جست‌وجوی استاد و پژوهشگر

Stephen Mitroffمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Visual Cognition
• Attention
• +۵ مورد دیگر

Stephen Mitroff is a Professor of Cognitive Neuroscience at George Washington University’s Department of Psychological and Brain Sciences. He leads the Visual Cognition Lab, focusing on mechanisms of visual memory, perception, and attention. Prior to GW, he held faculty positions at Duke University (2005–2015) after completing postdoctoral research at Yale and a Ph.D. in Cognitive Psychology from Harvard University. His research emphasizes individual differences in visual search performance, leveraging big data from smartphone apps and professional populations like radiologists and TSA officers. Education: Ph.D. in Cognitive Psychology (Harvard, 2002), B.A. in Cognitive Science (UC Berkeley). Research explores how individual traits, training, and environmental factors influence visual cognition. Recent work includes pandemic-related mental health impacts, error dynamics in decision-making, and optimizing visual search for real-world applications. The lab employs innovative methodologies such as consistency-based training protocols and drift-diffusion modeling to dissect cognitive processes. Advising and grants: While specific grant details are not provided, his work has been supported by industry-academic partnerships, particularly in aviation security and medical diagnostics. The Visual Cognition Lab collaborates with professional groups to translate research into practical tools like sports vision training and baggage screening assessments. Labs/Teams: Principal Investigator of the GW Visual Cognition Lab, which integrates computational modeling with behavioral experiments to address applied cognitive science challenges.

Yun Fuمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Pattern Recognition
• +۲ مورد دیگر

Yun Fu is a tenured Professor in the Department of Electrical and Computer Engineering at Northeastern University, with a joint appointment in the Khoury College of Computer Science. He has established himself as a leading researcher in Artificial Intelligence, with over 500 publications in top-tier venues including IEEE/ACM transactions and major AI conferences. His work spans both theoretical foundations and practical applications, with significant impact in computer vision and machine learning. Professor Fu earned his Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign. His academic career progressed from Assistant Professor at SUNY Buffalo to his current position as tenured Professor at Northeastern University, where he has held appointments since 2012. His educational background includes a Beckman Graduate Fellowship at UIUC (2007-2008). His research focuses on advancing Artificial Intelligence with particular emphasis on Computer Vision, Pattern Recognition, and Machine Learning. His seminal work includes the "Residual Dense Network for Image Super-Resolution" presented at CVPR 2018, which was ranked among the Top 10 Most Influential CVPR papers. His research interests span image processing, anomaly detection, multimodal learning, and trajectory prediction, with applications ranging from healthcare to consumer technology. Analysis of his recent publications reveals a strong trend toward developing efficient and robust AI systems that bridge computer vision with language understanding. His work increasingly focuses on multimodal learning, trajectory prediction for multi-agent systems, anomaly detection in complex environments, and model validation techniques for black-box systems, while maintaining practical applications in real-world scenarios. Professor Fu's extensive recognition includes: Fellow of IEEE (2018), OSA (2019), SPIE (2018), IAPR (2016), AAIA (2021), and AAAI (2025) Member of Academia Europaea (2022) and European Academy of Sciences and Arts (2023) Fellow of National Academy of Inventors (2023) Multiple Young Investigator Awards from NAE, ONR, ARO, IEEE, ACM, and INNS 12 Best Paper Awards from major conferences Industrial Research Awards from Google, Amazon, Samsung, JPMorgan, and others Professor Fu has successfully mentored numerous Ph.D. students who now hold prominent positions in academia and industry at institutions including Amazon, Microsoft, Meta, Adobe, and major universities. His entrepreneurial ventures include founding Giaran (acquired by Shiseido in 2017) and co-founding TVision Insights, demonstrating his commitment to translating research into real-world impact. He has secured significant research funding from both government agencies and industry partners. As the PI and Founding Director of the SmiLe Lab at Northeastern University, Professor Fu leads a dynamic research group focused on advancing the state-of-the-art in AI and Computer Vision. The lab fosters interdisciplinary collaboration across computer science, electrical engineering, and applied mathematics, with ongoing projects in efficient deep learning, multimodal understanding, and practical AI applications.

