Find Academic Supervisors

• Data Mining
• Big Data
• Fuzzy Systems
• +9 more

Carlos Jesús Fernández Basso is a Post-Doc Researcher at the Universidad de Granada in the Department of Computer Science. His work focuses on the intersection of Big Data, Data Mining, and Fuzzy Systems, with applications in energy efficiency, social network analysis, and medical diagnostics. His research interests include: Energy-efficient building management using IoT and Big Data Distributed frequent itemset mining and association rule discovery Fuzzy logic for data interpretability in complex systems Text mining and pattern detection in social media Recent publications highlight his contributions to Spark-based distributed algorithms, predictive control systems, and visualization of fuzzy association rules. He collaborates with researchers across disciplines, including applications in healthcare and cybersecurity.

Saday SadayappanView profile

Professor

• Compiler Optimization
• High-Performance Computing
• Sparse/Dense Matrix Computations
• +3 more

P. (Saday) Sadayappan is a Professor at the University of Utah's School of Computing, specializing in high-performance computing and compiler optimizations. His research focuses on performance optimization for parallel systems, particularly for tensor computations, sparse matrix operations, and machine learning workloads. He leads multiple NSF and DARPA-funded projects focused on GPU optimization, tensor computations, and scalable machine learning frameworks. Research Interests: Dr. Sadayappan's work spans compiler optimizations for high-performance systems, optimization of sparse/dense matrix/tensor computations, scalable machine learning, and algorithm-architecture co-design. His recent projects include developing performance-portable frameworks for tensor applications and optimizing data locality for scientific computing. Publication Trends: His recent publications (2020-2022) predominantly focus on GPU acceleration of machine learning workloads (especially CNNs), automated I/O complexity analysis, and optimization techniques for sparse matrix/tensor operations. Earlier work (2018-2019) established foundations in GPU code generation for tensor contractions and cache optimization. Awards and Honors: ACM SIGPLAN Most Influential PLDI Paper Award (2018) for A Practical Automatic Polyhedral Parallelizer and Locality Optimizer Active Grants and Projects: NSF: A Comprehensive Framework for Efficient, Scalable, and Performance-Portable Tensor Applications (2022-2027) NIH SBIR: Enabling next generation machine learning for large scale image analysis (2023-2025) NSF: AI Institute for Intelligent CyberInfrastructure (ICICLE) (2021-2026) NSF: Data Locality Optimization for Sparse Matrix/Tensor Computations (2020-2024) DARPA SBIR: Performance Portable Framework for Developing Graph Applications (2017-2022) Teaching: He currently teaches CS 4230/6230 (Parallel and High-Performance Computing) at the University of Utah.

Leonidas AkritidisView profile

Lecturer

• Natural Language Processing
• Deep Machine Learning
• Data Mining
• +5 more

Leonidas Akritidis serves as a Lecturer at the Department of Science and Technology within the School of Science and Technology at International Hellenic University. He holds a PhD and Diploma in Electrical and Computer Engineering from University of Thessaly and Aristotle University of Thessaloniki respectively. His educational background includes: PhD in Electrical and Computer Engineering, University of Thessaly (2007-2013) Diploma in Electrical and Computer Engineering, Aristotle University of Thessaloniki (1997-2003) Akritidis specializes in Natural Language Processing, Deep Machine Learning, and Data Mining with particular expertise in short text clustering, dimensionality reduction, and rank aggregation techniques. His research addresses challenges in sparse data environments and high-dimensional feature spaces, developing novel algorithms for text representation and processing. He has contributed significantly to parallel and distributed computing approaches for big data analytics. His publication trends reveal a consistent focus on NLP and machine learning applications, with recent work expanding into generative models for tabular data, medical image analysis, and advanced fault detection systems. The research demonstrates progression from foundational text processing techniques to more complex applications in healthcare, e-commerce, and software engineering. Akritidis has taught multiple courses including Big Data and Cloud Computing, Machine Learning Principles and Concepts, Mobile Application Development, and Web Programming. His research projects include CyberPi (intelligent cyber threat detection), NANOTRIM (transistor sizing optimization), and iMuSe (virtual museum platform). He collaborates extensively with researchers like Panayiotis Bozanis, Miltiadis Alamaniotis, and Athanasios Fevgas, primarily within the Greek academic community.

