جست‌وجوی استاد و پژوهشگر

Damon L. Woodardمشاهده پروفایل

استاد

• Artificial Intelligence
• Hardware Security
• Biometrics
• +۵ مورد دیگر

Damon L. Woodard is a Professor in the Department of Electrical and Computer Engineering at the University of Florida (UF) and serves as Director of the Florida Institute for National Security (FINS) and the Applied Artificial Intelligence (AAI) Group. His research focuses on applied artificial intelligence, hardware security, and biometrics, with particular emphasis on adversarial AI, AI-enabled hardware assurance, and explainable AI (XAI). He holds IEEE and ACM Senior Member status and is a Kavli Frontiers Fellow. Education: Ph.D. in Computer Science and Engineering, University of Notre Dame M.E. in Computer Science and Engineering, Penn State University B.S. in Computer Science and Computer Information Systems, Tulane University Research Interests: Dr. Woodard explores cutting-edge areas such as AI hardware acceleration, multi-modal AI systems, and counter-AI strategies. His work bridges cognitive science and technology through projects like text stylometry and psychological analysis frameworks. Key focus areas include semiconductor reverse engineering, hardware trojan detection via SEM imaging and machine learning, and secure IoT biometric systems. Highlighted Awards: National Academy of Science Kavli Frontiers Fellow IEEE Senior Member ACM Senior Member AAAI Membership Leadership & Contributions: As FINS Director, he oversees national security initiatives integrating AI and hardware assurance. His AAI Group develops resource-efficient AI solutions for constrained environments. Notable projects include the SECURE segmentation metric for IC reverse engineering and the MaGNIFIES GAN framework for electronic system inspection. Labs & Teams: Leads the Applied Artificial Intelligence Group and collaborates across disciplines through FINS, fostering innovation in AI-driven security and semiconductor integrity.

Risi Kondorمشاهده پروفایل

دانشیار

• Machine Learning
• Graph Theory
• Quantum Mechanics
• +۶ مورد دیگر

Risi Kondor is an Associate Professor in the Departments of Statistics and Computer Science at the University of Chicago. His research focuses on machine learning, group theory applications, and equivariant neural networks. He develops algorithms respecting geometric and physical symmetries, with contributions to graph learning, quantum mechanics modeling, and multiresolution matrix factorization. Key projects include the development of Covariant Compositional Networks (CCNs) for graph-structured data and N-body networks for molecular simulations. He has created software tools like GraphFlow, SnOB (FFT for symmetric groups), and Mondrian for high-performance computing. His work bridges algebraic methods (e.g., Fourier analysis on permutation groups) with machine learning, addressing challenges in multi-object tracking, computer vision, and materials science. Risi Kondor holds grants including a DARPA Young Faculty Award ($500K, 2016–2018) and NSF funding for non-commutative harmonic analysis in machine learning. His research emphasizes theoretical foundations and practical applications, advancing areas like equivariant architectures, multiscale analysis, and symmetry-aware machine learning systems.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Dr. Kit Yan Chan is a Senior Lecturer at the School of Electrical Engineering, Computing and Mathematical Sciences (EECMS) at Curtin University. His research focuses on Artificial Intelligence, Machine Learning, Deep Learning, and Optimization, with applications in wireless communications, signal processing, and power systems. He has held editorial roles in journals such as Neurocomputing, Sensors, and the International Journal of Ad Hoc and Ubiquitous Computing. His teaching spans courses like Transmission and Interface Design, Advanced Research in AI, and Mobile Cloud Computing. Dr. Chan's work emphasizes interdisciplinary approaches, combining computational intelligence with engineering challenges. He has contributed to over 100 publications in areas such as resource allocation in heterogeneous networks, deep learning for load forecasting, and underwater acoustic communication systems. His research bridges theoretical advancements and practical implementations, addressing real-world problems in telecommunications, energy systems, and smart technologies. His recent projects include optimizing energy efficiency in 5G networks, developing robust power control strategies, and advancing neural network architectures for real-time applications. Collaborations with industry and global institutions underscore his commitment to impactful research.

