Find Academic Supervisors

Jingling XueView profile

Professor

• Programming Languages
• Compilers
• Program Analysis
• +5 more

Jingling Xue is a Scientia Professor at the School of Computer Science and Engineering at the University of New South Wales (UNSW) in Sydney, Australia. As an IEEE Fellow of the Computer Society, he leads the Programming Languages and Compilers research group, focusing on practical applications of compiler optimization and program analysis techniques. His work bridges theoretical foundations with real-world software systems, particularly in developing open-source tools for large-scale program analysis. Professor Xue received his B.Eng and M.Eng degrees from Tsinghua University in 1984 and 1987, respectively, followed by a PhD from the University of Edinburgh in 1992. His academic journey has established him as a leading figure in programming languages and compiler technology. Xue's research spans programming languages, compiler technology, and program analysis with emphasis on practical relevance. His current projects include compiler techniques for improving parallelism and locality, pointer/alias analysis for million-line-scale programs, and static/dynamic analysis for detecting bugs and security vulnerabilities in real-world applications like web browsers and Android apps. His group actively develops open-source tools to support scientific replicability and reproducibility in these areas. His recent publications demonstrate a strong focus on applying program analysis techniques to modern challenges including AI compilers, homomorphic encryption, security vulnerability detection, and graph processing systems. The work shows evolution from traditional compiler optimization to addressing emerging domains like privacy-preserving computation and deep learning systems while maintaining rigorous theoretical foundations. Scientific Awards: Best Paper Award at CGO'13 Best Paper Award at CGO'16 Distinguished Paper Award at ECOOP'16 Distinguished Paper Award at ICSE'18 Distinguished Paper Award at ISSTA'19 Distinguished Paper Award at ASE'19 Distinguished Artifact Award at ISSTA'23 Best Artifact Award at FSE'23 Distinguished Paper Award at ASE'23 Test-of-Time Award at CGO'21 Professor Xue has successfully supervised 30 PhD students to completion, many of whom now work as professors or researchers in academia and industry. He has served as Program Chair for major conferences including LCTES'13, CC'18, CGO'20, and General Chair for LCTES'20. His group currently focuses on memory safety in Rust, smart contract analysis, AI compilers, compilation for privacy-preserving computation, and adversarial attacks in deep learning. The Programming Languages and Compilers group maintains strong connections with industry partners, translating theoretical advances into practical tools for real-world software development challenges. Their work on pointer analysis, memory safety, and compiler optimizations continues to influence both academic research and industrial practice.

Christian WinklerView profile

Professor

• Artificial Intelligence
• Natural Language Processing
• User Experience
• +4 more

Christian Winkler is Professor for AI-based UX optimization and general business administration at Nuremberg Institute of Technology since 2022. With over 25 years of experience spanning entrepreneurship, enterprise architecture, and academia, he brings substantial industry expertise to his academic role. His career includes founding multiple technology companies including an internet service provider (WWL Internet AG) that went public in 1999, Querplex GmbH through management buyout in 2003, and datanizing GmbH as an NLP SaaS provider in 2017. Professor Winkler's research focuses on practical applications of artificial intelligence in business contexts, with particular expertise in natural language processing, user experience optimization, and data-driven marketing strategies. His work bridges theoretical AI research with real-world business applications, emphasizing accessibility of complex technologies. He has published extensively on language models (including BERT and LLaMA implementations), text analysis techniques, and social media data analysis for business insights. Recent publications reveal a strong trend toward optimizing large language models for practical deployment, with significant focus on analyzing user-generated content from social platforms like Instagram and WallStreetBets. His work demonstrates how NLP can extract valuable business intelligence from unstructured data sources while making advanced AI techniques accessible to non-technical business professionals. Winkler teaches E-Commerce, International Marketing Tools - Quantitative Methods, Applied User Experience, and Communication Management, reflecting his interdisciplinary approach that combines technical AI knowledge with business administration expertise. He is an active contributor to the data science community through conference presentations at events like m3 Konferenz, MLsummit, and data2day, as well as educational content for Heise Academy on Python and NLP topics.

