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Yiying TongView profile

Professor

• Discrete Geometric Modeling
• Physically-Based Simulation/Animation
• Discrete Differential Geometry
• +7 more

Yiying Tong is a Professor in the Department of Computer Science and Engineering at Michigan State University (MSU), within the College of Engineering. His research focuses on discrete geometric modeling, physically-based simulation/animation, and applications in biometrics, biomolecular surfaces, and medical imaging. Tong received his Ph.D. from the University of Southern California (2004), M.S. from Zhejiang University (2000), and B.Eng. from Zhejiang University (1997). Education: Ph.D., Computer Science, University of Southern California, 2004 M.S., Computer Science, Zhejiang University, China, 2000 B.Eng., Computer Science, Zhejiang University, China, 1997 Research Interests: Tong's work spans discrete differential geometry, computational geometry, fluid dynamics, and biomedical applications. Key areas include: Development of efficient algorithms for mesh generation and surface reconstruction Physically-based simulations for fluids, deformable objects, and sound Applications in biomolecular modeling, face recognition, and medical imaging Geometric modeling techniques for computer graphics and engineering Publications: His research emphasizes geometric and topological methods, with notable contributions to fluid simulation, meshing, and biomolecular surface modeling. Recent work includes advancements in Hodge decomposition, spectral graph theory, and manifold learning. Grants & Collaborations: Tong leads NSF-funded projects on geometric integrators and biomolecular modeling. Collaborators include institutions like Caltech, Zhejiang University, and the University of Southern California. His work bridges computational mechanics and computer graphics, with applications in bioinformatics and engineering. Labs & Teams: Supervises a research group focused on geometric computing, with active projects in discrete differential geometry, fluid simulation, and biomedical applications. Notable alumni include researchers at Qualcomm, Apple, and Google.

Subhash SuriView profile

Professor

• Algorithms and Theory
• Networking
• Computational Geometry
• +5 more

Subhash Suri is a Distinguished Professor in the Department of Computer Science at the University of California, Santa Barbara (UCSB). He holds academic ranks such as Fellow of ACM, AAAS, and IEEE, and is an ACM Distinguished Scientist. His research focuses on algorithms, computational geometry, sensor networks, robotics, and social networks. He directs the Applied Algorithms Lab and the Center for Geometric Computing at UCSB. Education: Ph.D. Computer Science, Johns Hopkins University, 1987 M.S. Computer Science, Johns Hopkins University, 1984 B.S. Electronics and Communication, IIT Roorkee, India, 1981 Research Interests: Prof. Suri's work emphasizes geometric and network algorithms, with applications in diverse fields. His lab develops foundational methods in computational geometry, graph theory, and algorithmic decision-making. Recent projects include dynamic geometric set cover, polychromatic TSP approximation, and shortest paths in complex environments. Awards: His honors include ACM/AAAS/IEEE Fellowships and recognition for contributions to algorithm design and analysis. Students/Grants: Advised over 20 graduate students, including notable alumni like Neeraj Kumar (Facebook) and Luca Foschini (Evidation Health). Active in NSF workshops and program committees (e.g., SoCG, WAFR, SWAT). Teaching: Recent courses include CS 130a/b (Data Structures/Algorithms), CS 190A (Algorithmic Decision Making), and CS 235 (Computational Geometry).

David M. RosenView profile

Assistant Professor

• Nonlinear Optimization
• Differential Geometry
• Topology
• +7 more

David M. Rosen is an Assistant Professor at Northeastern University, affiliated with the Departments of Electrical and Computer Engineering, Mathematics, and the Khoury College of Computer Sciences (by courtesy). He leads the Robust Autonomy Lab (NEU-RAL), focusing on mathematical and algorithmic foundations for trustworthy autonomous systems. ScD in Computer Science (2016), Massachusetts Institute of Technology MA in Mathematics (2010), University of Texas at Austin BS in Mathematics (2008), California Institute of Technology His research combines nonlinear optimization, differential geometry, abstract algebra, and probability to design robust algorithms for machine perception and control. Recent work emphasizes convex relaxation techniques for problems like SLAM and rotation averaging, enabling provably optimal solutions in real-world settings. Recent publications highlight advancements in range-aided SLAM, distributed pose-graph optimization, and spectral synchronization methods. These works often integrate semidefinite programming and Riemannian optimization to address non-convex challenges in autonomous navigation. 2023: Grant from Draper Laboratory for decentralized multi-agent perception 2023: Grant from MIT Lincoln Laboratory for certifiable perception tools His awards include the WAFR Best Paper Award (2016), RSS Pioneer Award (2019), RSS Best Student Paper Award (2020), and IEEE T-RO King-Sun Fu Award Honorable Mention (2021). He has contributed to key tools like SE-Sync and Shonan averaging, widely used in robotics and computer vision communities.

