Find Academic Supervisors

• Rigidity Theory
• Discrete Geometry
• Combinatorics
• +4 more

Louis Theran is a Lecturer in Mathematics at the University of St Andrews , School of Mathematics and Statistics. His research bridges geometry, combinatorics, and algorithmic problems, with applications in physics, materials, and machine learning. Education: Ph.D. in Computer Science, University of Massachusetts, Amherst (2010) M.S. in Computer Science, University of Massachusetts, Amherst (2007) B.S. in Computer Science and Mathematics, University of Massachusetts, Amherst (2006) Theran’s research focuses on the rigidity theory of frameworks , exploring how geometric and combinatorial properties determine structural stability. He investigates discrete geometry, sparse hypergraphs, and pebble game algorithms, connecting these to machine learning (e.g., low-rank matrix completion) and materials science (e.g., auxetic metamaterials, sticky disks). His recent work analyzes rigidity transitions in random graphs , universal rigidity in one-dimensional frameworks, and maximum likelihood thresholds via graph rigidity. Collaborations span computational geometry, algebraic statistics, and physics, emphasizing interdisciplinary applications. Scientific Awards: Heilbronn small grants scheme (2021) NSF/KOSEF East Asia and Pacific Summer Institutes Fellowship (2006) Theran has supervised numerous BSc, MMath, and PhD projects , including topics like tensegrities, graphons, and geometric constraint systems. He has also contributed to Gaussian graphical models and universality theorems for Delaunay triangulations.

Marcello PelilloView profile

Professor

• Computer Science
• Machine Learning
• Pattern Recognition
• +7 more

Marcello Pelillo is a **Full Professor** at the Department of Environmental Sciences, Computer Science and Statistics at Ca' Foscari University of Venice. His research focuses on machine learning, pattern recognition, computer vision, and adversarial machine learning. He has contributed significantly to graph-based methods, clustering algorithms, and security in machine learning systems. Pelillo is affiliated with the European Center for Living Technology (ECLT) and the Research Institute for Complexity Communications . His work spans theoretical advancements in graph theory and practical applications in cultural heritage digitization, climate science (e.g., ice core analysis), and AI security. Recent projects include entropy-guided graph clustering, backdoor poisoning defenses, and benchmark datasets for puzzle-solving tasks. Publications emphasize interdisciplinary applications, such as AI-assisted historical document digitization and energy-latency attacks in networks. He has supervised multiple collaborative projects involving institutions like the Research Institute for Complexity and the European Interuniversity Research Center.

Nate VeldtView profile

Assistant Professor

• Graph algorithms
• Network science
• Matrix computations
• +3 more

Nate Veldt is an Assistant Professor in the Department of Computer Science and Engineering at Texas A&M University. His research focuses on combinatorial algorithms and computational methods for data analysis, particularly in graph and network modeling. He holds a Ph.D. in Mathematics with a concentration in Computational Science from Purdue University (2019), an M.S. in Mathematics from Purdue (2017), and a B.S. in Mathematics from Wheaton College (2013). Research Interests: - Graph algorithms and network science - Matrix computations - Computational optimization - Data science and machine learning applications in network analysis. His work bridges theoretical computer science, discrete mathematics, and practical data science challenges. Notable Awards: - AFOSR Young Investigator Program Award (2025) - Dimitris N. Chorafas Foundation Award (2019) - NSF East Asia and Pacific Summer Institute Award (2016) Recent Grants and Roles: - Course development grant for a new 'Computational Methods for Network Science' course (TAMIDS, 2025) - Co-chair of SIAM Symposium on Algorithm Engineering and Experiments (ALENEX26, 2026) - Secretary of SIAM Activity Group on Applied and Computational Discrete Algorithms (since 2025) Research Contributions: Develops algorithms for hypergraph clustering, cut improvement, and network analysis. His work emphasizes practical scalability and theoretical guarantees in combinatorial optimization problems. Active in interdisciplinary collaborations, including with Lawrence Livermore National Laboratory and the University of Utah.