Gagan AgrawalView profile

Professor

• Parallel Computing
• High-Performance Computing (HPC)
• Data Analytics
• +5 more

Gagan Agrawal is the UGA Foundation Professorship in Computing and a Professor at the School of Computing, University of Georgia. He serves as Director of the School and is affiliated with the Franklin College of Arts & Sciences. Agrawal holds a PhD and MS in Computer Science from the University of Maryland (1994-1996). His research focuses on high-performance computing, parallel algorithms, compiler optimization for deep learning, GPU acceleration, and interdisciplinary applications in health informatics. Notable contributions include frameworks like ForensiBlock (blockchain for data forensics) and DELITE (tensorized instruction compilation). Agrawal has secured over $2.5M in NSF grants for projects addressing extreme-scale computing challenges. Recent grants include: SHF: Small: Memory Hierarchy Optimizations Meet Transformers (MITTEN) ($600K, 2024-2027) DELITE compilation system for deep learning models ($600K, 2023-2026) Publications span parallel computing methodologies, cybersecurity frameworks, and health outcomes analysis. His work on social determinants of health in cancer survival has been systematically reviewed in top-tier medical journals. Agrawal leads UGA's computing initiatives, emphasizing interdisciplinary research and student mentorship in HPC and AI domains.

Stefano FerilliView profile

Professor

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

Stefano Ferilli is a Professor of Computer Science at the University of Bari, Italy, where he leads the ARA (Apprendimento e Ragionamento Automatico) research lab within the Department of Computer Science. His academic roles include former Director of the Interdepartmental Center for Logic and Applications (CILA) and current head of the Artificial Intelligence & Intelligent Systems node in the CINI national laboratory. He holds a PhD in Computer Science and has been a key figure in advancing machine learning, logic programming, and digital library technologies. Education: Laurea (MSc equivalent) in Information Sciences (1996), Specialist Laurea in Computer Science (2003), and PhD in Computer Science (2001). His research focuses on foundational aspects of machine learning, multi-strategy reasoning, process mining, and applications in cultural heritage, bioinformatics, and smart environments. Research Contributions: Developed frameworks like INTHELEX (incremental theory learner), WoMan (process mining), and DoMInUS (document management). Over 370 publications, including a Springer monograph and multiple award-winning papers. Active in organizing conferences like ECML-PKDD, ICDM, and IRCDL, and serves on editorial boards of journals like Information Sciences . Projects: Led or participated in over 30 national and European projects, including EU-funded initiatives on digital libraries (COLLATE, DELOS) and AI applications. Collaborates with industries like Samsung and institutions like the Italian Police for traffic analysis and cultural heritage preservation. Awards: Recognized for outstanding peer review (MDPI, ECMLPKDD), best paper awards, and contributions to AI education and cultural heritage. Member of prestigious associations like AI*IA (Italian AI Society) and AICA (Italian Computing Society).

Torsten HoeflerView profile

Professor

• Deep learning Systems
• Parallel programming models
• High performance and datacenter networking
• +8 more

Torsten Hoefler is a Full Professor of Computer Science at ETH Zurich, Switzerland, with an adjunct appointment in Electrical Engineering. He previously held roles at the National Center for Supercomputing Applications (University of Illinois at Urbana-Champaign) and Indiana University. Full Professor of Computer Science, ETH Zurich (2020–present) Adjunct Professor of Electrical Engineering, ETH Zurich (2020–present) Member at Large, ACM SIGHPC Executive Committee (2013–present) Leadership roles in the MPI Forum and Blue Waters project His research focuses on performance-centric system design , with emphasis on scalable networking, parallel programming models, and performance modeling. Key contributions include the Slim Fly network topology, Data-Centric Python framework, and innovations in parallel graph computations and RDMA-based systems. Recent publications span topics like LLM training networks , quantization geometry , chiplet interconnects , and AI-driven climate modeling , reflecting his interdisciplinary approach combining HPC, AI, and hardware-software co-design. ACM Gordon Bell Prize (2019) ERC Consolidator Grant (2020) IEEE TCSC Award for Excellence (2019) SIAM SIAG/SC Junior Scientist Prize (2012) Latsis Prize of ETH Zurich (2015) He has received multiple best paper awards at top conferences (SC10, SC13, SC14, SC19, IPDPS'15, HPDC'15, OOPSLA'16) and contributed to MPI-3 standardization.

Michalis MountantonakisView profile

Researcher

• Large-Scale Semantic Data Integration
• Linked Open Data
• Semantic Web
• +4 more