• Large Language Models
• Multilingual Machine Translation
• Vision-Language Models
• +3 more

Xiaocheng Feng is a prominent researcher in computational linguistics and large language models (LLMs), with extensive contributions to multilingual systems, vision-language integration, and knowledge alignment. His work spans from 2016 to 2025, focusing on advanced topics like Mitigating hallucinations in multimodal models Efficient knowledge editing across multiple models Length-controlled text generation for black-box LLMs Improving contextual faithfulness via retrieval heads Cross-lingual connection mechanisms for fine-tuning His recent publications address critical challenges in LLMs, including knowledge misalignment, entity-level unlearning, and Pareto optimization in multilingual translation. He has explored probability density estimation for text control, adaptive context modeling in visual storytelling, and cross-lingual annotation projection for low-resource languages. Collaborations with researchers like Bing Qin, Lei Huang, and Weitao Ma highlight his central role in advancing NLP methodologies.

Peter SpichtingerView profile

Professor

• Cloud Dynamics
• Atmospheric Physics
• Mathematical Modeling
• +4 more

Peter Spichtinger is a Professor for Theoretical Cloud Physics at the Institute for Atmospheric Physics (IPA) , Johannes Gutenberg-Universität Mainz , Germany. Since 2010, he has led research on cloud dynamics, multiscale modeling, and atmospheric pattern formation. His work bridges mathematical modeling with climate science , focusing on ice supersaturation , gravity wave-cloud interactions , and machine learning applications in atmospheric physics. Key Affiliations : Institute for Atmospheric Physics (Mainz), Mainz Institute of Multiscale Modeling (M3O), Waves to Weather (W2W) SFB, TPChange CRC Research Themes : Cloud-scale microphysics, ice nucleation pathways, tropopause dynamics, stochastic Galerkin methods, fractal properties of atmospheric systems His 15 most recent publications emphasize ice microphysics in climate models, GPU-accelerated weather front detection , asymptotic cloud modeling , and machine learning for atmospheric datasets . Current projects include BINARY (Big Data in Atmospheric Physics) and leadership of the SCALES Conference 2025 . He serves as Deputy Institute Director (2020-2022) and Spokesperson for M3O (since 2022) . Scientific awards include the Marie Curie Fellowship (2007-2009) . He supervises theses in interdisciplinary contexts and co-organizes seminars on cloud dynamics and machine learning for atmospheric systems.

Wang JunView profile

Assistant Professor

• Machine Learning
• Engineering Design
• Additive Manufacturing
• +10 more

Jun Wang is an Assistant Professor in the Department of Mechanical Engineering at Santa Clara University (SCU) . He received his Ph.D. and M.S. in Mechanical Engineering from SUNY Buffalo (2019, 2015) and a B.S. from Xi’an Jiaotong University (2012). He previously served as a Postdoctoral Associate in Mechanical Engineering at University of Maryland, College Park and worked in the IDEAL Lab, focusing on Engineering Design, Optimization, and Machine Learning. Education: Ph.D., Mechanical Engineering, SUNY Buffalo (2019) M.S., Mechanical Engineering, SUNY Buffalo (2015) B.S., Mechanical Engineering, Xi’an Jiaotong University (2012) Dr. Wang’s research lies at the intersection of Engineering Design , Machine Learning/Artificial Intelligence , Advanced Manufacturing , and Topology Optimization . His lab, GIFTED (Geometric Informatics For Technology, Engineering, and Design) , develops AI-driven methods for geometric modeling, metamaterial design, and additive manufacturing. Specific projects include inverse/generative design frameworks, surrogate models for manufacturing simulation, and high-resolution multi-scale material synthesis. His recent publications focus on machine learning integration with isogeometric analysis, implicit surface-based inverse design of cellular materials, and optimization of non-periodic metamaterials. Notable collaborations include work on vision transformers for image enhancement and generative adversarial networks for engineering applications. Trends in his research emphasize bridging geometric modeling with cloud computing and big data to accelerate design cycles. Scientific Awards: Best Paper Award (IDETC-CIE 2021) for non-periodic cellular design Dr. Wang mentors graduate researchers Shaoliang Yang (Ph.D. candidate) and Kevin Wang (Master’s graduate). His former students Matthew Franke (now at Intel) and Yunchen Li (now at Hymson USA Inc.) have transitioned to industry roles. His NSF-funded project ( $249,829 ) explores data-driven inverse design of additively manufacturable materials. At SCU, he leads the GIFTED Lab , which combines geometric modeling , machine learning , and advanced manufacturing to revolutionize design workflows. The lab’s work targets performance-equivalent designs generated via AI, with applications in aerospace, automotive, and biomedical industries.

