Find Academic Supervisors

• Robotics Perception
• Alternative Sensing
• Inertial Fusion
• +6 more

Teresa Vidal-Calleja is an Associate Professor and Research Director at the Robotics Institute, University of Technology Sydney (UTS). She holds a Deputy Head of School (Research) role in the School of Mechanical and Mechatronic Engineering. Her research focuses on robotics perception, alternative sensing, inertial fusion, SLAM, and applications in manufacturing, agriculture, mining, and construction. Education: B.Sc. Mechanical Engineering (UNAM, 2000), M.Sc. Electrical Engineering (CINVESTAV-IPN, 2002), Ph.D. Automatic Control (Polytechnic University of Catalonia, 2007). Postdoctoral Experience: LAAS-CNRS (France), Australian Centre for Field Robotics (Sydney). Research Interests: Teresa’s work spans robotics perception, sensor fusion, autonomous navigation, and collaborative robotics. She leads projects on next-gen infrastructure robotics (ARC Discovery Grant) and is co-lead of the Australian Cobot Centre. Her innovations target hazardous environments, infrastructure maintenance, and human-robot collaboration. Funded Research: Includes grants for robotic sensors, satellite docking systems, and multi-robot collaboration. Recent projects involve Navantia, the NSW Space Research Network, and industry partnerships. Professional Roles: IEEE Senior Member, Treasurer of the Australian Robotics and Automation Association, and Associate Editor for IEEE Transactions on Robotics and other journals. She chairs conferences like the Australasian Conference on Robotics and Automation. Labs/Teams: UTS Robotics Institute, collaborating with global institutions like ETH Zürich and DLR. Her work integrates robotics with industries such as agriculture and construction, emphasizing sustainability and safety.

David ChoppView profile

Professor

• Numerical Methods
• Scientific Computing
• Interface Motion
• +4 more

David Chopp is a Professor of Engineering Sciences and Applied Mathematics at Northwestern University's McCormick School of Engineering. His research focuses on numerical methods, scientific computing, and interface motion, with applications in bacterial biofilms, neurophysiology, and materials science. He holds the Charles Deering McCormick Professor of Teaching Excellence award. Chopp earned his Ph.D. in Mathematics from UC Berkeley and a B.S. in Mathematics and Applied Mathematics from the University of Washington. Research interests include level set methods, computational neuroscience, fracture mechanics, and biofilm modeling. Notable contributions span phase field modeling, microbial fuel cell simulations, and adaptive algorithms for neural systems. His work bridges computational tools with real-world applications in engineering and biology. Key Projects: Phase field modeling with large driving forces (2023), biofilm potassium signaling (2021), non-planar crack tracking (2016). Awards: Charles Deering McCormick Professor of Teaching Excellence. Chopp advises students in biofilm dynamics, computational materials, and fracture mechanics. His lab develops algorithms for interface tracking and neural simulations, collaborating on biofilm bioclogging and material failure analysis. Active in teaching and publishing, he authored textbooks on high-performance computing and contributed to over 60 peer-reviewed articles.

Sebastian KurtekView profile

Professor

• Shape Analysis
• Functional Data Analysis
• Image Analysis
• +4 more

Sebastian Kurtek is a Professor of Statistics at The Ohio State University, affiliated with the College of Arts and Sciences. He holds roles in the Interdisciplinary PhD Program in Biostatistics and Translational Data Analytics. His research focuses on statistical shape analysis, functional data analysis, and Bayesian statistics, addressing challenges in medical imaging, computer vision, and biometrics. Education: PhD in Biostatistics, Florida State University (2012) M.S. in Biostatistics, Florida State University (2009) B.S. in Mathematics, Tulane University (2007) Research Interests: Development of statistical models for complex functional data, including shapes and images Applications in medical imaging, biology, and computer vision Computational tools for Riemannian manifold analysis Publications & Trends: Recent work emphasizes geometric frameworks for analyzing eye tracking heat maps, radiogenomics in tumors, and Bayesian approaches for functional data registration. His articles highlight interdisciplinary applications, combining statistics with fields like computer vision and medical imaging. Awards: Best Paper Awards in computer vision, pattern recognition, and medical imaging NSF funding (multiple grants since 2016) IMS and AMS Travel Awards Grants & Collaborations: NSF DMS-1613054 (2016-2020) NIH R37-CA214955 (2017-2024) Collaborations on endometrial tissue modeling and fossil classification Labs/Teams: Involved in CBMS Conference on Elastic Functional and Shape Data Analysis (EFSDA), advancing methodologies for shape and functional data analysis.