Michalis Mountantonakis is a Postdoctoral Researcher at FORTH and Laboratory Teaching Staff in the Department of Computer Science at the University of Crete, Greece. He holds a PhD (2020), MSc (2016), and BSc (2014) in Computer Science from the University of Crete, all with top grades. His research focuses on Large-Scale Semantic Data Integration, Linked Open Data, and Semantic Web technologies, with over 45 publications in top venues like ACM VLDB, ISWC, and ECML. He has been awarded the prestigious SWSA Distinguished Dissertation Award (2020) and the Maria Michael Manasaki Fellowship (2020). His work includes tools like LODsyndesis and LODChain, addressing challenges in knowledge graph connectivity and validation of AI-generated content. Education: PhD in Computer Science (2016-2020), University of Crete (Excellent GPA 9.74/10) MSc in Computer Science (2014-2016), University of Crete (Excellent GPA 9.87/10) BSc in Computer Science (2010-2014), University of Crete (2nd in class with GPA 8.42/10) Research Interests: His work bridges semantic web technologies with modern AI challenges, emphasizing large-scale data integration, knowledge graph applications, and validation frameworks. He has contributed to cultural heritage informatics, machine learning-augmented semantic systems, and cross-lingual NLP solutions. Recent trends include leveraging LLMs for query generation and semantic enrichment while ensuring factual accuracy through knowledge graph-driven validation. Key Achievements: Developed LODsyndesis, a global-scale semantic integration service Pioneered real-time validation of ChatGPT responses using RDF knowledge graphs Won Best Paper Award (ISWC 2022) for entity enrichment techniques Recipient of Stelios Orphanoudakis Undergraduate Fellowship (2013-2014) Participated in Roche Continents 2019 (top 100 European science students) Grants & Labs: His research has been supported by GSRT/HFRI. He collaborates with FORTH-ICS and leads projects in EU-funded initiatives like iMarine and BlueBridge. Current work focuses on governance models for ontologies, interoperable thesaurus creation (e.g., FoodEx2), and semantic analytics for cultural heritage datasets.

Divya MahajanView profile

Assistant Professor

• Computer Architecture
• Distributed Systems
• Machine Learning Systems
• +2 more

Divya Mahajan is an Assistant Professor holding dual appointments in the College of Computing and College of Engineering at Georgia Institute of Technology. She directs the Systems Infrastructure and Architecture Research Lab, focusing on sustainable computing platforms for large-scale AI, machine learning, and data storage systems. Her research integrates computer architecture, distributed systems, and database technologies to optimize end-to-end data pipelines. Mahajan earned her PhD from Georgia Tech and Bachelor's from IIT Ropar (President's Gold Medal). Prior to academia, she was a Senior Researcher at Microsoft Azure, leading communication collective designs for distributed DNN training. Awards include NCWIT Collegiate Award (2017) and HPCA Distinguished Paper Award (2016). Research spans hardware-software co-design for ML systems, efficient recommendation architectures, federated learning optimization, and PIM/NPU acceleration. Recent projects address challenges in large-scale model serving, edge-cloud integration, and sustainable computing. Student advisees include PhD and MS researchers in systems and architecture. Publications demonstrate consistent innovation in accelerating ML workloads through novel hardware/software techniques, with work appearing in ISCA, MICRO, ASPLOS, NeurIPS, and VLDB. Current projects explore energy-efficient AI infrastructure and domain-specialized systems for emerging economies.

Vladimir VlassovView profile

Professor

• Machine Learning
• Data-intensive computing
• Cloud computing
• +7 more

Dr. Vladimir Vlassov is a full Professor in Computer Systems at the Division of Software and Computer Systems (SCS) , Department of Computer Science (CS) , School of Electrical Engineering and Computer Science (EECS) , KTH Royal Institute of Technology , Stockholm, Sweden. He leads the AVA project in ALEC2, an AI-powered system for mental health care. He is a member of the Distributed Computing research group (DC@KTH) . Education & Roles: Holds a PhD and is a member of ACM and IEEE. Previously visited MIT (1998) and UMass Amherst (2004). Teaches courses on Data Mining , Distributed Systems , and Concurrent Programming . Research Interests: Focus on scalable AI, Cloud computing, distributed systems, and NLP for mental health. Projects include ExtremeEarth (Copernicus data analytics) and EMJD-DC (distributed computing PhD program). Grants & Projects: Principal Investigator in ALEC2 (adaptive mental health care) and ExtremeEarth (EU H2020). Led EU projects like ENCORE (manycore systems) and PaPP (embedded systems). Labs & Teams: Directs the Distributed Computing group, contributing to Hopsworks (machine learning feature store) and Maggy (hyperparameter optimization).

