جست‌وجوی استاد و پژوهشگر

• Biomedical Engineering
• Entropy Analysis
• Bubble Entropy
• +۱۳ مورد دیگر

George Manis is an Associate Professor in the Department of Computer Science and Engineering at the School of Engineering, University of Ioannina, Greece. He is a member of the IPAN lab and serves as a Guest Editor for a special issue on 'Entropy in Biomedical Engineering' in the journal Entropy . His research bridges biomedical engineering and computational systems, with a strong focus on entropy-based methods and machine learning for physiological signal analysis. PhD in Computer Science, National Technical University of Athens (1993–1997) MSc in Advanced Methods in Computer Science, Queen Mary, University of London (1992–1993) Diploma in Electrical and Computer Engineering, National Technical University of Athens (1987–1992) His primary research interests include Biomedical Engineering , Entropy Analysis (especially Bubble Entropy), Fast Computation Algorithms , Biomedical Data Classification , Heart Rate Analysis , and Computing Systems with emphasis on parallelization. He teaches postgraduate courses in data analysis and biomedical data processing, and undergraduate courses in compilers. The analysis of his recent publications reveals a consistent trend in developing computationally efficient and robust methods for entropy estimation and classification in biomedical contexts. His work on Bubble Entropy eliminates the need for scale parameters and enhances stability, while his improvements to Random Forests and Support Vector Machines target enhanced diagnostic accuracy in diseases like Alzheimer’s and heart conditions. In computing systems, he has pioneered compiler technologies for parallelizing recursive functions and optimizing runtime scheduling. No scientific awards are explicitly mentioned in the provided text. George Manis has advised or collaborated with several researchers, including Evanthia Tripoliti, Dimitrios Fotiadis, Petros Arsenos, Argyro Kampouraki, and others, indicating active supervision and research leadership. He is currently involved in funded projects such as Palimpsest , an interactive museum system, and Homore , a smart monitoring system for elderly individuals. These projects reflect his interest in applying advanced computing to real-world biomedical and societal challenges. He is affiliated with the IPAN lab and contributes to the development of innovative algorithms for entropy computation and parallel processing. His work on the C2μTC/SL compiler and Ariadne system demonstrates deep expertise in compiler design for specialized architectures like the SVP processor.

Hui Liمشاهده پروفایل

دانشیار

• Information Retrieval
• Data Mining
• Data Management
• +۸ مورد دیگر

Hui Li is an Associate Professor in the Department of Computer Science and Technology at the School of Informatics, Xiamen University. He is affiliated with the Media Analytics & Computing Laboratory (MAC Lab) and holds a Ph.D. in Computer Science from the University of Hong Kong. Ph.D., Computer Science, University of Hong Kong His research centers on Information Retrieval, Data Mining, and Data Management, with a strong emphasis on Recommender Systems. Key areas include shilling attacks and defenses, bias analysis and debiasing, large language model-based recommendation, generative information retrieval, code retrieval, and multi-modal large language models. His work bridges theoretical innovation with practical applications in real-world platforms. His recent publications span top-tier journals and conferences such as IEEE TKDE, TNNLS, TOIS, KDD, SIGIR, AAAI, ACL, and ICLR. The publications demonstrate a consistent focus on robustness, fairness, and efficiency in recommender systems, increasingly integrating large language models and graph-based methods. Themes include adversarial attacks, self-supervised learning, and data augmentation using LLMs. Hui Li actively supervises graduate students, many of whom have published in CCF-A and CCF-B ranked venues. Notable students include Zhang Sheng, Liu Hao, and Li Lianyun. He has contributed to open science by releasing datasets like the Dianping datasets for sequential and social recommendation research. His work has practical implications in e-commerce, social platforms, and software engineering tools. He collaborates with industry partners such as Alibaba, Tencent, and Pinduoduo, where several of his students have been placed. His research is supported by publications in high-impact journals and participation in leading AI conferences.