Risi KondorView profile

Associate Professor

• Machine Learning
• Graph Theory
• Quantum Mechanics
• +6 more

Risi Kondor is an Associate Professor in the Departments of Statistics and Computer Science at the University of Chicago. His research focuses on machine learning, group theory applications, and equivariant neural networks. He develops algorithms respecting geometric and physical symmetries, with contributions to graph learning, quantum mechanics modeling, and multiresolution matrix factorization. Key projects include the development of Covariant Compositional Networks (CCNs) for graph-structured data and N-body networks for molecular simulations. He has created software tools like GraphFlow, SnOB (FFT for symmetric groups), and Mondrian for high-performance computing. His work bridges algebraic methods (e.g., Fourier analysis on permutation groups) with machine learning, addressing challenges in multi-object tracking, computer vision, and materials science. Risi Kondor holds grants including a DARPA Young Faculty Award ($500K, 2016–2018) and NSF funding for non-commutative harmonic analysis in machine learning. His research emphasizes theoretical foundations and practical applications, advancing areas like equivariant architectures, multiscale analysis, and symmetry-aware machine learning systems.

Zhizhong HanView profile

Assistant Professor

• 3D Computer Vision
• Digital Geometry Processing
• Artificial Intelligence
• +1 more

Zhizhong Han serves as an Assistant Professor in the Department of Computer Science at Wayne State University, specializing in 3D computer vision and artificial intelligence. Prior to his current position, he completed a three-year postdoctoral fellowship in the Department of Computer Science at the University of Maryland, College Park. His academic credentials include: Bachelor of Engineering (B.E.) in Pattern Recognition and Intelligent Systems from Northwestern Polytechnical University (2009) Master of Engineering (M.E.) in Pattern Recognition and Intelligent Systems from Northwestern Polytechnical University (2012) Doctor of Philosophy (Ph.D.) in Pattern Recognition and Intelligent Systems from Northwestern Polytechnical University (2017) Dr. Han's research centers on 3D Computer Vision , Digital Geometry Processing , and 3D Deep Learning , with emphasis on interpreting 3D structures through point clouds, meshes, and voxel grids. His work tackles fundamental challenges in shape recognition, segmentation, and reconstruction, often leveraging unsupervised learning frameworks to model complex 3D geometries without extensive labeled data. His publication record reveals a concentrated research trajectory in geometric deep learning, evidenced by 10+ papers at premier venues including CVPR, ICCV, and NeurIPS. Key innovations include differentiable rendering techniques, multi-view representation learning, and novel architectures for 3D shape completion—demonstrating consistent contributions to advancing 3D scene understanding capabilities. No scientific awards were documented in the source material. Teaching responsibilities include graduate and undergraduate courses such as Introduction to Deep Learning (CSC4760) and specialized topics in 3D shape analysis (CSC6991), with upcoming instruction in Computer Vision (CSC4860) for Fall 2025. No student advising details or grant funding information were provided. Research activities appear centered on algorithmic development for 3D vision tasks, though specific laboratory affiliations or collaborative teams were not mentioned in the available text.

