جست‌وجوی استاد و پژوهشگر

• Cybersecurity
• Cryptography
• Malware Analysis
• +۳ مورد دیگر

Tudor Dumitras is an Affiliate Associate Professor at the University of Maryland, College Park, holding appointments in the Department of Electrical and Computer Engineering (ECE) and the Department of Computer Science (CS). He is affiliated with The Maryland Cyber Security Center (MC2), where he leads research initiatives in cybersecurity and cryptography. His work focuses on malware detection, system security, and analyzing real-world vulnerabilities like the Heartbleed bug. Dumitras has collaborated with institutions such as Northeastern and Stanford Universities on critical security challenges, including SSL certificate reissuance and revocation strategies. His research interests span machine learning applications in cybersecurity, network security protocols, and adversarial attack mitigation. Notable contributions include developing automated tools for vulnerability exploitation prediction (SCAVY) and investigating the robustness of machine learning models against adversarial examples. Dumitras advises PhD students Simge Tekin and Kamala Varma, focusing on advancing cybersecurity through data-driven approaches. Key projects include analyzing software adoption patterns, studying zero-day attacks, and improving PKI security. His work often bridges academic research with industry practices, leveraging big data from sources like Symantec's WINE system. Dumitras has published extensively on topics ranging from malware behavior analysis to hardware fault attacks on neural networks.

Peter Desainمشاهده پروفایل

استاد

• Brain-Computer Interfaces
• Neuroscience
• Electrophysiology
• +۳ مورد دیگر

Peter Desain is a Professor and Principal Investigator at the Donders Institute for Brain, Cognition and Behaviour, Radboud University. His work focuses on developing advanced brain-computer interfaces (BCI) leveraging evoked potentials, particularly through code-modulated visual and auditory stimuli. He pioneers methods like noise-tagging and Bayesian dynamic stopping to enhance BCI efficiency and accessibility. His research spans neurotechnology, electrophysiological modeling, and clinical applications such as objective EEG audiometry and ALS communication aids. Recent studies emphasize gaze-independent systems, semantic decoding, and minimizing BCI calibration requirements. Key contributions include optimizing c-VEP code-books, real-time fMRI neurofeedback for memory contexts, and literature reviews on BCI design trends. Experimental pilot studies explore auditory attention and high-frequency SSVEP dynamics. No scientific awards are explicitly mentioned. His work integrates multidisciplinary approaches, bridging neuroscience, machine learning, and engineering to advance human-computer interaction and clinical tools.

• Computer Vision
• Applied Machine Learning
• Computer Graphics
• +۳ مورد دیگر

Fahim Hasan Khan is an Assistant Professor in the Computer Science and Software Engineering Department at California Polytechnic State University, San Luis Obispo (Cal Poly). His research focuses on computer vision, applied machine learning, and citizen science applications, with a special emphasis on environmental monitoring and education. He holds a PhD in Computer Science and Engineering from UC Santa Cruz, where he was advised by Professors Alex Pang and James Davis, and a Master's in Computer Science from the University of Calgary. Key research contributions include real-time rip current detection systems (RipFinder, RipScout), mobile citizen science platforms (SmartCS), and educational tools to engage high school students in STEM research. His work has received media attention for innovations in drowning prevention and environmental safety. Notable awards include the Best Poster Presentation Award at ICIAR 2019 and the Best of the Baskin School of Engineering Award at UC Santa Cruz in 2022. Dr. Khan collaborates extensively with industry and academic partners to develop practical solutions for challenges in marine safety, autonomous systems, and healthcare diagnostics. He actively mentors students and seeks to democratize access to machine learning tools through no-code platforms.