Jason SimonView profile

Lecturer

• Fluid Dynamics
• Thermodynamics
• Atmospheric Modeling
• +3 more

Jason Simon is a Lecturer in the Department of Mechanical Engineering at the University of New Hampshire, College of Engineering and Physical Sciences. He teaches courses such as ESCI 502: Beaches and Coasts , ME 503: Thermodynamics , and ME 608: Fluid Dynamics . Education: Ph.D. in Civil and Environmental Engineering from University of California - Berkeley His research focuses on Fluid Dynamics , Thermodynamics , Atmospheric Modeling , and Land-Atmosphere Interactions . He has contributed to studies on turbulence modeling, weather prediction algorithms, and coastal hydrodynamics. Recent work includes leveraging LES simulations to study cloud development and subgrid-scale climate modeling. Publications highlight his expertise in land-surface heterogeneity , storm surge analysis , and numerical weather prediction . He has collaborated with researchers like A. D. Bragg and F. K. Chow on turbulence modeling and WRF-LES simulations.

• Computational Social Choice
• Algorithmic Game Theory
• Approximation Algorithms
• +2 more

Krzysztof Sornat is a Lecturer at the Institute of Computer Science , Faculty of Computer Science, AGH University of Science and Technology in Kraków. His research focuses on computational aspects of voting theory, including multiwinner elections, participatory budgeting, and liquid democracy systems. His recent work analyzes approximation algorithms for submodular optimization (2024), complexity of subelection isomorphism (2024), and fine-grained hardness results in voting mechanisms (2021-2025). Publications demonstrate expertise in algorithmic fairness, preference aggregation, and parameterized complexity. Key collaborations include studies on Schulze voting methods (2021), minimax approval voting approximations (2018), and multiwinner rule comparisons (2023). Active in both theoretical algorithm design and practical implementation challenges.

• Logics
• Databases
• Formal languages
• +5 more

Gabriele Puppis is an Associate Professor at the Department of Mathematics, Computer Science, and Physics of Udine University since 2022. Previously, he held a tenure-track position at the same university (2019-2022) and was a tenured CNRS researcher at LaBRI, Bordeaux (2012-2019). He completed postdoctoral research at Oxford University (2009-2012) and Udine University (2006-2008). His academic journey began with a PhD in Computer Science at Udine University (2002-2006). Education: PhD in Computer Science, Udine University (2006) Research Interests: Logics and formal languages String transducers and automata theory XML schema validation and database repairability Interval temporal logic and spatial reasoning Higher-order query languages Academic Contributions: His work focuses on the decidability and complexity of problems in automata theory, particularly for finite-valued transducers and their equivalence. He has advanced understanding of bounded repairability for tree and word languages, dynamic data structures for timed automata, and resynchronizability in transducer models. His recent publications explore decomposition theorems for streaming string transducers and their equivalence to two-way models. Teaching and Leadership: He teaches courses in Distributed Systems, Algorithms and Data Structures, and Verification techniques in AI and Cybersecurity. He has organized events like the GandALF 2023 summer school and participated in workshops on unambiguity in automata theory and regular cost functions.

Afrouz Jabal AmeliView profile

Researcher

• Approximation Algorithms
• Graph Theory
• Learning Augmented Algorithms
• +3 more

Afrouz Jabal Ameli is a Postdoc Researcher in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e) since November 2021, supervised by Professor Laura Sanita. Previously, she obtained her Ph.D. in Computer Science from the Dalle Molle Institute for Artificial Intelligence Research (IDSIA) in 2021 under Professor Fabrizio Grandoni and worked there as a postdoctoral researcher for 8 months. Education: B.Sc. in Computer Engineering (Software) from Sharif University of Technology (2010-2015) M.Sc. in Algorithms and Computation from Sharif University of Technology (2015-2017) Ph.D. in Computer Science (Approximation Algorithms for Survivable Network Design) from IDSIA (2017-2021) Her research lies at the intersection of Approximation Algorithms and Survivable Network Design , with significant contributions to connectivity augmentation problems and geometric packing. She has also explored applications in Fairness in Sports and Learning Augmented Algorithms . Recent publications include work on Flexible Network Design (ESA 2024), Exact Distance Graphs (Discrete Applied Mathematics 2025), and Skewed Strip Packing (Algorithmica 2023). These works span subfields like eigenvalue bounds, geometric optimization, and parameterized connectivity requirements. Scientific Awards: Best ICALP Paper Award (2023) NWO-VIDI grant recipient (2024-2024) National Elite Foundation Flowship (2010, 2015) Bronze Medal in Asia Pacific Olympiad in Informatics (2010) Gold Medal in National Olympiad in Informatics (2009) She has lectured on Graphs and Algorithms at TU/e and contributed to Discrete Mathematics education in national olympiads and high schools.

