Find Academic Supervisors

• Medical Image Analysis
• Distributed Systems
• Acoustic Location
• +2 more

Kuo-Ming Chao is a Researcher at the Centre for Research in Arts and Creative Exchange within the School of Arts, Humanities, and Social Sciences. His work bridges computational methods and applied research, with focus areas spanning medical imaging systems, distributed computing architectures, acoustic positioning technologies, social network analysis, and multimodal misinformation detection. Chao maintains active involvement in the academic community through sustained editorial contributions and conference participation. Research Focus: His interdisciplinary research integrates computer science with domain-specific applications. Key themes include: Development of orchestrated workflows for medical image analysis systems Multimodal fusion techniques for information verification Acoustic signal processing for location tracking Graph-based algorithms for social network prediction Professional Activities: Peer reviewer for Service-Oriented Computing and Applications journal since 2006 Invited keynote speaker for the 2025 IEEE Conference on Artificial Intelligence

• Operations Research
• Scheduling
• Machine Learning
• +6 more

BEZOUI Madani is a Researcher-Lecturer at CESI, affiliated with the 'Engineering and Numerical Tools' research team. He holds a PhD in Operational Research from the University of Science and Technology Houari Boumediène (2019), with a focus on multi-objective programming in portfolio optimization. His academic roles include serving as a pedagogical tutor for FISA training courses and heading the 'Data Sciences' program for 5th-year Computer Science Engineers at CESI. His research interests center on Industry 4.0/5.0, optimization of complex systems, machine learning, IoT/BIM technologies, and scheduling. Notable work includes integrating human-centricity and sustainability into digital twin models and advancing hybrid metaheuristics for multi-objective manufacturing optimization. Recent publications (2021–2024) address preference-driven optimization methods, tabu search algorithms, and IoT network vulnerability detection. He has authored a book on Euclidean graph boundaries and contributed to frameworks for flexible job shop scheduling. His ongoing research focuses on decision-maker preference integration in dynamic scheduling under Industry 5.0 contexts. Advising and grants: No specific advising roles or grants mentioned in the CV. His educational activities emphasize pedagogical leadership in data science and operational research. Labs/teams: Active member of CESI’s Engineering and Numerical Tools group, collaborating on IoT, digital twins, and optimization projects.

Apurva NarayanView profile

Adjunct Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +6 more

Dr. Apurva Narayan is an Affiliate Professor in Computer Science and Data Science, and an Associate Member in Mechanical Engineering at the University of British Columbia's Irving K. Barber Faculty of Science. His research focuses on artificial intelligence, machine learning (particularly explainable AI and quantum ML), data mining, cybersecurity of cyber-physical systems, and decision-making under uncertainty. He holds a PhD from the University of Waterloo (Systems Design Engineering) and a Bachelor's in Electrical Engineering from Dayalbagh Educational Institute. Education: PhD in Systems Design Engineering, University of Waterloo (2015) Bachelor of Engineering in Electrical Engineering, Dayalbagh Educational Institute (2008) Research Interests: Explainable AI/ML and quantum machine learning Data analytics and mining Safety/security of cyber-physical systems Graph-theoretic analysis of complex systems Decision-making under uncertainty Reverse engineering complex software systems Awards: Systems Society of India Young Scientist Award (under 40) MITACS Accelerate Award (2013) Dr. T.E. Unny Memorial Award (2011) Multiple teaching nominations at University of Waterloo Advising & Grants: Supervises graduate students in AI/ML and cybersecurity. Secured grants including MITACS Accelerate and Waterloo scholarships. Active in patent filings related to timed regular expressions. Labs/Teams: Engaged in collaborations within the UBC Department of Computer Science and cross-disciplinary projects with Mechanical Engineering on system reliability and quantum computing applications.

Pascal SunView profile

Part-Time Lecturer

• Computer Science
• Machine Learning
• Data Mining
• +5 more

Pascal Sun is a PhD student in the School of Physics, Mathematics and Computing at The University of Western Australia, concurrently serving as a Casual Teaching Lecturer and Research Assistant at the Centre for Software Practice. His research focuses on advanced topics in machine learning and data mining, including graph representation learning, knowledge graph construction, and multimodal sentiment analysis. He has contributed to peer-reviewed publications at prestigious venues such as the ACM Web Conference and IEEE Transactions on Affective Computing. His work explores innovative applications of graph embeddings, human-LLM collaboration systems, and methods for handling non-IID data challenges. Education: Multi-Discipline Doctor of Philosophy (current) Award recognition includes the 2024 Best Demo Award and the 2023 Students Choice Award for enhancing learning experiences through academic programs. Collaborations involve researchers like Prof. Wei Liu and Dr. Du Huynh, with contributions to both theoretical advancements and practical systems development in AI and data science domains.