Adam Chlipalaمشاهده پروفایل

استاد

• Programming Languages
• Formal Methods
• Computer Systems
• +۱۲ مورد دیگر

Adam Chlipala is the Arthur J. Conner (1888) Professor of Computer Science at the Massachusetts Institute of Technology (MIT), where he is a faculty member in the Department of Electrical Engineering and Computer Science (EECS), the Computer Science and Artificial Intelligence Laboratory (CSAIL), and leads the Programming Languages & Verification Group. He has been a faculty member at MIT since 2011, following a postdoctoral position at Harvard University. PhD in Computer Science, UC Berkeley (2007) BS in Computer Science, Carnegie Mellon University (2003) His research lies at the intersection of programming languages and formal methods, with a strong emphasis on using the Coq proof assistant to build verified compilers, cryptographic systems, and hardware-software stacks. His work spans from high-level language design to gate-level hardware verification, aiming for end-to-end correctness proofs. Recent efforts focus on high-performance parallel computing systems with full formal assurance. The 15 most recent publications highlight a consistent trend in verified compilation, cryptographic security, hardware verification, and tensor/ML program optimization. His work increasingly integrates software and hardware verification, emphasizing modular, extensible frameworks and end-to-end correctness. Key themes include side-channel resistance, automated proof techniques, and practical deployment of formally verified systems. Advisory Board Member, BlueRock Systems (formerly BedRock Systems) Member, DARPA Information Science and Technology (ISAT) Study Group (2018–2022) Advisory Board Member, SiFive Former Advisor, krypt.co (acquired by Akamai) Chlipala has advised numerous PhD and Master’s students and regularly teaches core MIT courses such as 6.009 (Fundamentals of Programming), 6.042 (Mathematics for Computer Science), and 6.822/6.5120 (Formal Reasoning About Programs). He is the author of the widely used textbook Certified Programming with Dependent Types and co-developer of the FRAP (Formal Reasoning About Programs) educational materials. He is also the founder of Nectry, a startup based on Ur/Web and UPO, aiming to democratize enterprise application development through AI-assisted, type-safe programming. His research group develops tools and frameworks for modular verification, verified compilation, and formal analysis of complex digital systems. The work is deeply collaborative, involving students, industry partners, and open-source contributions via GitHub. Projects like Fiat Cryptography have been deployed in major web browsers, demonstrating real-world impact.

• Innovation Management
• Digital Platforms
• Mobile App Development
• +۵ مورد دیگر

Nilam Kaushik is an Honorary Research Fellow at the University College London (UCL) School of Management. She is part of the Operations & Technology group, focusing on innovation management in digital platforms such as mobile apps and crowdsourcing systems. Her research employs interdisciplinary methods like text-mining, econometrics, and field experiments. She is affiliated with the IoT Open Innovation Lab (http://beta.iotopeninnovation.org/). Starting June 2019, she will join the Indian Institute of Management Bangalore (IIM Bangalore) as an Assistant Professor. Her research interests include understanding how digital platforms organize for innovation, the operational drivers of innovation in tech ecosystems, and the impact of gender differences in competitive organizational settings across STEM and non-STEM fields. Methodologically, she combines quantitative and experimental approaches to analyze innovation dynamics and software engineering challenges. Key contributions include studies on defect detection in software systems, prioritization in regression testing, and sequential innovation models in mobile app development. Her work bridges operational management, technology innovation, and gender studies in tech environments. Nilam’s publications span topics from IoT platform analysis to industrial software practices, reflecting her dual focus on theoretical innovation management and applied software engineering challenges.

Jay Ponderمشاهده پروفایل

استاد

• Computational Chemistry
• Molecular Dynamics
• Force Field Development
• +۳ مورد دیگر

Jay Ponder is Professor of Chemistry, Biochemistry and Molecular Biophysics, and Biomedical Engineering at Washington University in St. Louis. His research develops computational methods for structural biology and protein engineering, focusing on next-generation polarizable force fields (AMOEBA) for accurate prediction of molecular structures and interactions. Research areas include molecular dynamics with multipole electrostatics, potential smoothing methods for conformational search, distance geometry algorithms for protein folding, and crystal structure prediction. The TINKER software package implements these computational approaches. Ponder's group integrates high-performance computing with advanced algorithms for large-scale biomolecular simulations and free energy calculations.

