Find Academic Supervisors

David F. GleichView profile

Professor

• High Performance Computing
• Large Scale Data Analysis
• Network Science
• +5 more

David F. Gleich is a Professor and University Faculty Scholar in the Department of Computer Science at Purdue University, with a courtesy appointment in Mathematics. His work focuses on high-performance large-scale matrix computations to analyze data from biology, social networks, and scientific simulations. Key research areas include higher-order network analysis, algorithmic anti-differentiation, spectral graph theory, community detection, and energy-efficient computing. Recent publications highlight trends in topological analysis of neural networks (2023), higher-order network analysis (2021), and correlation clustering via graph drawing (2020). Awards: NSF CAREER Award 2018 SIAM Outstanding Paper Prize Teaches graduate courses like CS520 - Computational Methods in Optimization and CS515 - Matrix Computations , with an online/virtual teaching adaptation since 2020. Collaborates with student co-authors such as Charles Colley, Yufan Huang, and Omar Eldaghar on network and matrix problems. Maintains active presence on social platforms (Bluesky, Mastodon) advocating for open-source tools, clean air, and responsible AI.

Mark SmuckerView profile

Professor

• Information Retrieval
• Search Engines
• Natural Language Processing
• +3 more

Mark Smucker is a Professor at the University of Waterloo, specializing in Information Retrieval and Search Engine Technology. His primary research focuses on evaluating search systems, combating health misinformation, and understanding user behavior in search contexts. He has been actively involved in TREC tracks since 2019, contributing to tracks like Health Misinformation, Decision, and Lateral Reading. His work emphasizes developing robust evaluation measures and preference judgment tools. Research interests include: Advanced evaluation metrics for search systems User interaction and behavior analysis Health misinformation detection and mitigation Design of preference-based ranking algorithms Optimization of high-recall retrieval systems Recent work highlights innovative approaches to: Expanding benchmark datasets for evaluation Visualizing gaze patterns in search interactions Reducing misinformation through trustworthy web source learning Simulating user behavior for predictive system evaluation His contributions to TREC tracks demonstrate leadership in collaborative research. Though no academic awards are listed, his extensive publication record reflects significant scholarly impact.

Demetres ChristofidesView profile

Associate Professor

• Combinatorics
• Random Graphs
• Extremal Graph Theory
• +10 more

Dr. Demetres Christofides is an Associate Professor in Mathematics and Course Leader of BSc (Hons) Mathematics & Statistics at the University of Central Lancashire Cyprus (UCLan Cyprus). He holds a PhD in Mathematics from the University of Cambridge (2008), alongside advanced qualifications from Cambridge including a BA (2003), MA (2007), and Certificate of Advanced Study in Mathematics (2004). Affiliations: School of Sciences, Department of Mathematics. Administrative Roles: Former Senate Member (2015–2020), Deputy Head of School (2017–2018), and Academic Standards Lead (2017–2020). Research Interests: Focuses on combinatorics, including random graphs, extremal graph theory, positional games, and algorithmic graph theory. His work spans Hamilton cycles, Cayley graphs, information theory, and structural graph properties. He has contributed to over 10 peer-reviewed publications in journals like Journal of Combinatorial Theory and SIAM Journal on Discrete Mathematics . Funding & Grants: Secured research grants from the Cyprus Research Promotion Foundation and the EPSRC, including projects on extremal combinatorics and graph algorithms. Awards: Smith Prize (University of Cambridge). Outreach: Trains Cypriot Mathematical Olympiad teams, creates problems for international competitions (e.g., IMO Shortlist 2020), and co-authored a training book for high-school mathematics competitions. Teaching: Leads and teaches modules across all undergraduate years, including Real Analysis, Algebra, and Complex Analysis. Previously taught at Charles University, Warwick, and Birmingham.

