جست‌وجوی استاد و پژوهشگر

Eun Jung Kimمشاهده پروفایل

استاد

• Computer Architecture
• Power Efficient Systems
• Parallel/Distributed Systems
• +۳ مورد دیگر

Eun Jung Kim is a Professor in the Department of Computer Science and Engineering at Texas A&M University, affiliated with the College of Engineering. Her research focuses on computer architecture, power-efficient systems, parallel/distributed systems, cluster computing, performance evaluation, and fault-tolerant computing. Ph.D.: Computer Science and Engineering, Pennsylvania State University (2003) M.S.: Computer Science and Engineering, Pohang University of Science and Technology (1994) B.S.: Computer Science, Korea Advanced Institute of Science and Technology (1992) Her work explores innovative solutions for optimizing hardware and software systems, including energy-efficient interconnects, security enhancements, and adaptive network-on-chip designs. Notable achievements include the NSF Early CAREER Award (2009) and contributions to publications like IEEE Transactions on Parallel and Distributed Systems . Recent research trends include advancements in near-data processing, secure computing abstractions (e.g., WHISTLE), and mitigating hardware vulnerabilities (e.g., cache timing attacks). She also integrates IoT and low-cost cluster systems for environmental research applications.

Chia-Che Tsaiمشاهده پروفایل

استادیار

• Operating Systems
• Software System Security
• Secure Hardware
• +۴ مورد دیگر

Chia-Che Tsai is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University, part of the College of Engineering. He joined the faculty in Spring 2019 after earning his Ph.D. from Stony Brook University in 2017 and completing a postdoc at UC Berkeley's RISE Lab. His educational background includes an M.S. from Columbia University and a B.S. from National Taiwan University. His research focuses on operating systems, software security, and secure hardware, with emphasis on compatibility, performance, and hardware support. Key interests include library operating systems (e.g., Graphene), SGX-based security, and cloud computing. He has received notable awards, including the 2016 EuroSys Best Paper Award and NSF grants for confidential cloud applications and resilient systems. Teaching responsibilities span courses like Operating Systems (undergraduate/graduate), Software Security, Distributed Systems, and Secure Computer Systems. His work integrates practical system design with theoretical principles, addressing challenges in multi-tenant environments and secure execution. Current research explores enclave-based security, deterministic debugging, and virtualization techniques. He leads projects funded by NSF and collaborates with industry partners, emphasizing real-world impact in secure computing systems.

• Medical Research
• Biomedical Informatics
• Data Stream Processing
• +۲ مورد دیگر

Dr. Ke Wang is an Associate Research Scientist at the Yale School of Medicine, Yale University. His work focuses on advancing biomedical informatics through innovative data processing techniques, including algorithm design for dynamic graphs and streaming data frameworks. His research intersects computer science and healthcare, addressing challenges in real-time data analysis and cybersecurity for web applications. Academic Role: Researcher in biomedical data science and algorithm development Key Areas: Streaming algorithms, graph theory, cybersecurity, and persistent memory optimization

Christof Ferreira Torresمشاهده پروفایل

استادیار

• Blockchain Security
• Smart Contract Analysis
• Decentralized Finance (DeFi)
• +۴ مورد دیگر

Christof Ferreira Torres is an Assistant Professor at the Department of Computer Science and Engineering (DEI) at Instituto Superior Técnico (IST), University of Lisbon, and a researcher at INESC-ID in the Distributed, Parallel and Secure Systems (DPSS) group. His research focuses on program analysis, software security, and blockchain systems, particularly addressing vulnerabilities in smart contracts and decentralized finance (DeFi). He holds a Ph.D. from the University of Luxembourg and Technical University of Munich under Professors Radu State and Claudia Eckert. Education: Ph.D. in Computer Science, 2022 (University of Luxembourg & TU Munich) Postdoctoral Fellow at ETH Zurich (2022–2023) His research interests include blockchain security, MEV (Maximal Extractable Value) analysis, cross-chain interoperability, and privacy in Web3. Notable contributions include frameworks like Horus for attack detection in smart contracts and Elysium for automatic vulnerability patching. He actively participates in academic service, serving on program committees for major conferences like S&P, CCS, and USENIX. Recent work highlights cross-chain arbitrage dynamics, privacy leaks in web wallets, and centralized risks in blockchain infrastructure. His findings emphasize the need for decentralized solutions to counteract threats to blockchain liveness and finality. Key Awards: TLDR Research Fellowship (2024) Excellent Doctoral Thesis Award (2022) UBRI Impact Award (2022) He teaches courses on information security, dependable systems, and computer science foundations at IST and ETH Zurich. His work bridges theoretical computer science with practical blockchain security challenges, addressing both academic and industry needs.

