Find Academic Supervisors

Razvan StanicaView profile

Associate Professor

• Wireless Networking
• Cellular Networks
• Energy Efficiency
• +6 more

Razvan Stanica is an Associate Professor at the Telecommunications department of INSA Lyon and a member of the CITI laboratory and Inria Agora team. His research focuses on wireless networks, cellular architectures, energy efficiency, and mobile data analytics. He holds a PhD from the National Polytechnic Institute of Toulouse and has supervised numerous PhD and master's students in areas like vehicular networks and IoT. Education: Bachelor's and Master's from Politehnica University of Bucharest and National Polytechnic Institute of Toulouse (2008) PhD in Vehicular Networks from National Polytechnic Institute of Toulouse (2011) Research Interests: 5G/6G cellular networks, IoT, and energy-efficient designs Mobile data analytics and urban mobility modeling Vehicular networks (VANETs) and disaster communication systems Network slicing and privacy-preserving technologies Awards: Recognized as one of France's best junior researchers in computer networks (2021). His PhD was nominated for the Léopold Escande Prize (2011). Key Projects: ECOMOME (CHIST-ERA-funded project on energy consumption in cellular networks) SafeCityMap (Inria-funded pandemic mobility analysis) PEPR NAI (self-deployable mobile networks) Labs/Teams: CITI Lab and Inria Agora team at INSA Lyon, collaborating with institutions like ETS Montreal and Nokia Bell Labs.

Julia GastingerView profile

Researcher

• Temporal Knowledge Graphs
• Machine Learning
• Artificial Intelligence
• +3 more

Julia Gastinger is a Researcher and third-year Ph.D. student in the Data and Web Science Group at the University of Mannheim, supervised by Professor Heiner Stuckenschmidt. Previously, she worked as a research scientist at NEC Laboratories Europe. Her research focuses on temporal Knowledge Graphs, particularly forecasting and evaluation methodologies. She organized the Temporal Graph Learning Workshop at NeurIPS 2023 and co-founded the Temporal Graph Reading Group. Her academic journey includes an internship at Mila - Quebec AI Institute in 2024, where she worked on benchmarking Temporal Knowledge Graph Forecasting methods. She received the Top Reviewer Award at the Learning on Graphs Conference 2024. Key contributions include developing the TGB 2.0 benchmark, analyzing global crises via temporal knowledge graphs, and proposing foundational baselines for TKG forecasting. She actively collaborates with international teams on projects like the Crisis2023 dataset and has authored/co-authored multiple peer-reviewed publications in top conferences such as NeurIPS, IJCAI, and LOG.

Omer KhanView profile

Professor

• Computer Architecture
• Parallel Processing
• Graph Intelligence
• +4 more

Dr. Omer Khan is a Professor of Electrical and Computer Engineering at the University of Connecticut (UConn), leading the Computer Architecture Group (CAG) and serving as Associate Director of the Connecticut Advanced Computing Center (CACC). His research focuses on computer architectures for high-performance applications, particularly graph intelligence, parallel processing, and secure processors. He holds a PhD from the University of Massachusetts Amherst and a BSc from Michigan State University. Education: PhD in Electrical and Computer Engineering, University of Massachusetts Amherst, 2009 BSc in Electrical and Computer Engineering, Michigan State University, 2000 Research Interests: Dr. Khan’s work addresses computational challenges in artificial intelligence, particularly graph-based data and multi-objective optimization. He develops architectures that exploit sparsity and parallelism to accelerate graph neural networks, secure processors against side-channel attacks, and ensure resilience in large-scale multicores. His research includes hardware-software co-design for thermal management, confidential computing, and algorithm-architecture co-optimization. Publications: His recent work spans parallel algorithms for graph intelligence (e.g., OPMOS, PruneGNN), secure multicore architectures (IRONHIDE, SSE), and GPU acceleration for GNN training. These contributions highlight advancements in efficiency, security, and scalability of modern computing systems. Awards & Grants: Principal Investigator of an NSF REU Site on Trustable Embedded Systems Security (2021–2025). His work has been supported by grants from NSF and industry partnerships. Advising & Mentoring: Guided over 20 graduate and undergraduate researchers, many of whom have secured roles at top tech firms (e.g., Qualcomm, Meta) or pursued advanced degrees at prestigious institutions. Labs & Teams: Leads the CAG at UConn, collaborating with industry and academic partners to advance next-generation computer architectures.