Heather MacbethView profile

Senior Lecturer

• Kähler Geometry
• Geometric Analysis
• Formal Verification
• +3 more

Dr. Heather Macbeth is a Senior Lecturer in Pure Mathematics at Imperial College London, specializing in Kähler geometry, geometric analysis, and the formalization of mathematics. Her research develops geometric analysis techniques for complex manifolds while advancing proof verification through the Lean theorem prover. She leads the development of Lean's Mathlib library, creating formalizations for differential geometry, functional analysis, and representation theory. Her textbook The Mechanics of Proof introduces proof writing through Lean, integrating computer verification with mathematical pedagogy. Funded by a Microsoft Research Lean Award, Dr. Macbeth organizes workshops on formal mathematics and serves on the AMS Committee on Publications. Her geometric research examines Ricci solitons, Yamabe invariants, and Kähler-Einstein metrics, while her formalization work includes Sobolev inequalities and semilinear functional analysis. Research Contributions: Geometric analysis of Ricci solitons and Kähler metrics Formal verification of functional analysis theorems Proof assistant pedagogy and textbook development Contributions to Lean's mathematical library

Samuel Clowes HunekeView profile

Assistant Professor

• Modern European History
• History of Sexuality
• Legal History
• +4 more

Samuel Clowes Huneke is Assistant Professor of History at George Mason University, specializing in modern European history with particular focus on Germany. His research intersects queer theory, legal history, and Cold War studies, examining how state structures interact with marginalized communities. PhD from Stanford University (2019) MSc in Applicable Mathematics (LSE, 2012) BA in German and Mathematics (Amherst College, 2011) Major publications include States of Liberation (2022), analyzing gay experiences in Cold War Germany, and A Queer Theory of the State (2023), exploring queer theory's engagement with state structures. His co-edited volume Reimagining Citizenship (2025) addresses postwar European identity politics. Recent work examines both GDR and Weimar-era queer histories, with a forthcoming monograph I Will Not Abandon You (2026) on Nazi-era queer women. He directs the East German Poster Database project and contributes to public discourse through media outlets like CNN, Washington Post, and Boston Review. David Barclay Book Prize (2023) Charles E. Smith Award (2022) Waterloo Centre Finalist Active in digital humanities initiatives and Cold War oral history projects, Huneke bridges mathematical precision with historical analysis. His teaching includes courses on Nazi Germany, Cold War Europe, and global sexuality history.

• Combinatorial group theory
• Geometric semigroup theory
• Algorithmic problems in algebra
• +6 more

Robert D. Gray is a Professor of Mathematics at the School of Mathematics, University of East Anglia. His research focuses on combinatorial and geometric group and semigroup theory, algorithmic problems in algebra, decidability, homological finiteness properties, and group actions on graphs and topological spaces. EPSRC Research Fellow Editorial Board: International Journal of Algebra and Computation Available for PhD supervision in semigroup and inverse monoid theory His recent work explores: Topological finiteness properties of monoids Undecidability in one-relator inverse monoids Algorithmic properties of inverse monoids Maximal subgroups in special inverse monoids Key article trends include: Geometric and algorithmic aspects of inverse monoids Homological properties of semigroups Connections between group theory and semigroup theory Applications to graph theory and automata Scientific Awards: EPSRC Fellowship EP/V032003/1 EPSRC grant EP/N033353/1 EPSRC Postdoctoral Fellowship EP/E043194/1 Contact: Room S1.29, School of Mathematics, University of East Anglia, Norwich NR4 7TJ. Email: Robert.D.Gray@uea.ac.uk . Phone: +44 1603 591443.

Jonah GasterView profile

Assistant Professor

• Topology
• Geometric Group Theory
• Combinatorial Topology
• +1 more

Jonah Gaster is an Assistant Professor in the Department of Mathematical Sciences at the University of Wisconsin-Milwaukee, where he also serves as the Program Coordinator for the Topology Research Group and Colloquium Chair. His research focuses on mathematical topology, geometric group theory, and combinatorial structures in low-dimensional topology. His research explores the interplay between combinatorial methods and geometric structures, with specific interests in curve systems on surfaces, harmonic maps between manifolds, and the computational aspects of topological invariants. Recent work investigates combinatorial representations of geometric objects and their applications in low-dimensional topology. Analysis of Dr. Gaster's recent publications reveals a consistent focus on topological combinatorics, surface embeddings, and geometric group theory. His work bridges discrete mathematics with continuous geometric structures, particularly in the contexts of hyperbolic geometry and curve complexes. He leads the Topology Research Group at UWM, fostering collaborative investigations into fundamental questions in geometric topology and discrete mathematics.