• Optical Nanoscopy
• Quantitative Phase Imaging
• Deep Learning in Microscopy
• +5 more

Krishna Agarwal is a Professor in Ultrasound, Microwaves and Optics at the Department of Physics and Technology, UiT The Arctic University of Norway. His research spans multiple interdisciplinary fields including optical nanoscopy, quantitative phase imaging, and computational imaging techniques. He is an active member of the Ultrasound, Microwaves and Optics research group, with specialized focus on Optical Nanoscopy, and participates in research projects including VirtualStain and NanoAI. Professor Agarwal's research interests center on advanced imaging techniques, particularly in optical nanoscopy and quantitative phase imaging. His work bridges physics, computer science, and biology, developing novel computational methods for microscopy enhancement. His research focuses on applying deep learning to improve imaging resolution, developing frameworks for quantitative phase reconstruction, and creating new methodologies for 3D imaging of biological specimens. His work has significant applications in biomedical imaging, cellular analysis, and diagnostic technologies. Recent publications demonstrate a strong trend toward integrating artificial intelligence with traditional optical techniques, with increasing emphasis on computational approaches to solve longstanding challenges in microscopy. His research shows consistent progression from theoretical optical methods toward practical applications in biological imaging and medical diagnostics, with numerous publications in high-impact optics and imaging journals. Professor Agarwal teaches Optisk nanoskopi (Course FYS-3029) at UiT, contributing to advanced optics education. His research group appears to collaborate extensively with international researchers across multiple institutions, suggesting active grant funding and collaborative research efforts. Based at Teknologibygget Tromsø 3.058, Professor Agarwal leads research in the Optical Nanoscopy group, focusing on developing next-generation imaging technologies that combine optical physics with computational methods. His team appears to work at the intersection of physics, computer science, and biology, developing tools that push the boundaries of what's possible in cellular and sub-cellular imaging.

Audrey RepettiView profile

Associate Professor

• Optimization
• Fixed Point Algorithms
• Deep Learning
• +4 more

Audrey Repetti is an Associate Professor at Heriot-Watt University, affiliated with both the School of Mathematical and Computer Sciences and the School of Engineering and Physical Sciences. She holds a PhD in optimization from Université Paris-Est Marne-la-Vallée (2015) and an MSc in applied mathematics from Université Pierre et Marie Curie (2011). Her research focuses on optimization (convex/nonconvex/stochastic), deep learning, Bayesian inference, and inverse problems in imaging and graph processing. She received the Royal Society of Edinburgh Fellowship (2022) for her work in optimization for data science. Her teaching includes courses like Bayesian Inference and Computational Methods. She collaborates internationally on projects in radio astronomy imaging, medical imaging, and computational optics. Repetti’s work bridges optimization theory with practical applications, emphasizing scalable algorithms for large-scale data problems.

• Machine Learning
• Graph Neural Networks
• Algorithmic Reasoning
• +3 more

Dobrik Georgiev is a Lecturer in the Department of Computer Science and Technology within the School of Technology at the University of Cambridge. His research centers on bridging algorithmic reasoning with neural architectures, focusing on how neural networks can execute and generalize algorithmic processes. His primary research interests include: Neural algorithmic reasoning and its applications to combinatorial problems Graph neural networks and hypergraph learning systems Explainable AI through concept-based interpretability Deep equilibrium models for algorithmic execution Biological data analysis using neural architectures Georgiev's publication record demonstrates consistent innovation in neural execution models, with recent work exploring bottlenecks in algorithmic reasoning (2025), multi-solution reasoning frameworks (2024), and generalization beyond synthetic graph models (2023). His research shows strong interdisciplinary connections between theoretical computer science, machine learning, and computational biology. While no formal awards are documented in available sources, his work has established significant contributions to neural algorithmic reasoning frameworks. Georgiev maintains active research collaborations through the Department of Computer Science and Technology's initiatives, particularly in the areas of machine learning and neural architectures. His technical leadership is evident in software contributions like the LENs library for logic-explained networks.

Ning BaoView profile

Assistant Professor

• High Energy Physics
• Quantum Information Science
• Quantum Algorithms
• +4 more

Ning Bao is an Assistant Professor in the Departments of Physics and Mathematics at Northeastern University's College of Science. His research bridges high energy theory and quantum information science, focusing on topics such as holographic correspondences, quantum entanglement classification, and quantum algorithm development. He explores the interplay between quantum information principles and fundamental physics concepts like black hole thermodynamics and spacetime geometry. His work often involves cutting-edge techniques from machine learning and applied mathematics to address questions in quantum gravity and condensed matter systems. Key research interests include the information-theoretic properties of black holes, entanglement structure in holographic systems, and the application of quantum information tools to machine learning. He contributes to foundational studies in quantum error correction, entanglement wedge reconstruction, and the ER=EPR conjecture. His articles span topics from AdS/CFT correspondence to quantum cellular automata, reflecting his interdisciplinary approach to theoretical physics. Bao’s research is supported by contributions to holographic entropy inequalities, tensor network models, and the exploration of quantum algorithms through frameworks like classical shadows and reinforcement learning. His work has advanced understanding of entanglement in gauge theories, quantum complexity measures, and the interplay between geometry and quantum information in gravitational systems.

