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Xiajun JiangView profile

Assistant Professor

• Adaptive AI Computing
• Physics-Informed Deep Learning
• AI for Healthcare
• +3 more

Xiajun Jiang is an Assistant Professor in the Department of Computer Science at the University of Memphis, joining in Fall 2024. He holds a PhD in Computing and Information Sciences from Rochester Institute of Technology (2024), an M.S. in Computer Science from the University of Southern California (2018), and a B.S. in Electrical Engineering and Automation from Zhejiang University (2016). His research focuses on adaptive AI computing, physics-informed deep learning, and their applications in healthcare, particularly in medical imaging and cardiac simulation. Key contributions include hybrid neural state-space modeling for electrocardiographic imaging and physics-informed frameworks for bi-ventricular electrophysiological simulations. Education: PhD, Rochester Institute of Technology, 2024 M.S., University of Southern California, 2018 B.S., Zhejiang University, 2016 Research Interests: Machine learning for healthcare Adaptive computing in AI models Physics-informed deep learning His work bridges machine learning and biomedical engineering, with applications in cardiac imaging and electrophysiology. Recent articles highlight advancements in hybrid models for ECGI and meta-learning approaches for personalized cardiac simulations. He has reviewed for top conferences like ICLR, NeurIPS, and MICCAI, and contributed to projects like the Computational Biomedical Lab (CBL).

• Wireless Networks
• Signal Processing
• 5G/6G Communications
• +3 more

Dr. Wibowo Hardjawana is a Senior Lecturer in Telecommunications Engineering at the School of Electrical & Computer Engineering , University of Sydney. He holds a PhD from the University of Sydney and serves as an ARC DECRA Research Fellow. His research focuses on wireless network softwarisation, enabling programmable radio interfaces to address traffic elasticity in 5G/6G systems. Education : PhD (University of Sydney) Grants : ARC DP210100744 (2021), ARC DECRA DE140101114 (2014) His work spans 5G/6G network architectures , machine learning for wireless systems , and open radio interfaces . Key contributions include graph representation learning for interference management, Bayesian neural network detectors for OTFS modulation, and NOMA decoding techniques . Recent publications analyze ultra-reliable low-latency communications , UAV-enabled networks , and stochastic geometry in wireless systems . He has collaborated with institutions in China, Indonesia, and UAE, and engaged with industry partners like Telstra and Ausgrid.

• Biomedical Engineering
• Computational Fluid Dynamics
• Fluid-Structure Interaction
• +5 more

Dr. Xinying Liu is a Researcher at the University of Sydney's School of Chemical and Biomolecular Engineering, specializing in Computational Fluid Dynamics (CFD), Fluid-Structure Interaction (FSI), and biomedical engineering applications. She is a member of the University of Sydney Nano Institute and holds a PhD (2023) and bachelor's degree (2017) from the same institution. Education: Bachelor's Degree: The University of Sydney (2017) PhD in Chemical and Biomolecular Engineering: The University of Sydney (2023) Research Focus: Dr. Liu's work addresses biomedical challenges through advanced modeling techniques, including cardiovascular hydrodynamics, gastric flow systems, and bioinspired polymeric heart valve design. She bridges engineering and medicine to develop personalized healthcare solutions and non-thermal plasma technologies for PFAS remediation. Her projects emphasize multiphysics simulation and interdisciplinary collaboration. Current Projects: Polymeric heart valve replacements with growth capability Non-Thermal Plasma applications for PFAS remediation Advising & Collaborations: Dr. Liu advises Parham VATANKHAH on flow dynamics in human aortas. Her collaborations span computational modeling, experimental validation, and industry partnerships for healthcare innovation. Labs & Affiliations: Member of the University of Sydney Nano Institute, actively contributing to bioengineering and materials science research.

Chandrika SadanandView profile

Assistant Professor

• Low Dimensional Topology
• Geometry
• Hyperbolic Geometry
• +3 more

Chandrika Sadanand is an Assistant Professor in the Department of Mathematics at Bowdoin College. She holds a PhD from Stony Brook University and a BS from the University of Toronto. Her research focuses on low-dimensional topology and geometry, specifically exploring curves on surfaces, hyperbolic geometry, billiards, translation surfaces, and 3-manifolds. She has held postdoctoral positions at the University of Illinois Urbana Champaign, Technion, and Hebrew University of Jerusalem. Dr. Sadanand teaches courses in mathematical reasoning, geometry, and topology. Her recent courses include MATH 2020 (Introduction to Mathematical Reasoning), MATH 2404 (Geometry), and MATH 3402 (Topology). She has also mentored undergraduate research projects on flat surfaces and participated in outreach activities through programs like the Stony Brook Math Summer Camp and WISE. Her research contributions include studies on translation surfaces of infinite type, billiards dynamics, and geometric structures. She has contributed to innovative conferences like the Nearly Carbon Neutral Geometry and Topology Conferences through video presentations. Her work bridges pure mathematics with computational methods, addressing questions in geometric topology and dynamical systems.

