Find Academic Supervisors

• Computer Architecture
• Compilers
• GPUs
• +4 more

Antonio González Colás is a Full Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Facultat d'Informàtica de Barcelona (FIB) and the Department of Computer Architecture. He leads the ARCO research group (Microarchitecture and Compilers) and focuses on advanced computer architecture, including GPUs, compilers, and embedded systems. In 2024, he received the ICREA Acadèmia distinction for his research on future computer systems integrating cognitive tasks and autonomous decision-making. His work emphasizes energy-efficient architectures, memory optimization, and hardware acceleration for AI and real-time applications. He holds a Doctorate in Computer Science and has over 1000+ publications, including top-tier journals and conferences. His research spans topics like GPU microarchitecture, compiler-assisted caching, DNN acceleration, and autonomous driving hardware. Major awards include the HiPEAC 2024 Paper Award and EU-funded projects under Horizon 2020. He advises numerous PhD students on topics such as point cloud processing for autonomous systems, GPU rendering optimizations, and neural network efficiency. His contributions bridge theoretical computer architecture with practical hardware-software co-design, addressing challenges in modern computing systems.

Handan LiuView profile

Teaching Professor

• High-Performance Computing
• Artificial Intelligence
• Machine Learning
• +5 more

Handan Liu is a Teaching Professor in the Multidisciplinary Graduate Engineering Programs at Northeastern University, specializing in High-Performance Computing for AI, scalable machine learning, and large language models. With over 20 years of combined industry and academic experience, she integrates practical insights from her work at NASA, the US Air Force, and as a software engineer into her teaching and research. Her educational background includes: Ph.D. from Shanghai Jiao Tong University Dr. Liu's research centers on advancing AI through high-performance computing, with a focus on efficient scaling of large language models via quantization and parameter-efficient fine-tuning (PEFT). She also explores reasoning capabilities, cognitive aspects, and multimodal learning to enhance AI systems. Her work bridges theoretical algorithms and real-world applications in distributed environments. Her publication history shows an evolution from computational fluid dynamics and aerospace engineering (2008-2011) to biomedical applications (2008-2009), and now to AI and HPC (2014-2025). Recent work emphasizes educational integration of parallel computing in AI, reflecting her commitment to preparing students for industry demands. No major scientific awards are listed in the provided information. Dr. Liu mentors graduate students through course projects and research, developing unique curricula that merge HPC with AI. Her leadership in building Northeastern's HPC clusters since 2015 provides critical infrastructure for student research. Past collaborations with government agencies indicate a strong grant history, though current funding details are not specified. She has been instrumental in establishing Northeastern University's HPC clusters, creating a robust computational environment that supports interdisciplinary research in engineering and data science.

• Machine Learning
• Control Systems
• Computer Vision
• +3 more

Jian Sun is a researcher affiliated with the Chinese Academy of Sciences' Institute of Computing Technology. His work spans interdisciplinary fields including machine learning, control systems, robotics, and signal processing. He collaborates with institutions globally to advance theoretical and applied research in computational methods and their real-world applications. Research interests focus on machine learning for healthcare diagnostics, optimization algorithms for complex systems, and sensor fusion technologies for autonomous systems. Recent work emphasizes applications in cognitive impairment detection, UAV-based communication systems, and crystal structure prediction. Publications highlight advancements in adaptive control methodologies, cybersecurity in control systems, and 3D reconstruction techniques. His contributions bridge theoretical foundations with practical implementations in robotics, aerospace, and environmental monitoring.

