Find Academic Supervisors

Francisco A. Gómez VelaView profile

Associate Professor

• Machine Learning
• Data Mining
• Bioinformatics
• +3 more

Francisco A. Gómez Vela is an Associate Professor at Universidad Pablo de Olavide, Spain. His work bridges computational techniques with biomedical and energy data analysis. Education: PhD in Computer Science (Cum Laude) from Universidad Pablo de Olavide; BSc in Computer Science from University of Seville Research interests focus on: Machine learning for genetic and biomedical data Big data and high-performance computing (HPC) in bioinformatics Energy consumption analysis in smart buildings/cities Software tools for gene network reconstruction Recent publications highlight: Ensemble approaches for gene co-expression networks Biclustering methods in biological data mining GPU-accelerated bioinformatics software Applications in cancer biomarker discovery Educational data mining for microcompetency frameworks Teaching duties include: Computer Science (BSc and MSc) Bioinformatics Project Engineering History and Digital Humanities He has participated in national research projects and R+D+I transfer initiatives, developing tools like: bioScience (HPC bioinformatics library) CyEnGNet—App (Cytoscape gene network tool) BIGO (gene enrichment analysis) GNC–app (gene network validation)

Moath JarrahView profile

Assistant Professor

• Machine Learning
• IoT Security
• Data Management Systems
• +5 more

Moath Jarrah is an Assistant Professor at the School of Technology, Eastern Illinois University. He holds editorial roles in the journal Simulation Modelling Practice and Theory (Elsevier) and actively reviews for international conferences/journals. His research focuses on IoT security, machine learning for fake news detection, graph-based data systems, and DEVS-driven modeling/simulation of complex systems. Collaborations span global institutions, with recent work emphasizing smart agriculture and renewable energy integration. Research interests include: Graph-based data management IoT botnet behavior analysis Machine learning applications in cybersecurity Simulation of smart home/IoT networks Multi-objective optimization in edge computing Recent publications (2021-2024) highlight advancements in fake news detection, smart agriculture frameworks, and DEVS-based IoT simulations. His work bridges theoretical modeling with real-world applications in cybersecurity and sustainable technologies. No awards explicitly listed, but his contributions to editorial roles and frequent publication activity indicate academic recognition. Advising/grants: No specific advisees or grants detailed in provided texts, but his research outputs suggest active grant-supported projects given the scale of work described.

• Computer Science
• Information Systems
• Formal Methods
• +5 more

Keijo Tapio Heljanko is a Professor in the Department of Computer Science at the University of Helsinki, affiliated with the Helsinki Institute for Information Technology and the EUROFusion Advanced Computing Hub. He previously held an Associate Professor position at Aalto University (2008–2018) and currently serves as Principal Investigator at HIIT. His research focuses on formal methods, parallel computing systems, machine learning applications, and GPU architecture optimization. He leads the LUMI AI Factory project (2025–2028), an EU-funded initiative for AI infrastructure development. Education details are not explicitly listed in the provided text. His work spans theoretical computer science and applied machine learning, with notable contributions to bike-sharing optimization, GPU consistency analysis, and boreal ecosystem modeling. Media highlights include collaborations with NVIDIA on AI technology centers and innovations in search engine and disinformation detection systems. Professional roles include supervising the Doctoral Programme in Computer Science and managing multi-institutional research projects. He has collaborated internationally on topics ranging from exascale computing to environmental data analysis. His research outputs total 131 publications and 9 press engagements, emphasizing both foundational and applied computational challenges.

Bodan VelkovskiView profile

Academic

• Renewable Energy Systems
• Power Quality Analysis
• Energy Storage
• +3 more

Bodan Velkovski is an academic at the Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University in Skopje, holding the rank of Assistant. His research focuses on modern power systems, particularly renewable energy integration, smart grid technologies, and power quality analysis. With an institutional email (bodan@feit.ukim.edu.mk), he actively contributes to advancing sustainable energy solutions. Research Interests: Velkovski's work spans interdisciplinary domains including: Design and optimization of renewable energy communities Electric vehicle-grid integration frameworks Advanced power quality disturbance analysis and simulation tools GPU-accelerated computational methods for power systems Innovative e-learning platforms for engineering education His 15 most recent publications (2020-2024) demonstrate consistent focus on energy transition challenges, with 73% addressing renewable community operations, 20% on power quality instrumentation, and 7% on educational technology. This reflects a balanced portfolio bridging theoretical research with applied engineering solutions. Educational Contributions: Velkovski develops collaborative learning environments, notably creating remote laboratory platforms for vocational electrical engineering training that enable hands-on experimentation through digital interfaces.