John SylvesterView profile

Research Fellow

• Probability
• Combinatorics
• Random Graph Models
• +2 more

John Sylvester is a Research Fellow at the University of Cambridge's Computer Laboratory, under the supervision of Thomas Sauerwald, Agelos Georgakopoulos, and David Croydon. He holds a PhD from the MASDOC programme at the University of Warwick (2017) and an MSci in Mathematics from University College London. His research focuses on Probability and Combinatorics, particularly random graph models, random walks on graphs, and extremal combinatorics. Education: PhD in MASDOC (Mathematics and Statistics Centre for Doctoral Training), University of Warwick, 2017 MSci in Mathematics, University College London Research Interests: Probability and Combinatorics, including random graph models, random walks, extremal combinatorics, and their applications in graph theory. His work explores topics like effective resistance and hitting times in Erdős–Rényi graphs. Talks and Conferences: Presented at the 6th Polish Combinatorial Conference (2016), Warwick Random Processes Workshop (2016), and multiple other international events. Delivered talks on hitting times, electrical resistances, and random walk dynamics in Erdős–Rényi graphs. Teaching Experience: Teaching Assistant for Geometry (Warwick, 2014/15) and Probability A & B (Warwick, 2014/15). Tutor for Villiers Park residential course (2015–2017), delivering lectures on permutations, martingales, hyperbolic geometry, and Ramsey Theory.

• Computer Architecture
• GPU Systems
• Accelerators for Machine Learning
• +4 more

Jose Luis Abellan Miguel is a Ramón y Cajal Fellow and Tenure-Track Associate Professor at the University of Murcia's Department of Computer Engineering and Technology. He leads the EcoArTech team and is a European R3 researcher. Previously, he held roles at the University of Ferrara, Boston University, and Universidad Católica de Murcia. His research focuses on GPU architectures, accelerators for machine learning, and privacy-preserving computing, particularly Fully Homomorphic Encryption (FHE). He has authored over 70 peer-reviewed publications and contributed to conferences like ISCA, HPCA, and MICRO. Education: Bachelor's, Master's, and PhD in Computer Science and Engineering (University of Murcia, 2007–2012) Research Interests: Abellan's work emphasizes architectural enhancements for GPU systems, customized accelerators for ML and FHE, and efficient synchronization/communication in many-core architectures. His contributions include tools like MGPU-Sim and STONNE for simulation, and frameworks like FIDESlib for FHE on GPUs. Recognition: HiPEAC Paper Awards (2011, 2019–2024) Top Picks in Hardware and Embedded Security 2024 European R3 Certificate (2024) Editorial roles at ACM TACO and Frontiers in Electronics Grants & Leadership: Recipient of a Ramón y Cajal fellowship and a Consolidación Investigadora grant. He chairs sessions at conferences like ISPASS and serves on TPCs for venues including DATE, HPCA, and MICRO. His lab collaborates with institutions like Georgia Tech, Northeastern University, and Intel. Labs/Teams: He leads the EcoArTech team, focusing on next-gen computing systems via architectural simulators. Collaborators include researchers from MIT, Boston University, and industry partners like NVIDIA and Intel.

• Data Analytics
• Distributed Systems
• Query Optimization
• +5 more

Jorge-Arnulfo Quiané-Ruiz is a researcher at Qatar Computing Research Institute , with affiliations at institutions like Technical University of Berlin and University of Nantes (PhD, 2008). His work focuses on data analytics , distributed systems , and query optimization , particularly in cross-platform environments and trusted execution frameworks . Research Interests : Data analytics across heterogeneous platforms Secure and compliant data processing in trusted environments Efficient query allocation and optimization Scalable algorithms for big data and streaming graphs Machine learning in data management Table extraction and data cleaning Publications : Recent work includes benchmarks for TEE-based joins and cross-database query frameworks (2023). Explores space-efficient graph algorithms and ML-driven revenue optimization (2022). Developed RHEEMix , a cost-based optimizer for cross-platform systems (2018). Collaborations : Regularly partners with Volker Markl and Zoi Kaoudi on unified analytics frameworks. Contributed to projects like Apache Wayang and AgoRA .

