جست‌وجوی استاد و پژوهشگر

• Trustworthy AI
• Trustworthy Computing
• Privacy
• +۶ مورد دیگر

Jeannette M. Wing is a prominent academic and researcher in computer science. She holds the positions of Executive Vice President for Research, Avanessians Director of the Data Science Institute, and Professor of Computer Science at Columbia University. Additionally, she is a Consulting Professor of Computer Science at Carnegie Mellon University. Her research focuses on trustworthy AI, privacy, security, and foundational aspects of computing. She has held leadership roles, including Department Head of CMU's Computer Science Department and Assistant Director of the NSF's CISE Directorate. Wing's education includes a Ph.D. (1983), S.M. (1979), and S.B. (1979) in Computer Science from MIT. She has authored over 150 publications, including influential works on computational thinking and trustworthy systems. Her leadership in initiatives like the Data Science Institute emphasizes interdisciplinary collaboration and societal impact. Awards include the PROSE Award for her book Data Science in Context . Her service spans editorial boards (e.g., Communications of the ACM ), advisory roles (e.g., DARPA ISAT), and governance (e.g., National Academies). Wing's vision bridges technical innovation with ethical and societal considerations, shaping future directions in computing and data science.

Vassilis P. Plagianakosمشاهده پروفایل

دانشیار

• Machine Learning
• Neural Networks
• Bioinformatics
• +۵ مورد دیگر

Vassilis P. Plagianakos is an Associate Professor at the Department of Computer Science and Biomedical Informatics, University of Thessaly, Greece. He has held visiting academic roles at the University of the Aegean, University of Patras, and University of Central Greece. He currently serves as the Department Head and Director of the postgraduate program Informatics and Computational Biomedicine in the School of Sciences. His research focuses on machine learning, neural networks, bioinformatics, and parallel computing with applications in healthcare and education. Education : Bachelor’s in Mathematics (1996), University of Patras Ph.D. in Mathematics (2003), University of Patras Research Interests : Plagianakos explores neural networks, evolutionary algorithms, and machine learning applications in bioinformatics, medical diagnosis, and educational technology. His work bridges computational methods with real-world challenges in healthcare (e.g., precision medicine) and STEM education (e.g., flipped classrooms, AI integration). Recent Trends in Publications : Recent work emphasizes predictive precision medicine using big data, blockchain scalability solutions, and AI ethics in automated content detection. He has pioneered methods like the HCER hierarchical clustering-ensemble regressor and developed tools for analyzing single-cell RNA sequencing data. Professional Activities : Member of IEEE Neural Networks Society, IEEE BBTC, and former Board Member of the Hellenic AI Society. Active in collaborative projects like CrowdHEALTH for policy-driven health data analytics.

Tom Henzingerمشاهده پروفایل

استاد

• Formal Methods
• Software Engineering
• Computer Science
• +۵ مورد دیگر

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.

• Automated Theorem Proving
• ATP System Evaluation
• Distributed Reasoning Systems
• +۲ مورد دیگر

Geoff Sutcliffe is a Professor in the Department of Computer Science at the University of Miami's College of Arts and Sciences. He holds a PhD from the University of Western Australia (1992), MSc from the University of Natal (1986), and BSc (Hons) from the University of Natal (1983). His research focuses on Automated Theorem Proving (ATP), evaluation methodologies for reasoning systems, and distributed computational frameworks. His primary research areas include developing infrastructure for ATP systems through the TPTP (Thousands of Problems for Theorem Provers) World project, organizing the CADE ATP System Competition (CASC), and creating standards for logic representation. His work bridges theoretical foundations with practical implementations in AI and computational logic. Research publications demonstrate sustained focus on ATP system improvements, logical framework extensions, and historical analyses of automated reasoning. Recent works explore non-classical logics, semantic web integrations, and performance benchmarking techniques. Scholarship consistently emphasizes practical tool development alongside theoretical advances.