Maxim KontsevichView profile

Professor

• Mathematics
• Quantum Field Theory
• Algebraic Geometry
• +5 more

Maxim Kontsevich is a Permanent Professor at the Institut des Hautes Études Scientifiques (IHÉS) since 1995. He has held visiting positions at institutions including the University of California at Berkeley (1993-1995), Harvard University (1991-1992), and the Max-Planck-Institut für Mathematik (1993-1994). His work bridges mathematics, physics, and geometry, with a focus on string theory, quantum field theory, and non-commutative geometry. His research spans topics such as homological mirror symmetry, deformation quantization, and motivic Donaldson-Thomas invariants. Recent publications include groundbreaking work on WDVV equations, non-archimedean analytic spaces, and the interplay between algebraic geometry and quantum field theory. Fields Medal (1998) Henri Poincaré Prize (1997) Crafoord Prize (2008) Shaw Prize in Mathematical Sciences (2012) Fundamental Physics Prize (2012)

• AI Drug Discovery
• Biomedical Engineering
• Nanomedicines
• +3 more

Krish Ramadurai is a DPhil student at the University of Oxford, affiliated with St. Catherine’s College and St. Hilda’s College. He is a member of the Multimodal Medical Data Integration & Analysis (MultiMeDIA) Group at the Institute of Biomedical Engineering (IBME). Additionally, he serves as a Partner at AIX Ventures, focusing on AI-enabled healthcare and life sciences investments. His research spans predictive modeling of nanomedicines, Drug Development Digital Twins (D3Ts), and 3D bioprinting with novel biopolymers. Education: Dual master’s degrees in Biology (Harvard University) and Business Administration (Washington University in St. Louis) Bachelor’s degree in Integrative Biology and Economics from the University of Illinois at Urbana-Champaign Research Interests: His work bridges AI-driven drug discovery, biomedical engineering, and precision agriculture. He explores applications of machine learning in healthcare diagnostics (e.g., Clock Drawing Test automation) and agricultural robotics. His contributions include open-source agricultural robotics and bio-based materials for global health challenges. Teaching & Leadership: He has served as a Guest Lecturer at Harvard University, teaching courses on tissue engineering and biomedical product development. He holds advisory roles with Nucleate and the National Venture Capital Association (NVCA), and chairs multiple companies. His thought leadership is featured in outlets like the Wall Street Journal and World Economic Forum.

Indu BodalaView profile

Lecturer

• Human-Robot Interaction (HRI)
• Social Robots
• Affective Computing
• +2 more

Dr. Indu Bodala is a Lecturer in Computer Science at the University of Southampton. She holds a Ph.D. from the National University of Singapore, where her research focused on vigilance fluctuations using EEG and eye-tracking. She has conducted postdoctoral research at NUS and the University of Cambridge in areas such as Human-Robot Interaction (HRI), social robotics, and affective computing. Her work on a robotic mindfulness coach was featured in media outlets like BBC Look East and the Raspberry Pi Foundation's Hello World magazine. She is actively involved in interdisciplinary projects, including healthcare technologies and machine learning applications in oncology decision-making. She currently supervises multiple PhD students and leads research groups in robotics and AI. **Education:** Ph.D. in Computer Science, National University of Singapore Postdoctoral Research, NUS School of Computing Postdoctoral Research, University of Cambridge, Department of Computer Science and Technology **Research Interests:** Human-Robot Interaction (HRI) Social Robotics and Affective Computing Machine Learning in Healthcare Longitudinal User Studies **Awards:** Shortlisted for RSJ/KROS Distinguished Interdisciplinary Research Award (IEEE RO-MAN 2021) **Research Projects:** LLM-driven Social Stories Interventions for Child-Robot Interactions KTP With First Step Trust Her recent publications focus on advancing non-invasive neuroimaging techniques, predictive models for cancer treatment pathways, and robotic interventions for mental well-being.

• Cybersecurity
• Privacy
• Data management
• +4 more

Dr. Wanlun Ma is a Postdoctoral Research Fellow at the School of Science, Computing and Emerging Technologies , Swinburne University of Technology . He earned a Bachelor's and Master's in Information and Communication Engineering from the University of Electronic Science and Technology of China (UESTC) in 2017 and 2020, respectively, followed by a Ph.D. in Computer Science from Swinburne University of Technology in 2024. His research focuses on Trustworthy and Responsible AI , particularly in adversarial machine learning, network security, and privacy preservation. Education B.E., M.E. in Information and Communication Engineering, UESTC (2017, 2020) Ph.D. in Computer Science, Swinburne University of Technology (2024) Research Interests Trustworthy AI systems Adversarial machine learning Network security and privacy AI ethics and accountability Grants Australian Research Council (ARC) grant (2025-2029) for AI Models for Digital Manufacturing Supervision Available to supervise Ph.D. candidates