• Wireless Networks
• Mobile Networks
• Satellite Networks
• +۵ مورد دیگر

Evangelos Papapetrou is an Associate Professor in the Department of Computer Science and Engineering at the University of Ioannina, Greece. He holds a Diploma (1998) and PhD (2003) in Electrical and Computer Engineering from Aristotle University of Thessaloniki. His research focuses on wireless and mobile networks, including mobile ad-hoc networks, opportunistic networks, satellite networks, quality of service (QoS), and network coding. He has contributed to over 50 peer-reviewed publications and actively participates in EU/nationally funded projects. Research interests include: Wireless/Mobile Network Architectures Opportunistic and Social Mobile Networks Satellite Communication Systems Network Coding Techniques Routing Protocol Design His work emphasizes practical implementations of network protocols, with recent trends focusing on ultra-reliable low-latency communication, energy-efficient broadcasting, and protocol optimizations for 5G/IoT environments. He has developed simulation tools like Adyton for opportunistic networks and contributed to privacy-preserving routing solutions. Teaching responsibilities include courses on Computer Networks I and Wireless Networks (2024/25). He maintains active IEEE/ACM memberships and serves on conference review committees.

Philippe Kalmogoمشاهده پروفایل

استادیار

• Structural Engineering
• Concrete Design
• Structural Reliability
• +۳ مورد دیگر

Philippe Kalmogo is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of New Hampshire (UNH), part of the College of Engineering and Physical Sciences. His research focuses on structural reliability, concrete design, and advanced material applications in infrastructure rehabilitation. He teaches courses such as Statics for Civil Engineers, Reinforced Concrete Design, Pre-stressed Concrete, and Special Topics in Structural Reliability. Dr. Kalmogo's work emphasizes improving structural design methodologies for deep foundations and bridge systems using advanced materials like ultra-high-performance concrete (UHPC). His recent publications address interface shear capacity analysis, LRFD optimization for drilled shafts, and regional calibration challenges in geotechnical design. Office: Kingsbury Hall, Durham, NH 03824 | Email: Philippe.Kalmogo@unh.edu

• Cybersecurity
• Machine Learning Applications in Security
• Data Provenance Analysis
• +۷ مورد دیگر

Kangkook Jee is an Assistant Professor in the Department of Computer Science at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. His work focuses on cybersecurity, machine learning applications in security, data provenance analysis, and graph neural networks. He has developed systems like ProvIoT for IoT security and UTrack for enterprise user tracking. His research addresses challenges in adversarial machine learning, malware detection, and robust graph classification under adversarial conditions. He also explores federated learning, confidential computing, and blockchain-based secure data sharing. Key contributions include techniques for detecting stealthy attacks in IoT, improving graph neural network robustness against adversarial node modifications, and enhancing intrusion detection through provenance-based analysis. His work bridges theoretical advancements in machine learning with practical enterprise security solutions. Notable systems include AIQL for efficient attack investigation and SEAL for storage-efficient causality analysis in enterprise logs. Research trends across his publications emphasize combining provenance tracking with modern ML techniques to address evolving cybersecurity threats. Work in 2024-2025 focuses on decompilation challenges, federated edge-cloud security, and graph abstraction methods for robust classification. No scientific awards are explicitly listed in the provided texts. His grants and advising activities are not detailed here, though his extensive publication record suggests active collaborative research. His lab works on tools like Nodoze for automated threat triage and APTrace for agile causality analysis in enterprise systems.

Alex Gittensمشاهده پروفایل

استادیار

• Machine Learning
• Computational Linear Algebra
• Randomized Numerical Linear Algebra
• +۳ مورد دیگر

Alex Gittens is an Assistant Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI), joined in 2017. His research focuses on algorithmic trade-offs between computational efficiency and accuracy in large-scale linear algebra and machine learning contexts. He has expertise in kernel methods, randomized numerical linear algebra, and low-rank approximation techniques. Education: PhD in Applied and Computational Mathematics, Caltech (2013) Industry Postdoc at eBay Research Labs (2013-2015) Postdoctoral Scholar at International Institute of Computer Science (2015-2016) His research explores scalable machine learning algorithms, nonlinear and multilinear sketching applications, and sampling for low-rank tensor/matrix approximation. Current technical interests include attention mechanisms for knowledge graph completion, federated learning trade-offs, and causal inference in adversarial settings. Recent publication trends show active contributions in federated learning (privacy-fairness optimization), causal information extraction (financial text analysis), and adversarial machine learning (robustness-security trade-offs). His work emphasizes trustworthy ML systems and computational efficiency in high-dimensional data processing. Teaching includes foundational discrete mathematics (CSCI 2200) and advanced machine learning courses (CSCI 6968/4968). He offers advising through Slack channels and via email, focusing on course selection, research opportunities, and graduate school preparation.