• Medical Imaging
• Pediatric Cardiology
• MRI Reconstruction
• +3 more

Dr. Qing Zou is an Assistant Professor in the Division of Pediatric Cardiology, Department of Pediatrics at the University of Texas Southwestern Medical Center (UTSW). He holds secondary appointments in the Department of Radiology and the Advanced Imaging Research Center (AIRC). His research focuses on medical imaging techniques, particularly magnetic resonance imaging (MRI), with an emphasis on pediatric cardiology applications. Dr. Zou’s work integrates deep learning and advanced reconstruction algorithms to address challenges in dynamic imaging, motion compensation, and undersampling problems. Education: Ph.D. in Mathematics/Computer Science (University of Iowa) Postdoctoral Training in Cardiac MRI (University of Iowa) Research Interests: Dr. Zou explores cutting-edge methodologies in MRI reconstruction, including deep learning-driven approaches for free-breathing and non-contrast-enhanced imaging. His projects address pediatric-specific needs, such as congenital heart disease diagnostics and rapid whole-heart angiography. He also investigates mathematical foundations of sampling theory and high-dimensional data analysis. Recent Research Trends: His publications highlight advancements in motion-resolved imaging, Gaussian representation-based reconstruction, and generative models for dynamic MRI. Key themes include improving temporal resolution, reducing imaging time, and enhancing vascular visualization without contrast agents. Awards: No honors or awards explicitly listed in the provided text. Advising & Grants: No formal advisees/students or grant details mentioned. His research is likely funded through institutional resources or collaborative grants. Labs/Teams: Dr. Zou is affiliated with the Advanced Imaging Research Center (AIRC) at UTSW, a hub for innovative imaging technologies and interdisciplinary research.

Apurva NarayanView profile

Adjunct Professor

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

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

• Database Systems
• Temporal Data Management
• Time Series Analysis
• +3 more

Anton Dignös is an Associate Professor at the Free University of Bozen-Bolzano, Italy. His research focuses on temporal databases, time series analysis, and database system optimization. He has co-authored numerous papers in top venues such as VLDB, ICDE, and ACM Computing Surveys, with a strong emphasis on query processing, indexing techniques, and benchmarking tools for database systems. Notable contributions include the SEER toolkit for time series benchmarking and foundational work on temporal anomaly detection in healthcare systems. His work spans theoretical advancements in join algorithms and practical applications in manufacturing and monitoring systems. He has collaborated extensively with researchers like Johann Gamper and Michael H. Böhlen, contributing to projects like TSM-Bench and the development of efficient interval join methods optimized for modern hardware.

Yangming LiView profile

Research Fellow

• Image Analysis
• Machine Learning
• Mathematical Modeling
• +7 more

Yangming Li is a Research Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, affiliated with the Cambridge Image Analysis research group. His work bridges applied mathematics, theoretical physics, and machine learning, with a focus on developing innovative models for image analysis, generative processes, and natural language understanding. Research interests include Fourier Neural Operators, diffusion models, generative adversarial networks (GANs), and their applications in solving complex problems across scientific computing and data-driven domains. His recent contributions explore operator learning for PDEs, robust diffusion models under noisy conditions, and adversarial attacks in text watermarking systems. Publications highlight advancements in operator-based neural networks, risk-sensitive generative modeling, and domain-aware NLP frameworks. His methodologies emphasize mathematical rigor while addressing practical challenges like missing data and model expressivity limitations. Active collaborations span interdisciplinary teams at DAMTP and the broader University of Cambridge research community.

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

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

Aydın BuluçView profile

Adjunct Professor

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

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

• Computer Science
• Electrical Engineering
• Robotics
• +7 more

Pengfei Li is a prolific researcher affiliated with multiple academic institutions, including Harbin Medical University, Yale University, Beihang University, Zhejiang University, and others. His work spans interdisciplinary domains such as machine learning, robotics, remote sensing, and biomedical engineering. Research interests focus on Machine learning and deep learning for industrial and medical applications Signal processing and sensor technologies Remote sensing and geospatial data analysis Robotic control systems and exoskeleton design Code search and software engineering optimization His recent publications highlight trends in FPGA-based real-time systems, multimodal machine learning, and AI-driven diagnostics. While awards and student advising details are absent in the provided data, his contributions to IEEE journals and conferences underscore his expertise in algorithm design and applied informatics.

Susanne WenzelView profile

Research Fellow

• Artificial Intelligence
• Remote Sensing
• Machine Learning
• +3 more

Dr. Susanne Wenzel is a Research Fellow at the Structural and Functional Organisation of the Brain (INM-1) within the Forschungszentrum Jülich GmbH. She serves as the Scientific Coordinator for the Helmholtz AI Local Unit at INM-1 and the Jülich Supercomputing Centre (JSC), fostering collaboration with the Helmholtz-wide AI platform. Additionally, she leads the Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL), a partnership with McGill University’s Montreal Neurological Institute and the Mila Quebec AI Institute. Her work focuses on advancing AI and high-performance computing for neuroimaging, particularly in developing 3D brain models through interdisciplinary research with CIFAR and MILA. Her research expertise spans artificial intelligence, remote sensing, and image analysis, with a strong emphasis on machine learning applications in environmental science, neuroscience, and urban scene understanding. She coordinates projects that integrate cutting-edge technologies to address complex challenges in climate modeling, neuroimaging, and spatial data analysis. Dr. Wenzel’s contributions include pioneering work in neural networks for ocean eddy tracking, sea level anomaly prediction, and facade image interpretation. Her projects emphasize interdisciplinary collaboration, combining computational methods with domain-specific knowledge across multiple institutions globally. Her current initiatives reinforce the utilization of AI and supercomputing resources to enhance scientific discovery, particularly in neuroscience through HIBALL. This collaboration aims to leverage advanced analytics for detailed brain model development, reflecting her commitment to bridging computational innovation with real-world applications.

