Dr. Jing Ren is affiliated with the Department of Computer Science at ETH Zürich, holding a role within the Professorship for Computer Science. Their research focuses on computational geometry, 3D reconstruction, and computer graphics, with notable contributions to shape analysis, non-rigid matching, and fabric modeling. Dr. Ren’s work bridges theoretical advancements with practical applications in textile design, architectural modeling, and medical imaging. They collaborate extensively on projects involving functional maps, optimization algorithms, and geometric morphometrics. Key research interests include: Non-rigid shape correspondence and matching Computational modeling of woven fabrics and textiles 3D face and building reconstruction techniques Efficient spectral and discrete optimization methods Recent publications (2022–2024) emphasize innovations in fabric parameterization, Gaussian noise distribution, and rethinking 3D face reconstruction benchmarks. Their work often employs machine learning and functional map frameworks to solve geometric problems across disciplines. Laboratory and team affiliations are not explicitly detailed in the provided materials, but their research aligns with ETH Zürich’s broader initiatives in computer science and engineering. No grants or advising activities are specified in the current data.
Yi-Jia Zhang is a Professor at the School of Computer Science and Technology, Dalian University of Technology. They hold affiliations with multiple institutions, including Zhejiang Sci-Tech University and Jilin University. Their research focuses on biomedical informatics, machine learning, and healthcare applications, with significant contributions to medical knowledge fusion, drug recommendation systems, and multimodal analysis. Over 140 publications since 2011 reflect expertise in areas like graph neural networks, natural language processing, and medical image analysis. Key collaborations include work with Hongfei Lin, Mingyu Lu, and Jian Wang on projects such as drug-repositioning models and radiology report generation frameworks. A notable emphasis is placed on applying AI techniques to solve biomedical challenges, including ICD code classification and sentiment analysis in healthcare contexts. No formal awards are listed, but their extensive publication record underscores academic impact.
Zvi Galil is a distinguished academic and former Dean of Computing at Georgia Institute of Technology (2010-2019). He holds the title of Storey Chair and serves as Executive Advisor for Online Programs. His academic journey includes leadership roles at Columbia University (Fu Foundation School of Engineering Dean, 1995-2007) and Tel Aviv University (President, 2007-2009). He earned degrees in Applied Mathematics from Tel Aviv University and a PhD in Computer Science from Cornell University. Galil’s research focuses on algorithms, complexity theory, cryptography, and stringology. He has authored over 200 papers and edited 5 books, with contributions to graph algorithms, parallel computing, and data structures. He is a Fellow of the ACM and American Academy of Arts and Sciences, and a member of the National Academy of Engineering. His work has influenced fields like online education through initiatives like OMSCS (Georgia Tech’s Online Master of Science in Computer Science). Educations: BS and MS (summa cum laude), Applied Mathematics, Tel Aviv University PhD, Computer Science, Cornell University His research trends span dynamic graph algorithms, real-time string processing, and scalable graph isomorphism techniques. He has also contributed to foundational areas like suffix trees and text indexing. His awards include the Columbia Great Teacher Award (2009) for pedagogical excellence. Awards: ACM Fellow Member, American Academy of Arts and Sciences Member, National Academy of Engineering Columbia Society of Graduates Great Teacher Award Galil advises on online education programs and collaborates with the Algorithms and Randomness Center (ARC) at Georgia Tech. He has held editorial roles at major journals and advised Oxford University Press on computer science publications.
Marie-Louise Lackner is a Research Fellow (PostDoc Researcher) in the Department of Databases and Artificial Intelligence at Technische Universität Wien. She holds Diplom-Ingenieur and Dr. techn. degrees and actively contributes to the CD Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling (2017–2025). Her research focuses on Artificial Intelligence and Optimization , with emphasis on industrial scheduling, constraint programming, and algorithm design. Key domains include: Industrial oven scheduling and production leveling Metaheuristics (simulated annealing, large neighborhood search) Multi-objective optimization in manufacturing systems Combinatorial mathematics and discrete structures Her publications demonstrate a strong trajectory in applied optimization , with recent work concentrating on energy-efficient scheduling algorithms for electronic manufacturing, while earlier research explored permutation patterns and social choice theory. Methodologies consistently integrate theoretical computer science with industrial problem-solving. She supervises graduate researchers, including P. Malik's work on memetic algorithms for production leveling. Grant involvement includes the CD Laboratory project focused on AI-driven planning systems. Lackner is affiliated with the CD Laboratory for Artificial Intelligence and Optimization, conducting applied research in industrial scheduling systems within TU Wien's Databases and AI group.
