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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.

Fosca GiannottiView profile

Professor

• Mobility Data Mining
• Social Network Analysis
• Privacy-Preserving Data Mining
• +6 more

Fosca Giannotti is a Full Professor at Scuola Normale Superiore in Pisa, Italy, and leads the Pisa KDD Lab - Knowledge Discovery and Data Mining Laboratory, a joint research initiative of the University of Pisa and ISTI-CNR. Founded in 1994, the Pisa KDD Lab is one of the earliest research labs focused on data mining. Giannotti is a pioneering scientist in mobility data mining, social network analysis, and privacy-preserving data mining. Her educational background includes a Master Degree in Computer Science from the University of Pisa (1982) with 110/100 cum laude. She has held numerous visiting positions including at MCC in Austin, CWI Amsterdam, UCLA, and the Barabasi Lab at Northeastern University. Giannotti's research focuses on social mining from big data, encompassing smart cities, human dynamics, social and economic networks, ethics and trust, and diffusion of innovations. She has authored more than 300 papers and coordinated tens of European projects and industrial collaborations. Her current work increasingly centers on Explainable AI (XAI), as evidenced by her prestigious ERC Advanced Grant for the XAI project focused on "Science and technology for the explanation of AI decision making." Her recent publications reveal a strong emphasis on trustworthy AI, with research spanning privacy-preserving techniques, fairness in machine learning, human-AI collaboration frameworks, and medical applications of explainable AI. The breadth of her work demonstrates how data mining principles are being applied across diverse domains from social sciences to healthcare. ERC Advanced Grant for XAI project Premio Internazionale Tecnovisionarie 2021 Intelligenza Artificiale Giannotti has coordinated numerous significant projects including SoBigData (the European research infrastructure on Big Data Analytics and Social Mining), XAI, TAILOR (Foundations of Trustworthy AI), HumanE-AI-Net, and AI4EU. As former coordinator of SoBigData, she led an ecosystem of ten cutting-edge European research centers providing an open platform for interdisciplinary data science. She leads the Pisa KDD Lab, which serves as a hub for research on knowledge discovery and data mining. The lab has been instrumental in developing techniques for mobility data analysis, social network mining, and privacy-preserving data analytics, with applications ranging from smart cities to pandemic response.

• Stochastic Processes
• Operations Research
• Data-Driven Decision Making
• +4 more

Stella Kapodistria is an Associate Professor at Eindhoven University of Technology's Department of Mathematics and Computer Science, specializing in Stochastic Operations Research. She holds roles as EAISI High Tech Systems Associate Professor and editorial board member of journals like MCAP and PEIS. Her research focuses on data-driven decision-making, stochastic systems optimization, and maintenance policies, with applications in renewable energy, critical infrastructure, and cryptocurrency networks. She has secured grants including NWA-ORC, NWO Big Data, and TKI WoZ, and collaborates with industry partners in the Brainport region. Education: BSc (2003), MSc (2006, Hons.), and PhD (2009, summa cum laude) in Mathematics from the University of Athens. Postdoc at TU/e, followed by roles at Groningen University and TU/e's Stochastic Operations Research group. Teaching includes courses on Optimal Decision Making, Stochastic Performance Modeling, and Financial Mathematics. Research interests emphasize real-time learning, system resilience, and scalable algorithms for complex networks. Recent work addresses maintenance logistics, blockchain confirmation times, and wind energy prediction. She has published over 40 peer-reviewed articles and contributed to the 4TU Resilience Engineering Center. Awards include editorial leadership roles and grant funding. Advised 32 academic works and oversees industrial projects bridging theory and practice. Her labs and collaborations focus on adaptive systems, predictive analytics, and sustainable engineering solutions.