Sang-Woo Junمشاهده پروفایل

استاد

• Big Data Analytics
• Hardware Acceleration
• Non-Volatile Memory
• +۳ مورد دیگر

Professor Sang-Woo Jun is a leading researcher in systems and software for big data analytics, focusing on FPGA-based hardware acceleration and non-volatile memory (NVM) storage. His work spans applications such as graph analytics and bioinformatics, with a strong emphasis on cost-effective, high-performance computing architectures. He advises PhD students like Shengquan Ni and Yicong Huang, both of whom have achieved notable milestones (e.g., thesis defense, fellowship awards). Research Interests: Hardware Acceleration for Big Data FPGA-Based System Architectures Non-Volatile Memory Systems Graph Analytics and Bioinformatics Edge Computing and Low-Power Systems Recent Contributions: His articles highlight innovations in edge accelerators (e.g., IceSpy, Eciton), genomics acceleration (Bancroft), and scalable graph processing (Durin, Sting). These works emphasize reconfigurable systems, privacy-preserving techniques, and energy-efficient designs. Lab & Team: As part of the Intelligent Systems Group (ISG), he collaborates on events like the Southern California Database Day. His research bridges hardware-software co-design with real-world applications in IoT, environmental monitoring, and genomics.

Fabio Crestaniمشاهده پروفایل

استاد

• Information Retrieval
• Text Mining
• Digital Libraries
• +۳ مورد دیگر

Fabio Crestani is a Full Professor of Informatics at the Università della Svizzera italiana (USI) since 2007, serving as Pro-rector for Internationalisation since March 2024. He previously held roles at the University of Strathclyde (UK) and conducted sabbaticals at institutions like UC Berkeley and Xerox PARC. His expertise spans Information Retrieval, Text Mining, and Digital Libraries, with over 250 publications and editorial leadership roles, including Editor-in-Chief of Information Processing and Management (2008–2015). Education: PhD and MSc in Computing Science, University of Glasgow (UK) Degree in Statistics, University of Padova (Italy) Research Interests: Advanced information access systems Conversational search and user interaction models Machine learning for text analysis Early risk prediction (e.g., mental health via social media) Grants & Collaborations: Funded by Swiss National Science Foundation, Hasler Stiftung, and EU projects. Collaborations with institutions in UK, Italy, Spain, USA, and Malaysia. Labs & Teams: Lead the Information Retrieval Group at USI, which focuses on distributed IR, personalization, and mobile information access. The group includes 10+ researchers and has produced influential work in top-tier venues like SIGIR and ACL.

Bowen Xuمشاهده پروفایل

استادیار

• Software Engineering
• Machine Learning
• Program Analysis
• +۱۲ مورد دیگر

Bowen Xu is an Assistant Professor in the Department of Computer Science at North Carolina State University (NC State), College of Engineering. His research focuses on software engineering, machine learning, and program analysis, particularly in securing AI models and improving code quality. He holds a PhD from Singapore Management University (SMU), where he also conducted postdoctoral research. Education: PhD in Computer Science, Singapore Management University (SMU) Postdoctoral Researcher, SMU School of Computing and Information Systems Research Interests: AI for Code, Backdoor Attacks on Code Models, Vulnerability Detection Code Representation Learning, Model Compression, Safety of AI Systems Chatbot Development for Developers, Automatic Code Review Key Contributions: Developed PTM4Tag+, a Stack Overflow tag recommendation system using pre-trained models Explored stealthy backdoor attacks in code and reinforcement learning systems Pioneered work on automatic vulnerability repair using LLMs and broader input analysis Awards: 2022: Honorable Mention Award (ACSAC) 2018: Highly Commended Full Paper Award (ESEM) Service Roles: Editorial Board Member, Empirical Software Engineering Journal Program Committee Co-chair for ICSE/FSE Research Tracks Organized workshops like FORGE, MaLTeSQuE, and SEA4DQ Labs & Teams: Leads the Softmax Lab at NC State, advising 12+ students across PhD, Master's, and undergraduate levels. Alumni include industry professionals at Microsoft, Barclays, and Marvell Semiconductor.

