Find Academic Supervisors

• Artificial Intelligence
• Computational Sustainability
• Machine Learning
• +5 more

Carla Gomes is a Professor at Cornell University and a leading figure in Artificial Intelligence (AI), renowned for pioneering the field of Computational Sustainability. Her work bridges core AI advancements with multidisciplinary research, addressing critical sustainability challenges while driving innovation in computer science. Key Contributions: Established Computational Sustainability as a transformative subfield integrating computational methods with ecological and socio-economic problem-solving. Developed XOR-streamlining for model counting, enabling breakthroughs in probabilistic inference and combinatorial solvers. Advanced AI applications in materials discovery, including Deep Reasoning Networks for solving crystal-structures phase-mapping problems to identify solar fuel materials. Awards: ACM AAAI Allen Newell Award (2021) for foundational AI and Computational Sustainability contributions. Recognized as an ACM Fellow (2017) for transformative work in AI and technology advancement. Gomes’ research spans combinatorial optimization, heavy-tailed runtime distributions, and algorithm portfolios, with practical impacts on SAT, MIP, and SMT solvers. Her NSF Expeditions awards underscore her leadership in fostering interdisciplinary collaboration for global sustainability solutions.

Rob EggermontView profile

Assistant Professor

• Algebraic Geometry
• Discrete Mathematics
• Polynomial Representations
• +5 more

Rob Eggermont is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e), specializing in the Discrete Algebra and Geometry research group. His work focuses on finiteness properties in infinite-dimensional algebraic settings with symmetry, connecting algebraic geometry, representation theory, and combinatorics. His research has significant applications in fields including phylogenetics, chemistry, and algebraic statistics. Education: MSc in Mathematics from Leiden University (2011), cum laude PhD from Eindhoven University of Technology (2015), cum laude Rob Eggermont's research centers on how algebraic structures with symmetry maintain finiteness properties even in infinite-dimensional settings. His work on topological noetherianity demonstrates that in systems with sufficient symmetry, problems involving millions of variables can be as tractable as those with only a few variables. This has profound implications for computational algebra and geometric modeling. His research group plays a leading role in algebraic graph theory, finite and incidence geometry, and discrete Lie theory. An analysis of his recent publications (2017-2025) reveals a consistent focus on polynomial representations, GL-varieties, and symmetric matrix structures. His work frequently explores how finiteness properties emerge in infinite-dimensional settings through symmetry constraints. Collaborations with Jan Draisma and other mathematicians form a significant portion of his output, indicating strong research networks in algebraic geometry and representation theory. Scientific Awards: Jong Talent Aanmoedigingsprijs (2007) - awarded to the best first-year mathematics student at Leiden University Veni grant 016.Veni.192.113 from the Netherlands Organization for Scientific Research (NWO) Dr. Eggermont has secured research funding through competitive grants and has participated in numerous international workshops and conferences, including the AIM workshop on 'Representation stability' (San Jose, 2016) and the BIRS workshop on 'Free Resolutions, Representations, and Asymptotic Algebra' (Banff, 2016). His research group actively contributes to the DIAMANT symposium and the Intercity Number Theory Seminar. Within the Discrete Algebra and Geometry group at TU/e, Eggermont collaborates with colleagues on advancing the theoretical foundations of algebraic structures with symmetry. His work has helped establish connections between representation theory, combinatorics, and practical applications in scientific computing.

Viresh PatelView profile

Lecturer

• Extremal combinatorics
• Probabilistic combinatorics
• Graph polynomials / partition functions
• +5 more

Dr. Viresh Patel is a Lecturer in Optimisation at the School of Mathematical Sciences, Queen Mary University of London. His research focuses on extremal and probabilistic combinatorics, graph polynomials, phase transitions, and approximation algorithms. University: Queen Mary University of London School: School of Mathematical Sciences Email: viresh.patel@qmul.ac.uk Research Interests: Patel’s work bridges combinatorics, graph theory, and statistical physics. Key areas include Hamiltonian cycles in dense graphs, algorithmic approaches to graph polynomials, and complexity analysis of physical models. Publication Trends: Recent articles address structural graph theory (cycle/path decomposition), computational complexity (Potts/Ising models), and approximation algorithms using probabilistic and Taylor series methods. Collaborations: Regular co-authorship with researchers like Guus Regts, Allan Lo, and Matthew Jenssen indicates strong interdisciplinary engagement.

