جست‌وجوی استاد و پژوهشگر

Reynold Baileyمشاهده پروفایل

استاد

• Artificial Intelligence
• Applied Visual Perception
• Multimodal Human Sensing
• +۳ مورد دیگر

Reynold Bailey is a Professor in the Department of Computer Science at Rochester Institute of Technology (RIT), serving as the Associate Undergraduate Program Coordinator. He holds a BS in Mathematics and Computer Science from Midwestern State University and MS/PhD in Computer Science from Washington University in St. Louis. His research focuses on applied visual perception in computer graphics, multimodal human sensing, and AI-driven eye-tracking technologies. He leads the Computer Graphics and Applied Perception Lab and oversees NSF-funded initiatives including the AWARE-AI Research Traineeship and AI-PROWIL International Research Programs. Education: BS: Midwestern State University MS/PhD: Washington University in St. Louis Research Interests: Subtle gaze direction manipulation Eye-tracking in virtual environments Multimodal collaboration analysis Accessible visualization systems His recent work emphasizes AI applications in eye-tracking robustness (2024), international PhD mobility (2025), and multimodal dialogue analysis (2024). Over 100+ peer-reviewed publications span ACM/IEEE journals and conferences. Awards: NSF CAREER Award Best Interactive Paper Award (2011) Grants: AWARE-AI NSF Traineeship AI-PROWIL International Research He advises RIT's NSF REU programs and co-developed the Gaze3D framework for 3D gaze analysis. Active in curriculum design, he teaches foundational graphics courses (CSCI-610/716) and mentors MS thesis projects.

• Formal Methods
• Automata Theory
• Temporal Logic
• +۵ مورد دیگر

Daniel Neider is a Professor at TU Dortmund University, Germany. He holds former affiliations with Carl von Ossietzky University Oldenburg and the Max Planck Institute for Software Systems (Kaiserslautern). His research focuses on formal methods, automata theory, temporal logic, and their applications in verification, AI, and machine learning. Neider earned his PhD in 2014 from RWTH Aachen University and a second PhD in 2022 from Kaiserslautern University. His work bridges theoretical foundations with practical tools, such as developing algorithms for automata learning, robust temporal logics (rLTL), and neuro-symbolic verification frameworks for deep neural networks. Key research areas include: (1) learning-based formal methods for system verification, (2) robust temporal logics for handling noisy environments, (3) reinforcement learning with temporal logic constraints, and (4) automated specification mining from data. He has contributed to tools like libALF (Automata Learning Framework) and Sorcar for property-driven invariant synthesis. His recent work explores: (a) scalable LTL learning from noisy data using MaxSAT, (b) neuro-symbolic verification techniques, and (c) applying large language models to automate reinforcement learning with reward machines. He has published over 135 papers in top venues like AAAI, TACAS, and FMCAD, focusing on formal methods intersections with AI and systems verification. Current projects involve: (i) robustness-aware synthesis for reactive systems, (ii) temporal logic inference from positive examples, and (iii) developing interpretable verification tools for complex operational domains. His lab collaborates on applying formal methods to real-world systems like smart contracts and neural networks.