Erich Kaltofenمشاهده پروفایل

استاد

• Computational Algebra
• Symbolic Computation
• Number Theory
• +۴ مورد دیگر

Erich Kaltofen is a Professor of Mathematics and member of the Symbolic Computation group at North Carolina State University (NCSU). He holds an adjunct professorship in Computer Science at Duke University. His academic journey includes a PhD in Computer Science from Rensselaer Polytechnic Institute (1982) and an MS from the same institution (1979). Kaltofen’s research focuses on computational algebra, hybrid symbolic-numeric algorithms, sequential and parallel algorithms, and symbolic computation systems. Research Interests: Kaltofen’s work spans computational algebra and number theory, emphasizing the development of efficient algorithms for polynomial factorization, sparse interpolation, and error-correcting methods. He is renowned for contributions to symbolic computation systems like LinBox and FoxBox, and his research integrates theoretical insights with practical software development. Publications: His recent publications highlight advancements in sparse polynomial interpolation, error-correcting algorithms, and computational complexity. Key themes include symbolic-numeric computation, algorithm design, and applications in cryptography and algebraic geometry. Awards and Recognition: A Fellow of the ACM (2009) and recipient of the Jenks Memorial Prize (2017), Kaltofen’s contributions have shaped the field of symbolic computation. His work bridges theoretical foundations and real-world applications, impacting computer science and mathematics. Advising and Grants: Kaltofen has advised numerous PhD students and postdocs, fostering advancements in algorithmic research. His grants include NSF-funded projects on hybrid symbolic-numeric methods and error-correcting algorithms. He collaborates on initiatives such as the Genomic Sciences program at NCSU. Labs and Teams: Co-designer of the LinBox library, Kaltofen contributes to open-source software for linear algebra and symbolic computation. His work emphasizes collaborative, interdisciplinary projects addressing challenges in computational mathematics and computer science.

Antonio Guimarãesمشاهده پروفایل

پژوهشگر

• Fully Homomorphic Encryption (FHE)
• Verifiable Computation
• Privacy-preserving technologies
• +۳ مورد دیگر

Antonio Guimarães is a postdoctoral researcher at the IMDEA Software Institute in Madrid, Spain. His research focuses on practical aspects of Fully Homomorphic Encryption (FHE), including verifiable FHE, fast bootstrapping algorithms, and efficient homomorphic evaluation of cryptographic primitives. He holds a Ph.D. in Computer Science from the University of Campinas (2019-2023), where he also completed his MSc (2017-2019) and Computer Engineering degree (2012-2016). During his Ph.D., he was a visiting student at Aarhus University (2022-2023). His work emphasizes privacy-preserving technologies and verifiable computation, with applications in secure cloud computing and encrypted machine learning. Education: Ph.D. in Computer Science, University of Campinas (2019-2023) Visiting Ph.D. Student, Aarhus University (2022-2023) MSc in Computer Science, University of Campinas (2017-2019) Computer Engineering, University of Campinas (2012-2016) His research interests include advancing FHE efficiency, developing verifiable computation frameworks for encrypted data, and exploring practical implementations of post-quantum cryptographic algorithms. He has presented work at venues such as CRYPTO, ASIACRYPT, and CHES, and contributed to open-source cryptographic libraries like MOSFHET and HELIOPOLIS. His recent projects focus on homomorphic evaluation of neural networks and optimizing FHE for real-world applications. His publications highlight contributions to bootstrapping algorithms, verifiable computation over approximate arithmetic, and privacy-preserving machine learning. These works emphasize balancing cryptographic security with computational efficiency. Grants and Collaborations: Collaborations include projects on secure transciphering for MPC and high-performance seismic data processing in cloud environments. His work often bridges theoretical cryptography with practical software implementations. Labs/Teams: His research is conducted within the IMDEA Software Institute’s cryptography group, focusing on applied cryptography and privacy-preserving technologies.

