Find Academic Supervisors

• Networking
• Distributed Systems
• Network Security
• +3 more

Siegmar Sommer is a Researcher at the Humboldt University of Berlin , affiliated with the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . He has worked at the university since 1986 in various technical and scientific roles. Dipl.-Ing. in Computer Engineering (1982) Dr.-Ing. in Computer Science (1986) Postgraduate studies in Higher Education (1990) His research focuses on networking , distributed systems , and network security , with historical contributions to optical information transmission , LAN protocols , and security-relevant systems . His work bridges hardware-software co-design and educational theory in technical disciplines. The 1980s-1990s articles highlight expertise in LAN architecture, optical communication, and security frameworks. Keywords span Networking , Operating Systems , and Education , with subfields like Fiber Optic Networks , CSMA/CD Protocols , and Higher Education Curriculum . He contributed to the Computer Engineering Group and holds a patent for " Bus Access Method for a Local Computer Network " (1986). His teaching legacy includes courses on programming languages (BASIC, PASCAL, PEARL), computer graphics, and modern topics like Wireless Communication Systems and Reliable Distributed Systems . Contact: Email: sommer@informatik.hu-berlin.de Phone: +49 30 2093-41256 Office: Room IV.303, Rudower Chaussee 25, Berlin-Adlershof

Ondrej VaculinView profile

Professor

• Passive Vehicle Safety
• Automated Driving
• Vehicle Dynamics
• +3 more

Prof. Ondrej Vaculin, Ph.D., is a Professor at Technische Hochschule Ingolstadt (THI), specializing in passive vehicle safety, mechatronic systems, and automated driving. He joined THI in 2018 and previously held roles at TÜV SÜD Prague (2008–2018) as Vice President for Safety and Security in Automotive, and as a researcher at Czech Technical University (2005–2008) and DLR Oberpfaffenhofen (2000–2005). His research focuses on enhancing vehicle safety through advanced simulation, sensor integration, and machine learning applications. Education: Ph.D. in Mechanics of Solids, Deformable Bodies and Continua from Czech Technical University in Prague (2001), and a degree in Technical Cybernetics from the Faculty of Electrical Engineering at the same university (1991). His work bridges theoretical research and practical implementation, addressing challenges in automated driving systems, crash simulation, and human body diversity in safety design. Research interests include passive safety systems, autonomous vehicle dynamics, and infrastructure integration for safety enhancements. Recent publications emphasize collaborative smart infrastructure, sensor fusion for free space detection, and machine learning in crash detection. He actively contributes to FISITA, serving as a Council Delegate and member of technical committees. Awards/memberships: Active in FISITA, holding leadership roles in technical committees. No explicit awards listed, though his contributions to automotive safety standards are notable. Grants and advising: While no specific grants or students are detailed, his extensive industry and academic collaborations (e.g., IN2Lab testing field projects) highlight his role in applied research and development.

• Distributed Systems
• Fault-Tolerant Computing
• Asynchronous Algorithms
• +3 more

Prof. Ulrich Schmid is a full professor at TU Wien, affiliated with the Forschungsbereich Embedded Computing Systems. His research focuses on distributed systems, fault-tolerant computing, and asynchronous algorithms. He leads projects like 'Asynchronous Distributed Algorithms in the Theta-Model' and 'Gracefully Degrading Agreement in Directed Dynamic Networks.' Prof. Schmid has received honors at TU Wien and actively supervises numerous students in PhD and Master's programs. His academic work spans theoretical foundations (e.g., epistemic logic in Byzantine systems) and practical applications (e.g., digital circuit delay modeling). Key contributions include hybrid delay models for integrated circuits and formal verification frameworks for timed circuits. Collaborations involve institutions like the Institut für Technische Informatik and the E191-02 research group. Notable research areas: Byzantine fault tolerance, consensus protocols, and embedded systems Leadership in 4 major projects addressing distributed computing challenges Recipient of TU Wien honors (2021) Prof. Schmid's lab emphasizes bridging theoretical computer science with hardware-software co-design, producing over 220 publications and 10+ supervised theses since 2000.

Marinko SarunicView profile

Adjunct Professor

• Biomedical Imaging
• Optical Coherence Tomography
• Microscopy
• +2 more

Marinko Sarunic is an Adjunct Professor at the School of Engineering Science , Simon Fraser University . He holds a PhD in Biomedical Engineering from Duke University and has been recognized as a Michael Smith Foundation for Health Research Scholar . His research focuses on biomedical imaging , particularly optical coherence tomography (OCT) , microscopy , and low-coherence interferometry , with applications in diabetic retinopathy , Alzheimer’s disease , and age-related macular degeneration . Dr. Sarunic's work spans adaptive optics , deep learning , and sensorless OCT systems , emphasizing clinical translation and open-source software development (e.g., OCTAVA ). His Google Scholar publications highlight multimodal imaging , vascular heterogeneity analysis , and AI-driven diagnostics for retinal diseases. His contributions include the Michael Smith Foundation for Health Research Scholar award. Though not currently teaching courses, his collaborations and leadership in retinal imaging and medical device innovation are pivotal for advancing non-invasive diagnostics in neurodegenerative and diabetic conditions .

