Associate Professor Christopher Angstmann is an applied mathematician at the School of Mathematics & Statistics , University of New South Wales. His research spans complex dynamical systems, stochastic modeling, and fractional calculus, with applications in physics, biology, and finance. His work focuses on integrating fractional derivatives into differential equations, developing models for Anomalous diffusion Fractional compartment systems Time-delay SIR models Pattern formation in biological systems Recent publications highlight advancements in Space-fractional diffusion on finite domains Exact solutions for time-delay advection equations Stochastic simulation of fractional compartment models Applications to epidemiology and nanotechnology Contact: c.angstmann@unsw.edu.au . Affiliated with the University of New South Wales, Sydney, Australia.
Dr. Upanshu Sharma is a Lecturer at the School of Mathematics & Statistics , UNSW Sydney since 2023. His research spans partial differential equations , probability theory , and computational statistical mechanics , focusing on coarse-graining of stochastic dynamics, large deviations, and molecular dynamics sampling algorithms. 2023–Present: Lecturer, School of Mathematics & Statistics, UNSW Sydney 2021–2022: Humboldt Research Fellow, Computational Statistical and Biological Physics Group (FU Berlin) & Stochastics and Applications Group (BTU Cottbus-Senftenberg) 2019–2020: PostDoc, Institute of Mathematics (FU Berlin) 2017–2019: PostDoc, CERMICS (École des Ponts ParisTech) Education: PhD in Mathematics (2017), CASA (TU Eindhoven) Research Themes: Dr. Sharma's work bridges mathematical modeling and physical applications through rigorous analysis of stochastic systems. Key areas include variational structures for non-equilibrium systems, quantitative coarse-graining in multi-scale dynamics, and sampling algorithms for molecular simulations. Publication Trends: Recent articles (2024–2022) emphasize Markov chain analysis , non-equilibrium thermodynamics , and Hamiltonian stochastic systems , with sub-field coverage in error quantification, parallel computing, and hyperbolic PDEs. Scientific Recognition: Humboldt Research Fellowship (2021–2022)
Bodo Rosenhahn is a Full Professor at Leibniz University Hannover, heading the Institute for Information Processing since September 2008. His research focuses on automated image interpretation with profound expertise in Computer Vision, Machine Learning, and Big Data Analysis. He has established himself as a leading researcher through extensive contributions to the field and successful industry transfer of his work. Rosenhahn received his Computer Science education at the University of Kiel, earning his Dipl.-Inf. in 1999 and Dr.-Ing. in 2003. His academic journey included a postdoctoral position at the University of Auckland (2003-2005), funded by the German Research Foundation, followed by senior researcher work at the Max-Planck Institute for Informatics in Saarbruecken (2005-2008). His research interests span multiple cutting-edge areas including Computer Vision, Machine Learning, 3D Human Pose Estimation, Motion Capture, Object Tracking, Anomaly Detection, and Reinforcement Learning. His work bridges theoretical foundations with practical applications, particularly in medical imaging, autonomous systems, and industrial quality control. The group he leads has developed innovative approaches for video-based motion capture, semantic scene analysis, and multi-object tracking that have achieved state-of-the-art results in numerous challenges. His most recent publications demonstrate strong trends toward explainable AI systems, uncertainty quantification in vision models, robust multi-model fitting techniques, and the integration of quantum principles with machine learning. These works reflect his commitment to developing both theoretically sound and practically applicable computer vision solutions that address real-world challenges in industry and medicine. DAGM-Prize 2002 Dr.-Ing. Siegfried Werth Prize 2003 DAGM-Main Prize 2005 ERC-Starting Grant 2011 (EUR 1.43 million) CVPR 2017 Multi-Object Tracking Challenge PhysRev-A Editors Suggestion 2023 TÜV-Süd Innovation award 2018 As head coach of the LUH AI competition team, Rosenhahn has mentored numerous students who have achieved success in international competitions. His research has been supported by prestigious grants including the ERC Starting Grant and POC Grant. He has also received the Erskine Fellowship for research at the University of Canterbury. Since 2023, he serves as associate editor for IEEE TPAMI, the highest-ranked journal in computer science. Rosenhahn leads a vibrant research group focused on automated image interpretation with multiple ongoing projects including Multiple People Tracking, Relational Object Tracking, Physics-based modeling, Video-based Motion Capture, and Quantum Learning. His group has developed significant datasets such as the Multimodal Motion Capture Indoor Dataset (MPI08) and Multimodal Motion Capture Dataset (TNT15) that have become valuable resources for the computer vision community. The group maintains strong industry connections, successfully transferring research into practical applications while continuing to push the boundaries of fundamental research in computer vision and machine learning.