Faiçal AzaiezView profile

Professor

• Experimental Nuclear Physics
• Nuclear Astrophysics
• Microelectronics
• +6 more

Faiçal Azaiez is a leading figure in experimental nuclear physics, currently serving as Director of Laboratori Nazionale di Legnaro (INFN) in Italy. He previously held directorships at iThemba LABS (South Africa) and Institut de Physique Nucléaire d'Orsay (France). He holds academic appointments as Extraordinary Professor at Stellenbosch University and Honorary Professorships at the University of Cape Town and University of the Witwatersrand. Education: Baccalauréat C (Tunisia, 1976) Licence de Physique (Paris, 1980) Doctorat de 3ème cycle (IPN Orsay, 1984) Habilitation (IPN Orsay, 1999) Research Interests: Nuclear structure and astrophysics Detector technology (Ge arrays, LaBr3 gamma detectors) Applications in proton/hadron therapy and nuclear safety Front-end micro-electronics and data acquisition systems Scientific Impact: With over 280 publications, 3348 citations, and an h-index of 29, Azaiez has shaped modern nuclear physics through projects like AGATA, RISING, and SPIRAL2. His work bridges fundamental research with applications in medicine and cultural heritage, co-authoring reports on nuclear physics in medicine and cultural heritage. Awards: Fellow of the American Physical Society Leadership Roles: Chairman of Nuclear Physics Board of the European Physical Society (2012–2017) Coordinator of ECOS Network Activity (2010–2016) Member of IUPAP Working Group 9 (2016–2023)

Wolfram BauerView profile

Professor

• Analysis on Manifolds
• Operator Theory
• Operator Algebras
• +3 more

Prof. Wolfram Bauer is a Professor in the Faculty of Mathematics and Physics at Leibniz University Hannover, affiliated with the Institute of Analysis. His research focuses on operator theory, subriemannian geometry, and spectral analysis, with notable contributions to Toeplitz operators, geometric analysis, and microlocal analysis. He co-edits the journal Applied Analysis and Optimization and actively organizes international workshops on global and microlocal analysis. His teaching encompasses advanced topics such as operator algebras, functional analysis, and subriemannian structures, reflecting his research expertise. Bauer has presented at numerous conferences, including the Microlocal and Global Analysis series in Potsdam and specialized workshops on Lie groups and geometric analysis. Key Research Areas: Analysis on manifolds, pseudodifferential operators, subriemannian geometry. Recent Workshops Organized: 2025: Microlocal and Global Analysis Workshop, Potsdam 2024: Modern Aspects of Analysis on Lie Groups, Göttingen

Raimar WulkenhaarView profile

Professor

• Non-perturbative quantum field theory
• Noncommutative geometry
• Renormalisation group
• +3 more

Prof. Dr. Raimar Wulkenhaar is a Professor of Mathematics at the University of Münster's Faculty of Mathematics and Computer Science, affiliated with the Mathematical Institute. He is an investigator in the Mathematics Münster cluster and a member of CRC 1442 Geometry: Deformations and Rigidity. His research focuses on operator algebras and mathematical physics, particularly non-perturbative quantum field theories, noncommutative geometries, and renormalization. Wulkenhaar's work bridges abstract algebraic structures with physical models, such as matrix models and tensor field theories. He holds a leadership role in interdisciplinary projects, including the CRC 1442's D03 project on integrability and the Mathematics Münster focus areas T2 (moduli spaces) and T7 (field theory and randomness). His research emphasizes noncommutative quantum field theories, where he explores exact solutions and topological recursion techniques. Key collaborations include work on Kontsevich models, Calogero-Moser systems, and renormalization group methods. Recent publications highlight advancements in blobbed topological recursion, integrable systems, and stochastic quantization. Wulkenhaar frequently publishes in top journals like Communications in Mathematical Physics and Journal of Geometry and Physics . His work often intersects with algebraic geometry, combinatorics, and stochastic processes, reflecting a broad yet deep engagement with mathematical physics. He has contributed to foundational texts, including a chapter in Advances in Noncommutative Geometry , and has advised numerous collaborative research initiatives. Despite no explicitly listed awards, his prolific output and project leadership underscore his scholarly impact.