• Security and Reliability of VR/AR Systems
• Internet of Things and Mobile Sensing
• Blockchain and Distributed Systems
• +7 more

Dr. Henry Hong-Ning Dai is an Associate Professor in the Department of Computer Science at the Faculty of Science, Hong Kong Baptist University (HKBU). He previously held academic positions at Lingnan University and Macau University of Science and Technology, where he advanced from Assistant to Associate Professor. He holds a Ph.D. from the Chinese University of Hong Kong and a D.Eng. from Shanghai Jiao Tong University. Education: Ph.D. in Computer Science and Engineering, Chinese University of Hong Kong (2008) D.Eng. in Computer Technology Application, Shanghai Jiao Tong University (2012) M.Eng. in Computer Science and Engineering, South China University of Technology (2003) B.Eng. in Computer Science and Engineering, South China University of Technology (2000) Dr. Dai's research focuses on security and reliability of VR/AR systems , Internet of Things , blockchain and distributed systems , federated learning , and cyber-physical systems . His work integrates AI, networking, and software engineering to address real-world security and performance challenges in emerging technologies. He has published over 300 papers in top journals and conferences such as IEEE JSAC, TMC, ICSE, INFOCOM, and AAAI, accumulating more than 24,000 citations. The 15 most recent publications (2023–2025) highlight his leadership in VR/AR security (e.g., AcouListener, Meta VR study), blockchain scalability and fairness (e.g., Porygon, Auncel, Justitia), federated and robust learning (e.g., EBS-CFL, FedDP), and edge-AI and wireless security (e.g., HARBOR, Smart Shield). His recent work also explores AI-generated art evaluation and LLM-driven manufacturing systems , showcasing interdisciplinary innovation. Scientific Awards and Recognition: Holder of 1 U.S. patent and 1 Australia innovation patent Winner of more than 17 awards Senior Member of ACM, IEEE, and EAI Dr. Dai has been Principal or Co-Investigator on over 12 research projects totaling HK$16 million, funded by UGC, NSFC, FDCT, and HKBU. He serves as an Associate Editor for IEEE Communications Surveys & Tutorials , IEEE Transactions on Intelligent Transportation Systems , and several other IEEE journals. He has chaired program committees and served on the PC of top conferences including ICSE, KDD, and INFOCOM. He is actively recruiting Ph.D. students and RAs in security, blockchain, and AI. Laboratories and Research Teams: While not explicitly named, Dr. Dai leads a research group focused on secure and intelligent distributed systems, with active projects in blockchain, VR security, and edge AI. His team has developed open-source tools such as VR-SP Detector , PrettySmart , and RLF for smart contract analysis and security assessment.

• Computational biology
• Algorithm design and implementation
• Combinatorial optimization
• +3 more

John Kececioglu is a Professor in the Department of Computer Science at the University of Arizona, maintaining his office in Gould-Simpson Hall (GS 727). Holding a Ph.D. from the University of Arizona (1991), he bridges theoretical computer science with practical applications in biology and astronomy through rigorous algorithmic development. His research spans computational biology, algorithm design, and combinatorial optimization, with significant contributions to protein sequence alignment, metabolic network analysis, and astronomical alert systems. Recent work focuses on hypergraph-based pathway inference in cellular reaction networks and robust optimization for metabolic engineering, while his astronomy collaborations include the ANTARES broker system for real-time classification of transient events. Analysis of his 2018-2024 publications reveals a dual research trajectory: bioinformatics work emphasizing hyperpath algorithms for metabolic networks (60% of recent output) and astronomy projects developing machine-learning brokers for time-domain discovery (40%). Both streams demonstrate his signature approach of transforming complex biological and astronomical problems into combinatorial optimization challenges with efficient algorithmic solutions. No scientific awards were documented in the source material. While specific advising records and grant histories remain unreported in available texts, his extensive publication record spanning protein alignment (1989-2020) and metabolic engineering (2022-2024) suggests sustained mentorship of graduate researchers. His methodology of "parameter advising" for sequence alignment indicates innovative approaches to algorithm configuration that likely shaped student projects. Kececioglu leads computational efforts within the ANTARES (Arizona-NOAO Temporal Analysis and Response to Events System) collaboration, developing software infrastructure for next-generation astronomical surveys. His bioinformatics work implies active participation in interdisciplinary teams combining computer science, systems biology, and metabolic engineering, though specific lab affiliations are not explicitly stated in source materials.