Christof Büskensمشاهده پروفایل

استاد

• Optimization
• Optimal Control
• Control Systems
• +۵ مورد دیگر

Prof. Dr. Christof Büskens is a Professor of Technomathematics at the University of Bremen, leading the AG Optimierung und Optimale Steuerung (Optimization and Optimal Control Group) within the Faculty of Mathematics and Computer Science . His research focuses on Optimization, Optimal Control, and their applications in industrial and real-time systems. He holds leadership roles in interdisciplinary projects such as BESTVILLE and Safety Control Center for autonomous vehicle systems, and has contributed to maritime navigation, renewable energy management, and agricultural robotics. Büskens has supervised numerous PhD and master's students, advancing topics like autonomous exploration, neural architecture search, and trajectory optimization. His work integrates advanced numerical methods with practical applications, emphasizing real-world problem-solving in dynamic systems. Key affiliations include the ZeTeM (Center for Industrial Mathematics) and collaborations with industry partners. He has led over 20 projects since 2020, addressing challenges in autonomous systems, energy systems, and robotics. His educational contributions include courses on numerical analysis and optimal control, fostering interdisciplinary training for future researchers. Büskens' expertise bridges theoretical optimization and applied engineering, with over 100 publications and contributions to software tools like the WORHP solver.

Sven Scheweمشاهده پروفایل

استاد

• Automata Theory
• Game Theory
• Formal Verification
• +۱۲ مورد دیگر

Sven Schewe is a Professor in the Department of Computer Science at the University of Liverpool, affiliated with the School of Electrical Engineering, Electronics and Computer Science. He leads the AI Section and is a founding member and former leader of the Verification Group. He also has secondary affiliations with the Algorithms, Complexity Theory and Optimisation Group and the Institute for Risk and Uncertainty. Research Interests: His research centers on automata theory and game theory, particularly their applications in the verification and synthesis of reactive and safety-critical systems. He investigates infinite-duration games, automata over infinite words and trees, and develops algorithms and tools for automated verification, synthesis, and learning of optimal control strategies. His work extends to reinforcement learning with formal guarantees, cyber-physical systems, and AI safety. Recent Research Trends: His recent publications demonstrate a strong integration of formal methods with machine learning, particularly in adversarial training, neural network robustness, and model-free reinforcement learning under omega-regular objectives. He also applies formal reasoning to interdisciplinary domains such as chemical space exploration and materials science. Scientific Awards: Finalist for the ERCIM Cor Baayen Award 2010 Dr. Eduard Martin Preis 2009 GI Dissertation Award 2008 Advising and Grants: He actively supervises numerous PhD students and postdoctoral researchers. He is Principal Investigator (PI) or Co-Investigator (CI) on multiple major grants, including EPSRC Programme Grants, Royal Society Fellowships, and Horizon Europe projects. His funded research spans topics such as game theory, verification, synthesis, reinforcement learning, and risk analysis. He has hosted visiting researchers and collaborated internationally with institutions in Germany, France, India, Taiwan, and the US. Labs and Teams: He co-founded and led the Verification Group and previously led the AI Section at the University of Liverpool. These groups focus on formal methods, automata, games, and their applications in AI and safety-critical systems.