Alexander BarvinokView profile

Professor

• Computational Complexity
• Algorithms
• Algebra
• +6 more

Alexander Barvinok is a Professor in the Department of Mathematics at the University of Michigan, Ann Arbor. His office is located in East Hall (4066 East Hall), where he has been conducting research and teaching advanced courses in computational mathematics since receiving his Ph.D. from Leningrad State University in 1988. Professor Barvinok's research focuses on computational complexity and algorithms in algebra, geometry and combinatorics. He is particularly interested in connections between various notions of phase transition in statistical physics, analytical properties of partition functions and computational complexity. His work bridges theoretical mathematics with practical computational approaches, exploring how physical phenomena can inform algorithmic design and analysis. His research spans convex geometry, combinatorial optimization, and the computational aspects of polynomial systems. His recent publications (2016-2024) demonstrate a consistent focus on partition functions, computational aspects of convex bodies, and approximation algorithms for counting problems. He has made significant contributions to understanding the zeros of partition functions in statistical physics models, developing efficient volume estimation algorithms for polyhedra, and creating polynomial-time approximation schemes for problems previously thought to be computationally intractable. His work frequently connects algebraic properties of polynomials with computational feasibility. Professor Barvinok has authored several influential textbooks including "A Course in Convexity" (AMS Graduate Studies in Mathematics, 2002), "Integer Points in Polyhedra" (Zurich Lectures in Advanced Mathematics, 2008), and "Combinatorics and Complexity of Partition Functions" (Springer, 2016). He regularly teaches advanced graduate courses such as Math 669 on specialized topics including "Combinatorics, Geometry and Complexity of Integer Points" and "Topics in Convexity," with his lecture notes often evolving into significant research contributions.

• Quantum Computing
• Quantum Foundations
• Diagrammatic Reasoning
• +3 more

John van de Wetering is an Assistant Professor at the Theoretical Computer Science group of the Informatics Institute at the University of Amsterdam . He works with the QuSoft research center and co-develops the PyZX open-source quantum compiler. His research spans quantum computation, diagrammatic reasoning, and quantum foundations. Education: PhD in Computer Science (2022, Radboud University) Research: Focuses on ZX-calculus for quantum circuit optimization, verification, and simulation. Explores quantum foundations through algebraic and compositional methods. Recent work includes quantum circuit optimization with AlphaTensor, completeness proofs for ZH-calculus, and scalable spider nets for transversal non-Clifford gates. He directs the new Master's program in Quantum Computer Science at UvA and co-authored the book Picturing Quantum Software . Notable collaborations include PyZX development and EU Gender Equality Working Group participation. Supervision includes current PhD students Lia Yeh (Oxford), Sarah Li (UvA), and Marc Farreras (Leiden). Former students include Boldizsár Poór (Quantinuum), Julien Codsi (Princeton), and Yanbin Chen (TUM). Tools & Outreach: Maintains the ZX-calculus Wikipedia page, co-lectured courses like Quantum Processes and Computation , and develops the ZX-calculus educational website. Organized QPL2026 conference and contributed to open-access journal Quantum .