• Machine Learning
• Parallel and Distributed Computing
• Graph Theory
• +6 more

Giuseppe Agapito is an Associate Professor and senior research scientist at Magna Graecia University of Catanzaro, working within the Department of Law, Economics and Sociology. His academic profile spans both teaching responsibilities and active research in computational fields. His research focuses on computational intelligence techniques applied to complex real-world problems, particularly in machine learning, parallel and distributed computing, and graph theory. His work demonstrates strong interdisciplinary applications, especially in biology and omics sciences where he develops methods to leverage the vast amounts of data being produced. Agapito has published over 100 papers in international journals and conference proceedings. His recent publications show a strong focus on graph-based machine learning approaches, sentiment analysis techniques, and bioinformatics applications. His research trajectory indicates a progression from bioinformatics applications toward more general machine learning frameworks with applications across multiple domains. As an active member of the academic community, he serves as a reviewer for scientific journals and participates as chair and program committee member for national and international conferences. His editorial work for PeerJ Computer Science demonstrates his standing in the academic community. His teaching responsibilities include courses on Computer Science and Data Analysis within the Department of Law, Economics and Sociology, suggesting an interdisciplinary approach that applies computational methods to social sciences domains.

• Data Analysis
• Recommendation System
• Responsible AI
• +6 more

Dr. Angela Huo is an Associate Professor at the School of Computer Science within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). She holds multiple leadership roles including Deputy Director of Transnational Education for the Faculty of Engineering and IT, Responsible Academic Officer for Teaching and Learning of the School of Computer Science, Course Director for the Bachelor of Computing Science (Honours) program, and site coordinator for the South Pacific Programming Contests region of the International Collegiate Programming Contest (ICPC). Dr. Huo's research spans several key areas in computer science, with a primary focus on Data Analysis, Recommendation Systems, and Responsible AI. She specializes in utilizing AI and data analysis techniques to improve the efficiency and security of modern software systems. Her recent research interests include recommendation systems, anomaly detection, and privacy-preserving through data mining and deep learning. She has made significant contributions across multiple domains including multimodal learning, federated learning, causal inference, and human behavior recognition. Dr. Huo's publication record demonstrates a strong trajectory in cutting-edge AI research, with recent work focusing on conditional diffusion models for trajectory prediction, hybrid recommendation systems, causal learning to mitigate echo chambers, and adaptive waste detection. Her research shows a consistent pattern of addressing real-world challenges through innovative AI approaches, often combining multiple techniques to achieve superior results. Dr. Huo has secured research funding for projects including "Churn prediction scoping study" and "Army sovereign Software-Defined Battlefield Network." She has been actively involved in teaching and curriculum development, having created Industrial sub-majors for Google, AWS and iOS, Information Security sub-major, Data Analytics for Cybersecurity Microcredentials, and Cybersecurity and Privacy major since 2019. She has taught programming and cybersecurity management courses at the undergraduate level and served as HDR Coordinator of the School of Computer Science during 2021-2022.