Sridhar Mahadevanمشاهده پروفایل

استاد مدعو

• Machine Learning
• Artificial Intelligence
• Robotics
• +۷ مورد دیگر

Sridhar Mahadevan is an Adjunct Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst. He has held previous academic positions at Michigan State University, the University of South Florida, and was a visiting scholar at Carnegie Mellon University. His research focuses on machine learning, multi-agent systems, planning, perception, robotics, and probabilistic models, with recent emphasis on hierarchical frameworks for autonomous learning and sequential decision-making using Markov decision processes. Mahadevan is an Associate Editor of Machine Learning and Journal of Machine Learning Research , and has contributed to editorial boards of major AI journals. He co-founded LookAhead Decisions Inc. and has been recognized with the NSF CAREER Award (1995) and best paper awards at international conferences. Education: PhD in Computer Science, Rutgers University, 1990 Research Interests: Mahadevan’s work spans foundational AI topics including reinforcement learning, causal inference, and generative models. His applied research includes robotics navigation, active vision systems, and spectroscopic analysis for space science. He pioneered methods for autonomous learning from perceptual data streams and developed frameworks like manifold alignment for cross-domain data integration. Key Contributions: Developed hierarchical hidden Markov processes and semi-Markov decision processes Advanced manifold learning techniques for spectroscopic calibration under non-stationary conditions Contributed to theoretical foundations of proximal reinforcement learning and variational inequalities Awards: NSF CAREER Award (1995) Best Student Paper Award at Autonomous Agents Conference (2001) Best Paper Award Runner-Up at International Conference on Machine Learning (1999) Advising & Grants: Advised students Rajbala Makar and Mohammad Ghavamzadeh. His NSF-funded projects include research on spectroscopic analysis and reinforcement learning theory. Collaborative grants include work on generative AI for Mars mineral analysis. Labs & Teams: Co-founder of LookAhead Decisions Inc., a company advancing decision-making systems using causal inference and reinforcement learning. Active in interdisciplinary teams applying AI to space science and robotics.

Marco Colombettiمشاهده پروفایل

استاد

• Artificial Intelligence
• Multi-Agent Systems
• Semantic Web Technologies
• +۳ مورد دیگر

Marco Colombetti is a Full Professor at the Department of Electronics, Information and Bioengineering (DEIB) of Politecnico di Milano. His research focuses on artificial intelligence, multi-agent systems, and normative systems, with a particular emphasis on semantic web technologies and agent communication protocols. He has contributed to frameworks for norm enforcement, ontological reasoning, and institutional modeling in open multiagent systems. Research interests include the integration of deontic logic with computational systems, cognitive aspects of normativity in human interaction, and the application of formal methods to security and access control policies. His work bridges theoretical foundations (e.g., deontic logic, social commitment) with practical implementations in domains like autonomous systems and rehabilitation teams. Key projects include the ANEMONE network for academic communities and studies on joint commitments in human intersubjectivity. His publications analyze the role of communication in institutional reality, dialogue as a cognitive process, and the semantic web’s role in policy frameworks.

Olivia Brownمشاهده پروفایل

دانشیار

• Behavioral Science
• Psychology
• Digital Security
• +۵ مورد دیگر

Olivia Brown is an Associate Professor in Digital Futures at the University of Bath's School of Management, affiliated with the Management Institute for Digital Security and Behaviour (IDSB), Applied Digital Behaviour Lab, Centre for Qualitative Research, Centre for Future of Work, and Centre for Business, Organisations and Society (CBOS). She serves as Deputy Director of the Bath Institute of Digital Security and Behaviour and is part of the Centre for Research and Evidence on Security Threats (CREST), while also contributing to the Bath Beacon initiative focused on the value of digital data. Her research sits at the intersection of psychology and behavioral science, examining how intra- and inter-group processes shape behavior in online and offline contexts. Key focus areas include: High-reliability organizations and teamwork in extreme environments Using online communications data to predict offline behavior Online extremism and terrorism dynamics Psychological impacts of digital traces Behavioral data ethics and reproducibility Her recent work analyzes cognitive task analysis methodologies, ethical frameworks for digital data exploration (DECIDE Framework), computational tools for research attendance (Optimeet), and dementia-inclusive digital workplaces. Brown holds editorial roles as Associate Editor for the Journal of Cognitive Engineering and Decision Making and Editorial Board Member for the Journal of Occupational and Organizational Psychology. She chairs the Social Science Research Ethics Committee and serves as Research Staff Coordinator in the School of Management, actively supervising doctoral students. Her collaborative projects include: Policing Academic Centre of Excellence (2025-2028) Networks and Radicalisation - University Innovation Concept (2023-2024) CREST 3.0 funded by the Economic and Social Research Council (2020-2024)