Panagiotis PapastamoulisView profile

Assistant Professor

• Bayesian Statistics
• Computational Statistics
• Finite Mixture Models
• +5 more

Panagiotis Papastamoulis serves as Assistant Professor at the Department of Statistics within the School of Information Sciences and Technology at Athens University of Economics and Business (AUEB). He joined AUEB in April 2020 after working as an Adjunct Lecturer from 2018-2019 and completing extensive postdoctoral research at prestigious institutions including the University of Manchester (2012-2018) and INRA in France (2011-2012). His educational background includes a BSc in Mathematics from the University of Patras (2003), an MSc in Applied Statistics (2006), and a PhD in Statistics (2010) from the University of Piraeus. His doctoral thesis addressed the label switching problem in Bayesian analysis of mixtures of distributions under the supervision of Professor G. Iliopoulos. Dr. Papastamoulis's research program centers on Bayesian and computational statistics, with particular expertise in finite mixture models, model-based clustering, and bioinformatics applications. His methodological contributions span theoretical developments in label switching solutions, reversible jump MCMC algorithms, and practical implementations for RNA-seq data analysis. His work demonstrates a consistent trajectory from foundational statistical theory to real-world biological applications. Analysis of his publication record reveals a strong focus on developing statistical methodology for complex data structures, with significant contributions to mixture modeling, Bayesian factor analysis, and bioinformatics. His most recent work (2023-2025) extends into cure rate modeling, directional data analysis, and multinomial mixture models for spatial data, showing continued innovation while maintaining connections to his core research themes. As an educator, he teaches undergraduate courses including Linear Models and Bayesian Inference Methods, and graduate courses such as Statistical Genetics-Bioinformatics and High Dimensional Statistics. He has also developed multiple open-source R packages that have become standard tools in the statistical community, including label.switching, BayesBinMix, and fabMix, which address fundamental challenges in mixture model analysis. Dr. Papastamoulis actively contributes to the academic community through organizing research seminars at AUEB and participating in conference committees, including the 22nd European Young Statisticians Meeting in 2021. His research integrates theoretical statistical development with practical computational implementations, creating tools that advance both methodology and application in multiple scientific domains.

George NasrView profile

Assistant Professor

• Combinatorics
• Mathematics

George Nasr is an Assistant Professor in the Department of Mathematics at Augustana University, joining the institution in Fall 2023 after serving as a postdoctoral scholar at the University of Oregon. His academic appointments reflect a commitment to liberal arts-focused mathematics education. His educational qualifications include: Ph.D. in Mathematics, 2021, University of Nebraska-Lincoln M.S. in Mathematics, 2018, University of Nebraska-Lincoln B.S. in Mathematics and Computer Science, 2016, California Lutheran University Dr. Nasr specializes in combinatorics, which he describes as "counting, but without actually counting," situating his work within discrete mathematics and theoretical problem-solving frameworks. His research interests stem from foundational mathematical theory with potential interdisciplinary applications.

• Algebraic Combinatorics
• Geometric Combinatorics
• Coxeter Groups
• +8 more

Eleni Tzanaki is an Assistant Professor in the Department of Mathematics & Applied Mathematics at the University of Crete, Greece. Her office is located in room Δ330 at the Voutes Campus in Heraklion. She can be contacted via telephone at +30-2810-393747 or by email at etzanaki@uoc.gr. Her academic work spans both pure mathematics and educational research, with a particular focus on algebraic and geometric combinatorics as well as innovative approaches to science and physics education. Dr. Tzanaki's research interests center on algebraic and geometric combinatorics, with specific focus areas including: Combinatorics of Coxeter groups Hyperplane Arrangements Polytopes Poset structures, lattices, and patterns Real rootedness of polynomials Her publication record demonstrates a dual research trajectory. From 2016-2021, her work was predominantly in pure combinatorial mathematics, focusing on hyperplane arrangements, lattice paths, and combinatorial structures related to Coxeter groups. More recently (2022-2025), she has expanded her research to include educational applications, particularly examining how artificial intelligence tools like ChatGPT can be effectively integrated into junior high school physics education. This interdisciplinary approach connects her mathematical expertise with practical educational challenges, exploring innovative teaching methods like flipped classrooms and escape-room activities. Dr. Tzanaki appears to be actively involved in interdisciplinary collaborations that bridge mathematics, computer science, and education. Her work on fractal geometry across science, computer science, and art lessons demonstrates her commitment to cross-disciplinary approaches. She is investigating how emerging AI technologies can enhance physics instruction and address student misconceptions in areas like hydrostatic pressure and buoyancy, while maintaining her foundational work in combinatorial mathematics.