William Regliمشاهده پروفایل

استاد

• Artificial Intelligence
• Robotics
• Computational Modeling
• +۹ مورد دیگر

William Regli is a Professor at the University of Maryland's Clark School of Engineering, holding appointments in Computer Science, Electrical and Computer Engineering, and the Institute for Systems Research. He also directs the Applied Research Laboratory for Intelligence and Security (ARLIS), overseeing over 75 researchers focused on defense and intelligence challenges. Regli's career spans academia, government leadership (including DARPA's Defense Sciences Office), and industry, with over 250 publications and five foundational U.S. patents in 3D CAD search. His research integrates AI, robotics, and computational modeling to address interdisciplinary problems in engineering, materials science, and national security. Education: Ph.D. and B.S. in Mathematics from the University of Maryland and Saint Joseph's University, respectively. He is a Fellow of AAAS and IEEE, recognized for contributions to 3D search and intelligent manufacturing. Research interests include AI agents, cyber-infrastructure for engineering data sustainability, advanced materials design, and robotic interoperability using category theory. His recent work emphasizes applying AI to national security, including trustworthy AI evaluation and social science integration. Awards include the DARPA Meritorious Public Service Medal (2018), AAAS Fellowship (2020), and IEEE Fellowship (2017). His articles reflect a focus on multi-agent systems, robotic task planning, and formal methods in autonomous systems. He advises multiple PhD students and has spun off two tech startups. Regli's laboratories include ARLIS, where he develops solutions for defense missions, and collaborates with the Maryland Robotics Center. His work bridges academia and government, emphasizing real-world impact through innovative computing research.

• Machine Learning
• Deep Learning
• Computer Vision
• +۸ مورد دیگر

Dr. Amir Atapour-Abarghouei is an Assistant Professor in the Department of Computer Science at Durham University, UK, and a Fellow of the Wolfson Research Institute for Health and Wellbeing. He leads the VIViD (Vision, Imaging and Visualisation in Durham) research group. Previously, he held roles at Newcastle University and Shahid Bahonar University of Kerman (Iran). His research focuses on machine learning, deep learning, computer vision, 3D scene understanding, and natural language processing. Notable contributions include the GANomaly anomaly detection framework, now part of Intel's AI products. Education : Ph.D., Computer Science, Durham University (UK) M.Sc., Computer Science, Universiti Teknologi Malaysia (Malaysia) B.Sc., Computer Engineering, Shahid Bahonar University of Kerman (Iran) Research Interests : His work spans machine learning, deep learning, image processing, 3D scene analysis, and robotics. Key areas include depth estimation, domain adaptation, semantic segmentation, and causal-based models for action quality assessment. Recent projects involve datasets like DurTOMD and Dur360BEV for autonomous systems and image inpainting techniques (e.g., HINT, SEM-Net). Advising & Grants : He supervises over 15 postgraduate students and has contributed to grants focused on AI-driven systems in healthcare, robotics, and computer vision. His team's work on GANomaly and neural architecture search (NAS) has been widely cited and applied in industry. Labs/Teams : Leads the VIViD Research Group and collaborates on interdisciplinary projects involving healthcare imaging, autonomous vehicles, and ethical AI. Active in organizing workshops at CVPR, IEEE BigData, and the BMVA Summer School.

Rodney Howellمشاهده پروفایل

دانشیار

• Real-Time Systems
• Self-Stabilization
• Petri Nets
• +۵ مورد دیگر

Rodney Howell is an Associate Professor and Undergraduate Programs Director in the Department of Computer Science at Kansas State University (KSU). He holds a Ph.D. (1988) and B.S. (1984) in Computer Science from the University of Texas at Austin and Wichita State University, respectively. His research expertise includes Real-Time Systems, Self-Stabilization, Petri Nets, and Computational Complexity. He authored the textbook Algorithms: A Top-Down Approach (2023) and has directed seven Master’s theses. Key awards include the Kansas State Scholarship (1980–1984), McGregor Scholarship (1980–1984), and Microelectronics and Computer Development Fellowship (1984–1986). He has organized the KSU High School Programming Contest since 1992 and contributed to grants such as the NSF-funded Software Control Laboratory (1993–1995). His advising includes students like Muralidhar Venkatrao, Thiagarajan Rajagopalan, and Vineet Tadakamalla. His work spans over 30 years at KSU, including roles like Special Assistant to the Dean (2019–2020) and involvement in academic outreach.

• Artificial Intelligence
• Healthcare Informatics
• Data Science
• +۵ مورد دیگر

Wajahat Ali Khan is an Associate Professor in Artificial Intelligence at the University of Derby's College of Science and Engineering. His research focuses on AI-driven solutions for healthcare informatics, cybersecurity, and data integration. He leads interdisciplinary projects involving knowledge graphs, semantic web technologies, and clinical decision support systems. Research interests include applying machine learning to healthcare challenges such as disease subtyping, health data interoperability, and personalized medicine. He also explores cybersecurity in network systems and SDN environments. Key contributions include platforms like the Ubiquitous Health Profile (UHPr) for health data interoperability and the Intelligent Medical Platform for dialogue-based healthcare services. His publications span topics from anti-DDoS models to sentiment analysis for depression detection. Collaborations with institutions like Sungkyunkwan University (South Korea) highlight global impact. Khan’s work emphasizes bridging semantic gaps in healthcare systems through adaptive mediation frameworks and data-driven knowledge acquisition methods.