Erkki PesonenView profile

Senior Lecturer

• Computer Science Education
• Educational Technology
• Machine Learning
• +3 more

Erkki Pesonen is a Senior Lecturer and Deputy Head of Department at the School of Computing, University of Eastern Finland. His roles include teaching foundational computer science courses and spearheading modern pedagogical approaches such as flipped classrooms and student analytics. He actively contributes to institutional governance as a member of the University of Eastern Finland Board. His research focuses on computer science education, including student expectations, pedagogical collaboration, and the integration of technology in learning environments. Previously, he conducted pioneering work in medical informatics, particularly using neural networks for diagnostic support systems in acute appendicitis and abdominal pain. Education: Erkki holds a Doctor of Philosophy (PhD) in Computer Science, demonstrated by his 1998 dissertation on neural network-based decision systems. Research Interests: Computer Science Education Educational Technology Machine Learning Applications Medical Informatics Notable Contributions: Developed pedagogical strategies for first-year computing students Authored influential studies on work planning in academia Pioneered neural network applications for medical diagnosis Awards: None explicitly listed in provided materials. Grants and Funding: No specific grants mentioned.

• Integrable Models
• AdS/CFT Correspondence
• Yang-Baxter Models
• +7 more

Stijn J. van Tongeren is a theoretical physicist and Emmy Noether junior research group leader at Humboldt University, Berlin. His research focuses on integrable models within the context of the AdS/CFT correspondence, particularly exploring Yang-Baxter deformations, twist-noncommutative gauge theories, and mirror duality in string theory. He holds a PhD from Utrecht University (2013), supervised by Prof. Gleb Arutyunov, and previously conducted postdoctoral research under Prof. Matthias Staudacher in the 'Space-Time-Matter' group at Humboldt University. His work spans integrable sigma models, quantum deformations of spacetime geometries, and the application of Drinfeld twists to gauge/gravity duality. Key areas include the analysis of η-deformed AdS5×S5 superstrings, non-relativistic string actions, and the thermodynamic Bethe ansatz. His research bridges string theory, quantum field theory, and mathematical physics, emphasizing the role of exact solutions and symmetry structures. Scientific contributions include the development of quantum spectral curves for deformed backgrounds and the exploration of mirror duality in non-geometric settings. He has received the prestigious Emmy Noether grant to lead a research group investigating integrable deformations and their implications for AdS/CFT. His collaborative work often addresses foundational questions in string theory dualities and integrability preservation under deformations.

Hans VandierendonckView profile

Professor

• High-Performance Computing
• Runtime Systems
• Compilation
• +7 more

Hans Vandierendonck is a Professor in High-Performance and Data-Intensive Computing at Queen’s University Belfast’s School of Electronics, Electrical Engineering and Computer Science. His research focuses on compilers, runtime systems, and architectures for parallel systems, with special attention to programmability, cache architecture, and performance evaluation. He has authored over 150 publications and supervised multiple PhD students, including Jiawen Sun, a finalist in the EPSRC Connected Nation Pioneers competition. His work spans compiler optimization, graph processing, transprecision computing, and blockchain technology. He is a Senior Member of IEEE and ACM, an IEEE Computer Society Distinguished Contributor (2023), and a Fellow of the Higher Education Academy. He has held visiting roles at FORTH, INRIA, and the Universitat Politècnica de Catalunya. His projects include the RELAX Doctoral Network and the Kelvin Living Lab for Net Zero HPC. He teaches courses on energy-efficient computing, concurrent programming, and research projects at both BSc and MSc levels. Key research interests include runtime systems, high-performance computing, energy efficiency, and scalable algorithms for data analytics. His awards include the IBM Belgium Prize (2000, 2004) and Jozef Plateau Prize (2000). He actively reviews for top journals and conferences, including ISCA, SC, and ICS. As part of the LINAS network, he explores interdisciplinary algorithmic solutions for societal impact. His current projects address challenges in graph processing, edge computing, and sustainable wearable systems. Collaborations span academia and industry, reflecting his commitment to bridging theoretical and applied computing.