• Interactive learning and decision making
• Artificial Intelligence (AI)
• Machine Learning
• +۷ مورد دیگر

Frans A. Oliehoek is an Associate Professor at the Department of Intelligent Systems, Delft University of Technology. He leads the Sequential Decision Making group and directs the Mercury Machine Learning Lab and ELLIS Unit Delft. His research focuses on interactive learning and decision making, combining AI, machine learning, and game theory to develop intelligent systems capable of learning through interaction with dynamic environments. Notable contributions include work on influence-based abstraction, decentralized planning in multi-agent systems, and applications in robotics, traffic control, and human-AI collaboration. Education: PhD in Computer Science (2010) from the University of Amsterdam, with postdoctoral positions at MIT, Maastricht University, and the University of Liverpool. Research Interests: Active perception, Bayesian reinforcement learning, multi-agent coordination, and scalable algorithms for complex decision-making problems. His work emphasizes balancing task performance with uncertainty management and lifelong learning. Scientific Contributions: Over 88 publications, including ERC Starting Grant-winning research, and contributions to open-source tools like the MADP Toolbox. Recognitions include ELLIS Fellowship and Senior AAAI Membership. Grants & Labs: Led projects funded by ERC and Dutch national grants, overseeing labs advancing AI for societal challenges. Supervised 4 doctoral students and actively participates in academic outreach through editorial roles (JAIR, AIJ) and conference organization.

Taesoo Kimمشاهده پروفایل

استاد

• Systems Security
• Operating Systems
• Programming Languages
• +۳ مورد دیگر

Taesoo Kim is a Professor at Georgia Tech's College of Computing, jointly affiliated with the School of Cybersecurity and Privacy and the School of Computer Science. He serves as Director of the GTS3 lab and leads research in systems security, operating systems, programming languages, and distributed systems. His work emphasizes foundational security principles and practical tools for system resilience. Education: PhD in EECS from MIT (2014), SM from MIT (2011), BS in Computer Science/Electrical Engineering from KAIST (2009) Affiliations: Georgia Tech's School of Computer Science, School of Cybersecurity and Privacy, ML@GT, and Online Master of Science in Computer Science program Kim's research focuses on building secure computing systems through formal system design, implementation analysis, and trusted component isolation. Notable contributions include tools for automatic vulnerability detection, intrusion recovery frameworks, and secure enclave technologies like SGX-Tor. His work has secured grants from ONR, NSF, DARPA, and industry partners. Awards include the 2015 Internet Defense Prize and a finalist placement in the DARPA Cyber Grand Challenge. Key projects include QSYM (concolic execution engine), RAIN (attack tracing), and FREEDOM (DOM fuzzer). Kim teaches courses on information security and blockchain technologies. His lab (SSLab) collaborates with industry on critical security challenges, including hardware-software co-design for trusted execution environments.

• Artificial Intelligence
• Data Management
• Health Informatics
• +۶ مورد دیگر

María Elena García Barriocanal is a Professor in the Computer Science Department at the University of Alcalá, Spain. She specializes in Artificial Intelligence, Data Management, and Health Informatics, leading the I4 research group focused on intelligence, innovation, internet, and information systems. Her work integrates semantic web technologies, blockchain, and quantum computing with interdisciplinary applications in healthcare, education, and social media analysis. Education: She holds a Doctorate from the University of Alcalá (2004) with a thesis on knowledge models for usability evaluation using questionnaires. Her research spans over two decades, with a focus on developing practical frameworks for data-driven decision-making and educational technology. Research Interests: Her work bridges theoretical computer science with real-world problems. Key areas include AI ethics, biomedical decision support systems, blockchain-based metadata, quantum software engineering, and social media-driven epidemiological modeling. She also explores semantic integration in disaster management and learning analytics. Publications: Her recent work emphasizes AI tool development (e.g., madmpy library), trustworthy AI practices, and innovative applications like quantum code analysis. She frequently publishes in data science, cybersecurity, and health informatics domains. Labs/Teams: As part of the I4 group, she collaborates on projects like decentralized persistent identifiers and ontology-driven educational frameworks. Her work often involves cross-disciplinary teams addressing societal challenges through technological innovation.

