Find Academic Supervisors

• Wireless Sensor Networks
• Cyber-Physical Systems
• Network Protocols
• +3 more

John Harrison Kurunathan is an Integrated PhD Researcher affiliated with the CISTER Research Centre at the University of Porto, Portugal. He holds a PhD in Electrical and Computer Engineering (2021), a Master's in Very Large-Scale Integration (2014), and a Bachelor's in Electronics and Communication (2012). Education: PhD (2021) - University of Porto, Portugal MSc (2014) - SSN College of Engineering, Anna University BSc (2012) - SRM University His research focuses on Wireless Sensor Networks (WSNs) , Cyber-Physical Systems (CPS) , and Automotive Networks , with an emphasis on Quality-of-Service (QoS) optimization, secure communication, and vehicular platooning. Notable projects include SafeCOP for safety-related CO-CPS and work on IEEE 802.15.4e DSME networks. Recent publications (2023-2025) span areas like Visible Light Communication , Vehicular Security , and Machine Learning in UAV Operations , reflecting his interdisciplinary work bridging embedded systems and transportation technologies. Scientific Awards: Best oral communication Award (in ex aequo) at DCE 2019 Reviewing Roles: Conference: ICCPS, EWSN, MSN, RTN Journal: IEEE ACCESS, IEEE Transactions on Vehicular Technology, ACM Sigbed Harrison is actively involved in workshops and conferences, including chairing roles at WIN-WIN-4S 2024 and technical demonstrations at WoWMoM 2023. His work appears in venues like IEEE Transactions on ITS, IEEE COMST, and PDP 2025.

Hai (Helen) LiView profile

Professor

• Neuromorphic Computing
• Machine Learning
• Emerging Memory Technologies
• +3 more

Hai (Helen) Li is a Professor and Clare Boothe Luce Associate Chair at Duke University's Electrical and Computer Engineering department. She was a TUM-IAS Hans Fischer Fellow (2017) hosted by Prof. Ulf Schlichtmann in the Neuromorphic Computing focus group. Education: B.S./M.S. from Tsinghua University, Ph.D. from Purdue University Positions: Qualcomm, Intel, Seagate, Polytechnic Institute of New York University, University of Pittsburgh Her research spans neuromorphic computing systems , machine learning acceleration , emerging memory technologies , and low-power circuits . Publications demonstrate expertise in ReRAM/memristor-based accelerators, sparse neural networks, and processing-in-memory architectures. Key contributions include cross-layer optimization frameworks and robust neuromorphic designs. Her awards include: 9 Best Paper Awards (ASPDAC, ICMLA, ISVLSI, etc.) NSF Career Award DARPA Young Faculty Award IEEE Fellow (2019) ACM Distinguished Member (2017) IEEE TCSDM Outstanding Leadership Award (2021)

Kyle ChardView profile

Research Professor

• Cloud Computing
• Distributed Systems
• High Performance Computing
• +2 more

Kyle Chard is a Research Professor in the Department of Computer Science at the University of Chicago and a researcher at Argonne National Laboratory. He holds a Ph.D. in Computer Science from Victoria University of Wellington (2011) and focuses on cloud computing, distributed systems, and high-performance computing. His work emphasizes scalable data management and automation in scientific research. Education: Ph.D., Computer Science, Victoria University of Wellington, 2011 BSc (Hons) and BSc in Computer Science and Mathematics, Victoria University of Wellington Research Interests: Kyle’s research bridges computational systems and scientific domains such as biology, earth science, and astrophysics. Key areas include distributed function serving (e.g., funcX), reproducible research (Whole Tale), and cost-aware cloud computing. His interdisciplinary approach addresses challenges in data-intensive computing and research automation. Publications: Over 150+ publications in top venues like IEEE/ACM conferences, focusing on workflow systems, distributed computing, and scientific data management. Recent work explores AI-driven workflows and exascale computing. Awards: IEEE TCHPC Early Career Award (2020) R&D100 Award (Globus Team, 2019) New Zealand Top Achiever Doctoral Scholarship Grants & Leadership: NSF-funded projects on distributed computing, reproducibility, and cloud infrastructure. Co-leads Globus Labs and the CERES Center for Unstoppable Computing. Community contributions include Parsl (parallel Python), DLHub (ML model serving), and funcX (function-as-a-service). Labs & Collaborations: Globus Labs: Data management and automation CERES Center: Resilient computing systems Collaborations with NASA, DOE, and NSF initiatives