Nausheen Husainمشاهده پروفایل

استادیار

• Civil liberties
• Data journalism
• Muslim communities
• +۷ مورد دیگر

Nausheen Husain is an Assistant Professor in the Magazine, News and Digital Journalism department at Syracuse University's S.I. Newhouse School of Public Communication. She is a tenure-track faculty member specializing in data journalism, civil liberties issues, and coverage of Muslim communities in the context of the 'War on Terror.' As a 2023-2025 Lender Center for Social Justice faculty fellow, she leads research examining American news coverage of post-9/11 security infrastructures and their impact on Muslim communities. Her research interests focus on the intersection of data journalism and underrepresented communities, particularly examining how surveillance, incarceration, and civil liberties issues affect Muslim Americans. She investigates how media coverage shapes public understanding of 'War on Terror' infrastructures, including communication management units, FBI stings targeting young Muslim men, and surveillance operations in Muslim neighborhoods. Her work emphasizes using data analysis and documents-focused reporting to bolster stories about civil rights issues and inequities. Husain's recent publications demonstrate a consistent focus on civil liberties, incarceration systems, and the impacts of security policies on marginalized communities. Her investigative work often employs FOIA requests and data analysis to uncover patterns in prison conditions, surveillance practices, and policy implementation. She has developed expertise in using R and Python for data parsing, Datawrapper for visualization, and MAXQDA for research-based data analysis. Scientific Awards: 2023-2025 Lender Center for Social Justice faculty fellow Husain actively mentors students in data journalism practices and has incorporated Newhouse students into investigative projects, such as the analysis of Muslims affected by the 2017 Muslim Ban. Her academic research connects with practical journalism applications, focusing on how data can be used to cover underrepresented communities while maintaining ethical standards. She frequently speaks about news coverage of the 'War on Terror,' data journalism techniques, and the limitations of using data in reporting. Husain leads projects including 'Islam For Reporters,' which tracks anti-Muslim hate crime data and provides resources on Islam-related terminology, and 'Driving Force,' an investigation into New York state police vehicle crashes. Her approach combines technical data skills with deep community engagement, particularly with Muslim communities affected by security policies.

• Power Systems
• Renewable Energy
• Electric Vehicles
• +۲ مورد دیگر

Haitham Abu-Rub is a Professor at Texas A&M University at Qatar specializing in power systems, renewable energy integration, and power electronics. His research focuses on developing innovative solutions for grid stability, EV charging infrastructure, and intelligent control systems. He has published extensively in IEEE journals and conferences, addressing challenges in smart grids and sustainable energy systems. His work spans power converter design, fault diagnosis, and AI applications in energy management. Recent projects include decentralized PV trading systems, resilient inverter networks, and physics-informed neural networks for insulation diagnostics. Dr. Abu-Rub collaborates internationally on projects involving grid-interactive buildings, digital twins for power converters, and adaptive control techniques for electric vehicle charging.

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۴ مورد دیگر

Rizwan Qureshi is an active researcher and academic specializing in artificial intelligence, machine learning, and their applications in medical imaging and bioinformatics. With a robust publication record spanning from 2017 to 2025, he has established himself as a significant contributor to the fields of computer vision and biomedical AI. His research interests focus on Artificial Intelligence , Machine Learning , Medical Imaging , Computer Vision , and Biomedical Engineering . Qureshi's work demonstrates particular expertise in object detection systems (especially YOLO variants), medical image segmentation, vision-language models, and applications of AI to healthcare problems including lung cancer research and diabetic retinopathy detection. Analysis of his recent publications (2023-2025) reveals a strong trend toward medical applications of AI, with approximately 60% of his work focusing on healthcare-related problems. His research shows increasing emphasis on model robustness, explainability, and handling distribution shifts in real-world applications. The publications span top venues including IEEE Access, IEEE Transactions on Medical Imaging, CVPR, and BIBM. Qureshi maintains extensive collaborations with researchers across multiple institutions, with frequent co-authorship with Hong Yan, Tanvir Alam, Jia Wu, and Sheheryar Khan. His work demonstrates both technical depth in machine learning methodologies and practical application to significant healthcare challenges. While specific details about his academic advising are not evident from the publication record alone, his numerous publications with multiple co-authors suggest active participation in research teams and likely supervision of graduate students. His work shows consistent funding support through publication in reputable journals and conferences.

Kai Salomaaمشاهده پروفایل

استاد

• Theory of Computation
• Automata Theory
• Formal Languages
• +۵ مورد دیگر

Kai Salomaa is a Professor and Graduate Chair in the School of Computing at Queen’s University, Canada. He holds a Ph.D. from the University of Turku (1989). His research focuses on theoretical computer science, particularly automata theory, formal languages, and their applications. Key areas include descriptional complexity, cellular automata, and quantum computing innovations. Affiliations: Queen’s University, School of Computing. Education: Ph.D. in Computer Science from the University of Turku (1989). Research Interests: Prof. Salomaa explores foundational topics like automata state complexity, nondeterminism measures, and computational models. His work bridges classical theory with modern applications in quantum computing, vehicular networks, and algorithmic resource optimization. Notable contributions include studies on input-driven pushdown automata and the integration of quantum algorithms into practical systems. Publications: His recent work spans quantum-enhanced optimization (e.g., vehicle platooning), fair matching algorithms, and complexity analysis of automata. These studies emphasize innovative solutions for computational challenges in dynamic systems and distributed networks. Grants & Labs: Leads the Formal Languages and Automata Theory Research Group, actively organizing conferences like CIAA and DCFS. His work often addresses practical applications of theoretical computer science in areas like sensor networks and metaverse resource management.

