Find Academic Supervisors

Jean-Baptiste JeanninView profile

Assistant Professor

• Verification of cyber-physical systems
• Logics and semantics of programming languages
• Programming with coinductive types
• +3 more

Jean-Baptiste Jeannin is an Assistant Professor in the Department of Aerospace Engineering at the University of Michigan — Ann Arbor, leading the MARVL group. He is also affiliated with the departments of Computer Science and Engineering and Robotics. His research focuses on formal verification of cyber-physical systems, logics and semantics of programming languages, and coinductive programming types. He holds a Ph.D. in Computer Science from Cornell University (2013) and a Diplôme d'Ingénieur from École polytechnique (2007), with prior roles at Samsung Research America and Carnegie Mellon University. His research interests include verification of aerospace applications, program logics, and formal methods applied to robotics and distributed systems. Notable work includes formally verified algorithms for collision avoidance, network consensus protocols, and numerical methods. He teaches courses such as AERO 552 (Aerospace Information Systems) and EECS 590 (Advanced Programming Languages). Jeannin's recent publications emphasize formal verification in hybrid systems, rounding error analysis, and hardware-software co-design. His work bridges theoretical foundations with practical applications in autonomous systems, cybersecurity, and computational physics. Collaborations include projects on certified approximations of mathematical functions and verified distributed system protocols.

Barry E. MullinsView profile

Professor

• Cybersecurity
• Network Security
• Digital Forensics
• +3 more

Barry E. Mullins is a Professor in the Department of Electrical and Computer Engineering at the Air Force Institute of Technology (AFIT). He holds a Ph.D. in Electrical Engineering from Virginia Tech (1997), an M.S. in Computer Engineering from AFIT (1987), and a B.S. in Computer Engineering (Cum Laude) from the University of Evansville (1983). His research focuses on cybersecurity, network security, and digital forensics, with notable contributions to wireless communication security and critical infrastructure protection. He has advised numerous graduate students and authored/co-authored over 100 publications in peer-reviewed journals and conferences. Dr. Mullins has received prestigious awards, including the 2021 Dean’s Distinguished Teaching Professor and multiple best paper/poster awards. His work emphasizes practical cyber defense solutions, such as secure wireless protocols, intrusion detection systems, and advanced encryption techniques. He is actively involved in curriculum development for cyber warfare and information assurance programs, integrating real-world scenarios and military applications. His research teams have developed tools like TRAPP for tracking peer-to-peer file sharing and FPGA-based systems for malicious traffic detection. Collaborations with industry and government entities (e.g., NSA, US Cyber Command) underscore his commitment to bridging academic and operational cybersecurity needs. Dr. Mullins also leads initiatives in software-defined networking (SDN) security and blockchain applications for IoT and transportation systems. Key contributions include pioneering studies on ZigBee network vulnerabilities, secure multicast architectures for satellites, and the use of metamorphic software protections. His teaching excellence has been recognized with the Cage H. Crocker Award and AETC Educator of the Year honors. Current projects explore AI-driven network security, smart home IoT defense mechanisms, and decentralized ledger technologies for critical infrastructure.

• Petri Nets
• Parallel Computing
• Computer Networks
• +3 more

Dmitry Zaitsev is a Senior Lecturer in Software Engineering at the College of Science and Engineering. His work focuses on applying Petri Nets to diverse domains, including epidemiological modeling, parallel computing architectures, and blockchain analysis. Recent research includes GPU vs CPU performance studies for sparse matrix computations and formal verification of distributed systems. Research Interests: Petri Net modeling frameworks Mass-parallel algorithms for high-performance computing Embedded system reliability engineering Blockchain protocol analysis Formal verification of MPI programs Recent Publications Trends: Demonstrates strong focus on Petri Nets applications across: Public health modeling (Ebola/COVID) GPU-accelerated scientific computing Microcontroller/FPGA-based embedded systems Blockchain fork analysis Advising & Grants: No specific student/advisor relationships or grant details mentioned in the provided text. Labs/Teams: No specific laboratories or research groups explicitly associated in this data.

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

Xiaojun Zhai is a Lecturer in Computing at the Department of Electronics, Computing and Mathematics. His research focuses on machine learning, computer vision, FPGA design, and cybersecurity, with applications in biomedical engineering, embedded systems, and space exploration. He has collaborated extensively on projects involving real-time image processing, sensor networks, and hardware-accelerated solutions. His work emphasizes practical implementations on platforms like Zynq SoC and FPGAs. Zhai's research interests include privacy verification in multimedia systems, deep learning for visual content analysis, and efficient algorithms for planetary exploration. He has contributed to advancements in ECG encryption, gas classification systems, and automated number plate recognition. His publications span journals such as IEEE Access, Journal of Computational Science, and Future Generation Computer Systems. Zhai has co-authored over 25 papers, with topics ranging from biomedical signal processing to service-oriented architectures. His work on multi-sensor data fusion and heterogeneous system design highlights his expertise in interdisciplinary engineering solutions.