Ian Abrahamمشاهده پروفایل

استادیار

• Robotics
• Optimal Control
• Machine Learning
• +۵ مورد دیگر

Ian Abraham is an Assistant Professor of Mechanical Engineering at Yale University, with an additional appointment in the Department of Computer Science. His research focuses on robotics, optimal control, machine learning, and artificial intelligence, particularly in enabling robotic systems to autonomously gather information and adapt in unstructured environments. He leads the Intelligent Autonomy Lab, which develops algorithms integrating theory and applied research to enhance robotic learning, exploration, and multi-agent collaboration. Abraham holds a Ph.D. and M.S. from Northwestern University, and a B.S. from Rutgers University. His work spans optimal control, exploration strategies, sample-efficient learning, and multi-agent systems, aiming to create self-sufficient robotic systems requiring minimal human intervention. Research Highlights: Active sensing, ergodic exploration, Koopman operator-based control, and decentralized control for multi-agent systems. Key Contributions: Development of algorithms for data-driven control, hybrid control methodologies, and energy-aware exploration frameworks. His awards include the 2020 Belytschko Outstanding Research Award and the 2019 IEEE King-Sun Fu T-RO Best Paper Award. Abraham’s research has been published in top journals such as IEEE Transactions on Robotics and IEEE Robotics and Automation Letters.

Sandra Louise Halversonمشاهده پروفایل

استاد

• Cognitive Translation Studies
• Empirical Translation Studies
• Corpus Linguistics
• +۳ مورد دیگر

Sandra Louise Halverson is a Professor of Translation and Professional Communication at the University of Agder (UiA), leading the Master's program in Translation and Professional Communication. She holds leadership roles as Director of Agder Forum for Translation Studies and Chair of the Norwegian Association for Translation and Interpreting Research. Her research focuses on cognitive translation studies, empirical methodologies, sociocognitive constructs, and corpus-based approaches. Her work emphasizes the gravitational pull hypothesis in translation, lexical bundles in interpreting, and the integration of cognitive linguistic theories. She has published extensively in journals such as Translation and Interpreting Studies , Cognitive Linguistic Studies , and Translation, Cognition and Behavior , contributing to theoretical advancements in translation epistemology and methodology. Halverson collaborates internationally and chairs key organizations in her field, driving scholarly discourse on translation and interpreting. Her academic contributions include over 30 peer-reviewed publications since 2008, addressing topics ranging from metonymic extensions in Norwegian media to multimethod approaches in translation research.

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Yulong Chen is a Researcher at the Department of Computer Science and Technology, University of Cambridge. He specializes in Natural Language Processing (NLP), focusing on areas such as large language models (LLMs), constituency parsing, cross-lingual summarization, and dialogue systems. His work bridges theoretical advancements with practical applications, including automated claim verification, multimodal intelligence, and ethical AI considerations. Chen contributes to both teaching and research: he teaches courses on Natural Language Processing at both undergraduate and postgraduate levels. His research emphasizes interdisciplinary approaches, combining machine learning, computational linguistics, and cognitive models. Key projects include developing benchmarks for higher-order theory of mind reasoning in LLMs and designing frameworks to uncover model weaknesses. His publications span foundational surveys (e.g., on LLM hallucinations and text-to-SQL systems) to applied work like MACSum for controllable summarization and Hi-TOM for evaluating social cognition in AI. He collaborates extensively on tools like EASE for efficient data annotation and UniSumm for few-shot summarization, reflecting his commitment to advancing both NLP infrastructure and model capabilities.