Albert Alonso is a Research Fellow at the Department of Computer Science within the Faculty of Science at the University of Copenhagen . He is affiliated with the Niels Bohr Institute and holds the position of Guest PhD Student in addition to his postdoctoral role. Postdoctoral Fellow in Biocomplexity and Biophysics Guest PhD Student Image Analysis and Computational Modeling Albert’s research focuses on computational biology , biophysics , and image analysis , with particular emphasis on gradient sensing and biological transport networks . His work bridges theoretical physics and computer science to develop differentiable computational methods for understanding microorganism behavior and cell motility . Recent publications highlight advancements in optimal node positioning , non-reciprocal chaotic systems , and chemotaxis strategies . His studies often integrate computational modeling with biological systems , including applications in high-density microscopy data analysis and receptor dynamics for spatial gradient sensing. Albert’s work has been published in high-impact journals such as Physical Review Letters , PNAS , and Communications Biology . He employs open-access platforms and collaborates with institutions globally, with a focus on Denmark and international networks .
Dr. Sarah Razzaqi is an Honorary Fellow at the School of Mechanical and Mining Engineering at The University of Queensland. Her primary research focuses on hypersonics and scramjet propulsion systems, with special expertise in oxygen enrichment techniques for improving scramjet performance. Dr. Razzaqi's research interests span multiple areas within aerospace engineering: Hypersonic propulsion systems, particularly scramjet engines Oxygen enrichment techniques for scramjet performance enhancement Experimental hypersonic flow characterization Freejet and shock tunnel testing of hypersonic vehicles HIFiRE (Hypersonic International Flight Research and Experimentation) program Computational modeling of hypersonic combustion Her extensive publication record from 2006-2021 demonstrates a strong focus on experimental and computational approaches to hypersonic propulsion. Dr. Razzaqi has made significant contributions to the understanding of oxygen enrichment in scramjet systems, which has implications for improving the efficiency and performance of hypersonic vehicles. Her work has been integral to the HIFiRE program, an international collaboration between Australia and the United States focused on hypersonic flight research, particularly with the HIFiRE 7 scramjet flowpath testing at Mach 7.5. Dr. Razzaqi completed her PhD at The University of Queensland in 2011 with a dissertation titled "Oxygen Enrichment in a Hydrogen Fuelled Scramjet." Her early career also included research in phased array ultrasonic transducers and optimization algorithms, demonstrating a multidisciplinary approach to engineering problems that likely informs her current hypersonics research. Her contact information includes email s.razzaqi@uq.edu.au and phone +61 7 336 53668. As an Honorary Fellow, she maintains an active research affiliation with the university while potentially pursuing other professional activities in the aerospace sector.
Varvara Kouznetsova is an Associate Professor in Multi-scale Mechanics of Solids in the Mechanics of Materials group at the Department of Mechanical Engineering of Eindhoven University of Technology (TU/e). Her research focuses on understanding, predicting, and tailoring structure-property-performance relations in various materials based on underlying microstructural phenomena. Dr. Kouznetsova holds a degree in Applied Mathematics from Perm State Technical University, Russia, and a PhD in Mechanical Engineering from TU/e. From 2002 to 2009 she was a research fellow at the Netherlands Institute for Metals Research (NIMR) and the Materials innovation institute (M2i). She served as an Assistant Professor at Eindhoven University of Technology from 2006 to 2018 before becoming an Associate Professor. Her research interests include: Multi-scale mechanics of solids Computational homogenization techniques Metamaterials and their emergent properties Wave propagation phenomena in structured materials Damage and fracture mechanics Mechanics of advanced high-strength steels Dr. Kouznetsova's recent publications demonstrate a strong focus on multi-scale modeling approaches applied to metamaterials, porous solids, and advanced metallic materials. Her work bridges fundamental methodological developments with practical applications across various materials systems. She has made significant contributions to computational homogenization techniques, particularly for transient phenomena and locally resonant structures. She has received substantial research recognition with over 6,000 citations according to Scopus metrics. Dr. Kouznetsova teaches courses including "Composite and light-weight materials: design and analysis," "Advanced computational continuum mechanics," "Computer aided engineering," and "Material models." She also engages in research collaboration and co-supervision of PhD researchers with Keio University.