• Medieval Religious History
• Digital Humanities
• Inquisition Studies
• +3 more

David Zbíral is Professor and Deputy Head of the Department of Religious Studies at Masaryk University's Faculty of Arts, where he leads research initiatives through the Center for Digital Research on Religion. His academic position involves both administrative leadership and advanced research in medieval religious history with computational methodologies. His research focuses on applying digital humanities approaches to medieval inquisition records, particularly through: Computational analysis of religious dissent and heresy prosecutions Network modeling of social and religious relationships Gender studies in medieval religious contexts Digital reconstruction of historical judicial processes Semantic text modeling of historical documents Zbíral's recent publications demonstrate a strong emphasis on computational methods in historical research, particularly using network analysis, natural language processing, and quantitative approaches to study medieval inquisition records across Europe. His work frequently examines social dynamics, gender patterns, and spatial relationships in religious dissent cases from the 11th to 15th centuries. He leads the DISSINET project (Networks of Dissent), which develops computational models of dissident and inquisitorial cultures in medieval Europe. This interdisciplinary initiative combines historical research with cutting-edge digital methodologies to analyze patterns of religious nonconformism.

Radu BalanView profile

Professor

• Applied Mathematics
• Harmonic Analysis
• Signal Processing
• +6 more

Radu Balan is a Professor of Applied Mathematics in the Department of Mathematics at the University of Maryland, College Park. He is affiliated with the Center for Scientific Computation and Mathematical Modeling (CSCAMM) and the Norbert Wiener Center for Harmonic Analysis and Applications. His research bridges harmonic analysis, signal processing, and machine learning, with a strong focus on phase retrieval, frame theory, and deep neural network analysis. He earned his Ph.D. in Applied Mathematics from Princeton University in 1998, advised by Ingrid Daubechies, and holds B.Sc. degrees in Physics and B.E. in Electrical Engineering from Romanian institutions. His academic journey includes a postdoctoral stint at IMA and IBM, followed by research roles at Siemens before joining the University of Maryland faculty in 2006 as an Associate Professor, rising to full Professor in 2011. His research interests include harmonic analysis, statistical signal processing, machine learning, phase retrieval, and mathematical modeling. He has made significant contributions to the theory of frames, phaseless reconstruction, and the stability of deep neural networks. His recent work explores permutation-invariant representations, Lipschitz bounds in CNNs, and graph deep learning. His recent publications (2020–2024) reveal a strong trend toward theoretical machine learning and its mathematical foundations, particularly in the stability and invertibility of neural networks and representations. He frequently publishes in top-tier journals such as IEEE Transactions, SIAM Journal, and Foundations of Computational Mathematics, often on arXiv, indicating an active and current research program in mathematical signal processing and deep learning theory. Radu Balan has authored or co-authored over 60 peer-reviewed papers, edited five volumes of Excursions in Harmonic Analysis , and holds 25 U.S. and international patents in signal processing and source separation. His work has been supported by the National Science Foundation (NSF), Army Research Office (ARO), Simons Foundation, Siemens, and Verisk. He has advised numerous students and collaborators, with research projects spanning statistical signal processing, blind source separation, and deep learning. His lab and research group are associated with CSCAMM and the Norbert Wiener Center, fostering interdisciplinary collaboration in mathematical modeling and computation.

• Mathematical morphology
• Fuzzy set theory and bipolar fuzzy sets
• 3D image interpretation and computer vision
• +7 more

Isabelle Bloch is a full Professor at Télécom Paris (Institut Polytechnique de Paris) and Emeritus Professor at the University of Bordeaux . She heads the Image, Modeling, Analysis, Geometry, Synthesis (IMAGES) research team within the Information Processing and Communication Laboratory (LTCI) in the Image, Data, Signal (IDS) Department . Education & Affiliations Professor – Télécom Paris, Institut Polytechnique de Paris (current) Professeure Émérite – University of Bordeaux (honorary) Research Interests Her work lies at the intersection of computer science, applied mathematics and medicine . Core themes include mathematical morphology , fuzzy and bipolar logics , 3-D image interpretation , discrete 3-D geometry & topology , information fusion , structural pattern recognition , spatial reasoning and medical imaging . Recent projects extend these concepts to explainable AI , argumentation theory , computational musicology and robotic surgery guidance . Scientific Awards Blondel Medal (2008) – awarded by the French Electrical & Electronics Engineering Society to outstanding young scientists. Grants & Collaborative Projects While specific grant numbers are not listed in the text, Prof. Bloch coordinates and participates in large-scale interdisciplinary projects funded by national (ANR) and European programs, spanning medical AI, pediatric oncology imaging, neuro-informatics and ethical AI. Labs & Teams She leads the IMAGES team (≈ 25 researchers) inside the LTCI – a joint research unit of Télécom Paris and CNRS. The group develops theory and open-source software in mathematical morphology, deep learning and symbolic AI, and collaborates closely with clinical partners at Necker–Enfants Malades Hospital, AP-HP and Gustave Roussy.