Neha LodhaView profile

Researcher

• Algorithms and Complexity
• Graph Theory
• SAT/SMT Solving
• +3 more

Neha Lodha is a researcher in the Institut für Logic and Computation at TU Wien. Her work focuses on algorithms, complexity theory, and SAT/SMT solving techniques. She has contributed to graph encodings for combinatorial optimization problems and parameterized complexity analysis. Key research areas include SAT-based approaches for graph decomposition (branchwidth, treewidth), SMT methods for fractional hypertree width, and algorithm engineering for constraint satisfaction problems. Her work bridges theoretical foundations with practical algorithmic implementations. Notably, she received the 2016 SAT Conference Best Student Paper Award for her work on SAT encodings of branchwidth. This research was later expanded into a 2019 ACM Transactions publication. Her publications span conferences like IJCAI, CP, and SAT, with a focus on advancing the theoretical and practical aspects of computational logic and algorithm design.

• Algorithms
• Graph Drawing
• Computational Geometry
• +3 more

Alexander Dobler is a PreDoc Researcher at the Algorithms and Complexity Department of Technische Universität Wien. His research focuses on algorithmic visualization, graph drawing, and combinatorial optimization. He contributes to optimizing layout algorithms for diagrams, treemaps, and storylines, with a particular emphasis on minimizing crossings and corners in geometric representations. Key projects include 'Engineering Linear Ordering Algorithms' (2020–2025) and participation in the PACE competition with solver 'Touiouidth'. His work bridges theoretical computer science with practical visualization challenges, addressing both discrete mathematics and computational geometry. Education: BSc, Dipl.-Ing. (engineering degree) Labs/Teams: Part of the Algorithms and Complexity group at TU Wien Grants: Involved in HumAlgo (2018–2023) and REVEAL-AI (2020–2024) His recent publications (2023–2025) address topics like cluster vertex splitting complexity, hoop diagrams for set visualization, and optimizing linear diagrams. He has supervised Marcel Holzmüller’s 2025 diploma thesis on expanding planar storyplan problems.

Chris UmansView profile

Professor

• Theoretical Computer Science
• Computational Complexity
• Derandomization
• +4 more

Chris Umans is a Professor of Computer Science in the Computing and Mathematical Sciences department at the California Institute of Technology (Caltech), affiliated with the Theory Group. He earned his Ph.D. from UC Berkeley in 2000 and joined Caltech in 2002 after a postdoc at Microsoft Research. His research focuses on theoretical computer science, particularly computational complexity, including derandomization, algebraic complexity, and matrix multiplication algorithms. Education: Ph.D. in Computer Science, UC Berkeley (2000); Postdoc, Microsoft Research (2000-2002). Research interests span computational complexity, explicit constructions, and hardness of approximation. His work often intersects algebraic methods and group theory to advance algorithm design, such as group-theoretic approaches to matrix multiplication. Professional Activities: Program committee member for FOCS 2024, STOC, SODA, and others. Vice-Chair of SIGACT (2021-24). Editor for Theory of Computing (ToC), ACM Transactions on Computation Theory (TOCT), and Computational Complexity (CC). Member of the ECCC scientific board. Research Trends: Recent articles explore fast matrix multiplication via matrix groups, algebraic problems over finite fields, and generalized DFTs for finite groups. His work bridges theoretical foundations with algorithmic innovations in algebraic structures. Grants & Labs: His research is supported by NSF grants focused on algebraic methods in complexity theory. He advises students in theoretical computer science and has contributed to collaborative projects on computational algebra and combinatorics.

Eric VigodaView profile

Professor

• Markov Chain Monte Carlo (MCMC) algorithms
• Statistical Physics Phase Transitions
• Randomized Algorithms
• +3 more

Eric Vigoda is a Professor of Computer Science at the University of California, Santa Barbara (UCSB). Previously, he held positions at Georgia Institute of Technology, including Director of the Algorithms and Randomness Center (2016–2019). He earned his PhD in Computer Science from UC Berkeley (1999) and BS/MS from Johns Hopkins University (1994). His research focuses on Markov Chain Monte Carlo (MCMC) algorithms, phase transitions in statistical physics, and randomized algorithms for approximate counting/sampling problems. He has authored over 100 papers and has been recognized with awards such as the Fulkerson Prize (2006) and the Machtey Award (1999). Education: PhD (UC Berkeley, 1999), MS/BS (Johns Hopkins, 1994). Research interests include MCMC methods, randomized algorithms, and applications in evolutionary biology. His work bridges theoretical computer science and statistical physics, particularly in analyzing mixing times of Markov chains. Awards: American Mathematical Society Fellow (2019), Fulkerson Prize (2006), Machtey Award (1999), William A. Baird Teaching Award (Georgia Tech, 2019). Supervised 6 PhD students, including Zongchen Chen (MIT Postdoc), Andreas Galanis (Oxford), and Linji Yang (Entrepreneur). Teaching: Current courses include CS 130A (Data Structures and Graph Algorithms) and CS 190A (Randomized Algorithms). Former courses include Markov Chains in Evolutionary Biology and a MOOC on Graduate Algorithms for Georgia Tech's OMSCS program. Contributions: Organized the 2022 Summer School on 'Spectral Independence and Entropy Decay' at UCSB. Collaborated with researchers on topics like high-dimensional expanders and phase transitions in sampling algorithms.