• Database Systems
• Distributed Systems
• Cloud Computing
• +4 more

Dr. Kenneth Salem is a Professor at the University of Waterloo, Canada , with a distinguished career spanning over three decades in database systems, distributed computing, and data management. His work has significantly influenced transaction processing, replication, and scalability in modern databases. Institution: University of Waterloo Collaborations: Frequent co-author with Hector Garcia-Molina, Ashraf Aboulnaga, Umar Farooq Minhas, Khuzaima Daudjee Research Interests: Dr. Salem’s research focuses on database systems, particularly in transaction processing , distributed databases , cloud computing , and energy-efficient data storage . He has pioneered techniques for schema design in NoSQL , adaptive caching , and RDMA-based data transfer . His work often addresses challenges in high availability , workload-aware optimization , and cardinality estimation . Recent Article Trends: His latest publications (2023-2025) explore ad hoc transactions , eventual durability , and summary-based cardinality estimation , emphasizing practical solutions for real-world database scalability and consistency. Earlier works (2018-2022) covered memory power optimization , RDMA abstractions , and workload-aware CPU scaling , reflecting a sustained focus on performance and efficiency. Advising and Collaborations: Dr. Salem has mentored numerous co-authors in database research, including Ashraf Aboulnaga, Umar Farooq Minhas, and Khuzaima Daudjee. His collaborative projects span Apache Spark caching , RemusDB high availability , and DimmStore memory optimization . Labs and Teams: He has been a key contributor to the DaMoN and Versatile Database Systems workshops, focusing on hardware-aware database design. His work with teams at Waterloo and collaborations with institutions like ETH Zurich and MIT has advanced self-managing databases and edge replication technologies.

• Business Analytics
• Operations Research/Management
• Operational Analytics
• +7 more

Mehdi Toloo is a Reader in Business Analytics at the University of Surrey's Surrey Business School. He holds a BSc, MSc, and PhD, and is a docent. Previously, he was a Professor at Technical University of Ostrava (Czech Republic) and Sultan Qaboos University (Oman). His research focuses on Business Analytics, Operations Research, Data Envelopment Analysis (DEA), and Decision Analysis. He has supervised over 40 postgraduate students and contributed to top-tier journals like European Journal of Operational Research and Omega. He is an editor for journals including Computers & Industrial Engineering and Decision Analytics. Recognized globally, he ranks in the top 2% of scientists worldwide in Business Analytics & Operations Research (2020-2024). His research projects include performance evaluation with unclassified factors, economies of scope in network DEA, and selective measures in DEA. He collaborates internationally on projects like robust optimization and supply chain sustainability. His teaching spans undergraduate courses in Operations Research, Mathematics for Business, and Programming, alongside postgraduate modules on Quantitative Methods and Advanced DEA. His work bridges theoretical and applied research, with applications in healthcare, renewable energy, and public policy.

• Artificial Intelligence
• Computer Vision
• Natural Language Processing
• +5 more

Gwenn Englebienne is an Assistant Professor at the Digital Society Institute and Human Media Interaction group of Utrecht University. Their research focuses on Artificial Intelligence, Computer Vision, and Human-AI Interaction, with applications in robotics, health, and social computing. They have contributed to over 80 research outputs since 2007, emphasizing embodied AI, social robotics, and explainable machine learning. Research interests span activity recognition, teleoperation systems, and ethical AI design. Notable work includes developing GNN-based group detection algorithms and evaluating chatbot reliability through automated question-answering frameworks. Their studies often bridge technical innovation with human-centered design, such as measuring embodiment via pupil dilation or addressing asymmetry in video-conferencing interactions. Key collaborations include work on social robotics, telepresence systems, and health monitoring using ambient sensors. Publications span conferences like IDA, CogMI, and LREC-COLING, reflecting interdisciplinary impact. A dataset on robot social positioning behavior is publicly accessible via 4TU.Centre for Research Data. Current work explores semi-supervised domain adaptation, spiking neural networks, and the psychological dimensions of AI trustworthiness. They lead initiatives in the Digital Society Institute to align technological advancements with societal needs.

Olaf HartigView profile

Associate Professor

• Database Systems
• Semantic Web
• Data Management
• +5 more

Olaf Hartig is a Senior Associate Professor at Linköping University's Department of Computer and Information Science (IDA), affiliated with the Database and Information Techniques (ADIT) division. He is also an Amazon Scholar collaborating with the Neptune graph database team. His research focuses on data management, semantic web technologies, graph databases, and distributed data systems. Hartig holds a PhD from Humboldt-Universität zu Berlin and is a Docent at Linköping University. He has received numerous awards, including the SWSA Distinguished Dissertation Award and eight best paper awards, and was selected as a Wallenberg Academy Fellow in 2024. Education: PhD in Computer Science (Humboldt-Universität zu Berlin), Docent (Linköping University). Research interests span query processing for Linked Data, federated systems, RDF and GraphQL semantics, and knowledge graph construction. He leads research groups in Database and Web Information Systems and Semantic Web Technologies at IDA. Key achievements include pioneering traversal-based query execution, developing Triple Pattern Fragments, and contributions to standards like RDF* and SPARQL*. His work has been recognized through grants, patents (e.g., on graph acceleration techniques), and leadership roles in conferences like ISWC and ESWC. Teaching: Course leader for database technology courses (TDDD12, TDDD37) and advanced topics like big data analytics and bioinformatics databases. Active in curriculum design and interdisciplinary education. Labs/Teams: Database and Web Information Systems Group, Semantic Web Research Group, Sports Analytics Group (IDA) Grants: Wallenberg Academy Fellowship, Swedish Research Council funding