John van de WeteringView profile

Assistant Professor

• Quantum Computing
• Quantum Foundations
• ZX-calculus
• +4 more

John van de Wetering is an active Assistant Professor at the Theoretical Computer Science group of the Informatics Institute of the University of Amsterdam (UvA), working in collaboration with the QuSoft research center. He joined the university in December 2022 after completing his PhD at Radboud University in 2020 and a postdoc position at Oxford University. His research spans two primary domains: diagrammatic techniques in quantum computation (particularly ZX-calculus applications for quantum circuit optimization, classical simulation and verification) and quantum foundations where he explores the nature of quantum mechanics through algebraic and compositional methods. He is the co-creator of PyZX, an open-source optimizing quantum compiler, and has co-authored the book "Picturing Quantum Software" with Aleks Kissinger. His work bridges theoretical computer science with practical quantum computing applications. His publication record shows consistent high-impact output with multiple papers in Physical Review A, Quantum, and proceedings of major conferences like QPL. His recent work focuses on quantum circuit optimization, ZX-calculus completeness, and applications to quantum error correction. His research demonstrates a strong trajectory with publications extending into 2025. VENI grant recipient for quantum computing research He currently supervises three PhD students (Lia Yeh, Sarah Li, and Marc Farreras) and has previously mentored numerous Master's and Bachelor's students whose work has led to publications. He serves as program director for the new Master in Quantum Computer Science at UvA and is an editor for the diamond open-access journal Quantum. His professional activities include developing educational resources like the ZX-calculus website and contributing to the ZX-calculus Wikipedia page.

José A. Á. AntolínezView profile

Assistant Professor

• Coastal Engineering
• Climate Change
• Machine Learning
• +9 more

José A. Á. Antolínez serves as an Assistant Professor in Coastal Engineering within the Hydraulic Engineering department at Delft University of Technology's Civil Engineering & Geosciences faculty. His academic career bridges research, education, and practical applications in coastal engineering with a focus on addressing climate change challenges. His educational background includes a Ph.D. in Civil Engineering (Morphodynamic coastal evolution in a changing climate) from Universidad de Cantabria (2013-2018), an M.Sc. in Integrated Coastal Zone Management from Universidad de Cantabria (2011-2013), and an M.Sc. in Civil Engineering with specialization in structures from Universidade da Coruña (2004-2009). Antolínez's research focuses on understanding coastal dynamics and processes, connecting them with climate and anthropogenic drivers, developing parameterization schemes for coastal modeling, enhancing modeling workflows, and improving the usability of model predictions for coastal management. His experimental designs frequently employ hybrid workflows combining observations, numerical models, and AI. His research spans coastal hydrodynamics, nature-based solutions, geomorphology, climate adaptation, emerging technologies, and uncertainty analysis. His recent publications (2024-2025) demonstrate strong activity in shoreline dynamics, flood risk assessment, coastal modeling, and sea turtle conservation applications. His work shows particular strength in applying machine learning and statistical methods to coastal engineering problems, with publications appearing in journals like Natural Hazards and Earth System Sciences, Coastal Engineering, and Communications Earth and Environment. Among his scientific contributions are the FPU fellowship received during his doctoral studies and leadership of the Coastal Science with AI in a Changing Climate research group. He coordinates the working AI group of the DIGISHAPE network, connecting companies, institutes, and governmental organizations in the Dutch Water sector. Antolínez has supervised students as an External PhD Supervisor at Griffith University (2019-2023) and has held various visiting researcher positions at Texas A&M University, IHE Delft Institute for Water Education, Auckland University, Oregon State University, Duke University, and USGS. Previously, he worked as a medior advisor at Deltares' Applied Morphodynamics section (2018-2020).