• Software Engineering
• Search-Based Software Engineering
• Automated Refactoring
• +۴ مورد دیگر

Dr. Mel Ó Cinnéide is an Associate Professor at the School of Computer Science, University College Dublin. He holds a PhD from Trinity College Dublin (2001) and has over three decades of experience in academia and industry. His research focuses on automated refactoring, search-based software engineering, design patterns, and energy-efficient software development. He leads the Masters in Advanced Software Engineering program at UCD and has received multiple research grants and best paper awards. Prior to academia, he worked as a software engineer at Philips (Netherlands) and Motorola (Cork). Education: BSc in Computer Science, University College Cork MSc in Computer Science, University College Cork PhD in Computer Science, Trinity College Dublin Diploma in Gaeilge Fheidhmeach (Computing Irish), UCD Research interests emphasize practical applications of refactoring techniques to improve software quality and energy efficiency. He pioneers tools like Code-Imp and RefDetect for automated refactoring, integrating multi-objective optimization and interactive systems. His work bridges theoretical software engineering principles with real-world industry challenges. Awards and Grants: - Best Paper Awards in peer-reviewed conferences - Competitive research grants supporting software engineering projects Advising & Leadership: - Director of UCD's Masters in Advanced Software Engineering - Supervisor of numerous academic and industrial projects Labs & Projects: - Co-lead of the CodeImp Project (automated search-based refactoring) - Involved in international workshops on refactoring and software engineering

Luca Di Gasperoمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Combinatorial Optimization
• Metaheuristics
• +۱ مورد دیگر

Luca Di Gaspero is an Associate Professor of Information Technology at the University of Udine, specializing in metaheuristic optimization techniques. His research enhances combinatorial optimization through hybridization of algorithms for scheduling, routing, and industrial applications. Research spans artificial intelligence in optimization, scheduling algorithms for manufacturing/healthcare, and metaheuristic framework development. Recent publications focus on LLMs in optimization, parallel batch scheduling, and energy-efficient manufacturing. Key Contributions: Developed EasyLocal++ framework for local search algorithms Advanced multi-neighborhood simulated annealing techniques Applied metaheuristics to healthcare logistics and emergency services

• Cloud Computing
• Edge Computing
• Task Scheduling
• +۵ مورد دیگر

Dr. Zhijun Wang is an Associate Professor of Research in the Department of Computer Science and Engineering at The University of Texas at Arlington. His work focuses on cloud and edge computing, resource management, task scheduling, and network traffic control. He holds a PhD in Computer Science from UTA (2005), an MS in Electrical Engineering from Penn State (2001), and a BS in Physics from Huazhong University of Science & Technology (1992). His research explores internet traffic control mechanisms , cloud resource allocation , and microservices architecture . Recent projects include NSF-funded work on tail latency guarantees for microservices ($600k grant, 2022-2026) and industry collaborations with Alibaba on datacenter transport protocols ($148k grant, 2018-2021). Publications highlight innovations like CurTail (tail latency scheduling) and Tailguard (data-intensive task scheduling). His work often combines price-aware protocols with distributed scheduling algorithms . Teaching focuses on foundational topics: discrete math, computer networks, and cloud computing. Grants: NSF (2022), Alibaba (2018) Key Patents: Tail Latency Scheduling (2023), Database Target Enforcement (2024) Recent Publications: 15+ papers since 2020 in top-tier venues

Johnny Liمشاهده پروفایل

استادیار

• Precision Agriculture
• IoT Sensors
• Autonomous Robotics
• +۵ مورد دیگر

Johnny Li is an Assistant Professor in the Department of Soil and Water Systems at the University of Idaho, affiliated with the College of Agricultural & Life Sciences. He leads the Precision Agriculture and Intelligent Robotics Laboratory (PAIR) and is an affiliate professor at the Center for Intelligent Industry Robot (CI2R). Education: PhD in Mechatronics (Central South University, 2012), M.S. in Material Science (Central South University, 2005), B.S. in Agricultural Engineering (Hunan Agricultural University, 2002). Research focuses on IoT/robotics sensing, control systems, and AI integration for precision agriculture, environmental management, and infrastructure monitoring. Key areas include autonomous robotics, remote sensing, crop modeling, and explainable AI for decision support systems. Publications emphasize machine learning applications in disease detection (citrus, tomatoes, grapes), wildfire monitoring, and infrastructure health assessment. Over 40 peer-reviewed papers and $1.5M in grants secured since 2019. Awards include NSF CGCA Panel Fellow (2022) and editorial role at Agricultural Engineering International: CIGR Journal (2013). Labs/Teams: PAIR Lab (founded 2022), CI2R collaboration, interdisciplinary partnerships with soil scientists, engineers, and computer scientists.