Tan Chen serves as Assistant Professor in the Department of Electrical and Computer Engineering at Michigan Technological University, where he directs the Robotics, Locomotion, and Applied Control (RoLAC) Lab. His research bridges robotics, nonlinear control, and AI with applications in healthcare and smart manufacturing. His educational background includes: PhD in Aerospace and Mechanical Engineering, University of Notre Dame (2021) MS in Mechanical Engineering, Shanghai Jiao Tong University (2016) MEng in Optimisation et Automatisation des Processus Industriels, Mines Douai (2014) BS in Mechanical Engineering, Shanghai Jiao Tong University (2013) Chen's research focuses on legged locomotion systems where he develops novel control frameworks integrating geometric nonlinear methods with machine learning. His work spans rehabilitation robotics, collaborative robots for industrial applications, and learning-based control systems. Current projects include Human Locomotion analysis, FractionalNet dynamics modeling, and tethered robotics for complex environments. The RoLAC Lab maintains advanced facilities including GoFish quadruped robot, Ecer humanoid robot, and custom biped platforms. His publication trends show increasing integration of AI with traditional control theory, particularly in motion planning for legged robots and human-robot collaboration systems. Recent work demonstrates significant advancements in robust control for uncertain terrains and industrial assembly tasks. Notable scientific achievements include: Ralph E. Powe Junior Faculty Enhancement Award (2024) NSF CRII Award (2024) ICC Achievement Award for bipedal locomotion research (2024) DAAD AInet fellowship for Postdoc-NeT-AI in robotics Eiffel Scholarship recipient during French studies Chen actively advises multiple PhD students including Shivayogi Akki, Anders Smitterberg, and Dhanush Biligiri while securing competitive grants from NASA and NSF. His NASA-funded project with Prof. Steven Elmer focuses on human locomotion modeling, and NSF support enables fundamental research in robotic control systems. He mentors students through Michigan Tech's Summer Youth Program and maintains strong industry connections through advisory roles and collaborative projects. The RoLAC Lab operates as a multidisciplinary research hub with facilities including GoFish quadruped robot, Ecer humanoid platform, 3D printers, and instrumented treadmill systems. Recent acquisitions like the Ecer humanoid robot (June 2025) enable new research directions in whole-body control and human-robot interaction.
Michael Stone is Professor of Physics at the University of Illinois at Urbana-Champaign, with a joint appointment in the Department of Physics, College of Engineering. He has been on the faculty since 1981, following a PhD from the University of Cambridge (1976) and a period as Deputy Director of the Institute for Theoretical Physics at UCSB (1992–1994). He serves on the editorial boards of International Journal of Modern Physics B , Modern Physics Letters B , and Physical Review Letters , where he was divisional associate editor (1997–1999). Education Ph.D., Particle Physics (Applied Mathematics and Theoretical Physics), University of Cambridge, 1976 Research Interests Professor Stone’s work lies at the intersection of quantum field theory and condensed-matter physics. His current focus is the dynamics of vortices in superfluids and superconductors, including the long-standing puzzle of dissipation mediated by Abrikosov vortices under Magnus force. He has illuminated how topological constraints induce anomalous behaviors—linking these to Berry phases—and has extended bosonization techniques to broader model classes. Additional areas include quantum Hall edge states, chiral kinetic theory, Majorana fermions, and the role of geometric phases in strongly coupled systems. Scientific Awards & Honors Fellow, Institute of Physics (UK) – 2011 Fellow, American Physical Society – 2009 Teaching & Pedagogical Contributions Professor Stone regularly teaches core graduate courses and has authored extensive online lecture notes. Recent offerings include: PHYS 211 – University Physics: Mechanics PHYS 508 – Mathematical Physics I PHYS 509 – Mathematical Physics II He has also co-authored the graduate textbook Mathematics for Physics with Paul Goldbart and maintains a popular webpage of short pedagogical essays and research notes.