Aydın BuluçView profile

Adjunct Professor

• High-performance graph analysis
• Parallel sparse matrix computations
• Communication-avoiding algorithms
• +3 more

Aydın Buluç is a Senior Scientist at the Lawrence Berkeley National Lab (LBNL) in the Applied Mathematics and Computational Research Division and an Adjunct Professor in the Electrical Engineering and Computer Sciences (EECS) department at UC Berkeley. At LBNL, he leads the Performance and Algorithms group, and at UC Berkeley, he is part of the SLICE lab. He also serves as the Director of Sparsitute, a DOE Mathematical Multifaceted Integrated Capability Center (MMICC) focused on sparse computations. Dr. Buluç's research focuses on high-performance graph analysis, parallel sparse matrix computations, and communication-avoiding algorithms with applications in machine learning and computational genomics. His work bridges theoretical computer science with practical applications in scientific computing, particularly in bioinformatics and large-scale data analysis. He has made significant contributions to the development of parallel algorithms for sparse linear algebra operations, which form the foundation for many graph analytics frameworks. His recent publications demonstrate a strong trend toward optimizing sparse computations for modern hardware architectures, particularly GPUs and distributed systems. The research spans theoretical algorithm design, practical implementation challenges, and applications in computational biology. A notable pattern is the increasing focus on communication-avoiding techniques for distributed graph neural network training and large-scale genomic analysis. Dr. Buluç has been actively involved in numerous professional activities, serving on program committees for major conferences including EuroSys (2026), ALENEX (2019, 2026), SPAA (2025), and IPDPS (2013-2019, 2021-2022, 2025). He has also served on the SIAM George Pólya Prize for Mathematical Exposition Selection Committee (2025) and as Founding Associate Editor for ACM Transactions on Parallel Computing (2013-2020). He leads the PASSION Lab research group, which focuses on parallel algorithms and systems for irregular numerical workloads. The lab develops several important open-source software packages including Combinatorial BLAS, HipMCL, PASTIS, CAGNET, and BELLA. These tools address challenges in large-scale graph analysis, protein sequence alignment, and distributed machine learning.

Benjamin SudakovView profile

Professor

• Combinatorics
• Probabilistic Methods in Discrete Mathematics
• Extremal Graph Theory
• +4 more

Benjamin Sudakov is a Professor of Mathematics at ETH Zurich, where he has held positions since 2013. Previously, he served as a Professor at UCLA (2007-2014) and as an Assistant Professor at Princeton University (2002-2007). He has also been affiliated with the Institute for Advanced Study (2005-2006) and held prestigious fellowships including the Veblen Instructorship (1999-2002). His academic journey began with a Ph.D. from Tel Aviv University in 1999 under the supervision of Noga Alon. Education: Ph.D. in Mathematics, Tel Aviv University (1999) Advisor: Noga Alon Sudakov is a leading expert in combinatorics, with primary research interests in probabilistic and extremal combinatorics, random structures, and the application of combinatorial methods to theoretical computer science and mathematics. His work spans algebraic combinatorics, Ramsey theory, and graph theory, addressing foundational questions and developing novel methodologies. His publications and talks highlight his contributions to Ramsey graphs, equiangular lines, hypergraph theory, and submodular optimization. Key themes include structural graph theory, extremal problems, and probabilistic techniques in discrete mathematics. Scientific Awards: Humboldt Research Award (2014) Fellow of the American Mathematical Society (2013) Invited Speaker, ICM 2010 David Saxon Presidential Term Chair (2007-2011) NSF CAREER Award (2006-2011) Alfred P. Sloan Fellowship (2004-2006) Sudakov has mentored 21 Ph.D. students, including prominent mathematicians like Peter Keevash, Jacob Fox, and Matija Bucic, many of whom hold academic positions globally. He has received significant funding, including the SNSF grant 200021_196965, and actively participates in editorial boards and workshops on extremal combinatorics and related fields. Notable among his collaborative efforts is a series of workshops and seminars at institutions like Banff Research Center, IPAM, DIMACS, and BIRS, focusing on probabilistic and extremal combinatorics. His teaching includes graduate-level courses such as Graph Theory, Probabilistic Methods in Combinatorics, and Algebraic Methods in Combinatorics.