Hasan PirkulView profile

Professor

• Operations Research
• Network Design
• Scheduling Algorithms
• +3 more

Hasan Pirkul is the Dean and Caruth Chair of the Naveen Jindal School of Management at the University of Texas at Dallas (UTD). He has held this position since 1996, leading the school to significant national rankings in MBA programs and research output. Previously, he served as a Full Professor, Associate Professor, and Assistant Professor at The Ohio State University's College of Business from 1983 to 1996. His academic preparation includes a Ph.D. in Computers & Information Systems (University of Rochester, 1983), an M.S. in Management Science (University of Rochester, 1980), and a B.S. in Industrial Engineering (Bogazici University, 1977). His research focuses on operations research, network design, scheduling algorithms, telecommunications infrastructure, decision support systems, and logistics. Notable contributions include work on survivable networks, GDSS adoption dynamics, and heuristic solutions for complex optimization problems. Under his leadership, the Jindal School expanded enrollment by 280% and secured top 10 rankings for its Professional and Executive MBA programs. He oversaw the construction of a $38M facility in 2003, significantly enhancing the school’s academic infrastructure. He has held editorial roles at journals like the Journal of Information Technology and Management (Founding Editor-in-Chief) and served on numerous academic committees, including the College of Business Executive Committee and various curriculum committees. His professional activities include membership in the Dallas Morning News Technology Board and advisory roles for multiple academic journals.

Joel FriedmanView profile

Professor

• Algebraic Graph Theory
• Combinatorics
• Computer Science Theory
• +5 more

Joel Friedman is a Professor in the Department of Computer Science at the University of British Columbia (UBC), Faculty of Science. His research focuses on Algebraic Graph Theory, Combinatorics, and Theoretical Computer Science, with significant contributions to spectral graph theory, sheaf theory, and computational complexity. He has supervised numerous PhD and Master's students in these areas. Friedman teaches courses such as CPSC 421 (Introduction to the Theory of Computing) and CPSC 531F (Discrete Hodge Theory and Topological Data Analysis). His work bridges pure mathematics and computer science, including studies on Riemann-Roch theorems for graphs, eigenvalue conjectures, and applications of linear algebra in discrete structures. He has authored over 50 publications, with recent contributions on topological data analysis, graph expansions, and algorithmic methods. Awards and honors include the Izaak Walton Killam Memorial Faculty Research Fellowship. Friedman actively engages in graduate supervision, advising students on topics like sparsifier constructions and coded caching problems. His research is affiliated with the Institute of Applied Mathematics at UBC. He has held academic positions since the 1980s, with a consistent record of contributions to theoretical computer science and discrete mathematics.

Henry BradfordView profile

Lecturer

• Asymptotic Group Theory
• Expander Graphs
• Cayley Graphs
• +5 more

Henry Bradford is a Lecturer and Fellow at Christ’s College, University of Cambridge, affiliated with the Department of Pure Mathematics and Mathematical Statistics. His research focuses on asymptotic group theory, including expander graphs, Cayley graphs, word maps, and residual finiteness. He completed his DPhil at the University of Oxford in 2015 and held a postdoctoral research position at the University of Göttingen from 2016 to 2019. His work explores lawlessness quantification, local embeddings into finite groups, and properties like LEF and soficity. His publications span topics such as mixed identities in finite groups, stability in permutations, and diameter bounds in branch groups. He supervises undergraduate research projects and collaborates with mathematicians like Jakob Schneider, Andreas Thom, and Daniele Dona. His recent preprints highlight non-solutions to group equations and arbitrary lawlessness growth. His advising includes projects on uniform diameter bounds and asymptotic group theory. He is reachable via email at hb470@cam.ac.uk and maintains a personal homepage on WordPress.