Fan Yaoمشاهده پروفایل

دانشیار

• Computer Architecture
• Hardware Security
• AI Security
• +۵ مورد دیگر

Fan Yao is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Central Florida's College of Engineering and Computer Science. She received her Ph.D. in Computer Engineering from The George Washington University in 2018 and currently leads the Computer Architecture and Systems Research (CASR) lab. Her research focuses on the intersection of computer architecture, security, and machine learning, with particular emphasis on hardware-based security vulnerabilities and defenses. Dr. Yao's research interests span computer architecture, hardware and system security, AI security, energy-efficient computing, and cloud computing. Her work addresses critical security challenges in modern computing systems, particularly focusing on microarchitecture attacks, hardware-based model tampering in deep learning systems, and information leakage threats in emerging non-volatile memory systems. She has developed innovative defense mechanisms against cache timing channels, branch predictor vulnerabilities, and GPU-based side channels. Her recent publications demonstrate a strong focus on AI security (particularly Deep Neural Network vulnerabilities), hardware security (including cache and branch predictor attacks), and secure memory architectures. The research shows an evolution from traditional computer architecture topics toward the security implications of AI hardware and emerging memory technologies, with increasing emphasis on practical attacks and defenses in real-world systems. NSF GW I-Corps Site Grant Award, 2018 Best Dissertation Award, GWU, 2018 The Norris & Betty Hekimian Engineering Endowment Fellowship, GWU, 2017 Top Picks in Hardware and Embedded Security, 2019 NSF CAREER project award, 2024 Dr. Yao currently leads multiple NSF-funded research projects including 'Understanding and Taming Deterministic Model Bit Flip Attacks in Deep Neural Networks' (NSF SaTC, 2020-2023), 'Towards Secure-By-Design Integration of Emerging Non-Volatile Memory in Future System' (NSF CNS, 2020-2023), and 'Architecting Secure-by-Design Memristor-Based Memories' (NSF CNS, 2019-2022). She has successfully mentored numerous PhD students, many of whom appear as first authors on top-tier conference publications, demonstrating her commitment to graduate education and research mentorship. As the leader of the CASR lab, Dr. Yao oversees a vibrant research group focused on building secure-by-design, efficient, and advanced future systems through novel techniques spanning hardware, computer architecture, and systems. The lab actively publishes at top computer architecture and security conferences including ISCA, MICRO, HPCA, IEEE S&P, and USENIX Security, with multiple papers accepted to these venues annually. The group has developed several influential tools and frameworks for security analysis, including proof-of-concept code for BranchSpec exploits that has been widely cited in the hardware security community.

Tony Whiteمشاهده پروفایل

استاد مدعو

• Artificial Intelligence
• Swarm Intelligence
• Genetic Algorithms
• +۴ مورد دیگر

Dr. Tony White is an Adjunct Professor in the School of Computer Science at Carleton University. He holds a Ph.D. from Carleton (2000), an M.A. from Cambridge, and a B.A. in Theoretical Physics. His research focuses on complex adaptive systems, including influence measurement in social networks and swarm intelligence applications. He leads the Complex Adaptive Systems Group and has extensive industry experience, previously working at Nortel. Education: Bachelor of Theoretical Physics, Cambridge University (1981) Master of Physics, Cambridge University (1981) Master of Computer Science, Carleton University (1993) Ph.D. in Electrical Engineering, Carleton University (2000) Research Interests: Artificial Intelligence Swarm Intelligence Genetic Algorithms Neural Networks Recommender Systems Search Engines His work explores influence dynamics in social networks, adaptive information systems, and evolutionary computation. Recent publications address neural network topologies, distributed control strategies, and embedded systems administration. He has also contributed to trust models, referral networks, and traffic signal optimization. Dr. White’s research bridges theoretical computer science and practical applications in robotics, network management, and autonomous systems. Labs/Teams: Leads the Complex Adaptive Systems Group, focusing on interdisciplinary approaches to complex systems and swarm intelligence.