Daniel Huangمشاهده پروفایل

استادیار

• Quantum Computing
• Machine Learning
• Probabilistic Programming
• +۳ مورد دیگر

Dr. Daniel Huang is an Assistant Professor in the Department of Computer Science at San Francisco State University. His research focuses on quantum computing, probabilistic programming, machine learning, and theoretical computer science. He explores interdisciplinary areas such as hybrid classical-quantum systems, Gaussian process optimization, and computational chemistry modeling. His work bridges algorithmic design with practical applications, including quantum circuit simulation and molecular geometry optimization. Dr. Huang’s recent publications highlight advancements in GPU-based quantum computing, gradient-constrained neural networks, and probabilistic programming languages like Push. He emphasizes the integration of physical priors into machine learning models and explores disruptive technologies like quantum visualization tools. His research often involves collaborative projects, as seen in works on meta-Gaussian processes and data-parallel inference algorithms. His academic contributions span over a decade, with notable papers in probabilistic program semantics, logic in linear spaces, and compiler optimizations for probabilistic models. Though no awards or grants are explicitly listed, his active publication record reflects sustained scholarly engagement. Contact: danehuang@sfsu.edu , Thornton Hall 906.

• Mathematical Modeling
• Cell Motility
• Spatiotemporal Processes
• +۷ مورد دیگر

Prof. Franziska Matthäus is a Professor at Goethe University Frankfurt and a Fellow at the Frankfurt Institute for Advanced Studies (FIAS). Her research focuses on mathematical modeling of spatiotemporal processes in biological systems, particularly cell motility, cancer migration, and developmental biology. She leads a multidisciplinary group collaborating with experimental partners to integrate data analysis, agent-based models, and partial differential equations (PDEs) into theoretical frameworks. Notable contributions include the 2020 book The Art of Theoretical Biology , which showcases visually striking scientific images from biological research, and the development of QuickPIV software for 3D particle image velocimetry. Education: PhD in Biophysics (University of Warsaw, 2005), postdoctoral work at Heidelberg University, and a junior professorship at the University of Würzburg before joining FIAS in 2016. She currently holds the Giersch Endowed Professorship. Research Interests: Agent-based modeling of collective cell behavior, reaction-diffusion systems in developmental patterning, and force inference in epithelial tissues. Her work bridges computational methods with experimental data, addressing questions in organoid morphogenesis, cancer metastasis, and embryonic development. Teaching: Offers courses in theoretical biology and bioinformatics, including modules on data analysis, mathematical modeling, and programming for biological systems. Courses are taught in German and English. Labs/Teams: Active in FIAS's Life and Neurosciences group, focusing on multiscale analyses of biological systems. Collaborates with institutions globally, including the University of Leeds and the University of Alberta.

Robert H Dengمشاهده پروفایل

استاد

• Applied cryptography
• Data security and privacy
• Mobile and wireless security
• +۵ مورد دیگر

Professor Robert H. Deng is a distinguished academic and researcher at Singapore Management University's School of Computing and Information Systems, where he serves as Professor of Computer Science and Deputy Dean for Faculty & Research. With a PhD from Illinois Institute of Technology (1985), he has established himself as a leading authority in cybersecurity, data privacy, and applied cryptography. His research interests span applied cryptography, data security and privacy, mobile and wireless network security, trusted computing, and system security. Professor Deng has published extensively in top-tier security venues, with over 600 publications listed in DBLP. His work bridges theoretical foundations with practical applications, particularly in the domains of IoT security, blockchain systems, and privacy-preserving technologies. Professor Deng has received numerous prestigious awards, including the National Day Award (Public Administration Medal Silver) in 2020, the AXA Chair Professor of Cybersecurity (2017-2025), and the Lee Kuan Yew Fellow for Research Excellence at SMU in 2006. His recent publications demonstrate continued leadership in emerging areas like post-quantum cryptography, federated learning security, and hardware-assisted security systems. His research group has produced significant work on secure authentication protocols, privacy-preserving data analytics, and robust cryptographic systems for real-world applications. National Day Award, Public Administration Medal (Silver), 2020 AXA Chair Professor of Cybersecurity, AXA Research Fund, 2017-2025 Distinguished & Best Paper Awards at NDSS, ESORICS, IEEE ICPADS, etc. Asia-Pacific Information Security Leadership Achievements (ISLA), 2010 Lee Kuan Yew Fellow for Research Excellence, SMU, 2006 University Outstanding Researcher, National University of Singapore, 1999 As a research advisor, Professor Deng has mentored numerous students including SUN Bing. His active participation in the research community is evidenced by recent keynotes at major conferences including DSPP 2024 on post-quantum cryptography and ASIACCS 2024 on client-side encryption. His research group maintains strong industry connections and receives significant funding for cutting-edge security research.