Benjamin Brubaker is a Professor and Department Head at the School of Mathematics, University of Minnesota. He received his PhD from Brown University in 2003 and has held academic positions at Stanford University (2003-2006), MIT (2006-2012), and the University of Minnesota (2012-present). His primary research interests lie at the intersection of analytic number theory and representation theory, with specific focus on automorphic forms, representations of algebraic groups, and their generalizations on arithmetic covering groups. His work frequently employs combinatorial methods, particularly lattice models from statistical mechanics, to address problems in representation theory and number theory. His recent publications demonstrate a strong trend toward connecting metaplectic Whittaker functions with solvable lattice models, crystal bases, and combinatorial representation theory. This interdisciplinary approach bridges number theory, representation theory, mathematical physics, and algebraic combinatorics. Scientific Awards and Funding: NSF Grant DMS-2101392 (current) NSF Grant DMS-1801527 (previous) Professor Brubaker has advised numerous PhD students throughout his career, with nine students graduating from the University of Minnesota and MIT. His current research group includes three PhD students working on topics related to Hecke algebras, metaplectic forms, and p-adic representation theory. His collaborative work, particularly with Dan Bump, has significantly advanced the understanding of metaplectic Whittaker functions and their connections to combinatorial structures.
Robert Thomson is a Professor at Chalmers University of Technology’s Mechanics and Maritime Sciences (M2) department, specializing in Vehicle Safety . With over 35 years of experience since 1988, his work spans crash testing, accident analysis, numerical modeling, and vehicle test data interpretation. Key research themes include injury mechanisms in collisions, crash compatibility between vehicles and infrastructure, and biomechanical modeling of human reflexes and posture. Current Projects : Road Work Warning II (VINNOVA), Säkrare utformning av lastbilsfront (Swedish Transport Administration), Virtual Evaluation Tools for Pedestrian Safety (VINNOVA) Methodologies : Finite element human body models, driving simulators, uncertainty analysis, real-world data modeling Recent publications focus on: Pedestrian/cyclist trajectory prediction using Social-LSTM Gender-specific head-neck biomechanics in low-speed impacts Crash compatibility assessments for heavy goods vehicles Balance recovery strategies in public transport passengers He actively collaborates with: Organizations : VINNOVA, European Commission, Swedish Transport Administration, RISE Research Institutes Institutions : KTH Royal Institute of Technology, Lund University, Technical University of Denmark
Davide Stocco is a Research Fellow at the Department of Industrial Engineering, University of Trento. He also serves as a Teaching Assistant in the same department, contributing to courses like Computational Methods for Mechatronics and Mechatronic Systems Simulation. His research focuses on symbolic-numerical analysis, differential-algebraic equations (DAEs), and computational mechanics. He develops tools for real-time simulations, tire-ground modeling, and matrix factorization techniques. Recent publications reveal trends in DAE index reduction using symbolic computation, parallel processing of bordered matrices, and geometric modeling for mechatronic systems. His work bridges symbolic mathematics with practical engineering applications. As a Teaching Assistant, he collaborates with Professors Enrico Bertolazzi and Francesco Biral in the Mechatronics Engineering program. He actively contributes to academic research and teaching at the University of Trento.
Professor Junbin Gao is Professor of Big Data Analytics at the University of Sydney Business School. He previously served as Professor in Computing (2010-2016) and Associate Professor (2005-2010) at Charles Sturt University, and held academic positions at University of New England and University of Southampton. He is also a Member of the Sydney Southeast Asia Centre, demonstrating his regional academic engagement. Professor Gao earned his BSc and MSc from Huazhong University of Science and Technology (HUST) and PhD from Dalian University of Technology (DUT). His academic journey includes Professor at HUST (1997), Postdoctoral Research Fellow at Wuhan University (1991-1993), and multiple guest professorships at leading Chinese institutions including Wuhan University, HUST, and Beijing University of Technology. Professor Gao's research spans from approximation theory and multivariate spline functions to wavelet applications, and now focuses on machine learning and big data analytics. His recent work explores matrix neural networks, tensorial recurrent neural networks, and innovative approaches to data subspace clustering on manifolds. He integrates Riemannian geometry with machine learning to address challenges with manifold-valued data, finding applications in international relations, financial panel data, and computer vision. His publication record shows a clear progression toward increasingly sophisticated applications of machine learning in business analytics. Professor Gao has secured significant research funding including two Discovery Project grants from the Australian Research Council (ARC). His 260 academic papers and two books demonstrate substantial scholarly impact across multiple domains of data science and machine learning. Professor Gao supervises numerous research students working on cutting-edge topics including multivariate volatility forecasting, interpretable uncertainty systems, graph machine learning for consumer behavior, and deep learning for stock market prediction. His research group bridges theoretical advances with practical applications in business analytics, with particular interest in international relations research and financial panel data analysis.