Oktay BaysalView profile

Professor

• Computational Fluid Dynamics
• Aerodynamic Shape Optimization
• Microfluidics
• +7 more

Oktay Baysal is a Professor & Eminent Scholar in the Department of Mechanical & Aerospace Engineering at Old Dominion University (ODU). He holds a Ph.D. in Mechanical Engineering from Louisiana State University (1982), an M.Sc. from the University of Birmingham (1978), and an additional degree from Istanbul Technical University (1977). His expertise spans computational fluid dynamics (CFD), aerodynamic design optimization, microfluidics, and aeroacoustics. Research interests include design sensitivity analysis, unsteady aerodynamics, and thermal management of microelectronic devices. He has led grants such as the $482,192 'Collaborative Master’s and Ph.D. Programs' (2005–2007) and contributed to projects like the design of future intelligent Earth-observing satellites. His work bridges CFD with optimization, addressing challenges in aerospace, biomedical devices, and MEMS. Key publications focus on neural network-based CFD upscaling, microfluidic PCR devices, and aeroacoustic optimization. Awards include Fellowships from ASME (1996) and AIAA (1994), plus ODU’s Eminent Scholar title (1996). His research integrates computational methods with real-world applications, emphasizing innovation in fluid dynamics and engineering systems. Notable collaborations involve NASA, MIT, and international institutions. Ongoing efforts explore machine learning in CFD and sustainable engineering solutions.

• Hardware Design for Network Devices
• Fault Tolerance
• Error Correction Codes
• +3 more

Salvatore Pontarelli is a Researcher at CNIT (Italian National Inter-University Consortium for Telecommunications), affiliated with the University of Rome Tor Vergata. He holds a Master's in Electronic Engineering from the University of Bologna (2000) and a PhD in Microelectronics and Telecommunications from the University of Rome Tor Vergata (2003). His work focuses on hardware design for network devices, fault tolerance, error correction codes, and high-speed packet processing. He contributed to EU-funded projects and received a CISCO Research Award (2011) for work on Bloom filters and Ternary CAM. Research interests include hash-based structures (e.g., Bloom filters, hash tables), stateful programmable data planes, and FPGA-based network intrusion detection systems. He led the Defect and Fault Tolerance Group (DFTGroup) under Prof. Adelio Salsano and has extensive experience in collaborative projects with academic and industry partners. Publications highlight innovations in flow monitoring, data plane optimization, and efficient memory management. His work often bridges theoretical algorithms (e.g., cuckoo hashing) with practical hardware implementations, emphasizing real-world applications in networking and telecommunications.

• Microelectronics
• Embedded Systems
• IoT Security
• +5 more

David Guerrero Martos is an Associate Professor in the Department of Electronic Technology at the University of Seville. His primary research focuses on microelectronics, embedded systems, and hardware security solutions. He leads the 'Digital Research and Development' group and has participated in numerous national and international grants, including the 'USECHIP: Chair in Microelectronics' project (TSI-069100-2023-001) and the 'Easy-to-deploy intelligent network for independent living for our seniors (RIING)' initiative (IPT-2012-0645-300000). His academic contributions include pioneering work on FPGA-based IoT security (e.g., E-LUKS), high-precision timing systems, and low-power digital circuit design. He has published extensively in journals like IEEE Access and Electronics , and his research spans topics such as trigonometric function computation, hardware bootloaders (NanoBoot), and SoC integration frameworks (evercodeML). Guerrero Martos has over 20 years of experience in both academic and applied research, with notable contributions to teaching methodologies in electronics. He holds patents for innovative digital circuits and has collaborated on open-source hardware projects, emphasizing practical applications of embedded systems.