Tamer ÖzsuView profile

Professor

• Distributed Data Management
• Graph Databases
• RDF Systems
• +2 more

Tamer Özsu is a University Professor at the David R. Cheriton School of Computer Science, University of Waterloo, where he previously served as Director (2007-2010) and currently holds the role of Associate Dean of Research. He is also a Distinguished Visiting Professor at Tsinghua University. Professor Özsu's research spans distributed data management, graph/RDF systems, and database fundamentals. His current work focuses on: Distributed graph processing algorithms SPARQL query optimization for RDF systems Streaming graph analytics Indexing techniques for modern hardware Distributed database architectures He has authored the seminal textbook Principles of Distributed Database Systems and co-edited the Encyclopedia of Database Systems . As founding Editor-in-Chief of ACM Books, he has shaped computing literature. His recent publications demonstrate continued innovation in graph partitioning, streaming graph algorithms, and scalable RDF processing. Özsu maintains active research leadership in database systems with over 30 years of influential contributions.

Akhlaque AhmadView profile

Assistant Professor

• Algorithms
• Computer Networks
• Theoretical Computer Science
• +4 more

Dr. Akhlaque Ahmad is an Assistant Professor in Software Engineering at TED University's Faculty of Engineering. He earned his Ph.D. in Electronics and Telecommunications from Politecnico di Torino, Italy, with industry experience at STMicroelectronics Milano and contributions to EU FP7 REVERIE project on virtual tele-immersion systems. His research spans: High-performance computing architectures Parallel algorithms for graph processing GPU optimization techniques Intelligent systems design Professor Ahmad teaches courses including Database Systems, Artificial Intelligence, and Operating Systems. He actively promotes interdisciplinary connections between software engineering and liberal arts education.

• Database Systems
• Information Systems
• Distributed Systems
• +9 more

Prof. Dr. rer. nat. habil. Gunter Saake is a full professor for Databases and Information Systems at the Otto von Guericke University Magdeburg since 1994. He holds a diploma (1985) and PhD (1988) from Technical University of Braunschweig, followed by a habilitation (1993). His roles include twice serving as Dean of the Faculty for Computer Science (2012 and 1996-1998). Research Focus: Database systems and query optimization Adaptive data management in heterogeneous hardware Graph databases integrating machine learning Software product lines and feature modeling High-performance computing and storage systems Current projects include LARDS (adaptive database engines), SMASH (storage engines for modern hierarchies), and EXPLANT (migrating software variants into product lines). His work addresses challenges like real-time OLTP/OLAP processing (COOPeR project), entity resolution under big data, and GPU-accelerated database operations. He has authored books on object-oriented modeling, databases, and Java. Collaborations: Active in EU projects (BRA working groups) and industry partnerships (e.g., MetaProteomeAnalyzer service). His research spans database theory, system architecture, and practical implementations across academia and industry.

Stefan BrucknerView profile

Professor

• Biomedical Visualization
• Volume Rendering
• Visual Data Exploration
• +1 more

Stefan Bruckner is Professor of Visualization at the University of Bergen, specializing in biomedical visualization, volume rendering, and visual data exploration. His work develops novel techniques for analyzing complex scientific datasets across meteorology, medicine, and materials science. Dr. Bruckner's research group develops interactive visual analytics tools for weather forecasting, medical diagnostics, and ensemble data analysis. His methodological innovations include GPU-accelerated rendering, visual parameter exploration, and uncertainty visualization. He received the 2011 Eurographics Young Researcher Award for contributions to illustrative visualization. Professional service includes program committee roles for IEEE VIS, Eurographics, and ECRTS conferences. His pedagogical contributions span visualization, computer graphics, and programming languages at institutions including École normale supérieure and École polytechnique.

• Compiler Optimization
• High Performance Computing
• Sparse Matrix Computations
• +4 more

P. Sadayappan is a Professor at the School of Computing, University of Utah, specializing in high performance computing, compiler optimization, and scalable machine learning. His research focuses on developing efficient computational methods for scientific applications, particularly in the areas of sparse/dense matrix and tensor computations. His research interests include: Compiler Optimization for High Performance Computing Optimization of Sparse/Dense Matrix/Tensor Computations Scalable Machine Learning Algorithm-Architecture Co-Design Optimization Sadayappan's recent publications demonstrate a strong focus on tensor computations, GPU acceleration, and compiler optimizations for machine learning workloads. His work spans from fundamental compiler theory to practical implementations that improve performance across various architectures. A significant trend in his recent work involves the development of frameworks for efficient tensor operations, sparse matrix computations, and domain-specific code generation, with particular emphasis on performance portability across heterogeneous computing platforms. His notable scientific achievement includes receiving the ACM SIGPLAN Most Influential PLDI Paper Award in 2018 for his work on polyhedral compilation. Sadayappan has been principal investigator or co-investigator on numerous significant research grants, including: NSF award #2217154 (2022-2027): A Comprehensive Framework for Efficient, Scalable, and Performance-Portable Tensor Applications NSF award #2112606 (2021-2026): AI Institute for Intelligent CyberInfrastructure with Computational Learning in the Environment (ICICLE) NIH SBIR-Phase 2 (2023-2025): Enabling next generation machine learning for large scale image analysis NSF award #2009007 (2020-2024): Data Locality Optimization for Sparse Matrix/Tensor Computations DARPA SBIR-Phase 2 (2017-2022): Performance Portable Framework for Developing Graph Applications He teaches CS 4230/6230 (Parallel and High-Performance Computing) at the University of Utah and collaborates extensively with researchers across multiple institutions on projects involving computational chemistry, physics simulations, graph analytics, and machine learning.