• Artificial Intelligence
• Cognitive Neuroscience
• Robotics
• +3 more

Prof. W.F.G. Haselager is a Professor of Artificial Intelligence at Radboud University Nijmegen, affiliated with the Donders Institute for Brain, Cognition and Behaviour and the Donders Centre for Cognition. He holds dual expertise in philosophy, psychology, and AI, focusing on societal implications of emerging technologies. His research integrates empirical methods (e.g., robotics, computational modeling) with philosophical inquiry into agency, responsibility, and human self-understanding. Education: MSc degrees in Philosophy and Psychology, PhD (1995) from Vrije Universiteit Amsterdam. Teaching responsibilities include bachelor/master programs in AI and Cognitive Neuroscience. Active in public engagement through lectures/workshops on AI ethics, neurotechnology, and free will. Key research themes include neuroethics, AI equity, and brain-computer interfacing. Current projects include the ELSA Lab (AI for Health Equity) and 'Healthy Data' initiatives addressing algorithmic bias and medical AI oversight. He advises Zander Labs on neurotechnology ethics and co-leads multi-institutional grants exploring conversational AI and neurodiverse education. Media appearances include Dutch TV/NOS coverage of brain implants and podcasts on AI sentience. Ancillary activities include the 'Pim Haselager Lectures' series and advisory roles in neurotechnology ethics.

Xiaojun RuanView profile

Associate Professor

• Energy-Efficient Systems
• Cloud Computing
• Storage Systems Optimization
• +7 more

Xiaojun Ruan is an Associate Professor in the Department of Computer Science at California State University, East Bay. He holds a Ph.D. in Computer Science from Auburn University (2011) and a B.E. in Computer Science and Technology from Shandong University (2005). His primary research focuses on energy-efficient systems, cloud computing optimization, storage systems, and security-aware resource management. He has extensive experience in thermal modeling, parallel I/O performance, and distributed deep learning frameworks. Dr. Ruan’s work emphasizes balancing energy efficiency, reliability, and performance in storage and cloud environments. Notable projects include DuoFS (hybrid storage system), energy-aware VM allocation strategies, and securing cloud infrastructure against co-residence attacks. His research bridges hardware-software co-design principles with practical system optimizations. His publications span topics from NVMe SSD performance optimization to text augmentation for spam detection, reflecting a blend of storage systems and machine learning applications. He has actively contributed to improving Shuffle I/O in big data processing, thermal management in clusters, and secure virtualization techniques. Dr. Ruan collaborates on interdisciplinary projects involving distributed computing, cybersecurity, and real-time systems. His lab focuses on deploying energy-efficient solutions while maintaining robust reliability, evidenced by over 50 peer-reviewed articles and ongoing contributions to academic conferences.

• Machine Learning
• Deep Learning
• Cybersecurity
• +4 more

Dr. Yi Wang is an Associate Professor and Department Chairperson of the Electrical and Computer Engineering Graduate Programs at Manhattan College, New York. He also serves as Director of the Electrical & Computer Engineering Graduate Program. His research focuses on machine learning, deep learning, cybersecurity, blockchain, and their applications in cyber-physical systems. Dr. Wang is an IEEE Senior Member (since 2021) and has secured NSF grants totaling over $149,000. Education: Ph.D. Computer Engineering, University of Alabama in Huntsville M.S. Computer Science, Wuhan University of Science and Technology B.S. Information Systems, Wuhan University of Science and Technology Research Interests: Machine learning and deep learning algorithms Cybersecurity for IoT and smart grids Blockchain applications in attribute-based access control Adversarial machine learning defense mechanisms Optical fiber communication systems Recent Publications Trends: His work spans blockchain platform comparisons, adversarial attack mitigation in power systems, and AI-driven smart home solutions. He frequently publishes in IEEE journals and conferences. Awards: Best Paper Award at 2017 IEEE UEMCON Best Paper Award at 2015 ICDIP Grants and Advising: Principal Investigator for NSF-funded UIRiSCS project (2022–2025). Co-Principal on NYC DOT Loading Zone Study. Recipient of Manhattan College Faculty Summer Grants (2021, 2017). Advises graduate students in cybersecurity and IoT research. Labs/Teams: Collaborates with the University of Zaragoza, Spain on smart systems research. Leads projects on plastic optical fiber networks and AI-driven smart home systems.