Risteski Aleksandarمشاهده پروفایل

استاد

• Telecommunications
• Cybersecurity
• Blockchain Technology
• +۵ مورد دیگر

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.

Glen Chouمشاهده پروفایل

استادیار

• Robotics
• Trustworthy Autonomy
• Machine Learning
• +۴ مورد دیگر

Glen Chou is an Assistant Professor in the Department of Electrical and Computer Engineering at Georgia Tech. He holds dual B.S. degrees from UC Berkeley (Electrical Engineering & Computer Science and Mechanical Engineering) and M.S./Ph.D. degrees from the University of Michigan (Electrical & Computer Engineering). Prior to Georgia Tech, he was a postdoctoral researcher at MIT CSAIL. Chou directs the Trustworthy Robotics Lab, focusing on algorithms for general-purpose robots and autonomous systems to ensure capability, safety, and resilience in real-world conditions. His research integrates control theory, machine learning, optimization, and formal methods. He emphasizes both theoretical foundations (e.g., proving algorithmic properties) and practical deployment on real robots. Key research areas include trustworthy autonomy, human-robot interaction, and constraint learning from demonstrations. His lab addresses challenges in perception, motion planning, and system reliability through interdisciplinary approaches. Chou has received notable awards including the Robotics: Science & Systems Pioneer (2022), NDSEG Fellowship (2019), and NSF Graduate Fellowship (2019). His work bridges foundational theory with real-world applications, with a focus on safety-critical systems.

Moharram Challengerمشاهده پروفایل

استادیار

• Cyber-physical Systems
• Multi-agent Systems
• Domain-specific Modeling Languages
• +۵ مورد دیگر

Moharram Challenger is a tenure-track Assistant Professor in the Department of Computer Science at the University of Antwerp's Faculty of Sciences. Previously, he served as an assistant professor at Ege University (2017-2018) and as a post-doctoral researcher at the University of Antwerp (2019-2020) working on Flanders Make projects PACo and DTDesign. His academic journey includes R&D leadership roles at UNIT IT Ltd. (2012-2016), post-doctoral research at Wageningen University (2016-2017), and tenure-track faculty positions at IAU-Shabestar University (2005-2009). His research spans Cyber-physical Systems , Multi-agent Systems , and Domain-specific Modeling Languages , with recent publications focusing on quantum machine learning, digital twinning, and IoT optimization. Key projects include ITEA ModelWriter, ITEA Assume, and Flanders Make initiatives. His work demonstrates strong integration of model-driven engineering with emerging technologies like quantum computing and reinforcement learning. Challenger actively contributes to the academic community as a member of IEEE and ACM . His publication record shows consistent output across top venues, with 2025 featuring significant work in quantum-enhanced learning and CPS security. Current research emphasizes practical applications in drone energy modeling, medical diagnostics, and industrial IoT systems. His advising activities focus on cyber-physical systems and agent-based modeling, supported by grants from TUBITAK and Flanders Innovation & Entrepreneurship. Key collaborations include European ITEA projects and partnerships with industrial entities through UNIT IT Ltd. Challenger maintains active development through GitHub repositories related to code refactoring, model-driven engineering, and legacy system modernization, reflecting his commitment to practical software engineering solutions.

• Distributed Systems
• Service-Oriented Computing
• Cloud Computing
• +۳ مورد دیگر

Marco Aiello is affiliated with the Vienna University of Technology (TU Wien), Department of Distributed Systems within the Faculty of Informatics. His work focuses on distributed systems, service-oriented computing, and cloud computing, with contributions to edge computing and IoT. He has edited multiple conference proceedings, including the 2023 SummerSOC conference and the 2016 Service-Oriented and Cloud Computing volume. His research includes projects like TEADAL (2022–2025) and SM4ALL (2008–2011), exploring middleware for pervasive environments and home automation. Aiello has authored influential papers on web service indexing, QoS composition, and embedded systems. He received the 2006 Web Service Challenge award for his indexing work. Education details: PhD in Informatics (not explicitly listed but inferred from role). His research interests span distributed systems' theoretical foundations and practical implementations, emphasizing accessibility and scalability. Recent publications highlight trends in serverless architectures and edge-based IoT processes. He is actively involved in academic publishing, serving as an editor and conference organizer. Key Projects : TEADAL (2022–2025), SM4ALL (2008–2011) Awards : 2006 Web Service Challenge (2nd place) Grants : FFG-funded project (2009–2011) Labs/Teams: Part of TU Wien's Distributed Systems research group, collaborating on middleware and service-oriented technologies.