Myra SpiliopoulouView profile

Professor

• Data Mining
• Knowledge Discovery
• Machine Learning
• +4 more

Myra Spiliopoulou is a Professor of Business Information Systems at the Faculty of Computer Science, Otto-von-Guericke-University Magdeburg since February 2003, leading the Knowledge Management and Data Mining (KMD) working group. Her research bridges theoretical data mining with practical healthcare and business applications. Education: 1982-1986: Mathematics, University of Athens, Greece 1988-1994: PhD Student & Research Assistant, Department of Informatics, University of Athens 1992: PhD in Parallel Query Optimization, University of Athens 2000: Habilitation in Business Information Systems, Humboldt-University Berlin Research Focus: Prof. Spiliopoulou specializes in temporal pattern mining , ethical AI , and healthcare data analytics , with notable contributions to epidemic surveillance systems. Her work integrates machine learning with domain-specific knowledge for real-world decision support. She has delivered authoritative tutorials at premier conferences including KDD (2015, 2018, 2019), IEEE ICDM (2017), ECML/PKDD (2014, 2016), and AIME (2022, 2023), establishing her as a leading educator in data science methodologies. Contact: myra@iti.cs.uni-magdeburg.de

Rui WangView profile

Associate Professor

• Computer Graphics
• Global Illumination Algorithms
• Real-Time Rendering
• +5 more

Rui Wang is an Associate Professor in the Department of Computer Science at the Manning College of Information and Computer Sciences, University of Massachusetts Amherst. His research focuses on computer graphics, including global illumination algorithms, real-time rendering, 3D scanning, and graphics hardware. He received a PhD in Computer Science from the University of Virginia (2006) and a Bachelor's from Zhejiang University (2001). He leads the Computer Graphics Research Group at UMass Amherst and holds an NSF CAREER Award (2008). He previously worked on open-source hardware projects (2013-2014) and has affiliations with the Center for Data Science and the Computer Graphics Laboratory. Education: PhD, Computer Science, University of Virginia, 2006 Bachelor's, Computer Science, Zhejiang University, 2001 Research interests include: Global illumination techniques Interactive photorealistic rendering Appearance modeling Graphics hardware optimization Key contributions involve advancing GPU-based graph algorithms, neural rendering methods, and real-time lighting effects. His work bridges theoretical computer graphics with practical applications in visualization and hardware acceleration. Awards: NSF CAREER Award (2008) Lab affiliations include the Computer Graphics Research Group and collaborations with the Center for Data Science.

Andi WangView profile

Assistant Professor

• Data Science
• Machine Learning
• Manufacturing Systems
• +3 more

Andi Wang is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison, joining in August 2024. Previously, he held an Assistant Professor position at Arizona State University's School of Manufacturing Systems and Networks. His research focuses on applying data science and machine learning to address challenges in engineering systems, including predictive analytics, monitoring, and design optimization. He holds a PhD (2021) from Georgia Institute of Technology, a PhD (2016) from Hong Kong University of Science and Technology, and a BS (2012) from Peking University. Research interests include multi-sensor data fusion for manufacturing process prediction, surrogate modeling for multi-stage engineering systems, and distributed/federated learning applications. His work spans domains like nuclear engineering, additive manufacturing, and semiconductor production. Awards: Multiple IISE and INFORMS awards, including the 2023 IISE Transactions Best Application Paper Award and the 2020 QCRE Division Best Student Paper Award finalist. Advising & Grants: Actively involved in research grants and advising students, though no formal advisee list is currently available. Labs/Teams: Engaged in collaborative projects with industry partners, focusing on industrial systems improvement through data-driven solutions.

Basil EllView profile

Researcher

• Semantic Web
• Natural Language Processing
• Knowledge Base Population
• +5 more

Basil Ell is a Researcher at the University of Oslo's Department of Data and Knowledge Management, affiliated with SIRIUS labs. He holds a PhD from Karlsruhe Institute of Technology (KIT) and has held postdoctoral positions at Bielefeld University's CITEC. His work bridges Semantic Web technologies and Natural Language Processing, focusing on knowledge base population, semantic parsing, and information extraction from text/tables. He splits his time between Oslo and Bielefeld, contributing to projects like semantically enhanced Virtual Research Environments and table understanding systems. Education: B.Sc./M.Sc. in Computer Science from Mannheim University of Applied Sciences, Pohang University of Science and Technology, and IIT Madras. PhD (2015) from KIT under Prof. Rudi Studer. Research Trends: Recent publications emphasize semantic data integration (e.g., materials informatics), link prediction in knowledge graphs, and clinical trial evidence synthesis. His work often combines RDF data structures with NLP techniques. Labs/Teams: Active in SIRIUS labs (Oslo) and previously at CITEC/Bielefeld's Semantic Computing group.

