Find Academic Supervisors

• Accelerator Physics
• Machine Learning
• Reinforcement Learning
• +4 more

Dr Andrea Santamaria Garcia is a Lecturer in Artificial Intelligence for Particle Accelerators at the University of Liverpool and a member of the Cockcroft Institute. Her work bridges accelerator physics with machine learning, focusing on reinforcement learning and Bayesian optimization for real-time particle accelerator control. Current Research: Reinforcement learning in autonomous accelerators, Bayesian optimization, differentiable simulations Key Projects: RL4AA collaboration, Cheetah simulation code, microbunching instability control Education: PhD in Accelerator Physics (CERN), postdoctoral work on electron light sources Her research explores the synergy between accelerator physics and machine learning, particularly through reinforcement learning and Bayesian optimization . Recent work focuses on zero-shot reinforcement learning, meta-algorithms, and deploying machine learning on hardware for low-latency beam control. She leads collaborations with KIT, DESY, and CERN, including the development of the first differentiable beam physics simulation code. Scientific highlights include the 2024 Physical Review Accelerators and Beams Editors' Choice for her work on online reinforcement learning in accelerators. Her publications demonstrate cross-disciplinary impact in Scientific Reports and Physical Review Accelerators and Beams . PhD Supervision: Chenran Xu (KIT/DESY), Luca Scomparin (real-time RL platform) Teaching Philosophy: Emphasizes precision, ethics, and interdisciplinary collaboration She collaborates with international institutions and co-founded the Reinforcement Learning for Autonomous Accelerators (RL4AA) initiative, which organizes annual workshops on cutting-edge control techniques.

Xin XingView profile

Assistant Professor

• Bioinformatics
• Metagenomics
• Single-cell analysis
• +7 more

Overview Xin Xing is an Assistant Professor in the Department of Statistics at Virginia Tech. He holds a PhD from the University of Georgia (2018), an M.S. and B.S. from the University of Science and Technology of China (2013, 2010). His research focuses on statistical methodology development in bioinformatics, machine learning, and computational biology. He has received grants including the NSF ATD (2022-2024) and NVIDIA GPU Grant (2019). Education Ph.D. – Statistics – University of Georgia (2018) M.S. – Statistics – University of Science and Technology of China (2013) B.S. – Statistics – University of Science and Technology of China (2010) Research Interests Dr. Xing's work spans three core areas: Bioinformatics: Metagenomics, single-cell analysis, epigenomics, neuroimaging. Statistics: Minimax nonparametric testing, smoothing splines, dimension reduction, controlled variable selection, causal inference. Machine Learning: Deep learning, generative models, transfer learning, neural network compression. Key Awards New Faculty Mentoring Grant, Virginia Tech (2020-2022) Best Senior PhD Student Award, University of Georgia (2018) Outstanding Dissertation Award, USTC (2010) Teaching Recent courses include CMDA 3654 (Intro to Data Analytics & Visualization) and CMDA 4654 (Deep Learning). Office: D&DS 308.

Dimitrios NikolopoulosView profile

Professor

• High-Performance Computing
• Systems Runtime Systems
• Memory Management
• +6 more

Dimitrios Nikolopoulos is the John W. Hancock Professor of Engineering at Virginia Tech's College of Engineering, within the Department of Computer Science. His research focuses on high-performance computing, systems runtime systems, memory management, and edge computing. He holds a Chartered Engineer (CEng) certification. Education: M.Eng., University of Patras, Greece (1996) M.Sc., University of Patras, Greece (1997) Ph.D., University of Patras, Greece (2000) Research Interests: His work spans transprecision computing, parallel programming paradigms, efficient inference frameworks for large language models (LLMs), and energy-efficient server ecosystems. Recent efforts emphasize optimizing edge computing systems, GPU resource sharing, and adaptive memory management in cloud-edge environments. Recent Trends in Publications: Recent studies highlight advancements in edge-serving frameworks (e.g., SLED), multi-agent systems for HPC code optimization (MARCO), and novel approaches to GPU and memory resource utilization in constrained settings. Themes include reducing latency, improving scalability, and integrating AI-driven techniques into HPC workflows. Grants & Advising: Details on current grants and advisees are not explicitly listed in the provided materials. Labs/Teams: His research is conducted through collaborative groups within Virginia Tech's Department of Computer Science, focusing on systems, parallel computing, and AI/ML infrastructure.