Andrea Gasparellaمشاهده پروفایل

استاد

• Building Physics
• Energy Engineering
• Indoor Environmental Quality
• +۵ مورد دیگر

Andrea Gasparella is a Tenured Full Professor and Dean of the Faculty of Engineering at the Free University of Bozen-Bolzano, located at NOI Techpark in Bozen-Bolzano, Italy. His research focuses on building physics, energy systems, and inclusive design, with a particular emphasis on indoor environmental quality for neurodivergent individuals and sustainable urban development. He leads projects like the Flexibots initiative (biodegradable medical microrobots) and BeSENSHome (autonomy support for neurodivergent people). His work integrates advanced modeling (e.g., CFD, machine learning) with real-world applications in energy efficiency, climate adaptation, and human-centered design. Key research areas include thermal and acoustic comfort, renewable energy integration, and smart building technologies. He collaborates with industry partners like Galileo High School and regional institutions to advance automation and robotics. Notable contributions include studies on urban heat islands, energy retrofit strategies for historic buildings, and the impact of ventilation on contagion risks in educational settings. As Dean, he promotes innovation through interdisciplinary initiatives at NOI Techpark, including new engineering programs and infrastructure like the university canteen. His publications span over 50 peer-reviewed articles in journals and conferences like Building Simulation Applications (BSA), addressing topics ranging from solar irradiance prediction in mountain regions to the psychophysical needs of neurodivergent populations. He actively contributes to policy discussions on decarbonization and energy equity in Alpine contexts. Professional activities include organizing events such as the 'nextFSE' fire safety conference and engaging with local organizations like Rotary Clubs to foster community ties. His work bridges academic research with practical solutions for climate resilience, accessibility, and technological advancement.

Chanchal K. Royمشاهده پروفایل

استاد

• Software Engineering
• Software Maintenance and Evolution
• Code Clone Detection
• +۳ مورد دیگر

Dr. Chanchal K. Roy is a Professor of Software Engineering/Computer Science at the University of Saskatchewan (USask), Canada, and Director of the NSERC CREATE SOAR program. He leads the Software Research Lab (SRLab) and is renowned for his work on code clone detection (NiCad tool) and software maintenance. His research spans software evolution, big data analytics, and quantum computing applications in software engineering. Dr. Roy holds a Ph.D. from Queen’s University, an M.Sc. from RWTH Aachen University, and a B.Sc. from Khulna University. Research interests include software clone detection, maintenance, and evolution, with emphasis on semantic analysis and cross-language clones. He has published over 240 papers (h-index 52) and attracted $6M+ in funding, including NSERC grants and CFI-JELF support. Awards include the GSA Advising Excellence Award, Outstanding Young Computer Science Researcher Award, and multiple Most Influential Paper awards. Key contributions include developing NiCad, advancing Stack Overflow search techniques, and leading collaborative projects in software analytics. His work has been featured in ACM Tech News, TechRepublic, and Stack Overflow blogs. Dr. Roy actively engages in keynotes at conferences like WCRE, IWSC, and BIM.

Kevin Schneiderمشاهده پروفایل

استاد

• Software Architecture and Design
• Software Evolution and Maintenance
• Software Analysis and Navigation
• +۶ مورد دیگر

Dr. Kevin Schneider is a Professor in the Department of Computer Science at the University of Saskatchewan. His research focuses on software architecture, evolution, analysis, and visualization, with notable work in quantum computing applications and machine learning. He also explores collaborative software teams and domain-specific languages to enhance development processes. Education: Ph.D., Computing and Information Science, Queen’s University (2000) Research Associate, Computing and Information Science, Queen’s University (1991–94) M.Sc., Computing and Information Science, Queen’s University (1990) B.Sc.(Hon), Computational Science, University of Saskatchewan (1980) Research interests include software design principles, maintenance strategies, and the integration of AI/quantum computing into software engineering. He emphasizes reproducibility in scientific workflows and tools like VizSciFlow. His work often bridges theory and practice, addressing challenges in code clone stability, user feedback management, and healthcare-related machine learning frameworks. Scientific Awards: Most Influential Paper at SCAM 2001 for his work on software engineering via source transformation. Advising and grants: While no specific advisees are listed, his research involves large-scale projects such as the Nutrient App and automated polyp segmentation tools. He collaborates on grants related to quantum computing applications and cloud-based software systems. Labs and teams: His work centers on collaborative scientific data analysis groups and developing tools for real-time groupware systems in complex workflows. He contributes to projects like CloneCognition and FSECAM, aiming to improve software design and maintenance through advanced analytics.