Daniel RANCView profile

Senior Lecturer

• Big Data
• Cloud Computing
• IP Multimedia Subsystem
• +5 more

Daniel RANC is a Senior Lecturer at Telecom SudParis, affiliated with the NeSS research group within the SAMOVAR laboratory. His academic work focuses on Big Data processing, cloud computing, and IP Multimedia Subsystem (IMS) networks, with particular expertise in data modeling, NoSQL databases, and quality of service management. His research interests span multiple domains including Big Data analytics, where he has developed preprocessing and modeling tools; cloud computing with predicate-based resource management; and telecommunications with a focus on IMS networks and QoS management. His work demonstrates strong interdisciplinary connections between data science, network engineering, and educational technology. RANC's publication record shows consistent contributions to his fields from 2007 through 2016, with a particular concentration on Big Data processing frameworks and network management systems. His research demonstrates a progression from eLearning systems interoperability toward more complex Big Data and cloud computing challenges. Daniel RANC collaborates extensively with researchers including Hadi Hashem and Brahim Raouyane, with whom he has co-authored numerous publications across multiple conferences and journals. His work appears in reputable venues including Foundations of Computing and Decision Sciences, International Journal of Computer Science and Applications, and various IEEE conferences.

• Distributed Systems
• High-Performance Computing
• Fault Tolerance
• +5 more

Ozalp Babaoglu is a Full Professor at the Department of Computer Science and Engineering, University of Bologna. He has held this position since 1987, following a PhD in Computer Science from the University of California, Berkeley in 1981. His research focuses on distributed systems, High-Performance Computing (HPC), fault tolerance, self-organization, and machine learning applications in data-driven autonomics. He has led numerous European research projects like BROADCAST, CABERNET, ADAPT, BISON, and DELIS, contributing to foundational work in biology-inspired distributed algorithms and gossip-based systems. He co-founded the Bertinoro International Center for Informatics (BiCi) in 2001 and the IEEE SASO conference series in 2007. Babaoglu is a Fellow of the ACM (2002) and has received prestigious awards, including the Sakrison Memorial Award (1982) and the USENIX Lifetime Achievement Award (1993). His contributions to BSD Unix and open standards are seminal, including virtual memory extensions during his PhD. Babaoglu has served on editorial boards for ACM Transactions on Autonomous and Adaptive Systems, and previously for ACM Transactions on Computer Systems and Distributed Computing. His recent work addresses HPC fault classification, data-driven resource allocation, and energy-efficient computing in hybrid systems. He actively advises on European projects and explores cognitive paradigms in distributed systems.

• Data Integration
• Big Data
• Entity Resolution
• +7 more

Sonia Bergamaschi is a Professor in the Department of Engineering 'Enzo Ferrari' at the University of Modena and Reggio Emilia. She holds dual roles as Contract Professor and Senior Professor, focusing on advanced data management, big data integration, and AI applications in cultural heritage. Her work emphasizes entity resolution, data preparation, and scalable systems for diverse domains such as energy communities and Islamic studies. Her research spans interdisciplinary areas including data science, machine learning, and database systems. Recent publications highlight contributions to entity resolution methodologies, sustainable data practices, and AI-driven analysis of religious texts. She collaborates with international teams on projects like the Digital Maktaba initiative, which digitizes non-Latin cultural heritage materials. Key research themes include: Big Data Platforms for Energy Communities AI in Islamic Studies and Digital Libraries Schema-Aware Entity Resolution Efficient Stream Processing Techniques Data Privacy and Auditable Tokenization Her work bridges theoretical advancements with practical applications in industry and cultural preservation.