• Quantum Computing
• Quantum Algorithms
• Quantum Error Mitigation
• +۳ مورد دیگر

Yunseong Nam is an Adjunct Assistant Professor at the University of Maryland. His research focuses on advancing quantum computing through innovations in trapped-ion systems, quantum algorithms, and error mitigation techniques. He is particularly known for developing methods to enhance gate fidelity, optimize quantum circuits, and improve fermionic simulations for quantum chemistry applications. Research Interests: Nam’s work spans quantum hardware optimization, entangling gate implementation, and hybrid quantum-classical computing architectures. He emphasizes practical applications like materials science simulations and fault-tolerant protocols, leveraging symmetries and resource-efficient algorithms to address computational challenges. His contributions include breakthroughs in pulse engineering, SPAM error handling, and the design of robust quantum circuits. Key Research Trends: Recent articles highlight advancements in trapped-ion gate optimization (e.g., small-angle Mølmer-Sørensen gates), error mitigation strategies, and quantum circuit compilers. His work frequently intersects with experimental quantum systems, emphasizing real-world implementations over purely theoretical models. Awards & Grants: No specific awards or grants are mentioned in the provided texts. Labs & Teams: While specific lab affiliations are not detailed, his collaborations likely involve quantum computing hardware groups and theoretical teams focused on algorithm optimization.

• Logic Programming
• Knowledge Representation
• Artificial Intelligence
• +۶ مورد دیگر

Bart Bogaerts is an Associate Professor in the Department of Computer Science at KU Leuven's Faculty of Engineering Science. He is affiliated with the Declarative Languages and Artificial Intelligence (DTAI) research unit and is a member of Leuven.AI - KU Leuven Institute for Artificial Intelligence. Bogaerts serves on the Council of the Faculty of Engineering Science as senior academic staff and participates in the Programme Committee for Artificial Intelligence curriculum development. His research focuses on foundational aspects of logic programming and knowledge representation, with particular expertise in approximation fixpoint theory, higher-order logic programming, and non-monotonic reasoning. Bogaerts investigates the theoretical underpinnings of stable model semantics, justification frameworks, and executable query languages. His work bridges theoretical computer science with practical applications in artificial intelligence and knowledge-based systems. Bogaerts' publication record demonstrates consistent contributions to top venues in logic programming and artificial intelligence. His recent work shows increasing focus on category-theoretic approaches to approximation theory, distributed web traversal specifications, and certified model expansion techniques. The publications reveal a strong emphasis on formal methods with applications spanning from theoretical mathematics to practical AI systems. As a promotor for multiple significant research projects, Bogaerts leads investigations into certified answer set programming (CertifASP), first-order model expansion (CertiFOX), proof generation for combinatorial optimization, distributed configuration problems, and knowledge integration paradigms. These projects, funded through 2028-2029, demonstrate his leadership in advancing the theoretical foundations of AI and logic programming. Bogaerts is actively involved in teaching courses on knowledge representation and reasoning, contributing to the development of next-generation AI researchers. His work within the DTAI research unit positions him at the forefront of declarative AI approaches in Belgium's leading research university.

Valentino Santucciمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Evolutionary Computation
• Machine Learning
• +۷ مورد دیگر

Valentino Santucci is an Associate Professor of Computer Engineering at the University for Foreigners of Perugia, Italy, affiliated with the Department of International Human and Social Sciences (SUSI). Since 2021, he has served as the Rector's Delegate for Technological Innovation and Information Flows, highlighting his leadership in digital transformation within the institution. His research spans key areas in Artificial Intelligence, particularly Evolutionary Computation, Natural Language Processing, Machine Learning applications in e-learning and sustainability, and digital technologies in education. He employs algebraic techniques to analyze combinatorial search spaces and evolutionary algorithm dynamics, contributing to both theoretical and applied advancements. The most recent publications reflect a strong trend in combinatorial optimization using algebraic frameworks, hybrid evolutionary-swarm algorithms, and applications in text complexity classification and educational technology. His work bridges computer science with humanities and sustainability, reflecting a multidisciplinary approach. PhD in Computer Science and Mathematics, University of Perugia (2012) He teaches courses in Artificial Intelligence, Computer Science for Humanities, Cybersecurity, and Sustainability across various degree programs. His editorial roles include contributing to WoS/Scopus-indexed journals, and he has taught at the University of Perugia and Hong Kong Baptist University. Dr. Santucci actively mentors through teaching and research supervision. While specific student names are not listed, his involvement in academic projects and publications suggests advisory roles. He has no explicitly mentioned grants, but his research output and leadership position indicate active project engagement. He is involved in institutional innovation through his role in technological advancement and has contributed to projects integrating soft skills and learning technologies. His work emphasizes practical applications of AI in education and sustainability, positioning him at the intersection of technical innovation and societal impact.