Patrick McCormickمشاهده پروفایل

استادیار

• Radar Systems
• Signal Processing
• RF Engineering
• +۳ مورد دیگر

Patrick McCormick is an Assistant Professor and Assistant Scientist in the Department of Electrical Engineering and Computer Science (EECS) and the Institute for Information Sciences (I2S) at the University of Kansas. He joined the university in 2021 after working at the Air Force Research Laboratory - Sensors Directorate (2018-2021). His research focuses on RF systems, signal processing, and waveform design for radar and communication systems. B.S. Mechanical Engineering, University of Kansas (2008) B.S. Electrical Engineering, University of Kansas (2013) Ph.D. Electrical Engineering, University of Kansas (2018) McCormick’s research areas include optimal emission design, multifunction transmissions, hardware characterization/compensation, and adaptive model-based parameter estimation. He leads the Radar Systems and Remote Sensing Laboratory (RSL) and has published extensively in radar waveform diversity, spectrum sharing, and adaptive signal processing. Recent work trends emphasize dual-function radar-communications co-design, low-cost systems, nonlinear hardware characterization, and digital array optimization. His publications span topics like waveform optimization, mutual coupling compensation, and power-efficient joint systems. IEEE Aerospace and Electronic Systems Society 2018 Robert T. Hill Best Dissertation Award He advises graduate students and collaborates with researchers at institutions like the Air Force Research Laboratory (AFRL), University of Oklahoma (OU), and international conferences. His service includes conference chairs, special session organization, and journal reviewing for IEEE Transactions on Signal Processing and Aerospace Systems. McCormick holds memberships in IEEE, Signal Processing Society, and Young Professionals. His lab seeks motivated graduate students for research in radar waveform design and RF systems.

Rui Daiمشاهده پروفایل

دانشیار

• Wireless Sensor Networks
• Multimedia Communications
• Video Analytics
• +۲ مورد دیگر

Dr. Rui Dai is an Associate Professor in the Department of Computer Science at the University of Cincinnati's College of Engineering and Applied Science. Her research focuses on wireless sensor networks, multimedia communications, and video analytics for healthcare and surveillance applications. She directs multiple NSF and NIST-funded projects on perceptual-quality-aware video systems. Research interests include quality-of-experience optimization for video analytics, compressed domain feature extraction, and edge computing frameworks for intelligent surveillance. Recent work develops deep feature compression techniques, multi-camera fall detection systems, and quality-aware video distribution strategies for 5G networks. Publications demonstrate consistent innovation in video processing for resource-constrained environments, with applications spanning healthcare monitoring, public safety networks, and embedded vision systems. Current projects investigate metaverse communication challenges for 6G networks and PHP vulnerability detection through hybrid static-fuzzing analysis.

• Programming Languages
• Software Engineering
• Compilers
• +۵ مورد دیگر

Manuel Rigger is an Assistant Professor at the National University of Singapore in the School of Computing and leads the Trustworthy Engineering of Software Technologies (TEST) Lab . His research focuses on improving data-centric systems , particularly their reliability, having found over 1,000 unique bugs in database systems. Education : PhD in Computer Science (2019) and MSc in Software Engineering (2015) from Johannes Kepler University Linz; MPhil in Chinese Philosophy (2015) from Xiamen University Research Highlights : Developed SQLancer – an automated testing framework that found 500+ bugs in DBMSs; created Query Plan Guidance (QPG) for efficient logic bug detection; received best paper awards at ICSE '23 and EuroSys '24 Scientific Awards : Recipient of 6 distinguished artifact/reviewer awards Major industry support from Google, AWS, and Microsoft Developed tools adopted by Oracle GraalVM and SQLite Teaching : Lecturer for CS3213 Foundations of Software Engineering and CS6223 Advanced Topics in Software Testing . Supervises multiple PhD/MSc theses on database testing and compiler reliability.