayan Gol, and Chinese Three Gorges, yielding data critical to palaeogeographic reconstructions of East Gondwana. Publication Trends: Betts’ 2024-25 output reveals intensive focus on integrating high-precision U-Pb ID-TIMS dates with biostratigraphic and chemostratigraphic signals to resolve the Cambrian Series 2–Miaolingian boundary, particularly in South Australian basins. Additional papers explore brachiopod and bradoriid evolution, extinction events (e.g., Sinsk event), and novel shell microstructures, signalling a dual emphasis on high-resolution chronostratigraphy and evolutionary palaeoecology. Scientific Awards & Recognition: Superstar of STEM, Science & Technology Australia (2021-2022) NSW Young Tall Poppy Award (2021) A.H. Voisey Medal, Geological Society of Australia (2021) Australian Research Council DECRA Fellow (2022) Teaching & Supervision: She coordinates GEOL120 Dynamic Earth and GEOL202 Introductory Palaeontology, teaches into GEOL110 Blue Planet and GEOL311 Palaeontology & Stratigraphy, and supervises a vibrant team of Honours and HDR students. Together with Drs Tim Chapman and Luke Milan she co-founded LithoLabUNE , a multidisciplinary geoscience research and teaching hub connecting on-campus and online geoscience students. Outreach & Impact: Committed to diversity in STEM, Betts volunteers as a STEM Coach for Curious Minds Australia and hosts the Sci-Flicks science-film events and podcast in Armidale. She wrote, directed and produced the short film ROLA [STONE] linking geology, landscape and culture.
Dr. Keaton Hamm is an Assistant Professor in the Department of Mathematics and Division of Data Science at The University of Texas at Arlington. His research bridges theoretical mathematics and computational data science, with postdoctoral experience at the University of Arizona and Vanderbilt University. Primary research domains include computational mathematics, manifold learning techniques, optimization algorithms, and tensor decompositions. His work develops novel methods for high-dimensional data analysis with applications in machine learning and scientific computing. Recent publications demonstrate strong focus on Wasserstein space methodologies (2023-2025), advancing techniques in optimal transport, dimensionality reduction, and geometric learning. Machine learning research explores adversarial robustness and federated learning systems, while mathematical contributions include innovations in matrix decompositions and approximation theory. No awards or student information was available in the provided profile.