• Smart Systems
• Optimum Resource Allocation
• Massive MIMO Systems
• +13 more

Dhammika Jayalath is an Associate Professor at Queensland University of Technology (QUT) in the School of Electrical Engineering & Robotics within the Faculty of Engineering. He has been with QUT since 2007, initially as a Senior Lecturer and later promoted to Associate Professor. Prior to joining QUT, he worked as a Senior Researcher at National ICT Australia Ltd and held a Fellowship at the Australian National University. His educational background includes a PhD in Wireless Communications from Monash University and a Graduate Certificate in Higher Education from QUT. He is a Senior Member of IEEE and active in multiple IEEE societies including Communications, Signal Processing, and Vehicular Technology. Research Interests: Dhammika's research focuses on Smart Systems with particular expertise in wireless communications and networking. His work spans Physical Layer Security, Massive MIMO Systems, Internet of Things, Optimum Resource Allocation, Cooperative communications, Cognitive radio, and Vehicular communications. He has made significant contributions to 5G New Radio, Chaotic Communications, Orthogonal Frequency Division Multiplexing (OFDM), and Space-Time Signal Processing. Publication Trends: His recent publications demonstrate a strong focus on 5G/6G networking technologies, physical layer security for IoT devices, and optimization of wireless communication systems. His work bridges theoretical communications theory with practical implementation challenges, particularly in vehicular networks, secure communications, and resource allocation for heterogeneous networks. The articles show increasing interdisciplinary work, combining machine learning techniques with traditional communications engineering approaches. Scientific Awards: 2007: Early Career Academic Recruitment and Development (ECARD) award from QUT 2009: Elevated to Senior Member Grade of IEEE 2000: IEEE travel grant Multiple scholarships during graduate studies at Monash University Supervision and Grants: Professor Jayalath has supervised numerous PhD students to completion with research topics including chaotic communication systems, resource allocation in heterogeneous networks, and vehicular communication systems. He has secured multiple research grants totaling over AU $300,000, including projects from ARC, QUT internal grants, and industry partnerships with Queensland Fire and Emergency Services. His current research includes physical layer security frameworks for IoT devices and optimization of massive MIMO systems for dense mobile networks. Laboratory and Team Work: He has been instrumental in establishing wireless communications research capabilities at QUT, including securing equipment grants for Software Defined Radio platforms. His work often involves interdisciplinary collaboration with researchers in signal processing, cybersecurity, and transportation systems.

Michelle ZhuView profile

Professor

• Parallel and Distributed Computing
• Big Data Analytics
• Cloud Computing
• +4 more

Dr. Michelle Zhu is a Professor and Associate Director for Faculty and Academic Affairs at the School of Computing, Montclair State University. She previously held roles as Associate Professor and Director of Undergraduate Programs at Southern Illinois University Carbondale. Dr. Zhu holds a Ph.D. in Computer Science from Louisiana State University and a B.S. in Biomedical Engineering from Zhejiang University. Her research focuses on parallel/distributed computing, big data analytics, and high-performance networking, supported by grants from NSF, DOE, and NVIDIA. She has authored over 150 peer-reviewed publications. Education: Ph.D., Computer Science, Louisiana State University (2005) M.Sc., Computer Science, Louisiana State University (2002) B.S., Biomedical Engineering, Zhejiang University (1996) Her research interests span parallel computing architectures, cloud workflow scheduling, and cybersecurity. She has led initiatives integrating computational thinking into STEM education and developed robotics-based learning tools. Her work has been funded through NSF grants such as the $1.1M "Assimilating Computational and Mathematical Thinking into Earth and Environmental Science" project (2017–2022). Dr. Zhu’s articles explore topics like blockchain-based cloud security, GPU-accelerated Gibbs sampling, and edge computing deployment strategies. She actively contributes to academic governance, serving on Montclair State’s Middle States accreditation committee and the University Academic Assessment Council. Key Grants: NSF MRI: Multimodal Collaborative Robot System (MCROS), $321,737 (2021–2024) DOE: Scalable Application Support Platform for E-Sciences, $389,398 (2009–2013) Service Roles: Curriculum Committee Chair, Computer Science Department Blue Ribbon Task Force for Gen Ed Redesign (2019–2020) She collaborates on robotics projects like MCROS and leads outreach efforts to engage pre-university communities in AI and robotics education.