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 .

• Cyber Security
• Cyber-Physical Systems
• Formal Methods
• +3 more

Samir Ouchani is a Research Director at the CESI LINEACT laboratory (Aix-en-Provence, France), affiliated with the CESI Engineering School. He holds a PhD in Computer Science from Concordia University (2013) and an HDR (Accreditation to Supervise Research) from CNAM Paris (2022). His research focuses on securing cyber-physical systems (CPS) through formal methods, blockchain, and AI-driven approaches. Key roles include leading projects on resilient CPS architectures, IoT security, and federated learning in industrial contexts. Education: 2022: HDR in Security and Reliability of Smart CPS (CNAM Paris) 2013: PhD in Computer Science (Concordia University, Montreal) 2006: Master in Computer Science (Lorraine University, France) 1997: Engineering Degree in Computer Science (Djillali Liabess University, Algeria) Research Interests: His work emphasizes secure CPS design, including cryptographic protocols for IoT, formal verification frameworks, and AI applications for intrusion detection. He explores blockchain for smart cities, federated learning in distributed systems, and resilience engineering for autonomous vehicles. Recent projects include developing PUF-based authentication protocols and digital twin architectures for resource-constrained systems. Advising & Collaborations: Supervised PhD theses on IoT security (Fahem Zerrouki), smart city formal verification (Walid Miloud Dahmane), and federated learning in industrial CPS (Souhila Bedra Guendouzi). Collaborates with institutions like Blida University (Algeria) and HESAM University. Active in conferences such as CRISIS, ICFNDS, and IEEE WETICE. Labs & Teams: Leads the Engineering and Numerical Tools research team at CESI LINEACT, focusing on model-based design, CPS simulation, and cybersecurity tool development. Engaged in EU-funded projects on Industry 4.0 and smart infrastructure security.

Richard Joseph SutcliffeView profile

Professor

• Discrete Mathematics
• Combinatorics
• Graph Theory
• +3 more

Richard Joseph Sutcliffe is a Professor of Mathematics and Computing Science at Trinity Western University (TWU), where he has served for four decades. He has taught over 45 distinct courses and held multiple administrative roles, including Assistant/Associate Dean of Science, Acting/Interim Dean of Science, and Chair of Mathematical Sciences. His expertise spans discrete mathematics, combinatorics, graph theory, programming languages, and the ethical implications of technology. He is also a published novelist and columnist, with notable awards in science fiction writing. Education: MSc in Computing Science (Simon Fraser University) BSc PDP (Trinity Western University) Research Interests: Combines theoretical computer science with practical applications, focusing on secure and reliable systems design. Explores ethical dimensions of technology adoption, particularly in programming languages and standards. His novels critically examine societal impacts of technological advancements. Awards: 2003: Best Electronically Published Science Fiction Novel 2003: Runner-up for Best Electronically Published Science Fiction Novel Professional Activity: Serves on ACM, CSA, and IEEE (life member). Columns appear in The Northern Spy and syndicated tech platforms. Active in academic accreditation and science policy via Canadian Council of Deans of Science. Owns Arjay Enterprises for consulting and web services.