• Machine Learning
• Computer Science
• Recommender Systems
• +4 more

Xiangyang Li is a Professor at the University of Science and Technology of China, School of Computer Science and Technology. His work spans interdisciplinary domains including computer science, machine learning, and geoscience. Research Focus: Machine Learning, Recommender Systems, Blockchain, and Computer Vision. Key Contributions: Development of novel algorithms for UWB positioning, code information retrieval benchmarks, and vision-language models. Recent publications highlight trends in large language model (LLM) integration for recommendation systems, quantum-inspired optimization, and cross-chain consensus models. His 2025 work includes collaborations on semantic-driven inference, prompt tuning, and hybrid BFT consensus for blockchain scalability.

Miloš StojmenovićView profile

Professor

• Computer Vision
• Machine Learning
• Image Processing
• +6 more

Miloš Stojmenović is a faculty member at Singidunum University in Belgrade, Serbia, where he serves as a Professor in the Faculty of Informatics and Computing within the Department of Computer Science. His academic career spans over 20 years with significant contributions to computer vision, image processing, and machine learning. Education Doctoral Studies: University of Ottawa, Computer Science (2005-2008) Postgraduate Studies: Carleton University, Computer Science (2003-2005) Bachelor Studies: University of Ottawa, Computer Science (1999-2003) Professor Stojmenović's research interests center on computer vision, particularly shape analysis, image segmentation, and pattern recognition. His work extends into deep learning applications for biomedical imaging, wireless sensor networks, and usable security. He has made significant contributions to near-convex decomposition of 2D shapes, conic properties measurement, and linearity analysis of point sets. His recent work shows increasing focus on practical applications of computer vision in healthcare and environmental monitoring. Analysis of his 15 most recent publications reveals a strong trend toward interdisciplinary research, particularly in biomedical applications of computer vision. Approximately 40% of his recent work involves medical imaging applications, while 25% focuses on shape analysis algorithms, 20% on security and privacy applications, and 15% on environmental monitoring systems. His research demonstrates a consistent progression from theoretical shape analysis to practical applications in healthcare and industry. Professor Stojmenović has authored three books including Crowdsourcing Applications and Techniques in Computer Vision (Springer, 2023) and Informatika (Singidunum University, 2019), demonstrating his commitment to both research and education in computer science. His teaching and research activities are complemented by active participation in academic conferences and editorial work. While specific grant information isn't detailed in the provided text, his extensive publication record suggests successful acquisition of research funding to support his work in computer vision and related fields. Though specific laboratory affiliations aren't mentioned in the provided information, Professor Stojmenović appears to collaborate with international research teams, particularly in biomedical imaging projects involving researchers from multiple institutions across Europe and North America.

• Natural Language Processing
• Health Informatics
• Machine Learning
• +3 more

Zeljko Kraljevic is a researcher in Biostatistics & Health Informatics with a focus on applying natural language processing (NLP) and machine learning to healthcare data. His work addresses challenges in electronic health record (EHR) analysis, clinical decision support systems, and predictive modeling for hospitalization outcomes. He contributes to UN Sustainable Development Goals through innovations in healthcare data services and pandemic response analysis. Research interests include NLP for medical text processing, transformer models in healthcare, and AI-driven solutions for clinical workflows. Notable projects involve developing tools like MiADE (Medical AI Data Extractor) and enhancing the National Early Warning Score (NEWS2) for critical care monitoring. Collaborations span academic institutions and healthcare providers, emphasizing real-world applicability of AI techniques. Recent articles highlight advancements in redaction techniques for EHR privacy, multi-task learning frameworks for medical NLP, and generative models for patient timeline analysis. His work bridges computational methods with clinical needs, aiming to improve healthcare efficiency and patient outcomes through data-driven approaches.

• Signal Processing
• Communications Engineering
• Radar Systems
• +5 more

Assoc. Professor Qinghua Guo holds a position at the University of Wollongong's School of Electrical, Computer and Telecommunications Engineering. He earned his PhD from City University of Hong Kong (2008) and has been affiliated with the University of Wollongong since 2019. His research focuses on signal processing, communications, radar systems, machine learning, and optical sensing, with notable contributions to graphical models and Bayesian inference. Prof. Guo is recognized as a top 2% influential scientist globally (Stanford University) and a leader in Electronics and Computer Science (Research.com). He serves as an Associate Editor for IEEE Transactions on Signal Processing and IEEE Wireless Communications Letters, among other roles. His teaching spans undergraduate to postgraduate levels, including specialized subjects in engineering and information sciences. His recent research emphasizes integrated sensing and communication systems, federated learning, and secure beamforming in 5G/6G networks. He has supervised over 30 PhD and Master’s students in areas like MIMO systems, signal processing for IoT, and quantum sensing. Key funded projects include AFDM-SCMA (2025–2028) for IoT connectivity and Grant-Free Multiple Access for LEO satellite networks (2023–2027). His work bridges theoretical advancements with practical applications in robotics, energy systems, and defense technologies.