• Video Streaming
• Virtual Reality
• Multi-Access Edge Computing
• +3 more

Matti Siekkinen is a Lecturer in the Department of Computer Science at Aalto University. He is also affiliated with the Helsinki Institute for Information Technology (HIIT), the Professorship Di Francesco Mario, and the Computer Science Lecturers group. His research focuses on latency-critical mobile multimedia services, cloud-based virtual reality (VR), and edge computing solutions to optimize network performance and user experience. Education: Doctoral degree in Engineering and Technology (2007) from Université Côte d'Azur Master's degree in Engineering and Technology (2003) from Helsinki University of Technology Research Interests: His work emphasizes improving Quality-of-Experience (QoE) in mobile and cloud gaming, optimizing video streaming for VR applications, and deploying edge computing to reduce latency. Key topics include foveated rendering, dynamic resource allocation, and energy-efficient mobile service delivery. He has led projects such as CloudVR (2019–2020) and Latency-Critical Mobile Multimedia Services (2016–2019), which investigate network architecture and latency reduction strategies. Recent Research Trends: Recent publications highlight advancements in foveated compression techniques, head-pose prediction for remote VR, and neural network-based depth map packing. His work often bridges theoretical models with practical implementations in edge and cloud environments. Grants & Projects: Principal Investigator for CloudVR: High-Quality Mobile VR Using Cloud Acceleration (Business Finland) Principal Investigator for Latency-Critical Mobile Multimedia Services (Academy of Finland) Labs & Groups: He leads the Siekkinen Matti group, focusing on innovative solutions for mobile and cloud-based multimedia systems. Collaborations include Tsinghua University and industry partners like Elisa.

Vincent HeuvelineView profile

Professor

• Uncertainty Quantification
• High Performance Computing
• Data Intensive Computing
• +3 more

Professor Vincent Heuveline serves as Professor and Head of the Engineering Mathematics and Computing Lab (EMCL) at Heidelberg University's Interdisciplinary Center for Scientific Computing (IWR). He concurrently holds the positions of Chief Information Officer (CIO) and Director of the Computing Centre at the university, while leading the Data Mining and Uncertainty Quantification (DMQ) group at HITS gGmbH. His academic background includes: Studies in Mathematics, Physics, and Computer Science at Caen (France) and Würzburg (Germany) PhD in Computer Science (1997) from Université de Rennes and INRIA Habilitation in Mathematics (2002) at Heidelberg University Research focuses on uncertainty quantification (UQ) , high-performance computing (HPC) , and data-intensive computing with applications in medical engineering. He is deeply engaged in IT security research and teaching, developing practical frameworks for industrial deployment of numerical simulations and secure computing infrastructures. Recent publications reveal converging trends in biomedical image segmentation (e.g., Biomedisa platform), metabolic modeling for newborn diagnostics, and cybersecurity solutions like DGA detection. Key interdisciplinary themes include machine learning for medical applications, HPC algorithm optimization, and network security frameworks addressing real-world industrial challenges. He leads the EMCL and DMQ research groups, driving innovation in computational science. As CIO, he shapes Heidelberg University's digital infrastructure strategy while maintaining active industry collaborations for translating academic research into simulation and security solutions.