Wojciech Szpankowski is the Saul Rosen Distinguished Professor of Computer Science at Purdue University. He holds concurrent positions as a Guest Professor at ETH Zurich and a Professor at Jagiellonian University, Krakow. His research focuses on analysis of algorithms, information theory, analytic combinatorics, and random structures. He has directed major initiatives like the NSF Science & Technology Center on Science of Information (CSoI), a $50M 10-year project. Szpankowski is a Fellow of the IEEE and has received prestigious awards including the Flajolet Prize (2020) and Humboldt Research Award (2010). He has held visiting roles at institutions worldwide, including Stanford University and the Newton Institute, Cambridge. His academic journey includes tenure as Full Professor at Purdue (since 1992), Assistant Professorships at McGill University (1984–1992) and Technical University of Gdańsk (1980–1984). His scholarly contributions span monographs like "Average Case Analysis of Algorithms on Sequences" (2001) and "Analytic Pattern Matching" (2015), with a forthcoming work on Analytic Information Theory (2022). He actively contributes to scientific boards, including HIIT Helsinki and NeuroMat Brazil. Szpankowski’s publications emphasize interdisciplinary research at the intersection of computer science, mathematics, and biology. His work on privacy-preserving data analysis, dynamic network inference, and protein superfamily evolution showcases his broad impact. Keynote talks at major conferences (e.g., SODA 2019, AofA 2016) reflect his leadership in theoretical computer science. His grants and recognitions underscore institutional trust: the NSF Science & Technology Center (2010) and Arden L. Bement Jr. Award (2015) highlight his pioneering role in redefining information science. Collaborations span academia and industry, with affiliations at Hewlett-Packard and INRIA.
Ting Lei is an Associate Professor in the Department of Geography at the University of Kansas, located in Malott Hall #1021. His primary research interests focus on Geographic Information Science (GIS), including algorithmic development, geospatial computational methods, network analysis, location theory, and web GIS. He also explores remote sensing applications and advancements in GIS technology such as data structures, databases, computational geometry, and open-source software. His teaching covers GIS principles, transportation geography, and geo-computational methods. Dr. Lei's publications emphasize spatial data conflation, transportation network vulnerability, and optimization models for facility location. Recent works include studies on optimal spatial data matching, hub center interdiction problems, and unified location-allocation approaches integrating GIS and distributed computing. His research addresses real-world challenges in urban planning, water resources management, and celestial imaging analysis. Key research trends in his articles include computational GIS advancements, transportation infrastructure resilience, and interdisciplinary applications of geospatial technologies. No specific scientific awards are listed, though his extensive publication record highlights scholarly contribution. Advising and grants details are not provided in the text, but his active research in multiple geospatial domains indicates engagement with academic and applied projects.
Prof. Dr. Ulrich Bauer is a Professor at the Technische Universität München (TUM) , leading the Applied and Computational Topology research group within the TUM School of Computation, Information and Technology . His academic career includes positions at Freie Universität Berlin, Georg-August-Universität Göttingen (where he earned a doctoral degree in Mathematics), and the Institute of Science and Technology Austria. Bauer specializes in applied and computational topology, focusing on multi-scale data connectivity and developing computational methods for large datasets. He is a key member of the Collaborative Research Center Discretization in Geometry and Dynamics and the Centre for Topological Data Analysis . Research Interests: Bauer’s work bridges theoretical foundations and practical applications in topology. He explores methods like persistent homology and discrete Morse theory to uncover global data structures. His contributions include advancing algorithms for topological data analysis, with applications in medical imaging, computational biology, and geometric modeling. Bauer’s software tool Ripser is widely recognized for efficient computation of persistence barcodes. Awards: ATMCS Best New Software Award (2016) Best Paper Award TopoInVis (2013) Apple Design Award (2003) O’Reilly Mac OS X Innovators Award (2003) Grants & Leadership: Bauer’s leadership roles include the executive board of the CRC Discretization in Geometry and Dynamics. His research has been supported by grants focusing on topological methods in data science and geometry. He actively contributes to advancing interdisciplinary collaborations between mathematics, computer science, and applied fields. Labs & Teams: As founder of the Applied and Computational Topology group at TUM, Bauer fosters innovation in computational topology, mentoring researchers and students in developing cutting-edge methodologies. His work intersects with the TUM School’s broader mission in computational and data-driven science.