Benjamin RaichelView profile

Associate Professor

• Computational Geometry
• Algorithms
• Theoretical Computer Science
• +3 more

Benjamin Raichel is an Associate Professor in the Department of Computer Science at the University of Texas at Dallas, affiliated with the Erik Jonsson School of Engineering and Computer Science. His research focuses on computational geometry and geometric approximation algorithms, with contributions to areas such as Voronoi diagrams, Fréchet distance, clustering, and algorithmic efficiency. Dr. Raichel holds a PhD from the University of Illinois Urbana-Champaign under the supervision of Sariel Har-Peled. He currently teaches Computational Geometry (CS 6319) and has advised multiple PhD students, including Md. Billal Hossain and Jonathan James Perry. His work is supported by NSF grants such as 'Shape Matching in a Messy World Using Fréchet Distance' and 'Metric Violation Distance: Hardness and Approximation.' He is a member of the Algorithms and Theory Group at UT Dallas.

Chengzhou TangView profile

Assistant Professor

• 3D Computer Vision
• Deep Learning
• Robotics
• +3 more

Chengzhou Tang is an Assistant Professor in the Department of Computer Science at the University of Manitoba. His research focuses on 3D computer vision, deep learning, robotics, and embodied artificial intelligence. He leads the Spatial Intelligent System (SPINS) Lab, exploring topics such as 3D object reconstruction, camera localization, and video processing techniques. His work spans applications in robotics, virtual reality, and computational imaging. Dr. Tang's research interests include developing novel algorithms for multi-view diffusion models, deep recurrent optimizers, and subspace methods. He has contributed to advancements in video depth estimation, point cloud processing, and stabilization of 360-degree videos. His recent work emphasizes generative models and embodied AI, addressing challenges in sparse-view 3D reconstruction and scene-agnostic camera localization. His publications reflect a trend toward integrating deep learning with traditional computer vision problems, such as structure-from-motion and SLAM (Simultaneous Localization and Mapping). The SPINS Lab collaborates on projects involving global bundle adjustment networks and initialization-robust monocular SLAM systems. While no awards or grants are explicitly listed, his active publication record indicates sustained research engagement in these areas. Dr. Tang’s academic profile highlights his contributions to foundational techniques in computer vision and their practical applications in robotics and AI systems. His work bridges theoretical advancements with real-world challenges in imaging and spatial intelligence.

Jesus Martinez-GarciaView profile

Senior Lecturer

• Algebraic Geometry
• Complex Geometry
• Birational Geometry
• +3 more

Dr Jesus Martinez-Garcia is a Senior Lecturer in the Department of Mathematical Sciences at the University of Essex, within the School of Mathematics, Statistics and Actuarial Science (SMSAS). His research focuses on algebraic and complex geometry, particularly birational geometry of Fano varieties, K-stability, moduli spaces, and computational algebraic geometry. He has held postdoctoral positions at the University of Bath, Max Planck Institute for Mathematics in Bonn, and Johns Hopkins University, and briefly served as an Adjunct Professor at Korea University. Dr Martinez-Garcia holds a PhD in Mathematics from the University of Edinburgh (2013), an MASt in Mathematics from the University of Cambridge (2009), and dual degrees from Universidad Autónoma de Madrid: MEng in Computer Engineering (2008) and MSci in Mathematics (2008). His research interests include Kähler-Einstein metrics, classification of Fano varieties, geometric invariant theory (GIT), and computational methods in algebraic geometry. He has contributed to foundational work on K-moduli spaces, asymptotic log Fano varieties, and moduli compactifications. Dr Martinez-Garcia’s publications span theoretical advancements and computational tools, including the Variations of GIT quotients package. His work bridges algebraic geometry with differential geometry, emphasizing connections between stability conditions and geometric structures. Grants and funding details are listed on his profile, though specific grants are not detailed here. He actively advises students in his field and contributes to teaching at Essex, with academic support hours coordinated via Moodle. He is affiliated with STEM 5.9 on the Colchester Campus and can be reached via email at jesus.martinez-garcia@essex.ac.uk .