LI YuchenView profile

Associate Professor

• Artificial Intelligence
• Data Mining
• Graph Mining
• +6 more

Yuchen Li is an Associate Professor at the School of Computing and Information System (SCIS) at Singapore Management University (SMU), where he also holds the Lee Kong Chian Fellowship for Research Excellence. He earned his Ph.D. in Computer Science from the National University of Singapore (NUS) in 2017. His research focuses on social analytics, high-performance graph mining, and fintech applications leveraging large language models (LLMs). He has led significant projects funded by MoE, including 'Next-Gen Competitive Intelligence' and 'CONQUEROR' for concurrent graph query processing. His academic contributions span influential publications in top-tier conferences like SIGMOD, KDD, and ICML, with notable works on graph algorithms, fraud detection, and knowledge graph robustness. He advises Ph.D. students and has supervised over a dozen researchers, including current students Xiao Hanhua, Wang Sha, and Ye Chang. His awards include the Lee Kong Chian Fellowship and the Best Demo Paper Award at CIKM 2021. Li’s research bridges theoretical foundations and practical applications, addressing challenges in graph processing, social media analysis, and fintech. His team develops systems like 'Dupin' for fraud detection and 'ThunderRW' for in-memory graph processing, showcasing expertise in GPU acceleration and scalable algorithms.

Sebastian WilczekView profile

Researcher

• Plasma Technology
• Plasma Simulation
• Gas Conversion
• +3 more

Sebastian Wilczek is a researcher at Technische Hochschule Georg Agricola (THGA) in Bochum and works part-time at enaDyne GmbH in Leipzig. He completed his PhD in plasma technology at Ruhr University Bochum and serves as a visiting researcher there, teaching Numerical Methods in Electrodynamics . His academic work spans research and teaching in plasma modeling, CO₂ conversion, and simulation techniques. Research Focus: Wilczek’s research centers on plasma technology , particularly radio-frequency (RF) plasma systems , with emphasis on non-thermal plasma catalysis for greenhouse gas conversion . His work includes detailed studies of electron dynamics in low-pressure capacitively coupled plasmas (CCPs), sheath behavior , and ionization mechanisms using advanced simulation tools like PIC/MCC and nonPDPSIM . Publications & Conferences: Wilczek has contributed to multiple peer-reviewed conference papers and simulations on topics such as CO₂ dissociation , electron temperature dynamics , and waveform tailoring for plasma optimization. His recent work compares microwave plasma torches with other sources for carbon recycling and investigates non-neutral discharge regimes at atmospheric pressure. Simulation Tools: He co-developed eduPIC , an educational PIC/MCC code for plasma simulation , available in multiple programming languages including Rust and C/C++. This tool is designed to help students and researchers understand and extend particle-in-cell methods for their own applications.

Matiyasevich YuriView profile

Professor

• Diophantine equations
• Computability theory
• Algorithms
• +1 more

Yuri Matiyasevich is a Russian mathematician and computer scientist affiliated with the Steklov Institute of Mathematics (Leningrad/St.Petersburg Branch) since 1980, where he serves as head of the Laboratory of Mathematical Logic. He also holds part-time professorships at Leningrad/St.Petersburg State University (since 2003) and previously at Polytechnical Institute (1980-1981). His research focuses on Diophantine equations, Computability theory, Algorithms, and Decidability problems. Full member, Russian Academy of Sciences (2008) Corresponding member, Bavarian Academy of Sciences (2007) Docteur Honoris Causa, Université Pierre et Marie Curie (2003) Humboldt Research Award (1997) Markov Prize, Academy of Sciences of the USSR (1980) His recent work involves computational approaches to Riemann's zeta function, probabilistic reformulations of graph theory problems, and algorithmic analysis of Diophantine representations. He has published extensively on connections between number theory and computational models, including studies on exponential Diophantine equations and their applications. Notable contributions include definitive solutions to Hilbert's Tenth Problem through Diophantine representations of recursively enumerable sets, and computational experiments supporting the Riemann Hypothesis. His publications span multiple languages and cover intersections between mathematical logic, number theory, and theoretical computer science.

Nikolay SimakovView profile

Researcher

• High-Performance Computing
• Machine Learning
• Scientific Software Optimization
• +3 more

Dr. Nikolay Simakov is a Computational Scientist at the University at Buffalo's Center for Computational Research (CCR), joining in 2012. He holds a PhD in Computational Chemistry from Carnegie Mellon University, alongside BA and MA degrees in Applied Physics and Mathematics from Moscow Institute of Physics and Technology. Research Interests: High-Performance Computing (HPC), Machine Learning, Scientific Software Optimization, Data Analytics for Large Datasets, and Simulation of Biomolecular Systems Teaching: Taught graduate courses like EAS509 Statistical Learning and Data Mining-II and CDA501 Introduction to Data Driven Analysis, mentoring undergraduate interns at CCR Key Contributions: Developed Slurm Simulator for HPC scheduling, contributed to XDMoD/ACCESS Metrics for resource auditing, optimized GPU-based Poisson equation solvers, and advanced multi-rheology geophysical flow modeling Technical Focus: Performance monitoring of HPC systems, node sharing analysis, and ARM architecture benchmarking His work bridges computational infrastructure optimization and biological system modeling, with publications spanning HPC resource management, biomolecular simulations, and parallel computing frameworks.