• High-performance computing
• Decimal arithmetic
• Parallel programming
• +4 more

Alvaro Vazquez Alvarez is an Assistant Professor at the Higher Technical School of Engineering, University of Santiago de Compostela. He holds a PhD in Computer Engineering (2009) from the same institution, focusing on High-performance decimal floating point units under the supervision of Dr. Elisardo Antelo Suárez. His research interests span high-performance computing, decimal arithmetic hardware design, parallel programming models, and LiDAR data processing. His work emphasizes FPGA implementation of arithmetic units, numerical algorithms optimization, and compiler/runtime systems for parallel environments. He leads the CograDe Research Group (Computer Graphics and Data Engineering), exploring advanced topics in parallel computing and data engineering applications. Key contributions include universal MPI binding frameworks for new languages and efficient LiDAR point cloud processing algorithms. Notable technical expertise includes: digit-by-digit algorithms, redundant number systems, CORDIC-based computations, and multi-operand decimal arithmetic implementations. His publications bridge theoretical numerical methods with practical hardware/software co-design solutions. Current research focuses on extending parallel programming interfaces, improving decimal arithmetic precision in modern architectures, and applying advanced geometric processing techniques to 3D data challenges.

David Lopez VilariñoView profile

Professor

• Computer Vision
• LiDAR Data Processing
• FPGA Acceleration
• +4 more

David Lopez Vilariño is a Professor at the Department of Electronics and Computing, Faculty of Physics, University of Santiago de Compostela. He holds a PhD in Electronics from the same university (2001), specializing in active contour algorithms and FPGA implementations. His research focuses on LiDAR data processing, reconfigurable computing architectures, and embedded vision systems. He is a member of the ARQCOMP (Computer Architecture) and Artificial Vision research groups. Education: PhD in Electronics (2001), thesis on 'Active contours at the pixel level: design and implementation on cellular network architectures' under Dr. Diego Cabello Ferrer. Research interests include: LiDAR-based 3D modeling for urban and environmental applications FPGA acceleration for high-performance computing Reconfigurable hardware architectures for embedded vision systems Real-time video surveillance and motion estimation His work bridges hardware-software co-design with advanced sensing technologies, particularly in geomatics and medical imaging applications.

Antoine KaufmannView profile

Professor

• Computer Systems
• Operating Systems
• Network Systems
• +2 more

Antoine Kaufmann is a Tenure-Track Faculty Member (W2) at the Max Planck Institute for Software Systems (MPI-SWS) and serves as Adjunct Faculty at TU Munich's Chair of Distributed Systems and Operating Systems. He leads the operating systems research group focusing on post-Moore systems – specialized architectures integrating hardware and software components. Education includes a PhD from University of Washington's Allen School of Computer Science and Engineering, and Bachelor/Master degrees from ETH Zürich. His work bridges systems research and practical implementations, resulting in publications at top-tier venues including SOSP, OSDI, and EuroSys. Research Interests: Centers on the interplay of software and hardware in specialized systems. Key areas include network communication architectures, machine learning systems, and methodologies for efficient post-Moore systems with manageable complexity through reusable components. Publications: Focus on network systems, operating systems, and distributed computing, with recent work on simulation frameworks (SplitSim), virtualized network stacks (Virtuoso), and reconfigurable packet processing (Kugelblitz). Awards: Distinguished Artifact Award for Clockwork at OSDI 2020. Teaching & Service: Developed courses on Operating Systems and Accelerating Applications with Specialized Hardware. Regularly serves on program committees for EuroSys, MobiSys, and other top conferences.

• Particle Physics
• Flavour Physics
• New Physics Searches
• +3 more

Arantza Oyanguren Campos is a Professor at the University of Valencia, affiliated with the Instituto de Física Corpuscular (IFIC), a joint research center of the University of Valencia and the Spanish National Research Council (CSIC). She serves as a key researcher in the LHCb experiment at CERN, focusing on experimental particle physics through large-scale international collaboration. Her research program centers on: Experimental Flavour Physics investigations via rare decay processes Searches for physics beyond the Standard Model Real-time data analysis optimization using FPGA and GPU accelerator platforms Computational challenges in high-energy physics experiments Dr. Oyanguren Campos leads the Experimental Flavour Physics Group at IFIC, which operates under the LHCb collaboration framework. The group specializes in developing novel analysis techniques for rare decay channels while pioneering real-time processing solutions using heterogeneous computing architectures. Their technical innovations directly address critical data throughput challenges at the LHC. Comprehensive details about the group's research agenda and technical infrastructure are available at http://ific.uv.es/lhcb/ .