Zhen XieView profile

Assistant Professor

• High-Performance Computing (HPC)
• Machine Learning
• Parallel Computing
• +3 more

Zhen Xie is an Assistant Professor in the Department of Computer Science at Binghamton University (SUNY), serving as Director of the Parallel Computing and Intelligent System (PCIS) Lab. He holds a PhD from the Chinese Academy of Sciences and a BA from Wuhan University of Technology. His research focuses on high-performance computing (HPC), machine learning, and their intersections, particularly optimizing performance for HPC and AI/DL applications across heterogeneous architectures. Research Highlights: Dr. Xie’s work emphasizes system-level performance optimization for ML and HPC, including GPU acceleration, memory optimization, and AI accelerator selection. His team has won the ACM Gordon Bell Special Prize (2022) for their GenSLMs project predicting SARS-CoV-2 evolution. Recent grants include a 2024 gift from OpenAI for AI testbed initiatives. Awards: ACM Gordon Bell Special Prize (2022), Impact Argonne Awards (2023) Lab: PCIS Lab explores middleware for parallel computing, targeting scientific simulations and big data analytics. Collaborations include Argonne National Lab and Lawrence Berkeley National Lab. Teaching: Teaches Distributed Systems (CS 457/557) and oversees independent studies. Previously trained researchers at Argonne’s ATPESC program. Grants & Collaborations: Subcontract with Lawrence Berkeley Lab (HEVI-LOAD), Argonne testbed expeditions, and OpenAI-funded projects. Active in DOE labs like Summit and Aurora supercomputers.

• Wireless Communications
• MIMO Systems
• Neuromorphic Computing
• +4 more

Dr. Juan Carlos De Luna Ducoing is a Research Fellow at the University of Surrey, specializing in advanced wireless communication systems with a focus on MIMO (Multiple-Input Multiple-Output) technologies. His work integrates neuromorphic computing, quantum annealing, and non-linear processing to enhance the efficiency and scalability of next-generation wireless networks. Research Interests: MU-MIMO detection and precoding Neuromorphic computing applications Quantum computing in wireless systems 6G network architectures Non-linear signal processing Hardware-software co-design (e.g., SWORD platform) Key Publications: His recent work includes NeuroMIMO (2024), which explores neuromorphic principles for power-efficient MU-MIMO detection, and Scalable MU-MIMO User Scheduling (2023), addressing resource allocation in dense networks. He also contributed to quantum annealing-based detection (2022) and Gyre Precoding (2021), achieving significant SNR gains. Collaborations: Works closely with Konstantinos Nikitopoulos and the SWORD research team to develop open-source platforms for rapid prototyping of advanced communication systems.

Steven EppingerView profile

Professor

• Product Design and Development
• Complex Technical Project Management
• Agile Software Development
• +5 more

Steven Eppinger is a Professor of Management Science and Innovation at the MIT Sloan School of Management, holding the General Motors Leaders for Global Operations Chair. He focuses on interdisciplinary education and research in product design, systems engineering, and agile methodologies. His work bridges engineering and management, emphasizing complex project management and innovation processes. Educated at MIT (SB, SM, ScD in Mechanical Engineering), he has led major programs like SDM, IDM, LGO, and LFM. He co-authored Product Design and Development (7th/8th editions) and pioneered the Design Structure Matrix (DSM) methods. His research applies agile practices beyond software to hardware and systems engineering. He has received numerous awards, including the PICMET Medal of Excellence and POMS Distinguished Fellow. His executive courses cover systematic innovation and managing technical projects using DSM and agile frameworks. He co-directed the Center for Innovation in Product Development and served as Deputy Dean of MIT Sloan.

Neil BergmannView profile

Professor

• Wireless Sensor Networks
• Internet of Things (IoT)
• Embedded Systems
• +6 more

Neil Bergmann is an Honorary Professor at the School of Electrical Engineering & Computer Science, University of Queensland. His research focuses on wireless sensor networks, IoT, embedded systems, and machine learning with applications in cybersecurity, energy efficiency, and industrial monitoring. He has contributed extensively to fields like cryptographic acceleration, localization systems, and sensor network optimization. His work bridges hardware-software co-design and practical implementations in domains such as healthcare and industrial automation. His academic career includes significant contributions to journals and conferences, with a focus on advancing technologies like energy harvesting, real-time data processing, and secure communication protocols. Collaborations span academia and industry, addressing challenges in sensor networks, embedded systems, and smart environments. Notable research includes energy-efficient machine fault diagnosis using industrial wireless sensor networks and secure authentication systems leveraging kinetic energy harvesting. His publications highlight interdisciplinary approaches to solving complex technical problems, emphasizing practical applicability and innovation.