• Inverse Problems
• Machine Learning for Imaging
• Numerical Analysis
• +5 more

Ozan Öktem is a Professor at KTH Royal Institute of Technology , specializing in applied mathematics with a focus on inverse problems, machine learning, and numerical analysis. He works in the Division of Numerical Analysis, Optimization and Systems Theory and develops theory and algorithms for solving inverse problems, particularly in medical imaging and cryogenic electron microscopy (Cryo-EM). His research integrates mathematical analysis, machine learning, and numerical methods to address challenges in recovering hidden model parameters from indirect observations. He emphasizes regularization techniques to stabilize ill-posed problems and computational feasibility for large-scale applications. Key areas include tomographic reconstruction, deep learning-based methods, and applications in biomedical imaging. Recent publications highlight his work on learned primal-dual architectures for CT, Riemannian geometry in protein dynamics analysis, and regularization strategies for Cryo-EM. Collaborations span computational biology, medical imaging, and optimization. He serves as course responsible for advanced courses in differential equations, inverse problems, and scientific computing.

• Wireless Sensor Networks
• Internet of Things
• Mobile Networks
• +7 more

Sudip Misra is a Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kharagpur, West Bengal, India. With an extensive publication record of 168 publications and over 1,142 citations, he has established himself as a leading researcher in networking and sensor systems. His work spans multiple domains within computer science and engineering, with a particular focus on practical applications of theoretical concepts. Dr. Misra's research interests encompass Wireless Sensor Networks, Internet of Things (IoT), Mobile Networks, Routing Protocols, Network Security, Energy Efficiency, Underwater Sensor Networks, UAV Networks, and Machine Learning applications for networking. His work demonstrates a consistent focus on solving real-world problems through innovative networking solutions, particularly in resource-constrained environments. He has made significant contributions to security protocols, energy management, and connectivity solutions across various network paradigms. Analysis of his recent publications reveals a strong trend toward practical applications of networking technologies, with increasing focus on IoT security, vehicular networks, underwater communication systems, and UAV-enabled networks. His work shows evolution from fundamental networking protocols to more complex, application-specific solutions addressing contemporary challenges in smart transportation, precision agriculture, and secure cloud-based IoT services. Dr. Misra has mentored numerous students and researchers, as evidenced by his extensive co-authorship record. His collaborative work spans multiple institutions across India and internationally, demonstrating strong research leadership and networking capabilities. His research has been supported by various funding mechanisms, though specific grants are not detailed in the available information. His laboratory or research group appears to focus on sensor networks, IoT systems, and related communication technologies, with particular emphasis on security, energy efficiency, and practical deployment scenarios. The research output suggests a well-established team working on cutting-edge networking challenges with real-world applicability.

• Software Engineering
• Human Computer Interaction
• Computer Science Education
• +4 more