Maurice van TiggelenView profile

Researcher

• Polar Meteorology
• Glaciology
• Remote Sensing
• +3 more

Maurice van Tiggelen is a Researcher at Utrecht University's Institute for Marine and Atmospheric Research (IMAU), working within the Physics department specializing in Dynamics Meteorology. Based at the Buys Ballotgebouw in Utrecht, he focuses on polar meteorology, glaciology, and remote sensing, with particular expertise in ice-atmosphere interactions and surface mass balance of ice sheets. His research centers on the coupling between ice surfaces and the atmosphere, especially how atmospheric boundary layer turbulence contributes to ice ablation. Van Tiggelen employs a multi-method approach combining automatic weather station measurements on the Greenland Ice Sheet, drone imagery, satellite altimetry, and atmospheric modeling at various scales. His work has significant implications for understanding sea-level rise, as ice sheet mass loss has contributed substantially to global sea-level rise since 1901. The analysis of his recent publications reveals a consistent focus on quantifying turbulent heat fluxes over ice sheets, improving aerodynamic roughness parameterizations, and developing methods for monitoring surface energy balance. His research spans both the Greenland and Antarctic ice sheets, with increasing attention to high-resolution modeling techniques and remote sensing validation. Van Tiggelen completed his PhD in 2023 with a thesis titled 'Roughish ice, snow and turbulent heat fluxes on the Greenland ice sheet' and maintains active research collaborations with institutions worldwide. His technical skills include Python programming and Geographic Information Systems, which support his data-intensive research approach.

• Computational Subsurface Hydrology
• Discrete Fracture Network Modeling
• Anomalous Transport in Porous Media
• +7 more

Rishi Parashar serves as a Research Professor in Hydrologic Sciences at the Desert Research Institute (DRI), affiliated with the University of Nevada Reno's Graduate Program of Hydrologic Sciences. His work bridges computational hydrology, geothermal systems, and microbial transport processes within fractured rock environments. Education: Ph.D. in Civil Engineering, Purdue University (2008) M.S. in Civil Engineering, Purdue University (2003) B.S. in Civil Engineering, Indian Institute of Technology, Roorkee (2001) Parashar's research centers on computational subsurface hydrology , where he develops discrete fracture network (DFN) models to simulate anomalous transport in porous media and upscaling techniques for complex flow systems. His hydro-bio-chemical systems work examines microbial motility, biofilm growth, and biogeochemical reactions governing contaminant fate. In thermo-hydro-mechanical interactions , he investigates enhanced geothermal systems (EGS) and induced seismicity through coupled process modeling. Recent publications reveal increasing integration of machine learning with traditional hydrological modeling, particularly in particle tracking and reactive transport upscaling. Analysis of his 15 most recent articles (2021-2025) shows dominant focus areas: 40% on microbial transport in porous media (including bacterial motility and biofilm effects), 30% on fracture network modeling and upscaling, 20% on contaminant remediation (particularly arsenic), and 10% on geothermal system dynamics. His collaborative work spans environmental engineering, computational science, and microbiology, with frequent co-authorship patterns indicating strong mentorship of early-career researchers. Parashar actively secures research funding through Department of Energy contracts, particularly for Nevada National Security Site projects involving fractured rock characterization. His advising portfolio includes numerous graduate students leading publications in high-impact journals like Water Resources Research and Advances in Water Resources . Current projects involve quantum algorithms for well capture zone determination and heat-sensitive epoxy foams for geothermal permeability alteration. Research Infrastructure: His work leverages DRI's computational resources for large-scale DFN simulations and collaborates with DOE laboratories on experimental validation through microfluidic devices and field-scale tracer tests. The Hydrologic Sciences division provides access to advanced characterization facilities for fractured rock systems.

Anthony PEREZView profile

Lecturer

• Parameterized Complexity
• Graph Algorithms
• Community Detection
• +3 more

Anthony PEREZ is a Lecturer in Computer Science at the University of Orleans, affiliated with LIFO (Laboratory of Fundamental Computer Science of Orleans) and part of the Graphs, Algorithms, and Computational Models team. He previously held a full-time ATER position at the same university from 2011 to 2012 and completed his PhD at LIRMM (Montpellier II University) under the supervision of Stéphane Bessy and Christophe Paul. Research Focus: Parameterized complexity, graph modification problems, community detection in directed graphs, and sparse graph embeddings Software Development: Created SINr (sparse interpretable node representations), Directed Louvain (modularity optimization for directed networks), and GeST (graph embedding-based image segmentation) His research explores polynomial kernelization for graph editing problems, particularly in triangulated graph subclasses, and challenges the common practice of ignoring edge direction in community detection algorithms. He has co-supervised multiple PhD students including Joanne Dumont and Maël Dumas, and collaborates extensively on graph-theoretical problems with applications to social network analysis.