• Artificial Intelligence
• Deep Learning
• Image Analysis
• +9 more

Lia Morra is a fixed-term Researcher at the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin, where she is also a member of the SmartData@PoliTO – Big Data and Data Science Laboratory. She is actively involved in research, teaching, and editorial roles, focusing on artificial intelligence and its applications in vision and data science. She teaches in the College of Computer, Film and Mechatronics Engineering and the College of Management and Production Engineering. Research Interests: Her work centers on artificial intelligence, deep learning, image analysis, and neural-symbolic integration. She applies these techniques to medical imaging, satellite data, autonomous systems, and human-computer interaction. Her research bridges symbolic reasoning with neural networks, emphasizing explainability, robustness, and real-world deployment. Publication Trends: Her recent publications (2023–2025) show a strong focus on neuro-symbolic AI, explainable models, medical imaging, and efficient deep learning. She frequently publishes in top-tier journals and conferences such as Medical Image Analysis , IEEE ACCESS , and IJCNN, with applications in healthcare, autonomous driving, and privacy-preserving AI. Scientific Awards and Recognition: Open badge RIMINI SCHOOL 2025 “SUCCESSFULLY PARTICIPATE IN CALLS FOR RESEARCH FUNDING” Learning to Teach (L2T) Advising and Grants: She supervises multiple PhD students in Computer and Systems Engineering, including Pietro Basci, Rosario Milazzo, Alessandro Sebastian Russo, and Luca Piano. She is the Scientific Responsible for several research projects, including EU-funded CAMOUFLAGE and multiple commercial contracts on AI for medical imaging, satellite data, and digital transformation. She also serves on editorial boards for Frontiers in Medical Engineering and IEEE Consumer Electronics Magazine . Labs and Teams: She is a core member of the GRAINS (GRAphics and INtelligent Systems) research group at DAUIN, focusing on intelligent systems, computer vision, and data science.

Federico SicilianoView profile

Researcher

• Recommender Systems
• Information Retrieval
• Explainable Artificial Intelligence
• +5 more

Federico Siciliano is a Postdoctoral Researcher and Ricercatore (Researcher) at Sapienza University of Rome, affiliated with the RSTLess Lab under the supervision of Prof. Fabrizio Silvestri. He has been teaching Algorithmic Methods of Data Mining in the Master’s program in Data Science since 2024. His research spans recommender systems, information retrieval, explainable AI, and deep learning theory. Education: PhD in Data Science, Sapienza University of Rome (2020–2024) Master’s in Data Science, Sapienza University of Rome (2017–2019) Bachelor’s in Management Statistics, Sapienza University of Rome (2014–2017) His research interests lie at the intersection of Artificial Intelligence and Data Science , particularly focusing on sequential and graph-based recommender systems , robustness and explainability in AI models , and theoretical foundations of deep learning . His recent work explores architectural improvements in RAG systems, anomaly detection in 5G networks, and trustworthy AI through counterfactual explanations. The most recent publications (2023–2025) reveal a strong trend toward robust and interpretable AI systems , with recurring themes in recommendation , retrieval , and security . His work combines theoretical rigor with practical applications in healthcare, telecommunications, and misinformation detection. He frequently publishes in high-impact venues such as IEEE Access, ACM RecSys, and CVPR. Scientific Collaborations: University of Cambridge Meta Amazon University of Pisa (UniPi) INAF (Italian National Institute of Astrophysics) Federico actively contributes to the research community by organizing a special session on Neural Methods for IR and RecSys at IJCNN 2025. He has been involved in projects related to space weather monitoring and clinical outcome prediction in thyroid cancer. No formal students or grants are explicitly mentioned, but his collaborative projects suggest involvement in externally funded research. Laboratories and Teams: RSTLess Lab, Sapienza University of Rome