Artem SapozhnikovView profile

Professor

• Probability theory
• percolation
• random walks
• +1 more

Artem Sapozhnikov is a Professor at the Mathematisches Institut of Universität Leipzig, Germany. His research focuses on probability theory, particularly percolation, random walks, and random graphs. He maintains an active research program with numerous publications and supervises PhD students and postdocs. Professor Sapozhnikov teaches a range of mathematics courses for physicists, including Mathematics for Physicists 1-4, Probability Theory I & II, and specialized courses on percolation. His teaching spans both undergraduate and graduate levels, demonstrating his commitment to mathematical education. His research interests center around probability theory , with specific focus on: Percolation theory and phase transitions Random walks and their properties on various structures Random interlacements and Brownian motion Boolean models and geometric probability Models with long-range correlations His work bridges theoretical probability with applications in statistical physics. Analysis of his recent publications shows a strong focus on the properties of random interlacements, Brownian motion, and percolation models. His research examines connectivity properties, phase transitions, and geometric aspects of these stochastic processes. Recent work with Yingxin Mu has explored visibility windows and indistinguishability of components in various models. Earlier collaborations with Caio Alves, Deepan Basu, and Yinshan Chang investigated decoupling inequalities, crossing probabilities, and loop percolation. Professor Sapozhnikov has supervised several PhD students and postdocs, including: Deepan Basu (PhD 2013-2017) Caio Alves (postdoc 2016, 2017-2020) Yinshan Chang (postdoc 2013-2016) Lorenzo Taggi (PhD 2012-2015) Yingxin Mu (postdoc 2021-2023) He currently has open positions for PhD students through the IMPRS program.

• Positional games
• Discrete and computational geometry
• Discrete random structures
• +2 more

Miloš Stojaković is a Full Professor at the Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Serbia. He has held this position since 2016, following roles as Associate Professor (2011–2016) and Assistant Professor (2006–2011). His research focuses on positional games, discrete and computational geometry, discrete random structures, combinatorial algorithms, and graph theory. He leads the Foundations of Computer Science group since 2018. Stojaković earned his Ph.D. in Computer Science from ETH Zurich (2005), advised by Emo Welzl and Tibor Szabó, and holds M.Sc. and B.Sc. degrees from the University of Novi Sad. He has been recognized with the Dr Z. Đinđić Award (2008) for best young scientist in Vojvodina and the Best Student of University of Novi Sad Award (1998/99). He has advised three Ph.D. students: Mirjana Mikalački, Marko Savić, and Jelena Stratijev. His work spans over 60 publications, including seminal contributions to positional games and computational geometry. He serves on editorial boards of Discrete Mathematics & Theoretical Computer Science and the Novi Sad Journal of Mathematics . Stojaković teaches courses such as Combinatorial Algorithms, Graph Theory, and Theoretical Computer Science at the University of Novi Sad. He has also taught specialized courses on positional games at institutions like the University of Buenos Aires and Eötvös Loránd University Budapest. His research interests emphasize algorithmic and combinatorial aspects of games, geometry, and graph theory.

• Dynamical systems
• Mathematical biology
• Computational topology
• +4 more

Konstantin Mischaikow is a Professor of Mathematics at Rutgers, The State University of New Jersey, affiliated with the Department of Mathematics. His research focuses on dynamical systems, computational topology, mathematical biology, and machine learning applications in complex systems analysis. He specializes in rigorously analyzing nonlinear dynamics using topological methods, with emphasis on robotics control, ecological modeling, and parameterized ordinary differential equations. His work combines theoretical rigor with computational techniques, addressing challenges in global dynamics characterization, data-driven modeling, and topological data analysis. Notable contributions include the development of the DSGRN (Dynamic Signatures Generated by Regulatory Networks) database for systematically analyzing regulatory networks. His research often bridges pure mathematics with applied fields like robotics, systems biology, and granular materials physics. Recent studies focus on applying persistent homology to chaotic systems (e.g., Rayleigh-Bénard convection), identifying attractors via machine learning, and quantifying dynamics in high-dimensional spaces. He has collaborated extensively on projects involving granular media, synthetic biology circuits, and spatiotemporal chaos analysis. His academic contributions span over three decades, with a focus on merging topological tools with computational methods to solve real-world problems. While no specific awards are listed, his work has significantly impacted computational dynamics and topological data science.