Andrei Asinowski is a Researcher at the Institute of Mathematics at Alpen-Adria-Universität Klagenfurt. He has held academic appointments at institutions including Bielefeld University (2005–2006), University of Haifa (2006–2008), Technion (2008–2011), Free University of Berlin (2012–2015), and TU Wien (2015–2018). Since 2020, he leads the FWF-funded project 'Generic Rectangulations: Enumerative and Structural Aspects' (P32731). His research focuses on combinatorics, discrete mathematics, computational geometry, and enumerative combinatorics. Key interests include rectangulations, permutation patterns, lattice paths, and guillotine partitions. Research Interests: Combinatorics of rectangulations, permutation classes, enumerative combinatorics, geometric permutations, and algorithmic applications of discrete structures. His work bridges combinatorial theory and computational geometry, with recent emphasis on bijections between rectangulations and permutations, generating functions, and analytic methods. Grants & Projects: Principal Investigator of FWF Grant P32731 (since 2020), exploring enumerative and structural aspects of rectangulations. Collaborative projects include EuroGIGA's ComPoSe (2012–2015) and SFB F50's Algorithmic and Enumerative Combinatorics (2015–2018). Publications Trends: Recent work emphasizes rectangulation enumeration, permutation patterns, and analytic combinatorics techniques. Earlier contributions addressed geometric permutations, computational geometry, and lattice path enumeration.
Bruce Hajek is the Leonard C. and Mary Lou Hoeft Endowed Chair in Engineering and Professor of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the Coordinated Science Laboratory. He holds a PhD in Electrical Engineering from the University of California, Berkeley (1979). His research spans communication networks, stochastic processes, game theory, wireless systems, and data science. He has authored influential works, including the textbook Random Processes for Engineers (2015). Hajek's roles include Department Head of ECE (2019–2024) and membership in prestigious organizations like the National Academy of Engineering (since 1999) and IEEE. He has received accolades such as the ACM SIGMETRICS Achievement Award (2015), Guggenheim Fellowship (1992), and IEEE Fellow distinction (1989). His research interests emphasize stochastic analysis, network dynamics, and algorithmic game theory. Notable contributions include foundational work in random graph matching, blockchain protocols, and statistical inference. Teaching excellence awards span decades at UIUC, and his service includes leadership roles in the IEEE Information Theory Society. Hajek's interdisciplinary expertise bridges theory and applications, impacting fields from wireless communications to machine learning. His work on semidefinite programming for community detection and auction mechanisms exemplifies his innovative problem-solving approach.