Siti Fazilah Shamsudin is a Senior Lecturer at UOW Malaysia KDU University College with over 20 years of academic experience in Computer Science and Software Engineering. She serves as Program Leader for the Diploma Program in Computing & Creative Media and plays a pivotal role in program development, curriculum design, and accreditation processes for computing degrees, all of which have achieved full accreditation. PhD in Information System (ongoing) at University Technology Mara (UiTM) Master of Information Technology from University Technology Mara (UiTM), 2002-2004 BSc (Hons) in Computer Engineering from De Montfort University, UK, 1996-1999 Dr. Shamsudin's research spans multiple interdisciplinary domains including software engineering, human-computer interaction, and computer science education. Her applied research focuses on medical imaging (particularly kidney stone and ovarian tumor detection using ultrasound), digital forensics for mobile devices, and machine learning applications in aquaculture. Her current PhD research on visual appeal interface design for slow learners bridges educational technology with user-centered design principles. Her publication record demonstrates consistent output in applied computing fields, with recent work focusing on image processing algorithms for medical diagnostics and digital forensics. The research shows a clear trajectory from theoretical foundations to practical implementations with real-world applications in healthcare, cybersecurity, and aquaculture management. Recipient of multiple UOW Malaysia KDU research grants (2014-2021) totaling over RM120,000 Appointed as Assistant Consultant for KiplePay Sdn Bhd through Islamic Science University of Malaysia (2021) Reviewer for 6th International Conference User Science and Engineering 2021 (i-USEr 2021) As the main coordinator for Final Year Projects, Dr. Shamsudin provides comprehensive guidance to students through their research journey. Her grants portfolio demonstrates strong research capability across multiple domains including medical imaging, digital forensics, and aquaculture technology. She maintains active industry connections through consultancy work and applied research projects that address real-world problems.

Yuzhen LuView profile

Assistant Professor

• Optical Sensing
• Machine Vision
• Computer Vision
• +8 more

Yuzhen Lu is an Assistant Professor in the Department of Biosystems & Agricultural Engineering at Michigan State University (MSU), with a joint appointment between the College of Agriculture & Natural Resources and College of Engineering. Before joining MSU in January 2023, he was an Assistant Professor at Mississippi State University (2020-2022) and a Postdoc Research Scholar with USDA-ARS and North Carolina State University. Dr. Lu earned his Ph.D. in Biosystems Engineering from Michigan State University in 2018. His academic journey reflects a strong foundation in engineering applications for agricultural systems, with a focus on bridging technological innovation with practical farming needs. Dr. Lu's research focuses on developing and deploying sensing and automation/robotics technologies for smart agriculture and food systems. His expertise spans optical instrumentation, machine/computer vision, image analysis, and applied machine learning. His work addresses critical challenges across the agricultural value chain, from in-field applications to postharvest processing, with particular emphasis on specialty crop production. His research integrates engineering principles with agricultural science to create practical solutions for real-world farming challenges. Analysis of Dr. Lu's recent publications reveals a strong trajectory in agricultural technology development, with emphasis on machine vision systems for quality assessment, precision agriculture applications, and robotics for agricultural tasks. His work shows increasing integration of advanced AI techniques with traditional engineering approaches to solve practical agricultural problems, particularly in specialty crop production, livestock monitoring, and food processing. Recognized among the World Top 2% Scientists based on Standford and Elsevier Data in 2025 Dr. Lu actively mentors a diverse team of graduate students, postdocs, and undergraduate researchers. His research is supported by multiple competitive grants from USDA-NIFA, MSU AgBioResearch, MDARD, and other funding agencies, totaling over $2 million in active funding. His projects range from developing vision-guided robotic systems for selective harvesting to non-destructive sensing technologies for food quality assessment, demonstrating both academic rigor and practical applicability. Dr. Lu leads a dynamic research laboratory focused on non-destructive sensing (machine vision, optical imaging, and spectroscopy) and automation/robotics technologies for addressing practical needs in agricultural systems. His team collaborates with industry partners, government agencies, and academic institutions to develop and transfer engineering solutions for smart and sustainable agriculture & food systems, with particular emphasis on specialty crop industries where labor shortages and quality demands create significant challenges.