Zhuangdi ZhuView profile

Assistant Professor

• Machine Learning
• Federated Learning
• Reinforcement Learning
• +7 more

Zhuangdi Zhu is an Assistant Professor at the Department of Cyber Security Engineering, George Mason University (GMU). She holds a PhD in Computer Science from Michigan State University (2022) and a BSc from Nanjing University of Science and Technology (2015). Prior to academia, she worked as a Senior Data & Applied Scientist at Microsoft (2022–2023) and interned at Meta, Google, IBM, and others. Her research focuses on accountable, scalable, and trustworthy AI , particularly in federated learning, reinforcement learning, robustness, fairness, privacy, and edge computing. She has organized workshops such as FedKDD and FL4Data-Mining, and her work has been recognized through grants like the CCI Grant (2024) and NAIRR Pilot Program Grant (2024). Key research areas include federated learning with system heterogeneity , sample-efficient reinforcement learning , and de-biased representation learning . She teaches courses on federated learning and cybersecurity engineering at GMU. Her lab hosts PhD students Zhengbang Yang and Eason Zhong. She has published extensively in top venues including TPAMI, ICML, KDD, and AAAI. Professional activities include serving as Program Chair for FedKDD and session chair at KDD. Her recent work explores AI applications in healthcare (e.g., cognitive support for seniors) and sports analytics. She also maintains a research lab focused on secure and privacy-conscious threat detection in federated systems.

• Computer Science
• Electrical Engineering
• Computer Architecture
• +3 more

Oliver M. Vojinović serves as an Assistant Professor in the Department of Electrical Engineering and Computing at the Faculty of Electronic Engineering, University of Niš. Appointed to his current academic rank in 2017 after serving as an assistant trainee since 2001, he maintains active roles in teaching and research within Serbia's academic framework. His educational foundation includes a Bachelor's degree (2000) and Master's degree (2008), both in Electrical Engineering and Computer Science from the Faculty of Electronic Engineering. These qualifications established his technical expertise in computing systems and electrical engineering disciplines. Dr. Vojinović's research spans Computer Science, Electrical Engineering, and Education Technology, with concentrated efforts in Computer Architecture simulation tools and optimized Signal Processing implementations. His work bridges theoretical computing concepts with practical engineering applications, particularly through development of educational simulation environments like SimArch for computer architecture instruction. Analysis of his publication history reveals consistent contributions to folded FIR filter architectures and computer science education methodologies. The trajectory shows evolving focus from hardware implementation (2002-2004) toward pedagogical applications of computing (2005-2007), demonstrating interdisciplinary integration of engineering principles with educational innovation. Currently engaged in one national and one international research project, he contributes to academic advancement through project-based learning initiatives and TEMPUS mobility programs. His institutional involvement includes participation in faculty quality assurance frameworks and continuous education programs at the University of Niš.

Dan NICULAView profile

Professor

• Digital Systems
• Hardware Description Languages
• Electronics Design
• +4 more

Professor Dan NICULA is a faculty member at the University of Transilvania in Brașov, Romania. He holds a Doctorate in Engineering (2017) from the same institution and a Master's degree in Applied Electronics Engineering from the Polytechnic University of Bucharest (1990). His academic roles include teaching courses such as Digital Electronics, Hardware Description Languages, and Digital Media Communication across undergraduate and graduate programs in Electrical Engineering and Computer Science. His research focuses on digital systems, hardware description languages (Verilog/VHDL), and electronic circuit design. Key contributions include textbooks like 'Digital Electronics: Emergency Textbook' (2020) and 'Verilog Textbook' (2019), which are foundational resources for students and professionals. He has supervised over 150 student theses, with recent topics spanning FPGA-based AI accelerators, UVM verification methodologies, and embedded system implementations. Professional experience includes roles at Intel (1999-2015) and Fotonation (2015-present), alongside his academic career. He is affiliated with the Faculty of Electrical Engineering and Computer Science and contributes to research initiatives in VLSI design, digital signal processing, and hardware-software co-design.

Koushik ChakrabortyView profile

Professor

• Computer Architecture
• Near-Threshold Computing
• Hardware Security
• +3 more

Koushik Chakraborty is a Professor in Electrical and Computer Engineering at Utah State University. His research focuses on computer architecture, energy-efficient near-threshold computing systems, and hardware security. He holds a PhD from the University of Wisconsin-Madison and has received multiple best paper awards. Research encompasses fault-tolerant architectures, hardware security countermeasures, and machine learning accelerators. Recent work develops timing error-resilient systems for deep learning hardware and security frameworks for neuromorphic computing. His group investigates voltage noise mitigation in network-on-chip designs and process variation-aware GPU architectures. Dr. Chakraborty teaches courses in Computer Architecture, Systems Programming, and VLSI Design. He has mentored over 18 graduate students and received teaching recognition including the 2010 Outstanding Undergraduate Mentor Award.