Nicholas Boucherمشاهده پروفایل

پژوهشگر

• Security
• Machine Learning
• Cryptography
• +۵ مورد دیگر

Nicholas Boucher is a Researcher at the Computer Laboratory , University of Cambridge , and a Security professional at Microsoft . He completed his PhD at Cambridge in 2024 under Professor Ross Anderson , focusing on security vulnerabilities in software supply chains and compiler design. His research interests span Cybersecurity Adversarial Machine Learning Compiler Security Unicode Vulnerabilities He actively supervises students at Cambridge and contributes to open-source security projects like Trojan Source and Cryptovote . Recent publications highlight his work on Imperceptible NLP attacks Source code obfuscation Software supply chain threats He has presented at venues including USENIX Security and IEEE Security & Privacy .

Dominic Orchardمشاهده پروفایل

مدرس ارشد

• Programming Languages
• Verification
• Semantics
• +۵ مورد دیگر

Dominic Orchard is a Senior Lecturer in the Programming Languages and Systems group at the University of Kent and co-director of the Institute of Computing for Climate Science at the University of Cambridge. He works part-time at Kent while maintaining his role at Cambridge. His research focuses on programming languages, verification, and semantics, with applications to climate modeling and computational science. He leads projects like Granule (a programming language with graded modal types) and CamFort (static analysis for Fortran). Education background includes a PhD in Computer Science, though specific details are not provided. Research interests span type systems, formal verification, climate science software, and tool development. Key achievements include the Distinguished Paper and Artifact Award at ECOOP 2022, the Best Paper Award at LOPSTR 2020, and fellowship at the Software Sustainability Institute. He advises multiple PhD students, including Vilem Liepelt and Danielle Marshall. His work bridges theory and practice, with tools like Granule and CamFort addressing real-world challenges in scientific computing. He collaborates with climate scientists through the Institute of Computing for Climate Science, advancing interdisciplinary research.

Matthew P. Dubeمشاهده پروفایل

دانشیار

• Spatial Informatics
• Applied Data Science
• Data Literacy/Ethics
• +۴ مورد دیگر

Matthew P. Dube is an Associate Professor at the University of Maine at Augusta, specializing in Data Science, Computer Information Systems, and Applied Mathematics. He holds graduate faculty appointments at the University of Maine in Data Science and Engineering, Biomedical Sciences, and Computing. A first-generation interdisciplinary scholar, his research integrates spatial informatics, applied data science, and human factors design. His research focuses on spatial artificial intelligence, redistricting analysis, computational ecology, and cross-disciplinary collaborations. Key areas include topological modeling in GIS, data ethics, and applications in equine science and political geography. His work demonstrates consistent innovation in spatial reasoning frameworks and community-centered solutions. Dube's 15 most recent publications (2018-2025) reveal strong trends in geospatial computation (55% of works), applied interdisciplinary research (30%), and educational pedagogy (15%). Spatial informatics dominates his output, with growing emphasis on socio-environmental applications like rural housing security and agricultural adaptation. 2021 Sigma Phi Epsilon Grand Chapter Distinguished Alumnus Award 2019 UMA Distinguished Scholar Award (sole annual recipient) 4x Fraternity Advisor of the Year (University of Maine) He mentors 10+ graduate students in Data Science and Spatial Informatics. Secured NSF EPSCoR funding for biodiversity/climate change research and developed UMS's Bachelor of Science in Data Science. Leads GIS User Group Maine and coordinates Alternative Spring Breaks.

Aythami Moralesمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Biometrics
• +۶ مورد دیگر

Aythami Morales is a Professor at Universidad Autónoma de Madrid (UAM), supported by the Madrid Government under the V PRICIT program for Excellence in University Teaching Staff. His work bridges machine learning with real-world applications in auditable AI tools, security, privacy, and discrimination-aware learning . Key Research Themes: Biometrics, multimodal AI, fairness in machine learning, dietary assessment, and attention mechanisms Recent Projects: M2RAI (Multimodal and Multitask Responsible AI, 250K euros + PhD funding) and eMIRIAM (Risk Assessment in AI for Mental Health) His 15 most recent articles (2025) focus on AI auditability, biometric verification, and privacy-preserving techniques , spanning conferences like IEEE/IJCB, ICCV, and CVPR. He received the 2024 Stanford Top 2% Scientists in AI recognition and a Best Paper Award at CVPR 2025 . Morales actively engages in societal outreach through media (El País Retina) and public events like Pint of Science, addressing ethical AI and bias.