• Reliable Hardware Design
• Electronic Design Automation
• Edge Computing
• +4 more

Dr. Abusaleh Jabir is a University Reader at the School of Engineering, Computing and Mathematics, Oxford Brookes University. He holds a DPhil in Computing from the University of Oxford and leads the Advanced Reliable Computer Systems (ARCoS) group. His research focuses on reliable hardware design, memristive nanotechnology, edge computing, and secure authentication systems. He has over 80 peer-reviewed publications and multiple patents, including innovations in error-tolerant circuits and memristive architectures. **Education**: DPhil in Computing (University of Oxford). **Research Interests**: Reliable hardware design, electronic design automation, sensing at the edge, physical uncloneable authentication, and emerging memristor technologies. His work addresses challenges in IoT, edge computing, and cybersecurity through innovative electronic systems. **Funding & Projects**: Current projects include the Leverhulme Trust-funded MONITOR gas sensor array initiative. His research has been supported by the UK Ministry of Defence, EPSRC, and Finance South East. **Awards & Patents**: Multiple patents granted, including EU 17706875.6 (memristive logic) and GB 1914221.5 (reconfigurable memristive logic). Recognized with best paper awards. **Advising & Impact**: Supervised PhD students now leading semiconductor and automotive industries (e.g., Infineon Technologies, Continental Teves AG). Collaborates with academic and industrial partners globally. **Labs & Teams**: Leads the ARCoS group within the Artificial Intelligence, Data Analysis and Systems (AIDAS) Institute, fostering interdisciplinary innovation in secure and reliable electronics.

• Seismology
• Earthquake Early Warning Systems
• Volcanology
• +3 more

Frédérick Massin is a researcher at ETH Zürich's Swiss Seismological Service (SED), specializing in seismology, earthquake early warning systems, and volcanic monitoring. He is affiliated with the Bedretto Underground Lab (BULGG) and contributes to global seismic research. His work focuses on developing advanced algorithms for rapid earthquake rupture characterization (e.g., FinDer), improving earthquake early warning (EEW) systems in regions like Central America and New Zealand, and analyzing seismic data to enhance disaster preparedness. Key research areas include: Earthquake Source Physics and Real-Time Modeling Seismic Data Center Innovation and Global Networks Volcanic Seismicity and Lahar Detection Socio-Technical Aspects of EEW Systems Recent projects involve assessing EEW impacts in Central America, improving seismic catalogues (FEAR-1), and investigating lake ice seismicity. He collaborates internationally on initiatives such as the Nepal School Seismology Network and Yellowstone volcano-tectonic studies.

Xiang SunView profile

Assistant Professor

• Mobile Edge Computing
• Internet of Things (IoT)
• Drone-assisted Mobile Access Networks
• +5 more