Fatemeh Ahmadi AbkenariView profile

Adjunct Professor

• Machine Learning
• Deep Learning
• Natural Language Processing
• +4 more

Fatemeh Ahmadi Abkenari is an Adjunct Professor in the Department of Computer Science at New York Tech , Vancouver campus. Her expertise spans Machine Learning , Deep Learning , Natural Language Processing , and Cybersecurity . With over a decade of teaching experience, she has instructed undergraduate, graduate, and doctoral students in computer engineering courses. Joined New York Tech: 2024 Education: Ph.D. Her research focuses on enhancing opinion spam detection in tourism reviews and improving Intrusion Detection Systems via hybrid machine learning models. She also explores domain-specific web search engines, aspect elicitation in customer reviews, and software quality compliance for health systems. Recent publications address topics such as overfitting mitigation , feature reduction in cybersecurity, and clickstream-driven web crawling . She is proficient in Python/R programming , AWS Cloud Security , SQL/NoSQL databases , and tools like Power BI and Tableau . Abkenari serves as a reviewer for journals such as Springer Neural Processing Letters and IEEE Access . She has contributed to projects involving sentiment mining , web page ranking optimization , and network protocol simulation , reflecting her interdisciplinary expertise in AI and cybersecurity.

Alexander FraserView profile

Professor

• Machine Learning
• Natural Language Processing (NLP)
• Multilingual Systems
• +5 more

Prof. Alexander Fraser is a faculty member at the Technical University of Munich (TUM), holding the Chair of Data Analytics & Statistics within the TUM School of Computation, Information and Technology. He specializes in machine learning for natural language processing, particularly focusing on multilingual systems, data sparsity, and cross-lingual alignment. His research bridges theoretical advancements and practical applications, including projects on hate speech detection, low-resource language support, and ethical AI. Fraser earned his PhD in Computer Science from the University of Southern California (2007). Prior to TUM, he held professorial roles at LMU Munich (2013–2024) and the University of Stuttgart (2007–2013). He has led significant research initiatives, including ERC grants and EU-funded projects like Health in my Language (HimL). His work has been recognized with awards such as the ERC Advanced Grant (2023) and ERC Proof of Concept Grant (2022). Teaching responsibilities include courses on large language models, generative models, and machine learning at TUM. Fraser is also a Principal Investigator at the Munich Center for Machine Learning (MCML) and an active member of the ELLIS Unit Munich. His contributions span academic research, industry collaboration, and language revitalization efforts for minority languages like Sorbian. Key publications highlight innovations in multilingual benchmarks, cross-lingual alignment, and ethical NLP applications. His research addresses challenges in data scarcity, model bias, and responsible AI deployment across diverse linguistic landscapes.

Hubert T.H. ChanView profile

Associate Professor

• Algorithms
• Combinatorial Optimization
• Discrete Metric Spaces
• +4 more

Professor Hubert T.H. Chan is an Associate Professor at the Department of Computer Science, University of Hong Kong, and serves as Programme Director for the BEng(CompSc) programme. He holds a PhD from Carnegie Mellon University (2007) and previously worked as a postdoc at Max-Planck-Institut für Informatik in Germany. His research focuses on algorithms, combinatorial optimization, discrete metric spaces, and security & privacy, with applications in graph theory, network analysis, and privacy-preserving mechanisms. Education: PhD in Computer Science, Carnegie Mellon University, 2007 BEng (Computer Science), details not specified in text Research interests emphasize algorithmic design for dynamic systems, privacy in data aggregation, and efficient optimization techniques. His work bridges theoretical computer science with practical applications in network security and distributed systems. Key grants include Hong Kong RGC-funded projects on oblivious data structures, spectral hypergraph analysis, and dynamic metric problems (2012–2018). His publications span top conferences like SODA, FOCS, WWW, and ICML, addressing challenges in clustering, sorting, privacy, and graph decomposition. He oversees research groups exploring hypergraph learning, secure computation, and algorithmic privacy. His lab's work often intersects with real-world systems, emphasizing both theoretical rigor and practical relevance.