• Optimization methods
• Metaheuristics
• Heuristics
• +۱۲ مورد دیگر

Mina Mirhosseini is a Research Fellow at the Faculty of Logistics, Molde University College, Norway. She holds a PhD in Computer Science from Shahid Beheshti University of Tehran, Iran, and has prior academic experience as a faculty member and lecturer in Iran and as a remote teaching assistant at the University of Hertfordshire, UK. Her primary research interests include Optimization Methods, Metaheuristics, Heuristics, Linear Integer Programming, Parallel Processing, Machine Learning, Artificial Intelligence, and Logistics. She has made significant contributions to solving complex computational problems such as the n-similarity problem and Mixed Integer Linear Programming (MILP) models using hybrid and parallel algorithms, particularly in the context of high-level synthesis and wireless sensor networks. The analysis of her recent publications reveals a strong focus on developing and applying advanced optimization techniques, especially quantum-inspired gravitational search algorithms and parallel genetic algorithms, to real-world engineering and computational challenges. Her work consistently emphasizes performance improvement, scalability, and load balancing in distributed and heterogeneous computing environments. Mina Mirhosseini has an extensive publication record in high-impact journals such as IEEE Transactions on Parallel and Distributed Systems, Journal of Parallel and Distributed Computing, Journal of Supercomputing, and Computers and Electrical Engineering. Her research has been published across a range of venues, reflecting interdisciplinary work at the intersection of computer science, electrical engineering, and applied optimization. She has actively contributed to the academic community through roles such as program committee member and executive committee member for conferences on fuzzy systems, swarm intelligence, and evolutionary computation. Her academic journey includes teaching and research roles in Iran, demonstrating a sustained commitment to higher education and scientific inquiry. Mina Mirhosseini is part of the research group focused on Planning, Optimization and Decision Support at Molde University College. Her current work continues to advance the state-of-the-art in parallel and metaheuristic optimization methods, with applications in logistics, synthesis, and sensor network design.

Mile Šikićمشاهده پروفایل

استاد

• Computational Biology
• Genomics
• Deep Learning in Sequencing
• +۴ مورد دیگر

Prof. Mile Šikić is a Full Professor at the Department of Electronic Systems and Information Processing, Faculty of Electrical Engineering and Computing (University of Zagreb). His research spans computational biology, genomics, and machine learning applications in sequencing technologies. Focus on nanopore sequencing analysis, genome assembly, and protein interaction prediction Developed tools like GraphMap , RiNALMo , and Orthobalancer Active in metagenomics, RNA structure prediction, and CUDA-based algorithm acceleration Scientific contributions include: Advances in de novo genome assembly for error-prone long reads Deep learning models for base modification detection Efficient algorithms for sequence alignment and similarity searches Technical implementations cover: GPU-accelerated sequence alignment libraries (e.g., SW# ) Web platforms for comparative protein analysis Simulation tools for epidemic spread on complex networks