• Computer Science
• Artificial Intelligence
• Cybersecurity
• +۷ مورد دیگر

Luis de Marcos Ortega is a Full Professor at the Department of Computer Science, University of Alcalá. He belongs to the TIFyC research group (Information Technologies for Training and Knowledge), focusing on interdisciplinary intersections of computer science, education technology, and cybersecurity. He holds a PhD from the University of Alcalá (2009), with a thesis on adaptive educational content sequencing using AI agents. His research interests span Artificial Intelligence applications (e.g., ChatGPT for automated analysis), Cybersecurity (dark web threat patterns, malware detection), Education Technology (gamification, mobile learning usability), and Interdisciplinary studies (gender representation in academia, editorial board dynamics). Recent work emphasizes AI-driven solutions for content analysis, dark web monitoring, and healthcare IT security. Key contributions include methodological frameworks for gender detection via AI, optimization of dark web datasets for OpenAI processing, and UML profiles for accessible web design. His publications also address usability guidelines for mobile apps, continuous authentication systems (e.g., JBCA architecture), and gamification in MOOCs and classroom environments. He has collaborated on projects like the μDevOps platform for cyber ranges, the ProTego healthcare security initiative, and the ECO iMOOC gamification experiment. His work frequently bridges technical innovation with societal impact, emphasizing accessibility, ethical AI, and academic equity.

Craig A. Shueمشاهده پروفایل

استاد

• Computer Security
• Cyber Security
• Network Security
• +۷ مورد دیگر

Craig A. Shue is a Professor and Department Head of Computer Science at Worcester Polytechnic Institute (WPI). He holds affiliations with the Cyber Security department and leads initiatives such as the NSF Scholarship for Service and Department of Defense Cyber Scholarship Program. His expertise spans network security, applied system building, and empirical analysis of security tools. Shue’s research focuses on securing enterprise and residential networks using SDN, NFV, and system instrumentation. He has advised the WPI Cyber Security Club and the Collegiate Cyber Defense Competition Team. His work addresses vulnerabilities in multi-factor authentication, IoT device security, and zero-trust architectures. Education: BS (Ohio University, 2004), MS (Indiana University, 2006), PhD (Indiana University, 2009). Research Interests: Network security, software-defined networking, IoT device protection, edge computing, zero-trust models, and system-level security solutions. His recent projects include user-action linked network profiling, containerized attack mitigation, and smartphone-based traffic inspection. Media Contributions: Provided analysis on the July 2024 CrowdStrike outage for NPR and Boston Globe. His insights address cloud vulnerability, incident response, and cybersecurity provider reliance. Scientific Awards: 2017 NSF CAREER Award, 2016 NSF Transition to Practice Grant, 2014 DHS Young Faculty Award, and Best Paper Awards at IEEE Network of the Future (2022) and LCN (2020). Grants & Programs: Directs WPI’s NSF Scholarship for Service and DoD Cyber Scholarship Program. Active in curriculum design for cybersecurity education aligning with UN SDGs like Quality Education (SDG 4) and Gender Equality (SDG 5). Labs/Teams: Leads research on network profiling, SDN-based security, and IoT resilience. Collaborates with students on projects documented in Digital WPI and his personal website.

Zane Weissmanمشاهده پروفایل

استاد آموزشی

• Microarchitectural Security
• FPGA-CPU Systems
• Cloud System Security
• +۳ مورد دیگر

Zane Weissman is an Assistant Teaching Professor in the Department of Electrical and Computer Engineering at Worcester Polytechnic Institute (WPI). She holds a Ph.D. in Electrical and Computer Engineering from WPI (2024), following B.S. (2019) and M.S. (2019) degrees in the same field. Her teaching focuses on computer engineering with an emphasis on hands-on laboratory work and collaborative project-based learning. Her research explores microarchitectural security vulnerabilities in FPGA-CPU systems and cloud infrastructure, with publications in peer-reviewed journals and a book chapter. She also contributed to environmental studies on sound pollution in national parks. Education Ph.D., Electrical and Computer Engineering, WPI (2024) M.S., Electrical and Computer Engineering, WPI (2019) B.S., Electrical and Computer Engineering, WPI (2019) Research Interests Her research bridges hardware security and cloud computing, investigating vulnerabilities in FPGA-CPU co-designs and serverless platforms. She explores mitigations for microarchitectural attacks like rowhammer and IOTLB-SC leaks, while maintaining a secondary interest in environmental acoustics. Her work emphasizes practical defenses for modern computing systems. Publications Recent work includes analysis of high-frequency bit flip impacts (2025), Firecracker VMM security (2024), and FPGA-driven rowhammer attacks (2019). Earlier environmental studies on sound pollution (2017) reflect a multidisciplinary approach to research. Labs/Teams No specific lab affiliations listed, though her teaching emphasizes collaborative project work in WPI's lab facilities.