Xue Linمشاهده پروفایل

دانشیار

• Robust and Secure Machine Learning
• Deep Learning on the Edge
• Cyber-Physical Systems
• +۳ مورد دیگر

Xue Lin is an Associate Professor in the Department of Electrical and Computer Engineering at Northeastern University, with a courtesy appointment in Khoury College of Computer Science. She joined Northeastern in 2017 and holds a PhD from the University of Southern California (2016) and a bachelor’s from Tsinghua University. Her research focuses on robust and secure machine learning, deep learning on edge devices, and cyber-physical systems. She leads the High Energy-Efficiency & Performance System Lab, which develops efficient algorithms and systems for applications like autonomous vehicles and medical AI. Dr. Lin’s work is supported by NSF, DARPA, and the U.S. Department of Transportation, among others. Notable achievements include a $1M DARPA grant for adversarial diagnosis systems, a 1st Place ISLPED 2020 Design Contest win, and multiple best paper awards. She has advised students such as Kaidi Xu (PhD’21), Mengshu, and Siyue, who have contributed to impactful projects like adversarial T-shirt attacks and FPGA-based DNN accelerators. Her research also addresses security in autonomous systems and inclusive design challenges for older and visually impaired passengers. Key grants include NSF CPS Small Awards, SaTC Medium Awards, and collaborations with institutions like the University of Maine and Michigan State University. Awards include the 2024 Faculty Fellow Award and recognition in Stanford’s top 2% cited scientists. Her lab’s projects span secure autonomous systems, energy-efficient inference frameworks (e.g., GRIM), and robust neural network verification techniques.

Sam Lindleyمشاهده پروفایل

دانشیار

• Programming Languages
• Formal Methods
• Type Systems
• +۳ مورد دیگر

Sam Lindley is a Reader in Programming Language Design and Implementation at the School of Informatics, University of Edinburgh. He holds a UKRI Future Leaders Fellowship in Effect Handler Oriented Programming. His research focuses on foundational aspects of programming languages, including effect systems, session types, type theory, and functional programming. Key research interests span algebraic effects, concurrency models, type systems for resource management, and language implementation techniques. Lindley's work bridges theory and practice, addressing challenges in systems programming, web technologies (e.g., WebAssembly), and compiler design. Recent publications (2023–2025) emphasize effect handlers for low-level languages (e.g., C, WebAssembly), modal type systems for memory management, and formal semantics for concurrent systems. His work often explores how advanced type systems can improve expressivity and efficiency in programming models. Scientific Awards: UKRI Future Leaders Fellowship Labs/Teams: Laboratory for Foundations of Computer Science (LFCS) Grants: Active funding via UKRI Fellowship

Jonathan Balkindمشاهده پروفایل

استادیار

• Computer Architecture
• Operating Systems
• Programming Languages
• +۳ مورد دیگر

Jonathan Balkind is an Assistant Professor in the Department of Computer Science at the University of California, Santa Barbara (UCSB). His research focuses on the intersection of computer architecture, programming languages, and operating systems, with an emphasis on pragmatic system design and open-source hardware. He leads the ArchLab at UCSB and is affiliated with the OpenPiton project, an open-source manycore research framework. Education includes a PhD and MA in Computer Science from Princeton University (adviser: Prof. David Wentzlaff), an MSci in Computing Science from the University of Glasgow (advisers: Prof. Joseph Sventek and Dr. John O'Donnell), and exchange studies at UCSB. His work has been supported by awards such as the NSF Early CAREER Award (2023) and the Open Hardware Trailblazer Fellowship (2022). Research interests span heterogeneous computing, cache-coherent systems, FPGA integration, and domain-specific architectures. Notable projects include the 25-core Piton chip, the CIFER SoC with embedded FPGA, and the DECADES manycore processor. Recent publications address fused-kernel operating systems (Stramash), control logic synthesis, and hyperloop data-center architectures. His awards reflect contributions to open-source hardware and academic mentorship, including Siebel Scholarship (2018), Gordon Y.S. Wu Fellowship (2013–2017), and multiple teaching/research recognitions. He actively collaborates with industry (e.g., Microsoft Research, ARM) and advises on open-source projects.