Hoda MalekiView profile

Assistant Professor

• Cybersecurity
• Blockchain Technology
• Digital Forensics
• +4 more

Hoda Maleki is an Assistant Professor in the Department of Computer & Cyber Sciences at Augusta University's School of Computer and Cyber Sciences. She holds a Ph.D. (2019) and an M.S. in Computer and Information Sciences from the University of Connecticut. Her research focuses on cybersecurity, blockchain technology, digital forensics, and secure access control systems. She has contributed to frameworks like ForensiBlock, exploring blockchain for data provenance and auditability, and has published extensively on topics including machine learning in healthcare cybersecurity and multi-dimensional encryption techniques. Dr. Maleki serves on Augusta University committees including the Faculty Affair Committee and the MS Cybersecurity degree development workgroup. She advises the Women in Cybersecurity Club and the VIVID team. Her professional service includes roles as a grant proposal reviewer for NSF and CICI-NSF, and committee membership for conferences like CCSW'24 and IPDPS. Her recent work emphasizes integrating blockchain with AI for secure systems, such as DLACB (Deep Learning-Based Access Control using Blockchain) and ForensiBlock. Awards include the 2023 Faculty Inclusive Excellence Initiative and 2022 Faculty Recruitment and Retention honors. She teaches advanced courses on cybersecurity topics like secure pollbook systems and cryptographic protocols.

Chunming QiaoView profile

Professor

• Cyber-Physical Systems
• Optical Networks
• Wireless Networks
• +6 more

Dr. Chunming Qiao is a SUNY Distinguished Professor and Chair of the Department of Computer Science and Engineering at the University at Buffalo (SUNY) , leading the Lab for Advanced Network Design, Evaluation and Research (LANDR) since 1993. His work spans cyber-physical systems , optical networks , and Internet of Things (IoT) , with a focus on safety, reliability, and protocol design. Education: PhD in Computer Science from the University of Pittsburgh (1993) BS in Computer Science and Engineering from the University of Science and Technology of China (1985) Dr. Qiao’s research interests combine theoretical and applied network design, including autonomous vehicles , quantum computing , and cloud services . He pioneered optical burst switching (OBS) and iCAR systems for wireless convergence, cited in BusinessWeek and Wireless Europe . His recent publications emphasize quantum networking , federated learning , and autonomous driving security , with projects on entanglement routing , edge inference optimization , and LiDAR adversarial attacks . Articles span IEEE and ACM venues , and include best paper awards . Scientific Awards: TC-CSR Distinguished Technical Achievement Award (2015) SUNY Chancellor's Award for Excellence (2013) IEEE Fellow (2009) UB Exceptional Scholar-Sustained Achievement Award (2005) Dr. Qiao has secured over two dozen NSF grants and collaborations with Google , Cisco , and NEC Labs . His 7 US patents and consulting experience highlight his industry impact, while his editorial roles and conference leadership underscore academic influence. He actively contributes to multi-disciplinary research through the New York State Center of Excellence in Bioinformatics and Life Sciences and CEDAR , advancing high-performance computing and document analysis .

Lydia Bouzar-BenlabiodView profile

Assistant Professor

• Hardware-based Machine Learning
• Privacy-Preserving Machine Learning
• Explainable AI
• +3 more

Dr. Lydia Bouzar-Benlabiod is an Assistant Professor at the Jodrey School of Computer Science, Acadia University, Nova Scotia, Canada. Her research focuses on hardware-based machine learning, privacy-preserving techniques, and explainable AI. She holds a Ph.D. in Computer Science from Université d’Artois, France, and an M.Eng from Ecole nationale Supérieure d’Information (ESI), Algiers, Algeria. Her work bridges theoretical advancements with practical applications in cybersecurity, medical imaging, and intelligent systems. Research interests include adversarial machine learning, neural architecture search, and AI-driven healthcare solutions. Her lab explores cost-effective hardware implementations for medical diagnostics and anomaly detection systems. Key projects involve NeuroMem® chip integration for breast cancer detection and RNN-VED models to reduce false positives in cybersecurity. Dr. Bouzar-Benlabiod’s publications span 2013–2024, emphasizing data-driven modeling, case-based reasoning, and AI ethics. She contributes to special issues on heuristic data science and reuse in intelligent systems. Her work is accessible via CILS Lab and personal page .