Dr. Xiang Sun is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of New Mexico (UNM). He holds a Ph.D. in Electrical Engineering from New Jersey Institute of Technology (2018), and M.E. and B.E. degrees from Hebei University of Engineering (2011 and 2008). His research focuses on Mobile Edge Computing, IoT, Drone-assisted Networks, Green Computing, and Data Center Optimization. Dr. Sun has been recognized with awards such as the 2018 InterDigital Innovation Award and NJIT Hashimoto Prize. Education: Ph.D., Electrical Engineering, NJIT (2018) M.E., Computer Applications Technology, Hebei University (2011) B.E., Electronic Information, Hebei University (2008) Research Interests: His work emphasizes IoT resource caching, semantic discovery, green communications, and drone-based mobile access networks. He also explores machine learning applications in edge computing and cybersecurity. Publications & Contributions: Dr. Sun has authored over 40 journal/conference papers, including impactful work on cloudlet networks and IoT semantic mashup. His research has been published in IEEE Transactions, Communications Letters, and top-tier conferences like ICC and GLOBECOM. Awards: 2018 InterDigital Innovation Award 2018 NJIT Hashimoto Prize 2017 IEEE Communications Letters Exemplary Reviewer 2016 IEEE ICC Best Paper Award Professional Activities: He serves as a TPC member for IEEE CCNC and MobiEdge, and reviewer for top journals like IEEE Transactions on Cloud Computing and IEEE Internet of Things Journal. He co-chairs the Social Computing and Semantic Data Mining symposium at IEEE ICNC 2019. Labs & Teams: Active in UNM's Electrical & Computer Engineering labs, focusing on mobile edge computing and IoT innovation. Collaborates with industry partners like InterDigital Communications.

Gabriel NicolosiView profile

Assistant Professor

• Operations Research
• Optimal Control
• Applied Optimization
• +4 more

Dr. Gabriel Nicolosi is an Assistant Professor in the Department of Engineering Management and Systems Engineering at Missouri University of Science and Technology (Missouri S&T). He is affiliated with the Kummer College of Innovation, Entrepreneurship, and Economic Development. His research focuses on Operations Research, Optimal Control, Applied Optimization, and Machine Learning applications in industrial systems. Nicolosi holds a Ph.D. in Industrial Engineering and Operations Research from Penn State, with minors in Electrical Engineering, and a B.S. in Industrial Engineering from Universidade Federal de São Carlos (Brazil). He previously worked in investment banking in Brazil before pursuing academia. His teaching includes courses like Introduction to Operations Research and Optimization under Uncertainty. Education Ph.D. in Industrial Engineering and Operations Research, Penn State (2023) M.S. in Industrial Engineering, Penn State (2020) B.S. in Industrial Engineering, Universidade Federal de São Carlos (2017) Research Interests His work bridges Optimal Control and Machine Learning, particularly in evolutionary game dynamics. Key areas include Fourier-based approximations for control surfaces, differential games, and stochastic modeling in industrial systems. He emphasizes practical applications of optimization techniques in management and engineering contexts. Advising & Fellowships Prospective students are encouraged to apply to Missouri S&T’s graduate programs. Nicolosi administers the prestigious Kummer Fellowship, prioritizing US permanent residents. His advising philosophy emphasizes a rigorous yet collaborative approach, with students expected to align with his research themes. Teaching Courses include ENG MGT 5414 (Introduction to Operations Research) and ENG MGT 6415 (Optimization under Uncertainty). Past exams and materials are publicly accessible for study purposes.

Diala NaboulsiView profile

Professor

• Mobile networks
• Wireless networks
• Network virtualization
• +7 more

Diala Naboulsi is a Professor at the École de technologie supérieure (ÉTS) in the Department of Software Engineering and IT. Her research focuses on mobile networks, wireless systems, and cybersecurity, with a strong emphasis on machine learning applications in network optimization. She holds an M.Eng. from the Lebanese University and M.Sc. and Ph.D. degrees from INSA Lyon. Research Units: Summit Tech Research Chair, LASI Lab, Imagin Lab Expertise: Network virtualization, resource allocation, mobility management, UAV-based computing Her work spans resilience in wireless backhaul networks, energy-efficient frameworks in RAN slicing, and federated learning for privacy-aware traffic forecasting. She has advised numerous doctoral students, including Ahmed Abdelmoaty, Hnin Pann Phyu, and Philippe Lavoie. Key contributions include deep reinforcement learning approaches for network topology optimization and UAV-assisted MEC systems for Industry 5.0. Recent publications highlight advancements in 6G networks, network slicing, and edge computing. Her research aligns with strategic initiatives in sustainable and secure communication systems.