Kyoung-Don KangView profile

Professor

• Real-time embedded systems
• Cyber-physical systems
• Internet of Things
• +4 more

Kyoung-Don (KD) Kang is a Professor in the School of Computing at Binghamton University, State University of New York. He specializes in real-time embedded systems, cyber-physical systems, and IoT security. His research focuses on enhancing real-time data services' timeliness and power efficiency, supported by NSF grants and industry collaborations. Education: BS, MS (Kyungpook National University); MS, PhD (University of Virginia). Research Interests: Real-time embedded systems, cyber-physical systems, IoT, security, edge computing, and wireless sensor networks. His work emphasizes control-theoretic approaches, feedback mechanisms, and adaptive algorithms for real-time systems. Grants & Projects: NSF-funded projects include 'Enhancing Timeliness of Real-Time Data Services' and 'QoS-Aware Data Management.' He leads the Real-Time Embedded Systems Laboratory, developing frameworks like Chronos and RTMR. Awards: Best Paper nominations (DCOSS'19), NSF grants, and patents in surveillance systems. Over 16 MS students and 9 PhD students have graduated under his mentorship, many joining top tech firms. Labs & Teams: Director of the Real-Time Embedded Systems Lab, focusing on edge computing, real-time stream processing, and secure IoT applications. Collaborates with industry partners like Samsung and Intel.

• High-Performance Computing
• Parallel Computing
• Message Passing Interface (MPI)
• +7 more

William D. Gropp is the W. W. Grainger Chair and Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign. He holds additional professorships in Electrical and Computer Engineering, Coordinated Science Lab, School of Information Sciences, and Center for Global Studies. As Director of the National Center for Supercomputing Applications (NCSA) and the Center for Extreme-Scale Computation, he leads initiatives in high-performance computing (HPC) and computational infrastructure. His research focuses on parallel computing, MPI, quantum-centric supercomputing, and optimizing data movement on heterogeneous architectures. Gropp has contributed to seminal tools like PETSc and MPICH, advancing scalable scientific computing. Research Interests: High-Performance Computing, Parallel Algorithms, MPI, Quantum Computing Integration, Heterogeneous Architectures, and Scientific Software Libraries. His work addresses challenges in scalable systems, communication optimization, and exascale computing. Recent Contributions: Recent articles explore benchmarking tools for hierarchical networks, agent-based models, and quantum-centric supercomputing applications. His work emphasizes practical solutions for real-world HPC challenges, such as optimizing GPU performance and I/O systems. Awards & Honors: AAAS Fellow (2018) ACM-IEEE CS Ken Kennedy Award (2016) IEEE Fellow (2010) NAE Member (2010) Labs & Teams: Directs NCSA and oversees the Center for Extreme-Scale Computation. Collaborates on projects like the Delta Gateway for GPU resource access and the Quantum-centric Supercomputing initiative. Active in community-driven efforts to advance HPC standards and tools.

Maximilian R. OdenbrettView profile

Researcher

• Formal Methods
• Parallel Computing
• Model Checking
• +3 more

Maximilian R. Odenbrett was a PhD student at Eindhoven University of Technology (2010–2011), collaborating with RWTH Aachen University's MOVES group. His research focused on Efficient Multi-Core Model Checking , leveraging NVIDIA's CUDA for parallel processing on GPGPUs. He specialized in formal methods, stochastic model checking, and parallel programming for multi-core systems. Education : Diplom-Informatiker (Computer Science) from RWTH Aachen University (2004–2010), with a minor in Psychology. Research Interests : Development of parallel algorithms for formal verification, application of GPUs in bioinformatics (e.g., biological network analysis), and model-based analysis of software systems. His work bridges theoretical formal methods with practical parallel computing solutions. Publications : Focused on applying GPGPUs to model checking and bioinformatics problems, as seen in his BMC Bioinformatics paper (2012) and conference contributions on AADL specification analysis (2010). His research emphasizes scalability and efficiency in computational systems. Affiliations : Collaborated with Prof. Joost-Pieter Katoen's MOVES group at RWTH and Prof. P.A.J. Hilbers' BMI group at TU Eindhoven. His work contributed to the EMCMC project on multi-core model checking.

Michael ShahView profile

Senior Lecturer

• Computer Science Education
• Software Visualization Tools
• Performance Analysis
• +3 more

Michael Shah is a Senior Lecturer of Computer Science at Yale University's School of Engineering & Applied Science. His research focuses on software visualization tools, performance analysis frameworks, and innovative computing education methodologies. He has contributed to fields such as parallel computing, GPU execution visualization, and asynchronous programming patterns. Dr. Shah's work bridges technical software development with educational applications, including curriculum design for introductory graphics courses and teaching assistant training programs. His research emphasizes practical tool development for debugging and performance optimization in both academic and industry contexts. Notable projects include the Daisen GPU visualization framework, DrAsync for JavaScript anti-pattern detection, and the Iceberg static analysis tool for Java concurrency issues. His publications span topics from procedural content generation in games to runtime performance bug mitigation strategies.