Tomasz RadzikView profile

Professor

• Algorithm design
• Network optimization
• Wireless communication algorithms
• +5 more

Professor Tomasz Radzik is a Professor of Computer Science at King's College London's Department of Informatics, within the Faculty of Natural, Mathematical & Engineering Sciences. His research focuses on algorithm design, network optimization, wireless communication algorithms, and data structures. He holds a PhD from Stanford University and an MSc from the University of Warsaw. Education: Doctor of Philosophy, Algorithms for Some Linear and Fractional Combinatorial Optimization Problems, Stanford University (1992) Master of Science, Communication and Routing in Synchronous Parallel Machines, University of Warsaw (1985) Research Interests: Radzik's work spans algorithm design for network protocols, distributed systems, and wireless communication. He explores practical implementations of algorithms and their experimental evaluation. His current projects include the Finance Hub (FinTech), Machine Learning, and Algorithms and Data Analysis groups. Grants & Projects: Concept drift in distributed IIoT networks (Toshiba Research Europe, 2021–2025) Randomized Algorithms for Computer Networks (EPSRC, 2014–2017) Expander and Random Walks research (EPSRC, 2011–2014) Collaborations & Labs: Radzik leads King's College's Algorithms and Data Analysis group. His work intersects with the Finance Hub and Machine Learning initiatives, focusing on FinTech and distributed computing challenges.

Amin SaberiView profile

Professor

• Algorithms
• Social Network Analysis
• Market Design
• +3 more

Amin Saberi is a Professor of Management Science and Engineering at Stanford University, with a courtesy appointment in Computer Science. His research focuses on algorithms, social network analysis, market design, and optimization, with applications in economics and computer science. He holds a B.Sc. from Sharif University of Technology (2000) and a Ph.D. in Computer Science from Georgia Institute of Technology (2004). He has been honored with the Terman Fellowship, Alfred P. Sloan Fellowship, and multiple best paper awards at FOCS, SODA, and EC. As founder and CEO of NovoEd, he pioneered scalable social learning platforms used globally. His academic roles include membership in ICME and Bio-X. Education: B.Sc., Computer Science, Sharif University of Technology (2000) Ph.D., Computer Science, Georgia Institute of Technology (2004) Research Interests: Algorithms, market design, optimization, social networks, and their applications in economics and operations research. His work spans theoretical contributions in approximation algorithms, stochastic optimization, and practical applications in ride-sharing markets and online advertising. Recent publications explore dynamic matching markets, envy-free resource allocation, and algorithms leveraging graph neural networks. Awards: Terman Fellowship Alfred P. Sloan Fellowship Best Paper Awards at FOCS, SODA, and EC Advising & Grants: Supervised over a dozen doctoral and master’s students. His research has been supported by grants focusing on dynamic resource allocation and algorithmic market design. Collaborations include Uber, Amazon, and academic institutions globally. Labs/Teams: Leads the Stanford Center for Computational Market Design. Active in interdisciplinary teams at ICME and Bio-X, integrating theoretical insights with practical applications.

Simão CorreiaView profile

Associate Professor

• Partial Differential Equations
• Nonlinear Dynamics
• Mathematical Analysis
• +3 more

Simão Correia is an Associate Professor at the Department of Mathematics of the University of Lisbon, affiliated with the Research Center CAMGSD. His research focuses on nonlinear dispersive and elliptic partial differential equations, particularly on local existence, stability of solutions, and blow-up dynamics. Current Position: Associate Professor, University of Lisbon Research Center: CAMGSD Projects: Co-PI of FCT project NoDES His recent publications address topics such as self-similar solutions for the modified KdV equation, nonlinear smoothing effects in dispersive equations, and stability transitions of NLS ground-states on metric graphs. These works emphasize sharp existence results, multiplier estimates, and applications to mathematical physics. He actively collaborates with researchers like Raphaël Côte, Hugo Tavares, and Jorge Drumond Silva, contributing to the understanding of nonlinear PDEs and their dynamical properties. Notable current trends include: Global and local well-posedness for higher-order dispersive equations Stability analysis of stationary and blow-up solutions Applications to metric graphs and expanding spacetime models Correia's work bridges theoretical analysis with physical phenomena, including cosmic rigid rod dynamics and quantum media systems.