Omar AlamView profile

Associate Professor

• Software Engineering
• Model-Driven Software Engineering
• Aspect-Oriented Modelling
• +3 more

Professor Omar Alam is an Associate Professor in the Department of Computer Science at Trent University. His research focuses on software engineering, particularly Model-Driven Software Engineering, Aspect-Oriented Modelling, and Mining Software Repositories. Prior to Trent, he earned his PhD at McGill University, specializing in Model-Driven Engineering. He has received notable awards such as the ACM SIGSOFT Distinguished Paper Award (2019) and the Merit Award for Excellence in Research (2022). His teaching includes courses like Software Specification (COIS 3030), Software Design and Modeling (COIS 2240), and Software Architecture (COIS 3040). Key grants include the NSERC Discovery Grant (2017-2024) and past fellowships from NSERC and FRQNT. His work spans collaborative modeling, automated grading systems, and empirical studies on developer practices. Research often intersects software development methodologies, tool integration, and socio-technical aspects of computing. His recent publications explore topics like developer workplace communication, transit schedule deviations using real-time data, and three-way domain-specific model differencing. He actively contributes to journals like SoSyM and conferences such as SLE and ICSM. His teaching excellence is recognized through student awards and nominations, reflecting his commitment to pedagogy in computer science education.

• Digital Innovation
• Crowdsourcing
• Data-Driven Decision-Making
• +3 more

Michael Zaggl is a Full Professor at NEOMA Business School, specializing in Strategy & Entrepreneurship. He holds a Habilitation from TU Munich, a PhD from TU Hamburg, and degrees from the University of Koblenz. Previously, he served in tenured roles at Aarhus University and held visiting positions at Harvard, USC, and other institutions. His research focuses on digital innovation, crowdsourcing, and data-driven decision-making, with publications in top journals like Strategic Management Journal and Academy of Management Discoveries . He is an active member of professional organizations such as the Academy of Management and Association for Information Systems. Educations: Habilitation (TU Munich), PhD (TU Hamburg), Master's & Bachelor's (University of Koblenz). His research interests span digital innovation , crowdsourcing mechanisms , and open-source governance , with notable contributions to understanding distributed strategic search and organizational decision-making. Recent work examines crowdfunding dynamics , AI-human collaboration , and privacy calculus in mHealth . His articles analyze topics like crowd wisdom optimization and hierarchical biases in idea evaluation , reflecting a focus on practical applications for organizations. He has presented at leading conferences, including the Academy of Management and Hawaii International Conference on System Sciences.

Kyriakos DeliparaschosView profile

Researcher

• Autonomous Vehicles
• Control Systems Design
• High Performance Computing
• +6 more

Kyriakos Deliparaschos is affiliated with the Cyprus University of Technology as a Researcher. His work focuses on cutting-edge technologies in autonomous systems, control systems design, and FPGA-based implementations. He collaborates actively with institutions like the Institute of Communications and has co-authored papers with experts such as Argyrios Zolotas and Themistoklis Charalambous. His research interests span autonomous robotics, medical robotics, and high-performance computing, with a strong emphasis on real-time systems and embedded hardware-software co-design. Notable contributions include advancements in Delaunay triangulation algorithms for multi-core architectures and fault-tolerant AI systems for autonomous vehicles. Deliparaschos has published widely in journals like IEEE Access and Electronics, focusing on topics such as computational resource management in cloud environments and FPGA-driven control system paradigms. His work bridges theoretical research with practical applications in both academia and industry.

• High-performance Computing
• Energy-efficient Computing
• Machine Learning
• +3 more

Dr. Ngoc Nha Vi Tran is an Associate Professor of Computer Science at UiT The Arctic University of Norway. She holds a PhD from UiT and was a visiting scholar at Rutgers University, USA. Her research focuses on high-performance and energy-efficient computing, machine learning, and bioinformatics. She is a member of the NORA.startup Steering Group and leads the Arctic Green Computing Group. Education: PhD in Computer Science (UiT), M.Sc. in Software Engineering via Erasmus Mundus (Blekinge Institute of Technology, Sweden & Technical University of Kaiserslautern, Germany). Research interests include energy-efficient algorithms, bioinformatics tools (e.g., vCOMBAT), and applications of machine learning in healthcare and robotics. She teaches courses such as INF-2200 Computer Architecture, INF-2900 Software Engineering, and INF-2202 Concurrent Programming. Her work spans computational models for antibiotic target-binding, runtime energy optimization (REOH framework), and power models for embedded systems (RTHpower/ICE). She contributed to the EXCESS project on energy-efficient computing systems. Labs/Teams: Arctic Green Computing Group, EXCESS consortium.