Aakriti UpadhyayView profile

Research Fellow

• Robotics
• Motion Planning
• Topological Data Analysis
• +3 more

Aakriti Upadhyay is a Research Fellow at the Colorado School of Mines (CSM), working in the Dynamic Automata Lab (DyALab) under Dr. Neil T. Dantam. She holds a Ph.D. in Computer Science from the University at Albany, SUNY, where she was part of the Robotics Algorithms and Computable Systems (RACS) lab under Dr. Chinwe P. Ekenna. Her research integrates Computational Geometry, Applied Mathematics, Machine Learning, and Topological Data Analysis (TDA) to advance motion planning for robotics and computational biology applications. Her work focuses on optimizing robot motion paths using topological methods, with extensions to identifying protein interaction sites. Key areas include robust path planning, fault tolerance in robotic systems, and topological modeling of biomolecular surfaces. Her research trends emphasize interdisciplinary applications of TDA, bridging robotics and computational biology. Collaborations include developing tools for Intrinsically Disordered Proteins (IDPs) analysis and enhancing path planning algorithms through geometric and topological insights. Aakriti has contributed to academic initiatives like the Student Activities Committee and advocated for women in engineering during the pandemic. Her work reflects a commitment to advancing both technical and community-oriented academic goals.

Ioannis SgouralisView profile

Assistant Professor

• Data Science
• Applied Mathematics
• Mathematical Biology
• +7 more

Ioannis Sgouralis is an Assistant Professor in the Department of Mathematics at The University of Tennessee, Knoxville. He holds a Ph.D. from Duke University and specializes in Data Science and Applied Mathematics with a focus on interdisciplinary research in Physics, Chemistry, Biology, and Medicine. His research group integrates computational methods with experimental data to address challenges in biophysics, biochemistry, and biomedicine. His educational background includes a Ph.D. in a relevant field (specific details not provided). His research interests span Bayesian nonparametrics, computational statistics, mathematical biology, and multiscale modeling. He leads projects on single-molecule biophysics, image analysis, and cardiovascular system modeling. Key research areas include: Single molecule experiments and analysis Super-resolution microscopy and multi-particle tracking Biomedical systems modeling (cardiovascular/urinary) Statistical methods for noisy experimental data Ioannis actively recruits graduate/undergraduate students for projects in Data Science, Mathematical Biology, and Applied Mathematics. His group uses advanced computing techniques such as Hamiltonian Monte Carlo and topological data analysis. Labs/Teams: Sgouralis Research Group focuses on cross-disciplinary computational methods for biological systems. Their work involves collaborations across departments and institutions.

Archontia C. GiannopoulouView profile

Associate Professor

• Graph Algorithms
• Structural Graph Theory
• Parameterized Algorithms and Complexity
• +3 more

Archontia C. Giannopoulou is an Associate Professor at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Greece. Her research focuses on Graph Algorithms, Structural Graph Theory, and Parameterized Algorithms and Complexity. She explores advanced topics such as matching minors, tree-cut width, immersion minors, and kernelization. Her work emphasizes algorithmic design and structural graph theory with applications in combinatorial optimization. Giannopoulou’s academic contributions include seminal papers on edge deletion problems, cutwidth obstructions, and polynomial kernelization. Notable publications include studies on bipartite graphs and their structural properties, as well as algorithmic advancements in parameterized complexity. Her research bridges theoretical foundations with practical algorithmic solutions. Her contact information includes an email at archontia.giannopoulou@gmail.com and a postal address at the University of Athens. Her work spans international collaborations, reflected in co-authored articles with leading researchers in the field.