Michael StiberView profile

Professor

• Computational Neuroscience
• Cybersecurity
• Artificial Intelligence
• +5 more

Michael Stiber is a Professor and Division Chair in the Computing & Software Systems Division at the University of Washington Bothell (UWB), affiliated with the School of Science, Technology, Engineering & Mathematics. He is also the Director of Cybersecurity Initiatives and Principal Investigator at the Intelligent Networks Laboratory (formerly BrainGrid Laboratory). Education: Ph.D. in Computer Science from UCLA (1989), M.S. in Computer Science from UCLA, B.S. in Electrical Engineering from Washington University in St. Louis His research focuses on complex networks, high-performance simulation, and data management, applied to computational neuroscience, cybersecurity, artificial intelligence, and nonlinear dynamics. He leads projects modeling emergency services communications systems (e.g., 911 networks) and biological neural network development using the Graphitti simulation framework. He has supervised numerous graduate students, including Jardi Martinez Jordan, Divya Kamath, Ashwini Rudrawar, Snigdha Singh, Emily Hsu, Smriti Singh, Victoria Salvatore, Joe Conquest, Tori Salvatore, and Tony Varela, who have defended theses or projects related to graph-based simulation, GPU migration, neural networks, and AI-driven data analysis. Scientific awards: Fulbright Scholar (Czech Academy of Sciences) Grants: US National Security Agency (2-year funding), InterPARES Trust AI (international cybersecurity-AI collaboration) The Intelligent Networks Laboratory, under his leadership, rebranded from BrainGrid, expanded its scope to AI-driven data analysis, and developed tools like Graphitti (for simulating network-structured systems) and Workbench (for tracking simulation code and data provenance).

• Computer Vision
• Deep Learning
• 3D Point Cloud Processing
• +7 more

Yusuf Hüseyin Şahin is an Assistant Professor in the Department of Computer Engineering at Istanbul Technical University, Faculty of Computer and Informatics. He earned all his academic degrees—B.Sc., M.Sc., and Ph.D.—from the same institution in Computer Engineering. His research lies at the intersection of computer vision, deep learning, and 3D data processing, with applications in medical imaging, architectural heritage, and drone-based vision systems. B.Sc., M.Sc., Ph.D. in Computer Engineering, Istanbul Technical University His primary research interests include 3D point cloud processing, deep learning, image segmentation, adversarial attacks, and medical image analysis. He has published extensively on these topics, particularly focusing on point cloud registration, segmentation, and classification using neural networks. His recent work explores uncertainty modeling, active learning, and generative models for both synthetic data creation and real-world applications. The trend in his publications from 2017 to 2024 shows a clear progression from foundational work in CNN-based 3D classification and cerebral vessel analysis to advanced topics such as dynamic graph networks, conformal prediction, and heritage digitization. His work bridges theoretical machine learning with practical applications in healthcare and cultural preservation. He is currently leading a research project titled "Konformal Tahmin ile Sıcaklık Tahmin Modellerinde Doğruluğun Arttırılması" (Improving Temperature Prediction Accuracy Using Conformal Forecasting), funded under the SRP program from 2025 to 2026. This indicates an expanding interest in predictive modeling and uncertainty quantification. While no scientific awards are listed in the provided texts, his h-index of 5 and 293 citations on Scopus reflect an active and growing research profile. Dr. Şahin teaches undergraduate courses such as Data Structures (BLG 223E) and Object-Oriented Programming (BLG 252E). He has no listed advisees or thesis supervision records. He is part of a collaborative research network involving Gozde Unal and other researchers in medical and architectural computer vision. His lab activities appear to focus on deep learning for 3D data, with emphasis on robustness, efficiency, and real-world deployment.