Xiaoning DuView profile

Senior Lecturer

• Software Engineering
• Artificial Intelligence
• Cybersecurity
• +3 more

Xiaoning Du is a Senior Lecturer (equivalent to Associate Professor) in the Department of Software Systems & Cybersecurity at Monash University's Faculty of Information Technology. She holds a PhD from Nanyang Technological University (2020) and a Bachelor's from Fudan University (2014). Her research focuses on software security and quality assurance for traditional and AI-based systems, with notable contributions to DevOps for AI, vulnerability detection, and runtime verification. Education: PhD in Computer Science, Nanyang Technological University (2015–2020) Bachelor of Software Engineering, Fudan University (2010–2014) Research Interests: Security of intelligent software systems, AI-driven software testing, DevOps for AI, and trustworthy AI services . Her work emphasizes practical applications like Devign (vulnerability detection), DeepStellar (deep learning system analysis), and BigCodeBench (code generation benchmarking). Recent Projects: Collaborations include CSIRO cybersecurity initiatives, Algorand Center of Excellence, and IBM-funded research. She leads projects addressing AI ethics, federated learning security, and code completion robustness. Awards: 2024 Google Research Scholar Award, 2024 FIT Dean’s Early Career Award, and multiple distinguished paper awards at top venues like ACM SIGSOFT and ICLR. Labs/Teams: Active in Monash’s cybersecurity and AI research groups, contributing to open-source tools like DeepStellar and BigCodeBench . She advises PhD students and mentors on scholarships.

Robello SamuelView profile

Adjunct Professor

• Drilling Engineering
• Well Engineering
• Petroleum Engineering
• +5 more

Dr. Robello Samuel is an Adjunct Professor in the Department of Petroleum Engineering at the University of Houston's Cullen College of Engineering, concurrently holding this position for over 12 years while serving as a Technology Fellow at Halliburton. With 43 years of multi-disciplinary experience in oil/gas drilling operations, he specializes in drilling engineering innovations and well design. Education: Ph.D. in Petroleum Engineering, University of Tulsa M.S. in Petroleum Engineering, University of Tulsa M.S. in Mechanical Engineering, College of Engineering Guindy (Madras) B.S. in Mechanical Engineering, University of Madurai Research Focus: Dr. Samuel's work spans drilling optimization, wellbore mechanics, torque-drag modeling, vibration analysis, geothermal well design, and AI applications in drilling engineering. His research integrates field experience with computational methods to solve complex drilling challenges. Publications: His extensive publication record (150+ papers/books) focuses on drilling mechanics, well design innovations, and predictive modeling. Recent works emphasize machine learning applications, real-time drilling optimization, and sustainable energy solutions like geothermal well engineering. Awards & Honors: SPE International Drilling Engineering Award SPE Distinguished Lecturer (2014) SPE Honorary Member Award AIME Honorary Member Award Gulf Coast SPE Drilling Engineering Award (2013) SPE Distinguished Member Professional Leadership: Serves on editorial boards for SPE Research Partnership to Secure Energy for America (RPSEA), Ocean Energy Safety Institute, and SPE Research & Development Advisory Board. Regularly delivers keynote addresses at major energy conferences worldwide.

• Child and adolescent mental health
• Mental health service utilization
• School social work practice
• +3 more

Jennifer Murphy is an Assistant Professor in the School of Social Work at the University of Texas at Arlington, specializing in adolescent mental health service utilization and school-based interventions. Her research examines longitudinal impacts of mental health services, school social work practices, and interprofessional collaboration in educational settings. Education: PhD in Social Work, Virginia Commonwealth University (2023) MS in Disaster Resilience Leadership Studies, Tulane University (2016) MSW in Social Work, Tulane University (2015) BS in Sociology, Virginia Polytechnic Institute and State University (2014) Dr. Murphy's research focuses on critical intersections of child/adolescent mental health, service utilization patterns, and school-based interventions. Her work employs mixed-methods approaches to investigate barriers to mental health access, the role of school social workers, and impacts of adverse childhood experiences. Current projects examine longitudinal service utilization trajectories, campus food insecurity, and refugee mental health. Her publication trends reveal strong emphasis on adolescent mental health service systems (60%), school-based interventions (25%), and methodological innovation in mixed-methods research (15%). Recent work increasingly incorporates cross-cultural perspectives and pandemic-related service adaptations. Scientific Awards: Hans Falk Dissertation Award Scholarship, VCU School of Social Work (2022) RAACE Fellowship, VCU School of Social Work (2020) Dr. Murphy serves as thesis committee member for four Master's students and teaches graduate courses in social policy, research methods, and school social work. She actively reviews for journals including Journal of School Health and Children & Schools , while contributing to university committees on mental health and academic policy. Her professional service extends to the School Social Work Association of America and Society for Social Work and Research.