Luis Nunes VicenteView profile

Professor

• Continuous Optimization
• Computational Science and Engineering
• Machine Learning
• +4 more

Luis Nunes Vicente is the Timothy J. Wilmott '80 Endowed Faculty Professor and Chair of Lehigh University's Department of Industrial and Systems Engineering since August 2018. He previously served as a faculty member at the University of Coimbra's Department of Mathematics. His research focuses on Continuous Optimization, Computational Science and Engineering, and Machine Learning/Data Science. Education: Ph.D. in Applied Mathematics (Rice University, 1996), M.A. in Applied Mathematics (Rice University, 1994), B.S. in Mathematics and Operations Research (University of Coimbra, 1990). Honors include the Lagrange Prize (2015), SIAM Fellowship (2024), and Fulbright Scholarship (1996). He co-authored the influential book Introduction to Derivative-Free Optimization (2009). Research Interests: Development of optimization algorithms for derivative-free and stochastic scenarios, multi-objective optimization, machine learning applications, and computational methods for engineering problems. Key contributions include trust-region methods, bilevel optimization frameworks, and fairness-aware machine learning models. Grants and Leadership: Secured over $1M from the Office of Naval Research (2024) and the Air Force Office of Scientific Research (2023). Served as Editor-in-Chief of Portugaliae Mathematica (2013–2018) and on editorial boards of top journals like SIAM Journal on Optimization . Elected President of the Operations Research Association of Chairs (2024). Visiting Positions: IBM T.J. Watson Research Center (2002/2003), Courant Institute/NYU (2009/2010), and CERFACS/Toulouse (2010–2015). Active in international conferences, delivering plenary lectures at 13th French-German Conference on Optimization, ICCOPT III, and ISMP 2018.

Ziqi SunView profile

Professor

• Inverse Problems
• Partial Differential Equations
• Conformal Geometry
• +3 more

Ziqi Sun is a Professor and Chair of the Department of Mathematics, Statistics, and Physics at Wichita State University, affiliated with the Fairmount College of Liberal Arts and Sciences. He earned his PhD in Mathematics from UCLA in 1987. Before joining Wichita State University in 1990, he held positions at the University of Washington. His academic promotions included Associate Professor (1992) and Professor (1997). Research Interests: Dr. Sun focuses on inverse problems in partial differential equations (PDEs), conformal geometry, and nonlinear materials. His work includes solving classical issues in inverse boundary value problems for elliptic equations and inverse scattering theory. He has pioneered geometric frameworks linking nonlinear inverse problems to linear theory, yielding uniqueness results for anisotropic elliptic equations. Key Contributions: Supported by three NSF grants (1990–1998), his research has addressed isotropic/anisotropic Calderon problems and nonlinear elasticity. Current work emphasizes nonlinear inverse boundary value problems in anisotropic media. His collaborations have resolved foundational questions in inverse scattering and vector potential analysis. Awards/Grants: While no named awards are listed, his sustained NSF funding underscores research impact. His academic leadership includes departmental chairmanship, balancing administration with active research and teaching roles.

Robert SeiringerView profile

Professor

• Quantum many-body systems
• Mathematical physics
• Quantum statistical mechanics
• +5 more

Robert Seiringer is a Professor at the Institute of Science and Technology Austria (ISTA). His academic career spans institutions including McGill University (Associate Professor, 2011–2013) and Princeton University (Assistant Professor, 2003–2010; Postdoc, 2001–2003). His research focuses on quantum many-body systems , mathematical physics , and quantum statistical mechanics , with significant contributions to Bose-Einstein condensation, superfluidity, and polaron models. Research Trends : His recent work explores superconductivity (BCS theory), quantum gases (Bose-Einstein condensation, Landau-Pekar equations), and mathematical tools for non-perturbative analysis. Key subfields include stability of many-body systems, spectral theory, and Density Functional Theory. Awards : Fellow, American Mathematical Society Corresponding Member, Austrian Academy of Sciences ERC Advanced Grant Henri Poincaré Prize (IAMP) Alfred P. Sloan Fellow Erwin Schrödinger Fellow Collaborations : Leads a research group at ISTA with postdocs (e.g., Davide Desio, Lorenzo Pigozzi) and collaborators (e.g., C. Hainzl, B. Schlein).