• Computational Social Science
• Data Science
• Multivariate Social Networks
• +10 more

Termeh Shafie is a full-time Professor in the Department of Politics and Public Administration at the University of Konstanz, specializing in Computational Social Science and Data Science. With a strong statistical foundation, she develops advanced methodologies for analyzing multivariate social networks while bridging archaeological network reconstruction with modern data science techniques through projects like NEXUS 1492. Key Research Areas: Multigraph modeling, network entropy analysis, isotope geoprovenance, and hypergraph representations Projects: NEXUS 1492 archaeological network reconstruction Her 15 most recent publications demonstrate significant contributions to network methodology (random multigraph models, centrality index analysis), archaeological applications (Caribbean attack networks, Iroquoian settlement patterns), and data privacy frameworks. Articles span 2012–2025 with interdisciplinary focus on statistical sociology, archaeological theory, and computational modeling. Current teaching includes courses on social network analysis, data science, and statistical learning. Office hours available via ILIAS booking system. Contact details provided for both academic and administrative correspondence.

Dominique de WerraView profile

Academic

• Operations Research
• Combinatorial Optimization
• Graph Theory
• +5 more

Dominique de Werra is an Honorary Professor at the Swiss Federal Institute of Technology in Lausanne (EPFL), affiliated with the Department of Mathematics within the Faculty of Basic Sciences. He holds an Engineering degree and a Doctorate in Operations Research from EPFL. His career includes roles as Full Professor (1979–2008), Vice-President (1991–2000), and Dean of International Relations (2000–2004). He has served as President of IFORS (2010–2012) and EURO (1987–1988), and led academic networks like CLUSTER and EURECOM. His research focuses on Operations Research, Combinatorial Optimization, Graph Theory, and applications in scheduling, logistics, and multidisciplinary fields. He has authored over 200 technical publications, co-authored textbooks like Operations Research for Engineers , and edited numerous journal special issues. Awards include Honorary Doctorates from Paris-Dauphine, Poznan, and Fribourg, as well as the EURO Gold Medal (1995) and Distinguished Service Medal (2012). de Werra has supervised over 140 master’s theses and 350 student projects. He has held visiting professorships worldwide and served on editorial boards of journals like Discrete Applied Mathematics and European Journal of Operational Research . His contributions span academic leadership, conference organization, and interdisciplinary collaborations in archaeology, zoology, and engineering.

Juan G. RestrepoView profile

Professor

• Complex Networks
• Synchronization
• Nonlinear Dynamics
• +5 more

Juan G. Restrepo is a Professor in the Department of Applied Mathematics at the University of Colorado Boulder, where he maintains an active research program in complex systems and network science. His work bridges theoretical mathematics with applications in physical, biological, and social systems. Professor Restrepo's research focuses on complex network dynamics, particularly synchronization phenomena, nonlinear dynamics and chaos, spread of information and disease in social networks, and criticality in brain networks. His work spans theoretical foundations of network science while addressing real-world applications from cardiac dynamics to social contagion processes. He has made significant contributions to understanding higher-order interactions in networks through hypergraphs and simplicial complexes, revealing new synchronization phenomena and dynamical behaviors. Analysis of his recent publications reveals a strong trajectory toward higher-order network structures, with increasing focus on hypergraphs, simplicial complexes, and topological approaches to network dynamics. His work connects mathematical theory with applications in neuroscience, quantum systems, epidemiology, and social dynamics, demonstrating the unifying power of network science across disciplines. Recent papers show growing integration of machine learning techniques with traditional dynamical systems approaches. Professor Restrepo maintains an active research group investigating fundamental questions in network dynamics while exploring applications to real-world systems. His work on criticality in neural networks and resource transport mechanisms has implications for understanding brain function and developing more efficient networked systems.