• Statistical Network Analysis
• Non-parametric Statistics
• Causal Inference
• +7 more

Dr. Alexander Fuchs-Kreiß is a Junior Professor for Statistics at the Institute of Mathematics, Faculty of Mathematics and Computer Science at Leipzig University. He joined the university in April 2022 as a member of the Stochastics research group. He is also a scientific member of the International Max Planck Research School for Mathematics in the Sciences and affiliated with the HKMetrics Network. His research focuses on the statistical analysis of network data, particularly semi- and non-parametric methods for dynamic interaction networks. He has developed methodologies for relational event models using counting processes like Hawkes processes, and has contributed to causal inference, high-dimensional statistics, and quantile regression. His work often bridges multiple statistical areas, creating innovative approaches to complex data structures. His recent publications show a strong trend toward developing statistical methods for network data with applications in economics, epidemiology, and social sciences. His research combines theoretical developments with practical implementations, as evidenced by the R packages accompanying his publications. As an educator, he teaches Mathematical Statistics, Statistical Network Analysis, and Causal Inference at Leipzig University. He supervises PhD students working on network models and advises diploma theses in mathematics and business mathematics. Dr. Fuchs-Kreiß maintains active research collaborations with institutions including Heidelberg University, UC Davis, KU Leuven, and LSE. He co-organizes academic seminars and participates in international conferences on statistical learning and probability.

Olivier SerreView profile

Professor

• theoretical computer science
• automata theory
• formal languages
• +7 more

Olivier Serre is a CNRS Research Director working at the Institute for Research in Fundamental Computer Science (IRIF), a joint research unit of CNRS and University of Paris. Since September 2021, he serves as Deputy Scientific Director at the Institute for Information Sciences and Technologies (INS2I) at CNRS. His research focuses on theoretical computer science with particular emphasis on automata theory and formal verification frameworks. Dr. Serre completed his education at ENS Cachan (1999-2003) and earned his PhD in Computer Science from Paris-Diderot University (now University of Paris) in 2004. Following a postdoctoral position at RWTH Aachen, he joined LIAFA (now IRIF) as a research fellow in 2005 and became a CNRS Research Director in 2016. His academic journey reflects deep engagement with foundational theoretical problems and their practical verification applications. His research centers on automata theory and formal languages, with particular expertise in games and logic, infinite structures, and verification of higher-order functional programs. Dr. Serre has made significant contributions to collapsible pushdown systems, recursion schemes, and parity games, bridging theoretical foundations with practical applications in program verification. His work demonstrates exceptional depth in developing decision procedures for complex verification problems previously considered intractable. Dr. Serre's extensive publication record in premier theoretical venues shows consistent contribution to advancing the state of the art. His research trajectory reveals a clear evolution from foundational automata theory toward increasingly sophisticated systems involving higher-order computation and imperfect information, with notable breakthroughs in establishing decidability results for complex verification problems.

Vittorio AddonaView profile

Professor

• Survival analysis
• Sports statistics
• Election audits
• +6 more

Vittorio Addona is a Professor in the Department of Mathematics, Statistics, and Computer Science at Macalester College. He teaches across the Statistics curriculum including introductory modeling, probability, mathematical statistics, and an applied survival analysis course. He maintains regular office hours and serves on the Community and Global Health steering committee. His educational background includes: BS from McGill University (2000) Master's from McGill University (2002) PhD in Statistics from McGill University (2006) Professor Addona's research spans three primary domains: Survival analysis : Focusing on statistical methods for time-to-event data, particularly in medical studies, with expertise in analyzing survival data from prevalent cohort studies Sports statistics : Including research on adjusted plus-minus ratings in the NBA, relative age effects across multiple sports leagues, and quantifying performance impacts in baseball and football Election audits : Developing statistical procedures to verify computerized vote tallies and ensure election integrity His publication record shows consistent output across these domains, with recent work focusing on election verification methods and cross-sport age effect analyses. The publications demonstrate methodological innovation in statistical approaches applied to real-world problems across diverse fields. Professor Addona actively collaborates with undergraduate students on research projects and accompanies them to academic conferences. His teaching responsibilities include STAT 125-01: Epidemiology and multiple sections of STAT 155-01: Intro to Statistical Modeling. Personal background: Born and raised in Montreal, Quebec to Italian immigrant parents Fluent in French and Italian Married to Isadora (PhD in School Psychology from University of Minnesota) Two children: Fabian (10) and Lucia (8) Avid sports fan (Montreal Canadiens, NY Jets)