Margarida Carvalho is an Associate Professor in the Department of Computer Science and Operations Research at Université de Montréal, where she holds the FRQ-IVADO Research Chair in Data Science for Combinatorial Game Theory. She is also an Associate Academic Member at Mila (Quebec AI Institute), contributing to their research in AI for Humanity. Her academic journey spans from Portugal to Canada, where she has established herself as a leading researcher at the intersection of operations research and game theory. Carvalho earned her bachelor's and master's degrees in mathematics from the Faculty of Sciences of the University of Porto (FCUP), followed by a PhD in Computer Science from the same institution in 2016. Her doctoral work, which focused on game theory applications for kidney exchange programs, earned her the prestigious 2018 EURO Doctoral Dissertation Award, making her the first Portuguese woman to receive this honor. After completing her PhD, she worked as an IVADO Postdoctoral Fellow at Polytechnique Montréal before joining Université de Montréal as an Assistant Professor in 2018. Her research focuses on combinatorial optimization and algorithmic game theory, with applications spanning healthcare (kidney exchange programs, hospital operations), sustainable development (electric vehicle infrastructure, urban planning), and education (school choice systems). She develops novel mathematical programming approaches to model and solve problems involving multiple decision-makers with potentially conflicting objectives. Her work bridges theoretical advances in optimization with practical implementations that address real-world challenges in resource allocation and decision-making under uncertainty. Notably, her research on fairness in kidney exchange programs has contributed to more equitable organ allocation policies. Her 15 most recent publications reveal a strong trend toward integrating game-theoretic concepts with practical optimization challenges, particularly in healthcare and sustainable infrastructure. She has pioneered approaches that balance utilitarian objectives with fairness considerations, developed novel formulations for bilevel and multilevel optimization problems, and created learning-based frameworks for complex decision environments. Her work consistently demonstrates how mathematical rigor can inform practical policy decisions in critical domains. 2018 EURO Doctoral Dissertation Award for her PhD thesis on game theory applications for kidney exchange programs Mathematical Programming 2024 Meritorious Service Award Teaching Excellence Award from Université de Montréal Supervised student Maria Bazotte receiving the Dupačová-Prékopa Best Student Paper Prize in Stochastic Programming Carvalho actively advises graduate students, with Marylou Fauchard (Master's) and William St-Arnaud (PhD) among her current advisees. Her research is supported by grants from Hydro-Québec, the Natural Sciences and Engineering Research Council of Canada (Discovery grant 2017-06054 and Collaborative Research and Development Grant CRDPJ 536757–19), and FRQ-IVADO. She serves as an associate editor for INFORMS Journal on Computing, OR Spectrum, and Dynamic Games and Applications, and is a founding board member and treasurer of the Bilevel Optimization Society. She teaches courses in Mathematical Programming, Operational Research Models, and Discrete Mathematics at Université de Montréal. Carvalho is affiliated with Mila (Quebec AI Institute), where she contributes to research initiatives focused on AI for Humanity, particularly in the areas of algorithmic fairness and sustainable development. Her FRQ-IVADO Research Chair supports her work on combinatorial game theory applications, and she collaborates with researchers across disciplines through the IVADO research community. She has been instrumental in establishing the Bilevel Optimization Society, creating a dedicated forum for researchers working on hierarchical decision-making problems.
Hu Ding is a pre-tenure Professor in the School of Computer Science and Engineering at the University of Science and Technology of China (USTC), where he directs the Data Intelligence, Algorithms, and Geometry (DIAG) research group. He previously held positions as a tenure-track Assistant Professor at Michigan State University (2016-2018) and a Simons-Berkeley Research Fellow jointly at Tsinghua University and UC Berkeley (2015-2016). Education: • Ph.D. in Computer Science, State University of New York at Buffalo (2015) • B.S. in Mathematics, Sun Yat-Sen University (2009) Research Interests: Hu Ding's research focuses on developing efficient algorithms for geometric optimization problems with applications in machine learning, big data, and biomedical imaging. His work bridges theoretical computer science (especially computational geometry) with practical challenges in distributed systems, outlier detection, and high-dimensional data analysis. Key areas include constrained clustering, truth discovery in crowdsourced data, and geometric methods for biomedical image analysis. Publication Trends: His recent publications demonstrate a strong focus on scalable algorithms for high-dimensional geometric optimization, particularly in distributed environments with noisy data. A consistent theme is developing theoretically-grounded solutions with practical efficiency, evidenced by work on sublinear-time algorithms, coreset constructions, and approximation frameworks for problems like k-center clustering and SVM optimization with outliers. Awards and Honors: Young Investigator Award, Ministry of Science and Technology (2021) Simons-Berkeley Research Fellowship (2015-2016) CCF Committee Member for Theoretical CS and Big Data (2021) Grants and Projects: USTC Innovation Group Grant: 'Toward Electronic Design Automation: Theories and Algorithms from AI' (2021) MOST Young Investigator Grant: 'Optimal Transportation in Medical Imaging' (3M RMB, 2021) Research Group: Leads the DIAG group with focus on geometric algorithms for data intelligence. Current team includes 6 PhD students and 15 Master's students working on problems in clustering, distributed optimization, and biomedical applications. Former students hold positions at Alibaba, ByteDance, and academic institutions.