• Marine Engineering
• Renewable Energy Systems
• Risk Assessment
• +5 more

Veysi Başhan serves as an Assistant Professor in the Department of Marine Engineering within the Faculty of Naval Architecture and Ocean Engineering at Istanbul Technical University (ITU). His academic profile demonstrates active engagement in maritime engineering research with current publications extending through 2025 and an active research project on hybrid renewable energy systems analysis. Professor Başhan's research spans critical maritime engineering domains with particular emphasis on renewable energy integration, risk assessment methodologies, and marine propulsion systems. His work innovatively combines fuzzy logic, Bayesian networks, and multi-criteria decision-making approaches to address complex challenges in maritime operations, energy systems, and safety protocols. This interdisciplinary approach bridges traditional marine engineering with modern computational techniques for risk analysis and system optimization. His publication portfolio reveals a clear trajectory toward sustainable maritime technologies, with increasing focus on wind-solar hybrid systems, alternative fuels like methanol, and autonomous navigation reliability. The research demonstrates consistent application of fuzzy-based analytical frameworks across diverse maritime contexts from engine performance to ballast water treatment systems. Current research activities include the 2024 project "Hibrit Yenilenebilir Enerji Sistemlerinde Arızaların Analizi ve Çözüm Önerileri" (Analysis of Failures in Hybrid Renewable Energy Systems and Solution Proposals), indicating ongoing commitment to solving practical engineering challenges in renewable energy integration for maritime applications. His scholarly impact is evidenced by 32 research outputs, 366 Scopus citations, and an h-index of 9.

• Automation Engineering
• Mechanical Engineering
• Robotics
• +4 more

Pauli Mustalahti is a Research Fellow at Tampere University's Automation Technology and Mechanical Engineering department. His expertise lies in advanced control systems for heavy-duty robotic manipulators, hydraulic systems, and model-based control design. Mustalahti holds a Master of Science (Technology) in Automation Engineering from Tampere University (2016) and a Bachelor of Science (Technology) in the same field (2013). His research focuses on optimizing automated operations for industrial manipulators through innovative control strategies, including bilevel optimization frameworks, vision-aided positioning, and robust safety-constrained systems. Key contributions include improving long-reach manipulator accuracy via local calibration and enhancing electric linear actuator performance with safety protocols. Research interests: Robotics, Hydraulic Systems, Model-Based Control, Industrial Automation Notable achievements: Doctoral thesis on modular model-based control design for heavy-duty manipulators (2023) Collaborations: Global Fluid Power Society, IEEE, and international researchers in automation Mustalahti's work aligns with UN Sustainable Development Goals related to Industry, Innovation, and Infrastructure. His publications span peer-reviewed journals like IEEE Transactions on Automation Science and Engineering and conferences such as PEMC, addressing challenges in actuator safety, real-time control, and energy efficiency.

Brian MitchellView profile

Teaching Professor

• Software Engineering
• Software Architecture
• Cloud Native Computing
• +7 more

Brian Mitchell is a Teaching Professor in the Department of Computer Science at Drexel University's College of Computing & Informatics (CCI). He brings over two decades of combined industry and academic experience, transitioning fully into academia in 2022 after serving as a Distinguished Engineer at a Fortune 15 company. His work bridges cutting-edge research and practical innovation in software systems. Drexel University, College of Computing & Informatics, Department of Computer Science Education: PhD in Computer Science, Drexel University MS in Computer Science, Drexel University BS in Computer Science, Drexel University ME in Computer & Telecommunication Engineering, Widener University Brian Mitchell's research centers on the intersection of Software Engineering, Software Architecture, Cloud Native Computing, and AI . His early foundational work helped establish the field of Search-Based Software Engineering (SBSE) , particularly in automated software clustering and architecture recovery. Recently, his focus has shifted to modern challenges in cloud-native environments , including misconfiguration detection, malware analysis, and resilient system design. He integrates security, scalability, and intelligent automation into software engineering practices. His recent publications reflect a clear trend toward AI-enhanced cloud-native systems , emphasizing automated analysis, security, and architectural robustness. These works appear in AI and cloud computing venues, showing interdisciplinary engagement. The evolution from source code clustering to cloud-native engineering illustrates his adaptability and leadership in emerging domains. Scientific Awards: Best Paper Award, GECCO'03 Best Paper Award, WCRE'01 Brian is actively involved in mentoring students and encourages research collaboration, particularly with those seeking deeper engagement beyond coursework. He emphasizes hands-on learning and uses modern tools like GitHub and Discord in his teaching. While no specific grants are listed, his industry leadership in digital innovation and open-source contributions suggests strong applied research support. He previously led large engineering teams and drove disruptive technological adoption in enterprise settings. Though no formal lab name is mentioned, his research group appears focused on software architecture, cloud systems, and AI-driven engineering , likely operating under informal or course-based research initiatives. His website and GitHub presence (@ArchitectingSoftware) suggest an active, open, and collaborative environment for student research.