• Robotic Imaging
• Computational Imaging
• Light Field Processing
• +۱ مورد دیگر

Donald Dansereau is a Senior Lecturer in the School of Aerospace, Mechanical and Mechatronic Engineering at the University of Sydney and Perception Theme Lead for the Sydney Institute for Robotics and Intelligent Systems. His research develops novel imaging systems for robotic perception. His work pioneers light field and computational imaging techniques to enable robust robotic vision in challenging environments. Current projects address underwater imaging, low-light conditions, space applications, and privacy-preserving vision systems. Recent advances include event-based satellite docking simulations, adaptive neural radiance fields for 3D reconstruction, and task-specific camera optimization frameworks. Key research directions include: Real-time light field processing for robotic navigation Neural rendering techniques for scene understanding Privacy-preserving computer vision architectures Hardware-software co-design for specialized imaging He received the Best Paper Award at ACRA (2014) and Distinguished Poster Awards from Stanford SCIEN (2016, 2017). Teaching includes Mechatronic Design and System Design courses.

• Compiler Optimization
• Static Program Analysis
• Datalog Systems
• +۵ مورد دیگر

Professor Bernhard Scholz is a faculty member in the School of Computer Science at the University of Sydney . He specializes in compiler optimization, static program analysis, Datalog systems, blockchain technology, and parallel processing. His research focuses on improving compiler efficiency, analyzing complex programs, and developing tools for smart contract security. He holds a professorial position and has been involved in significant projects like the Fantom blockchain partnership, which funds specialized blockchain research. His work includes developing the Soufflé Datalog compiler and tools like MadMax for smart contract analysis. Research interests span compiler design, formal verification, distributed systems, and energy-efficient computing. His recent work emphasizes blockchain security, Datalog scalability, and parallel execution optimization. Notable contributions include EVMTracer for Ethereum analysis and Julia Cloud Matrix Machine for cloud-based matrix computations. Key grants include ARC Discovery Projects on heterogeneous systems and smart contracts. Awards include industry partnerships and sustained research funding. He advises on large-scale program analysis and has collaborated globally with institutions like ETH Zurich and Fantom Foundation. Labs and collaborations include the University of Sydney's Blockchain Lab and Fantom-funded projects. His work bridges theoretical compiler research with practical applications in security and distributed systems.

Jyh-haw Yehمشاهده پروفایل

استاد

• Cybersecurity
• Machine Learning
• Data Privacy
• +۵ مورد دیگر

Jyh-haw Yeh is a **Professor of Computer Science** at Boise State University, serving as the **Program Co-director of the MS Cybersecurity Program**. He holds a Ph.D. in Computer and Information Science and Engineering from the University of Florida. His research focuses on **cybersecurity**, **machine learning**, **data privacy**, and **cryptography**, with contributions to malware detection, adversarial machine learning, and secure communication protocols. He leads initiatives to enhance cybersecurity education, such as the **GenCyber Teacher Camps**, and collaborates on projects like intelligent IoT systems and cloud-based security solutions. Dr. Yeh advises students in the Cybersecurity Emphasis program and actively contributes to community outreach, including workshops for educators and high-school students. His work bridges academic research and practical applications, addressing challenges in network security, data integrity, and AI-driven threat detection. Education: Ph.D., University of Florida, Computer and Information Science and Engineering Affiliations: College of Engineering, Computer Science Department Key Roles: MS Cybersecurity Program Co-director, Cybersecurity Curriculum Developer His research spans technical advancements in encryption, malware analysis, and AI ethics, with a focus on real-world implementations in industries like nuclear power plant decommissioning and smart cities.