• Geometric Measure Theory
• Optimal Transport
• Calculus of Variations
• +3 more

Luigi Ambrosio is a Full Professor at the Scuola Normale Superiore di Pisa (SNS), specializing in geometric measure theory, optimal transport, and partial differential equations. His research focuses on the interplay between geometric analysis, functional analysis, and calculus of variations, with applications to metric measure spaces and stochastic processes. He has organized numerous conferences and schools on optimal transport and geometric analysis, including the 2025 'XXXV Convegno Nazionale di Calcolo delle Variazioni.' Key research interests include the theory of currents, regularity of flows, and the application of optimal transport to problems in probability and geometry. Notable contributions include foundational work on metric Sobolev spaces, RCD spaces, and the analysis of geometric flows. Ambrosio frequently collaborates with leading institutions and has supervised numerous seminars on topics ranging from gradient flows to non-smooth geometric structures. His publications span over 150 papers, addressing topics such as the regularity of vector fields, entropy flows in Carnot groups, and the stability of action functionals. Recent works (2021–2025) explore superposition principles for currents, sharp PDE estimates for random matching, and embedding theorems for metric spaces. Ambrosio is also active in academic leadership, contributing to editorial boards and international research networks.

Simon MasnouView profile

Professor

• Applied Mathematics
• Image Processing
• Shape Optimization
• +5 more

Simon Masnou is a Full Professor at Université Claude Bernard Lyon 1, affiliated with the Institut Camille Jordan (CNRS UMR 5208). He holds leadership roles as Head of the 'Applied Mathematics, Statistics' Master's degree and Head of the 'M2 Maths in Action' program. Previously, he served as Director of the Camille Jordan Institute (2018-2022). His research focuses on applied mathematics, image processing, shape optimization, and geometric measure theory, with contributions to variational models, geometric flows, and applications in computer vision and materials science. Education: PhD in Mathematics (1998, Paris Dauphine) and HDR (2008, Paris 6). Research projects include ANR STOIQUES (2024-2028), PEPR PDE-AI (2023-2028), and collaborations with industry on topics like defect prediction in aluminum production and high-dimensional data analysis. Teaching includes courses on linear algebra, optimization, and machine learning at undergraduate and graduate levels. Key contributions span phase field models, varifold-based surface approximation, and image inpainting. He supervises PhD students in geometric variational problems and computational methods. His work bridges theoretical mathematics with industrial challenges, addressing issues in materials science, medical imaging, and cultural heritage preservation.

• Quantum gravity
• Models of quantum spacetime
• Statistical systems of random geometry
• +9 more

Renate Loll is a Professor in Theoretical Physics at Radboud University Nijmegen, The Netherlands. She is a co-founder of Causal Dynamical Triangulations (CDT) , a groundbreaking nonperturbative path integral approach to quantum gravity. Her research focuses on the intersection of gravity, geometry, and quantum systems, with a strong emphasis on understanding the quantum structure of spacetime, the origin of the universe, and nonperturbative quantum field theory techniques. Education : Habilitation (1998) and PhD (1989) in Theoretical Physics, with additional degrees from Imperial College London (Diploma, 1986) and Freiburg University (Baccalaureus, 1984). Research Impact : Leads one of Europe's largest research groups in quantum gravity, with over 120 professional publications (76 in peer-reviewed journals). Leadership Roles : Chair of Perimeter Institute's Scientific Advisory Committee, Member of FOM's Board of Governors, and editorial roles at Living Reviews in Relativity and General Relativity and Gravitation . Her work spans fundamental physics, integrating concepts like quantum foam and wormholes into rigorous mathematical frameworks. She has secured over 6 million euros in external funding, coordinated European networks (ENRAGE, Eurogrid), and served as Scientist-in-Charge for multiple Marie Curie Fellowships. Loll's contributions to science communication include TV/radio appearances and international media coverage. Awards : VICI Award (2005), Heisenberg Fellowship (1999), Lise Meitner Lecturer (2012), DAAD Scholarship (1984). Service : Co-organized 7 international conferences, referee for funding agencies across 6 countries, and invited plenary speaker at 25+ events in recent years.

Camillo De LellisView profile

Professor

• Calculus of Variations
• Geometric Measure Theory
• Partial Differential Equations
• +3 more

Camillo De Lellis is a Professor at the Institute for Advanced Study since July 2018, with a distinguished career spanning multiple institutions including the University of Zürich, where he served as Full Professor from 2005 and Assistant Professor in 2004. Prior to that, he held postdoctoral positions at the Max Planck Institute for Mathematics in the Sciences (Leipzig) and ETH Zürich. Research Interests encompass calculus of variations , geometric measure theory , partial differential equations , and incompressible fluid dynamics . His work bridges deep analytical techniques with geometric insights, particularly in understanding regularity theory for area-minimizing surfaces and anomalous dissipation in fluid flows. Publications highlight groundbreaking contributions to geometric analysis and fluid dynamics, including regularity theory for currents, Onsager's conjecture, and convex integration methods for Euler equations. These works span subfields like center manifold theory , blow-up analysis , Hölder continuous flows , and Q-valued functions . Scientific Awards Maryam Mirzakhani Prize (2022) Feltrinelli Prize (2021) Bôcher Memorial Prize (2020) Caccioppoli Prize (2014) Stampacchia Medal (2009)