Christian HaubeltView profile

Professor

• Embedded Systems
• Cyber-Physical Systems
• Smart Sensors
• +9 more

Christian Haubelt is a Professor at the Institute of Computer and Network Engineering, School of Engineering, University of Rostock, Germany. He is actively engaged in research and teaching in the areas of embedded and cyber-physical systems, smart implants, and IoT. His work is supported by multiple national and international projects including ELAINE (SFB 1270), SmILE (EU), 6G-Health (BMBF), and GenerIoT (BMBF). His research interests include: Embedded and Cyber-Physical Systems Smart Sensors and Smart Implants System-Level Design Methodologies SystemC-based Modeling and Verification Design Space Exploration and Multi-Objective Optimization Industrial Internet of Things and 6G for Healthcare His recent publications focus on real-time communication protocols, 5G/6G localization, smart implants, and secure IoT systems. Trends show a strong emphasis on integrating embedded systems with medical and industrial applications, particularly leveraging TSN, MQTT-SN, and OPC UA for reliable and secure communication. His work bridges theoretical modeling with practical implementation in safety-critical domains. Christian Haubelt has supervised multiple researchers including Michael Nast, Benjamin Rother, Nico Kalis, and Nico Graumüller. He leads several funded research projects such as ELAINE, SmILE, 6G-Health, and SUSTAIN, which focus on smart implants, secure IoT, and next-generation medical systems. These projects involve collaboration with DFG, EU, and BMBF. He is involved in the following research labs and teams: Embedded Systems and Cyber-Physical Systems Group Smart Implants Research Team (SmILE, ELAINE) 6G-Health Localization Team Industrial IoT Security (SUSTAIN, CargoAssist)

Wolfgang WeberView profile

Professor

• Molecular Imaging
• Targeted Radionuclide Therapy
• Theranostics
• +3 more

Prof. Wolfgang Weber is a Professor and Director of the Department of Nuclear Medicine at the Klinikum rechts der Isar, Technical University of Munich (TUM). His academic career includes roles such as Associate Professor at UCLA (2003–2007) and Chair of Nuclear Medicine at Albert Ludwig University of Freiburg (2007–2013). He specializes in molecular imaging and targeted radionuclide therapy, with a focus on cancer theranostics. His research addresses precision oncology through advanced imaging techniques for diagnosis and treatment monitoring. Education: Doctorate in Medicine from TUM (1995). Key prior appointments include Director of the Molecular Imaging and Therapy Service at Memorial Sloan Kettering Cancer Center (2013–2017) and Professor at Weill-Cornell Medical College. His work spans prostate cancer imaging, neuroendocrine tumors, and radiopharmaceutical development. Research interests emphasize integrating imaging modalities like PET/CT and SPECT for cancer staging, therapy response prediction, and novel radiotracer development. Notable contributions include PSMA-ligand PET imaging for prostate cancer recurrence detection and PARP inhibitor engagement studies in lung cancer. Publications highlight innovations in theranostic agents, such as 18F-rhPSMA-7 and Ga-68-based tracers. His work frequently addresses clinical trial validation of imaging biomarkers and dosimetry safety in radiolabeled therapies.