Alexandre JoannouView profile

Researcher

• Computer Architecture
• Hardware Security
• Memory Safety
• +4 more

Dr. Alexandre Joannou is a Senior Research Software Engineer at the Department of Computer Science and Technology, University of Cambridge. His research focuses on secure hardware-software co-design, particularly in the CHERI (Capability Hardware Enhanced RISC Instructions) project. He specializes in computer architecture, memory safety, and RISC-V processors, with a focus on integrating security into low-level system components. His work spans theoretical contributions to capability-based systems and practical implementations in accelerators, GPUs, and embedded devices. Key areas include secure speculation, memory protection, and formal verification of hardware designs. Joannou's research has advanced temporal safety in heaps, compressed capabilities for efficiency, and architectural contracts to prevent vulnerabilities. His publications emphasize hardware security innovations, including CHERI-RISC-V integration, GPGPU memory protection, and randomized testing methodologies. Collaborations involve both academic and industry partners in secure computing ecosystems.

• Bioinformatics
• Health Informatics
• Affective Computing
• +3 more

Dr. Stamos Katsigiannis is an Associate Professor in the Department of Computer Science at Durham University. His research focuses on bioinformatics, health informatics, affective computing, and GPU computing. He holds a BSc, MSc, and PhD in Computer Science from the University of Athens and the Athens University of Economics and Business. Before Durham, he was a Postdoctoral Research Fellow and Lecturer at the University of the West of Scotland (2016–2020). His work spans biometric identification via EEG signals, medical image analysis, and deep learning applications in healthcare. Education: BSc (Hons.) in Informatics and Telecommunications from National and Kapodistrian University of Athens MSc in Computer Science from Athens University of Economics and Business PhD in Computer Science from National and Kapodistrian University of Athens Research interests include bioinformatics (e.g., microarray analysis), health informatics (e.g., ECG-based emotion modeling), and GPU computing for real-time applications. He has pioneered studies on EEG-based biometrics and affect recognition in human-horse interaction. Recent articles explore chest X-ray classification using deep learning , AI-generated content detection , and trajectory prediction with graph networks . His research emphasizes interdisciplinary applications of machine learning in healthcare and education. He advises six postgraduate students and collaborates on UK parliamentary AI governance reports. Notable contributions include the BED EEG dataset and the DREAMER emotion recognition database.

• Bioinformatics
• Health Informatics
• Affective Computing
• +4 more

Dr. Stamos Katsigiannis is an Associate Professor in the Department of Computer Science at Durham University. His research focuses on bioinformatics, health informatics, affective computing, machine learning, and GPU computing applications. He holds a PhD in Computer Science from the National and Kapodistrian University of Athens (Greece), with prior roles including Postdoctoral Research Fellow and Lecturer at the University of the West of Scotland (2016-2020). Education: BSc (Hons) Informatics and Telecommunications, National and Kapodistrian University of Athens (Greece) MSc Computer Science, Athens University of Economics and Business (Greece) PhD Computer Science, National and Kapodistrian University of Athens (Greece) Research Interests: Bioinformatics, health informatics, affective computing (e.g., emotion recognition using EEG/ECG signals), machine learning applications in medical imaging and video quality, GPU-accelerated algorithms for biomedical data analysis, and biometric identification systems. His work bridges computational methods with healthcare, education technologies, and human-computer interaction. Advising & Students: Supervising postgraduate students in AI-driven healthcare, computer vision, and affective computing. Recent collaborations include projects on AI-generated content detection (De-Factify 4.0), chest X-ray image analysis (CLN network), and trajectory prediction using graph neural networks. Labs/Teams: Active in Durham's AI research groups, contributing to interdisciplinary projects in medical imaging, cybersecurity, and educational technology. Collaborates internationally in EU-funded initiatives and UK parliamentary AI governance consultations (AGENCY project).

Theresa WindusView profile

Professor

• High Performance Computing
• Computational Chemistry
• Software Interoperability
• +3 more

Theresa Windus is a Distinguished Professor and Department Chair at Iowa State University's Department of Chemistry. She leads the Windus Group, which develops computational models to solve environmental and energy challenges, with research focusing on high-performance computing, software interoperability, heavy metal systems, and quantum chemistry methods. Her group specializes in massively parallel algorithms for state-of-the-art computational resources. Her research spans multiple disciplines including: Scalable ab initio Monte Carlo methods GPU implementations of quantum integrals Exascale computing development (NWChemEx project) Rare earth element extraction Aerosol nucleation modeling Her recent publications explore computational chemistry software sustainability, GPU-accelerated quantum chemistry, nanopore modeling, and rare earth element extraction. She has mentored numerous graduate students including Alex, Erin, Daniel Turley, and Zach who received prestigious scholarships.