Holger R. Dullin is a Professor of Applied Mathematics at the University of Sydney's School of Mathematics and Statistics. His research spans multiple areas of dynamical systems theory with particular emphasis on Hamiltonian systems. He maintains an active research program with consistent publications in top mathematical physics journals and teaches advanced courses including Lagrangian and Hamiltonian Dynamics (MATH3977), Nonlinear ODEs (MATH3063), and Linear Algebra (MATH1902). Dullin's research focuses on Hamiltonian Dynamical Systems , with significant contributions to Integrable Systems (particularly topology, action-angle variables, and Hamiltonian/Quantum Monodromy), Classical Mechanics (N-body problems, rigid body dynamics), Bifurcation Theory (twistless bifurcations, Hamiltonian Hopf), and Fluid Dynamics (Euler equations). His work often bridges pure mathematics with physical applications, especially in celestial mechanics and biomechanics. He has developed novel approaches to understanding geometric phases, symplectic invariants, and the dynamics of Hamiltonian maps including billiards. Analysis of his recent publications reveals a consistent focus on monodromy phenomena across different physical systems, regularization techniques for singularities in dynamical systems, and stability analysis of fluid flows. His work shows increasing interdisciplinary connections between mathematical physics, quantum mechanics, and celestial mechanics, with several papers exploring the geometric structure of integrable systems and their quantum counterparts. The research demonstrates sophisticated mathematical techniques applied to concrete physical problems. Dullin maintains an active research group evidenced by numerous collaborations with mathematicians internationally. His work on the Kovalevskaya top, documented in his PhD thesis and subsequent publications, remains influential in the field of integrable systems. He has developed visualization techniques for complex dynamical systems, including Poincaré sections and energy surfaces in action space.
Professor Geoff Webb is a world-leading data scientist at Monash University , serving as Director of the Monash University Centre for Data Science within the Faculty of Information Technology . His research focuses on leveraging data science to enable evidence-based decision making and derive actionable insights through artificial intelligence, machine learning, and big data analytics. Core expertise in data mining , bioinformatics , and computational biology Developed Magnum Opus software and contributed to the Weka machine learning workbench Recipient of the Eureka Prize for Excellence in Data Science and leadership roles in major data mining conferences Research Interests Professor Webb's work spans artificial intelligence , machine learning , and data analytics , with a focus on black-box user modelling , interactive data analytics , and statistically-sound pattern discovery . His recent publications highlight advancements in Bayesian networks , time series analysis , and genomic data interpretation , demonstrating his interdisciplinary impact. Scientific Contributions AI in healthcare : EHR-ML framework for clinical records analysis Biological applications : KcatNet for enzyme prediction, PFresGO for protein function Data mining innovations : OPUS search algorithm, proximity forest techniques As a technical adviser to data science company Froomle , he bridges academic research with real-world applications. His work has been recognized through numerous research awards and leadership as Editor-in-Chief of Data Mining and Knowledge Discovery for a decade.
Mahesh Sunkula is an Assistant Professor of Practice in the Department of Mathematics at Purdue University, part of the College of Science. His research focuses on Computational and Applied Mathematics , addressing complex problems at the intersection of theoretical frameworks and real-world applications. He holds a position emphasizing both teaching and research excellence. While no formal student advisees or awards are listed in the provided information, his scholarly work spans diverse mathematical domains. Recent publications reflect expertise in quantum systems, optimization algorithms, and interdisciplinary environmental modeling. His office is located in MATH 842, and he can be reached via email . No specific grants or labs are mentioned in the text, though his personal website likely provides further details on collaborative projects or ongoing research initiatives.