• Robot Vision
• Robot Navigation
• Computer Vision
• +۷ مورد دیگر

Helder Araujo is a Professor at the Department of Electrical and Computer Engineering, University of Coimbra. His research focuses on Robot Vision , Computer Vision , and Medical Imaging , with applications in Capsule Endoscopy , Autonomous Driving , and Sensor Fusion . Recent publications highlight advancements in 3D Object Detection using transformer networks (e.g., RetSeg3D , DDet3D ), Medical Robotics (e.g., Self-supervised monocular pose estimation ), and Federated Learning (e.g., FAIR-FATE ). Key subfields include Transformer Models , Deep Learning , and Autonomous Navigation . His projects span Medical Imaging (e.g., Multi-Cam Capsule Endoscopy ), Autonomous Robotics (e.g., Learning to Navigate Endoscopic Capsule Robots ), and Edge Computing (e.g., Benchmarking CNN Inference on Low-Power Devices ). Grants include funding from Fundação para a Ciência e a Tecnologia (e.g., PTDC/EEI-ROB/1155/2020) and the European Commission (e.g., AdvanCed Hardware/Software Components for Embedded Vision). He has served as an invited researcher at Université Blaise Pascal (2012–2014). Peer review activities cover journals like IEEE Transactions on Pattern Analysis and Machine Intelligence and Autonomous Robots . His work bridges Medical Imaging , Autonomous Systems , and AI Fairness .

Anna Farihaمشاهده پروفایل

استادیار

• Data Management
• Human-Centered Systems
• Usability Engineering
• +۴ مورد دیگر

Anna Fariha is an Assistant Professor in the Kahlert School of Computing at the University of Utah. She co-leads the Data Management Research Center for Human-centered, Efficient, and Scalable Systems. Her research focuses on enhancing data system usability, explainability, and trustworthiness through algorithmic innovations and practical implementations. She holds a Ph.D. from the Manning College of Information and Computer Sciences at the University of Massachusetts, Amherst, under Prof. Alexandra Meliou. Education: Ph.D. in Computer Science, University of Massachusetts Amherst (2021) Master's in Computer Science, University of Massachusetts Amherst (2020) Undergraduate work in Computer Science, unspecified institution Her research interests include data wrangling tools, constraint discovery, conversational AI for data science, and human-centered database systems. Recent work emphasizes automated data summary recommendations, constraint violation detection, and educational tools for data science programming. Grants & Awards: NSF CIRC: ENS/Grand: POWDER-ENS Award (2024) Stena Center Seed Grant for Fintech Data Analysis (2025) Microsoft Research Dissertation Grant (2020) SIGMOD 2022 Comprehensive Reproducibility Award Teaching: Recently taught Advanced Database Systems , Human-Centric Data Management , and Deep Learning courses at the University of Utah.

Siegfried Nijssenمشاهده پروفایل

استادیار

• Data Mining
• Pattern Mining
• Exploratory Data Analysis
• +۵ مورد دیگر

Siegfried Nijssen is an Assistant Professor of Data Mining and Artificial Intelligence at the Catholic University of Louvain (UCLouvain) in Belgium, working within the ICTEAM research institute's Artificial Intelligence and Algorithms group. He has been at UCLouvain since 2016, previously serving as an Assistant Professor at University Leiden (2012-2016) and completing postdoctoral work at KU Leuven (2006-2015). He earned his PhD in Computer Science from University Leiden in 2006. His research focuses on making data analysis simpler through intersections between pattern mining, exploratory data analysis, and programming paradigms in Artificial Intelligence, particularly constraint programming and probabilistic programming. He has developed techniques for analyzing diverse data types including graphs, networks, and multi-relational data. His work bridges theoretical foundations with practical applications in decision tree learning, probabilistic networks, and source code analysis. Nijssen's recent publications demonstrate a strong focus on optimal decision trees, constraint-based pattern mining, and applications in bioinformatics and education. His research shows consistent evolution from foundational graph mining work (including the Gaston algorithm developed in 2004) to current work integrating machine learning with constraint programming for interpretable AI solutions. As an educator, he teaches courses including Mining Patterns in Data, Databases, and Artificial Intelligence and Machine Learning seminars at UCLouvain. He has advised numerous PhD students and postdocs, primarily in collaboration with Pierre Schaus, with former students like Tias Guns now holding professorships.