Kunle Olukotunمشاهده پروفایل

استاد

• Computer Architecture
• Parallel Computing
• Multicore Processors
• +۵ مورد دیگر

Kunle Olukotun is the Cadence Design Systems Professor of Electrical Engineering and Computer Science at Stanford University, where he has been a faculty member since 1991. He is a pioneer in multicore processor design, leading the Stanford Hydra CMP project and founding Afara Websystems (acquired by Sun Microsystems), which developed the Niagara processor. Currently, he co-leads SambaNova Systems as Chief Technologist and directs the Pervasive Parallelism Lab (PPL), focusing on domain-specific languages (DSLs) and machine learning infrastructure. Education: PhD in Computer Engineering from the University of Michigan (1991). Research interests include parallel computing architectures, transactional memory, and scalable systems. Awards include ACM Fellow, IEEE Fellow, and the Harry H. Goode Memorial Award. Key projects include the Hydra chip multiprocessor, Transactional Coherence and Consistency (TCC), and modern initiatives in dataflow architectures and AI acceleration. His work spans over 100 publications, emphasizing compiler design, hardware-software co-design, and high-performance computing. Current roles: Director of PPL and DAWN Lab, advisor to multiple students, and leader in industry collaborations like SambaNova’s dataflow accelerators. His research bridges academic innovation with commercial impact, addressing challenges in parallelism and scalable systems.

Richard T. Gretzمشاهده پروفایل

دانشیار

• Applied Empirical Industrial Organization
• Quantitative Marketing Modeling
• Applied Econometrics
• +۴ مورد دیگر

Dr. Richard T. Gretz is an Associate Professor of Marketing and Ph.D. Coordinator in the Marketing Department at The University of Texas at San Antonio (UTSA). He holds a Ph.D. in Economics from Claremont Graduate University and undergraduate degrees in Economics and Political Science from Westfield State College. His research focuses on platforms/network effects, product lifecycles, marketing strategy, and entertainment industries (video games/motion pictures). He has published in top journals like Journal of Marketing and Strategic Management Journal . Teaching responsibilities include Applied Econometrics and Principles of Marketing. He received the UTSA Graduate School Outstanding Mentor Award (2022) and the University of Münster Teaching Excellence Award (2021/22). His research has been recognized with the Piccione Endowed Research Award and the Dean’s Distinguished Research Award. He consults for Fortune 500 companies and local governments. Key research themes include platform exploitation dynamics, software-hardware interactions, and the impact of professional vs. user reviews on entertainment industries. He has advised numerous Ph.D. students through UTSA’s Marketing Department program, which recently won an Innovation in Doctoral Programming Award.

• Network Security
• Systems Security
• Wireless Sensor Networks
• +۳ مورد دیگر

Dr. Martin Strohmeier is a post-doctoral researcher at the University of Oxford's Department of Computer Science, specializing in network and systems security with a focus on wireless aviation technologies, critical infrastructure protection, and satellite communication. His research bridges theoretical cybersecurity with real-world applications, particularly in securing air traffic control systems and space-based networks. Education: DPhil (Oxford) in Computer Science; MSc in Computer Science from TU Kaiserslautern, Germany. He previously conducted visiting research at Lancaster University’s InfoLab21 and Lufthansa AG. Research Interests: His work addresses vulnerabilities in aviation cybersecurity, satellite communication protocols, and adversarial machine learning. Notable projects include developing cyber-physical security solutions for air traffic control, analyzing GNSS disruptions, and co-founding the OpenSky Network to crowdsource aviation data for research. Awards: EPSRC Doctoral Prize Award (2021) for his doctoral work on wireless aviation security. OpenSky Network: Co-founder and active contributor to this collaborative platform that aggregates and analyzes global flight data to improve aviation safety and environmental impact studies. Current Projects: Investigating satellite authentication mechanisms, jamming-resistant systems, and personalized phishing defense strategies through augmented reality training.