Linghao SongView profile

Assistant Professor

• Accelerator Architecture
• FPGAs
• Hardware Acceleration Systems
• +4 more

Linghao Song is an Assistant Professor in the Department of Electrical & Computer Engineering at Yale University. His research focuses on accelerator architecture design, FPGA-based acceleration systems, and ReRAM-based computing. He is affiliated with the School of Engineering & Applied Science and holds expertise in sparse matrix processing, high-level synthesis, and task-parallel programming frameworks. Education: Ph.D. and M.S. from Duke University and University of Pittsburgh, respectively, with a B.S.E. from Shanghai Jiao Tong University. Research Interests: Song's work spans FPGA-accelerated computing, resistive memory (ReRAM) architectures for deep learning and graph processing, and high-performance sparse matrix operations. His recent projects include the TAPA framework for FPGA programming, ReFloat for iterative linear solvers, and the Sextans/Serpens accelerators for sparse matrix computations. Key Contributions: Over 20 peer-reviewed publications on topics like ReRAM-based accelerators (e.g., GraphR, PipeLayer), FPGA optimization frameworks (TAPA, RapidStream), and novel hardware designs for neural networks and graph analytics. His work emphasizes energy efficiency, scalability, and cross-domain applicability of hardware accelerators. Awards: Recipient of the Duke ECE Outstanding Dissertation Award (2021), EDAA Outstanding Dissertation Award (2020), and National Scholarship of China (2012). Labs/Teams: Leads research in FPGA acceleration and memory-based computing, collaborating on projects involving HBM integration, resistive memory architectures, and task-parallel dataflow frameworks.

Zhengwu ZhangView profile

Associate Professor

• Machine Learning
• Brain Connectomics
• Deep Learning
• +4 more

Zhengwu Zhang is an Associate Professor in the Department of Statistics and Operations Research at the University of North Carolina at Chapel Hill. His research focuses on developing statistical and machine learning methods for analyzing high-dimensional neuroimaging data, particularly structural and functional brain connectomics. He leads the UNC Education Program of Intelligence and Connectomics (EPIC), an interdisciplinary initiative training students in brain network analysis. His work addresses challenges in large-scale neuroimaging datasets, including computational efficiency and reproducibility. Zhang completed his Ph.D. in Statistics at Florida State University under Anuj Srivastava. His funding includes NIH grants for CRCNS, structural connectome analysis, and personalized cognitive training. He serves as an Associate Editor for the Journal of the American Statistical Association (Reproducibility). Key contributions include tools like the Surface-Based Connectivity Integration (SBCI) GitHub repository for brain network analysis pipelines. His awards include the 2022 UNC Junior Faculty Development Award and the Oak Ridge Powe Award. Teaching roles include courses on data science, machine learning, and statistical consulting. His research spans brain network dynamics, genetic contributions to connectome structure, and applications of deep learning in neuroscience.

Hamid ZargariaslView profile

Researcher

• Computer Networks
• IoT and Sensor Technology
• RFID Systems
• +3 more

Dr. Hamid Zargariasl is a Researcher in the Computer Engineering Department at BTU Cottbus-Senftenberg. His work focuses on IoT systems, RFID technology, social network analysis, and distributed computing. He leads the UBICO Team and contributes to the Computer Engineering Group, exploring interdisciplinary applications of telecommunications and sensor networks. His research integrates theoretical frameworks with practical implementations, addressing challenges in smart city infrastructure, healthcare systems, and educational technology. Key research areas include optimizing RFID and sensor performance, analyzing social object interactions in IoT networks, and developing protocols for distributed computing. His experimental studies bridge academic performance metrics with mobile social network dynamics, and he has pioneered methodologies for network integration and centrality analysis in evolving systems. Publications span 15 years (2009–2024), emphasizing real-world data analysis and system optimization. Notable work includes enhancing digital thread functionality in aerospace engineering and evaluating smart parking sensor technologies. His contributions inform both technical systems and socio-economic policy recommendations. Dr. Zargariasl collaborates within interdisciplinary teams, contributing to the BTU's research initiatives while maintaining active engagement in the academic community through lectures and student mentorship.