Enyue Annie LuView profile

Professor

• High Performance Computing
• Computer and Communication Networks
• Algorithm Design and Analysis
• +5 more

Dr. Enyue (Annie) Lu is a Professor of Computer Science at Salisbury University, affiliated with the Department of Computer Science within the Richard A. Henson School of Science & Technology. She holds a Ph.D. from the University of Texas at Dallas. Her research focuses on High Performance Computing, including parallel and distributed processing, cloud computing, and GPU computing. She also explores Computer and Communication Networks, such as network security and wireless sensor networks, alongside Algorithm Design and Graph Theory applications in bioinformatics and medical imaging. Dr. Lu has led and contributed to numerous research projects, including the REU (Research Experience for Undergraduates) program, which emphasizes emerging computing in science and engineering. Her work spans interdisciplinary areas like medical imaging algorithms and stable matching problems, leveraging GPU acceleration and parallel computing techniques. She has advised over 20 students in these areas and collaborates with institutions like the University of Maryland Eastern Shore and Johns Hopkins University. Her publications emphasize practical applications of theoretical computer science, such as improving tomographic imaging and network intrusion detection through machine learning and distributed systems. Despite not listing explicit awards, her extensive contributions to undergraduate research and algorithm development highlight her scholarly impact.

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.

Andreas NüchterView profile

Professor

• Robotics
• Computer Vision
• Sensor Fusion
• +5 more

Andreas Nüchter is a Professor and Chair of Robotics at Julius-Maximilians-University Würzburg (Germany), leading the Chair of Computer Science XVII. He holds additional roles including Visiting Chair at ENSTA - Institut Polytechnique de Paris, Dean of Studies at the Institute of Computer Science, and Vice President of Zentrum für Telematik e.V. His research focuses on robotics, 3D reconstruction, SLAM, and sensor systems for autonomous systems. He has authored influential papers in IEEE Transactions on Robotics, Acta Astronautica, and robotics conferences like ICRA and IROS. Education: Not explicitly detailed in text, but inferred through academic roles. Research Interests: Robotics, computer vision, sensor fusion, autonomous systems, 3D reconstruction, and space robotics applications. His work emphasizes real-time systems, planetary exploration, and open-source software frameworks like SceneFactory and libBICOS. Recent projects include CNN-based spacecraft pose estimation, spherical mobile mapping systems, and radar SLAM (RIV-SLAM). Advising & Grants: Mentors a team including MSc/BSc students (e.g., Luca Anteunis, Fabian Arzberger). Involved in grants related to robotics, mining automation (AMADEE-24), and space exploration. Collaborates with institutions like NASA (CADRE lunar rovers) and Fraunhofer IPM. Labs/Teams: Leads the Robotics Lab at JMU, develops spherical robots for planetary missions, and maintains open-source libraries (libBICOS, SceneFactory). Active in projects like AMADEE-24 Mars simulation for human-robot interaction.

Andrea DettiView profile

Professor

• Computer Networks
• Information-Centric Networking (ICN)
• Cloud Computing
• +3 more

Andrea Detti is a Professor at the Department of Electronic Engineering, University of Rome Tor Vergata. His research focuses on computer networks, particularly Information-Centric Networking (ICN), Cloud Computing, and Software Defined Networks (SDN). He has coordinated major EU projects like Fed4IoT and BONVOYAGE, addressing IoT and Intelligent Transport Systems. He serves as Associate Editor for IEEE Transaction on Network and Service Management and has guest-edited special issues on ICN. His teaching includes courses on Internet Technologies, Mobile Wireless Networks, and Cloud Computing, emphasizing hands-on labs with Linux, Cisco routers, and OpenStack. Key projects include leadership roles in EU initiatives such as ICN2020, GreenICN (energy-efficient ICN), and OFELIA (ICN over SDN). He coordinates industrial projects with Telecom Italia and SSI/Finmeccanica on publish-subscribe paradigms in MANET and DTN. His research spans ICN scalability, adaptive video streaming, and satellite communication optimizations. Publications (80+) cover ICN architecture, SDN integration, and network performance modeling. He actively participates in conferences and standardization efforts, contributing to the evolution of future Internet architectures.