Tom HenzingerView profile

Professor

• Formal Methods
• Software Engineering
• Computer Science
• +5 more

Tom Henzinger is a Professor at the Institute of Science and Technology Austria (ISTA), leading the Tom Henzinger Group. His research focuses on formal methods to enhance software quality, addressing challenges in concurrent systems, real-time and embedded systems, quantum computing, neural networks, and biochemical reaction networks. He has advised numerous PhD students and postdoctoral researchers, contributing to advancements in these fields. His work emphasizes mathematical rigor in software analysis and verification. Current projects include VAMOS (Middleware for best-effort third-party monitoring) and SPyCoDe (Semantic and Cryptographic Foundations of Security and Privacy by Compositional Design). Past projects involved model checking and runtime verification techniques. Research interests span concurrent software synthesis, quantitative modeling of reactive systems, predictability of real-time systems, and formal methods applied to quantum computation and neural networks. He is also engaged in exploring hardware-optimal solutions for quantum algorithms. Administrative Assistant Ksenja Harpprecht supports the group. Courses taught include 'Formalisms Every Computer Scientist Should Know,' focusing on logics, automata, and semantics. The group hosts regular seminars, such as the Tuesday group seminar and the Wednesday joint formal methods seminar with TU Wien's FORSYTE group.

• Power Electronics
• Semiconductor Device Modeling
• Electromagnetic Modeling
• +2 more

Dr. Ivana Kovacevic is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zürich. Her research focuses on power electronics, semiconductor device modeling, and electromagnetic analysis of wide bandgap devices. ETH Zürich, Department of Information Technology and Electrical Engineering Contact: kovacevic@aps.ee.ethz.ch Her research explores SiC power MOSFETs, emphasizing their dynamic performance, reliability, and optimization through advanced modeling techniques like the Partial Element Equivalent Circuit (PEEC) method. She investigates parasitic extraction, thermal behavior, and stability issues in power modules, contributing to design improvements for high-efficiency systems. Her publications highlight trends in electromagnetic modeling, device-circuit interactions, and reliability analysis under extreme conditions. Key subfields include gate resistance dynamics, frequency-dependent capacitances, and multi-chip module design. Current projects involve virtual prototyping for power electronics and mission profile-based optimization of wearable power systems.

Risteski AleksandarView profile

Professor

• Telecommunications
• Cybersecurity
• Blockchain Technology
• +5 more

Professor Risteski Aleksandar holds a Ph.D. in Telecommunications and has been a faculty member at the Faculty of Electrical Engineering and Information Technologies, University Ss. Cyril and Methodius, since 1996. He currently serves as a Professor and has held roles including Vice-Dean for Science and International Cooperation (2008–2016). His research focuses on telecommunications, cybersecurity, blockchain applications in education, and IoT security. He has extensive industry experience, including internships at IBM T.J. Watson Research Center in the U.S. Education: Ph.D. in Telecommunications (2001–2004) M.Sc. in Telecommunications (1997–2000) Dipl. Ing. in Electronics and Telecommunications (1991–1996) Research Interests: Blockchain technology for academic credential verification Cybersecurity in tactical communication systems IoT energy efficiency and reliability Network intrusion detection using deep learning Anti-forgery systems using AI and blockchain Publications Trends: Over 50 publications since 2000, with recent focus on blockchain applications in education, cybersecurity frameworks for 5G networks, and IoT energy optimization. His work bridges theoretical advancements with practical implementations in telecommunication infrastructure. Awards: No specific awards listed, though his contributions to blockchain-based educational systems and cybersecurity research are notable. Advising & Grants: No listed students, but active in research projects funded by industry partnerships. Collaborations include IBM Research and initiatives in Macedonia's telecommunication sector. Labs/Teams: Affiliated with the Institute of Telecommunications at his faculty, focusing on network security and emerging technologies.