Fraser StoddartView profile

Professor

• Supramolecular Chemistry
• Molecular Machines
• Nano-Electronics
• +3 more

Sir Fraser Stoddart (1942-2024) was the Board of Trustees Professor of Chemistry and Director of the Center for the Chemistry of Integrated Systems at Northwestern University. A pioneer in supramolecular chemistry, he pioneered mechanical bonds and molecular machines, co-winning the 2016 Nobel Prize in Chemistry. Education: B.S. and Ph.D. from University of Edinburgh (1964/1966), followed by honorary doctorates from multiple institutions globally. Research focused on molecular recognition, self-assembly, and mechanically interlocked molecules, leading to innovations in molecular switches, rotaxanes, and nano-electronic devices. His work enabled molecular lifts, muscles, and advanced computer chip technologies. Major awards include the Nobel Prize, Royal Medal, Davy Medal, and knighthood from Queen Elizabeth II. He established a laboratory at Hong Kong University and led Northwestern's chemistry department to international prominence. Known for mentorship, he inspired generations of researchers through his energy and vision. His legacy includes over 1,400 publications and foundational contributions to nanotechnology and molecular engineering.

Michael ShapiroView profile

Professor

• Cluster Algebras
• Quiver Representations
• Mutation-Finite Quivers
• +3 more

Michael Shapiro is a Professor in the Department of Mathematics at Michigan State University (MSU), located in Wells Hall. His research focuses on cluster algebras, quiver mutations, and integrable systems, particularly the classification of quivers of finite mutation type. He collaborates extensively, including with Michael Gekhtman and Alek Vainshtein, and has developed computational tools like quiver.cpp and min_inf.cpp to analyze mutation classes. He holds a M.Sci. and Ph.D., with his work appearing in top journals and arXiv preprints. His teaching includes MTH 864 (Spring 2009), and he maintains a research page detailing his contributions to cluster algebra dynamics, directed networks, and Coxeter-Toda flows. His publications explore exotic cluster structures on SL_n and generalized Bäcklund-Darboux transformations, reflecting his expertise in algebraic and geometric combinatorics.

Zdzislaw BrzezniakView profile

Professor

• Stochastic Partial Differential Equations
• Turbulence
• Geometric Analysis
• +5 more

Professor Zdzislaw Brzezniak is a Professor in the Department of Mathematics at the University of York, where he has been since 2005. He holds a PhD in PDEs from Jagellonian University, Krakow (1988). His research focuses on stochastic partial differential equations (SPDEs), turbulence, geometric analysis, and harmonic analysis, with notable contributions to Navier-Stokes and Euler equations. He has organized major international workshops, including events on stochastic PDEs at ICMS (Edinburgh) and the Isaac Newton Institute. Education: PhD in PDEs (Jagellonian University, 1988). Research Interests: SPDEs, stochastic geometric problems (e.g., Landau-Lifshitz equations), fluid dynamics, and applications in physics. His work bridges pure and applied mathematics, influencing theoretical frameworks in micromagnetism and quantum field theory. Key Awards: 2013 Best Paper Award, 1st Prize, Institute of Information Theory and Automation. Supervision: Advised over 10 PhD students, including Nimit Rana (2019) and Fabian Hornung (2018). Current students include Asma Alalyani and Hessa Alharbi. Active in mentoring across stochastic analysis and geometric PDEs. Labs/Groups: Member of Mathematical Finance and Stochastic Analysis Research Group, and Geometry and Analysis Research Group at the University of York.

• Pure Mathematics
• Mathematical Physics
• Quantum Physics
• +3 more

Ajay Chandra is an Associate Professor in Pure Mathematics at Imperial College London's Faculty of Natural Sciences. He specializes in stochastic partial differential equations (SPDEs) with applications to statistical mechanics and quantum field theory. His work bridges probability theory, mathematical physics, and rigorous analysis of singular SPDEs. He has affiliations with the Department of Mathematics and focuses on areas like regularity structures, renormalization, and non-linear dynamics. His research interests include the analysis of singular SPDEs, gauge theories, quantum field models, and phase transitions. Notable contributions involve stochastic quantization of Yang-Mills theory, Phi^4 models, and multi-layer KPZ equations. Chandra has collaborated with leading researchers such as Martin Hairer and Hendrik Weber, advancing the field of stochastic analysis through innovative techniques like analytic BPHZ theorems and a priori bounds. His advising record includes PhD students working on topics ranging from dynamical Yukawa models to tensor field theories. Chandra's work often intersects with applied mathematics and theoretical physics, addressing foundational questions in statistical mechanics and quantum systems. He maintains an active research program with publications in top-tier journals like Inventiones Mathematicae and Communications in Mathematical Physics.