Dr. Ian Pratt-Hartmann is a Senior Lecturer at the School of Computer Science, specializing in Formal Methods. He received his PhD in Philosophy from Princeton University (1987) and previously studied Mathematics and Philosophy at Brasenose College, Oxford. His research spans logic, artificial intelligence, and cognitive science, focusing on intersections between logic and complexity theory, logic and geometry, and logic and natural language. He has supervised 11 PhD students, including Dominik Schoop, Nick Player, and Yegor Guskov, with topics ranging from mereotopology to temporal logics.
Sylvie Hamel is a Full Professor and Department Director at the Department of Computer Science and Operations Research (DIRO), Université de Montréal. She is affiliated with the CRM (Centre de recherches mathématiques) and the LBIT (Laboratoire de biologie informatique et théorique). Her research bridges computer science, mathematics, and molecular biology, focusing on algorithmic problems in genomics, combinatorial analysis, and RNA structure modeling. Education: Her academic background includes a Ph.D. in algorithmic and combinatorial studies, with a thesis on vector algorithms and bioinformatics. Research Interests: Algorithm design for genome rearrangements and sequence analysis Combinatorial optimization and permutation medians RNA structure prediction and computational biology Consensus algorithms and voting theory Software evolution and design patterns Grants & Projects: Leading projects on consensus algorithms (2025–2031), comparative genomics (2016–2024), and CRM strategic initiatives (2022–2029). Funded by CRSNG (Natural Sciences and Engineering Research Council) and FRQNT (Québec research fund). Awards: CRSNG Postdoctoral Fellowship (2003–2008). Supervision: Currently advising M.Sc./Ph.D. students on topics like consensus algorithms, RNA structure analysis, and combinatorial optimization. Over 20+ completed theses under her supervision. Labs: Active in LBIT (bioinformatics) and CRM (mathematical research), fostering interdisciplinary collaboration.
Professor Daniel Reidenbach is the Head of School of Computing and Mathematical Sciences at Birkbeck, University of London. Previously, he served as Head of the Department of Computer Science at Loughborough University (2018–2022) and as Professor of Theoretical Computer Science and Head of the School of Computer Science and Mathematics at Keele University (2022–2025). He holds a PhD in Computer Science from the University of Kaiserslautern (Germany, 2006). His research focuses on formal language theory, combinatorics on words, and algorithmic learning theory. He has made significant contributions to solving longstanding open problems in these areas, particularly in pattern analysis, symbolic sequence properties, and ambiguity reduction in formal languages. His work has been recognized through international academic prizes. Reidenbach’s articles often address theoretical challenges in discrete mathematics, such as the Billaud conjecture and Parikh matrix ambiguity. His publications span leading journals and conference proceedings, including the 12th International Conference on Combinatorics on Words (2019). He is affiliated with the Mathematics Genealogy Project, where his academic lineage and supervision activities are documented. As a leader in his field, he has held administrative roles in multiple universities, contributing to departmental governance and strategic academic planning. His research interests bridge theoretical foundations and algorithmic applications, with a focus on advancing computational methods for linguistic and combinatorial problems.
Tanya Khovanova is a Lecturer at the Massachusetts Institute of Technology (MIT) in the Department of Mathematics . Her career spans academia and industry, with extensive experience in recreational mathematics, combinatorics, probability, and algorithm design. She has held positions at MIT since 2008, including Visiting Scholar and Research Affiliate, and has contributed to programs like PRIMES and RSI for mentoring high school students. Ph.D. in Mathematics from Moscow State University (1988), advised by I.M. Gelfand Former roles include Lead Analyst at BAE Systems (2003-2008) and Princeton University (2001-2003, 1996-1998) Her research bridges recreational mathematics with applications in game theory , coding theory , and combinatorial puzzles . She is a prolific blogger and popularizer of mathematics, known for her work in creating innovative puzzles and educational tools like the Math Alive course at Princeton. Recent publications include explorations of EvenQuads games (2025), Chip-Firing algorithms (2022-2025), and SET game generalizations (2025). Her work often intertwines group theory , graph theory , and error-correcting codes . Scientific Awards : 2021 MLK Leadership Award (MIT) 2019 Infinite Mile Award (MIT) 1976 Gold Medal at International Mathematics Olympiad 1975 Silver Medal at International Mathematics Olympiad She mentors students through programs like PRIMES and RSI , emphasizing puzzle-based learning. Her blog and public talks continue to engage global audiences with mathematics.