Marcus HolzingerView profile

Professor

• Space Policy
• Space Situational Awareness
• Information Theory
• +4 more

Dr. Marcus Holzinger is the Joseph T. Negler Professor and Hatfield Endowed Professor in Space Policy & Law at the University of Colorado Boulder's Department of Aerospace Engineering Sciences. He leads the Colorado Center for Astrodynamics Research (CCAR) and the VADeR Laboratory, focusing on advancing space exploration through interdisciplinary research in space policy, autonomy, and space situational awareness. His work emphasizes collaboration and sustainable practices in space. Education: PhD in Aerospace Engineering Sciences, University of Colorado Boulder (2011) MS in Aeronautics & Astronautics Engineering, University of Washington (2005) BS in Aeronautics & Astronautics Engineering, University of Washington (2003) Research Interests: Space Policy & Law Space Domain Awareness Autonomy & Decision Support Systems Cislunar Space Dynamics Information Theory & Estimation Space Traffic Management Recent Research Trends: Holzinger’s work prioritizes cislunar space exploration and decentralized decision-making for space systems, with applications in orbital debris mitigation, sensor tasking, and spacecraft autonomy. His publications frequently address challenges in initial orbit determination and spacecraft maneuver detection . Awards: American Astronautical Society Fellow (2022) AIAA Associate Fellow (2018) AFOSR Young Investigator Award (2017) Advising & Labs: Holzinger mentors students in the STARLIT and VADeR Observatories. Notable advisees include Sam Fedeler (2023 PhD) and Katie Melbourne (PhD student). His labs develop technologies like astrographic mapping and sensor networks for space surveillance. Labs/Teams: CCAR and the VADeR Lab collaborate on projects funded by NASA, AFRL, and industry partners, emphasizing hardware-in-the-loop testing and space policy frameworks.

• Combinatorics
• Graph Theory
• Extremal Graph Theory
• +4 more

Asaf Shapira is a Professor at the School of Mathematical Sciences, Tel-Aviv University. His research focuses on combinatorics, graph theory, and extremal problems, with notable contributions to Ramsey theory, probabilistic methods, and algorithmic applications. He has authored over 50 publications in top-tier journals and has taught advanced courses such as Extremal Graph Theory and Probabilistic Methods in Combinatorics. His research interests include extremal combinatorics, hypergraph theory, and structural graph theory. Recent work includes advancements in removal lemmas, partition properties, and algorithmic testing of graph properties. Shapira has also contributed to foundational results in property testing and combinatorial optimization. His teaching spans undergraduate and graduate levels, covering topics like combinatorial analysis, algebraic methods, and advanced algorithms. While no explicit awards are listed, his extensive publication record reflects significant scholarly impact.

Rosalba Garcia-MillanView profile

Lecturer

• Non-equilibrium physics
• Active matter
• Stochastic thermodynamics
• +3 more

Rosalba Garcia-Millan is a Lecturer in Disordered Systems at King’s College London, part of the Faculty of Natural, Mathematical & Engineering Sciences and the Department of Mathematics. She holds a PhD in Mathematical Physics from Imperial College London (2020). Prior to joining King’s in 2023, she was a postdoctoral researcher at the University of Cambridge and held an independent Research Fellowship at St John’s College, Cambridge. Her research focuses on non-equilibrium physics, active matter, and stochastic thermodynamics. She applies microscopic field theories to study active matter systems and agent-based models, with applications to biological physics, including DNA organization in cell nuclei, branching growth, and neuronal avalanches. Her work bridges theoretical approaches with interdisciplinary applications in biophysics and complex systems. Recent publications highlight contributions to entropy production in nonreciprocal systems, liquid-state theories for active matter, and chromatin dynamics. Her research emphasizes understanding collective behavior, nonequilibrium dynamics, and thermodynamic principles in both synthetic and biological systems. Rosalba is affiliated with the Disordered Systems group at King’s, known for its leadership in statistical mechanics of complex systems. No scientific awards are explicitly listed in the provided materials. She currently has no listed advisees or grants in the available data.