• Software Engineering
• Natural Language Processing
• Grey Literature
• +۳ مورد دیگر

Dr. Ashley Williams is a Senior Lecturer in Software Engineering at Manchester Metropolitan University. Their research focuses on software engineering practices, grey literature analysis, natural language processing applications, and cybersecurity education. They maintain an active research profile with publications spanning software engineering methodologies, NLP applications in security, and mental health game design. Key research areas include: Credibility assessment of practitioner-generated content Grey literature retrieval and mining techniques Natural language processing for cybersecurity Research-practice gaps in software engineering Therapeutic game design for mental health Their work frequently employs innovative methodologies like case survey approaches and corpus linguistics for analyzing professional knowledge sources. Recent publications demonstrate evolving interests in applied NLP for security challenges (homoglyph attacks) and expanding into interdisciplinary domains like mental health intervention design. Their cybersecurity skills gap research contributes to curriculum development and workforce planning. Dr. Williams teaches software engineering courses and supervises student research projects. They are based in the John Dalton Building and maintain office hours on Tuesdays and Thursdays.

Wenbin Zhangمشاهده پروفایل

استادیار

• Societal Aspects of AI
• Generative AI
• Health Informatics
• +۵ مورد دیگر

Wenbin Zhang is an Assistant Professor in the Knight Foundation School of Computing & Information Sciences at Florida International University and an Associate Member at the Te Ipu o te Mahara Artificial Intelligence Institute. His research focuses on the theoretical foundations of machine learning with societal impact, including fairness, generative AI, health informatics, and interdisciplinary applications in healthcare, digital forensics, and energy. He has received awards such as the NSF CRII Award and recognition in the AAAI’24 New Faculty Highlights. Zhang serves in organizing committees for major conferences like AAAI, WSDM, and AIES, contributing to academic leadership. Ph.D., University of Maryland, Baltimore County (2020) Research interests span societal aspects of AI, generative models, and fairness-aware machine learning. His work bridges theory and practice, addressing ethical challenges in AI deployment across domains like healthcare and cybersecurity. Key contributions include frameworks for fair graph learning, bias mitigation in LLMs, and adaptive pruning techniques for large models. Publications emphasize fairness in ML systems, digital forensics, and interdisciplinary applications. Over 50 papers span venues like FAccT, ICDM, and AAAI, with multiple best-paper recognitions. His NSF-funded research highlights innovation in fair AI and robust model adaptation. Scientific Awards: NSF CRII Award, FAccT’23 Best Paper Candidate, ICDM’23 Best Paper Academic service includes roles as Travel Award Chair (AAAI’24), Volunteer Chair (WSDM’24), and Student Program Chair (AIES’23). Teaching and mentorship are integral to his mission, fostering next-generation AI researchers through rigorous training in ethics-driven innovation. Labs/Teams: Active collaborations with the Te Ipu o te Mahara Institute, focusing on AI ethics and societal impact.

• Web Engineering
• Web Measurement
• Web Security
• +۳ مورد دیگر

Dr Yanlong Zhang is a Senior Lecturer at Manchester Metropolitan University. He holds a PhD in Software Engineering and a PGCE in Higher Education, complemented by industry experience as an Assistant Engineer, Engineer, and Teaching Assistant. His research focuses on web engineering, including web measurement, security, and games design, with particular attention to software metrics and user interface design. Teaching responsibilities include undergraduate modules on web design and development, computer systems, game design, and human-computer interaction, as well as postgraduate courses in information systems. His work explores GUI generation using GANs, structural similarity in source code, and navigability metrics for websites. Research outputs span topics like melanoma classification via image similarity and quality analysis of software architectures. His publications demonstrate expertise in both theoretical frameworks (e.g., HASARD model) and applied systems (e.g., MEIC design). Office hours are Monday 10-11, 1-2, and Thursday 10-11. Proficient in English, Chinese, and basic Japanese/German, Dr Zhang contributes to education and industry through his roles as a Fellow and teaching-focused academic. His work bridges software engineering principles with practical web development challenges.