Stephen Eikenberryمشاهده پروفایل

استاد

• Astrophysics
• Gravitational Waves
• Black Holes
• +۷ مورد دیگر

Stephen Eikenberry is a Professor of Optics & Photonics Physics at CREOL, The College of Optics and Photonics, University of Central Florida. His academic journey includes a Ph.D. in Astronomy from Harvard University (1997), a Sherman Fairchild Postdoctoral Prize Fellowship at Caltech, and prior tenured roles at Cornell University and the University of Florida. His research focuses on black holes, neutron stars, gravitational waves, and astronomical instrumentation, with applications in biomedical imaging and spectroscopy. Key professional milestones include the 2016 Breakthrough Prize in Fundamental Physics (as part of the LIGO Science Consortium), the NSF CAREER Award (2000), and multiple University of Florida Research Foundation Professorships. He has designed advanced optical instruments and contributed to LIGO's gravitational wave discoveries. Eikenberry's research group explores astrophotonics, dark energy, and extrasolar planets. His recent work includes analyzing gravitational wave data from LIGO/Virgo and developing lunar occultation missions. He advises multiple graduate students and collaborates on international projects like the PolyOculus Array (OPA!). Education: Ph.D. in Astronomy, Harvard University (1997) Postdoctoral Fellowship at Caltech (Sherman Fairchild Prize) Awards: Breakthrough Prize in Fundamental Physics (2016) Gruber Prize for Cosmology (2016) UK Royal Astronomical Society Team Achievement Award (2016) His publications emphasize gravitational wave astronomy, cosmology, and instrument design. He has pioneered methods to constrain cosmic expansion using gravitational wave 'standard sirens' and studies correlations between fast radio bursts and gravitational wave events.

Abdul Aliمشاهده پروفایل

مدرس

• Supply Chain Management
• Green Logistics
• Circular Economy
• +۳ مورد دیگر

Abdul Ali is Lecturer in Operations and Supply Chain Management at Northumbria University's London campus. He holds a Ph.D. in Supply Chain Management from the University of Bedfordshire and is a certified Lean Six Sigma Green Belt practitioner. Research Focus: His interdisciplinary work examines: Sustainable supply chain models Green logistics strategies Circular economy implementation Supplier collaboration mechanisms He also researches technology applications including blockchain and AI in supply chain contexts. Recent publications address cybersecurity in cyber-physical systems, multi-objective optimization techniques, and blockchain applications for electoral systems. His research aims to develop resilient and ethical supply chain frameworks.

• Network Optimization
• Infrastructure Resilience
• Supply Chain Risk
• +۳ مورد دیگر

Nafiseh Ghorbani-Renani is a Teaching Professor in the Department of Systems and Enterprises at Stevens Institute of Technology. Her research develops mathematical frameworks for managing risk in interconnected systems such as critical infrastructure, supply chains, and energy networks. Education includes: PhD in Industrial and Systems Engineering (University of Oklahoma) MS in Industrial Engineering (University Technology Malaysia) BS in Industrial Engineering (Yazd University) Research integrates optimization techniques with machine learning for resilience planning in interdependent networks. Current projects examine microgrid energy trading, supply chain disruption management, and infrastructure restoration scheduling. Recent publications focus on tri-level optimization models for critical infrastructure protection, energy equity in community microgrids, and machine learning applications for network resilience. Articles demonstrate consistent advancement in modeling complex interdependencies across infrastructure systems. As Faculty Advisor for the Software Division, she guides student projects bridging theoretical models with practical implementation. Course instruction spans software engineering fundamentals, agile methodologies, and systems development lifecycle management.

• Information Visualization
• Visual Analytics
• Human-Computer Interaction
• +۸ مورد دیگر

Paolo Buono is Associate Professor at the Department of Computer Science, University of Bari Aldo Moro, Italy. He holds a PhD in Computer Science with specialization in Visual Data Analysis. His research focuses on Information Visualization, Visual Analytics, Human-Computer Interaction, and Mobile Applications. Co-founder and CEO of LARE (2010), a university spinoff providing real-time surgical support through audio-video telestration Member (since 2002) and computer science coordinator at METEA Research Center for environmental protection Visiting scientist at AVIZ (France), University of Maryland (USA), and Fraunhofer IPSI (Germany) His work spans multiple application domains including: Cultural Heritage through interactive exploration systems Healthcare with smart therapeutic devices Environmental Monitoring via CET system IoT-based Smart Interactive Experiences He has contributed to: Dynamic hypergraph visualization techniques End-User Development frameworks (EUDroid) Usability evaluation methodologies Mobile health applications As project leader, he has coordinated: EU-funded VisMaster Coordination Action (2008-2010) Italian Ministry-funded LOGIN project (2014-2015) Apulia Region environmental projects His professional engagements include: Co-chair roles at INTERACT, AVI, IS-EUD conferences Program committee participation in VIS series and HCI conferences Member of ACM, IEEE, and SIGCHI Italy

Chengzhou Tangمشاهده پروفایل

استادیار

• 3D Computer Vision
• Deep Learning
• Robotics
• +۳ مورد دیگر

Chengzhou Tang is an Assistant Professor in the Department of Computer Science at the University of Manitoba. His research focuses on 3D computer vision, deep learning, robotics, and embodied artificial intelligence. He leads the Spatial Intelligent System (SPINS) Lab, exploring topics such as 3D object reconstruction, camera localization, and video processing techniques. His work spans applications in robotics, virtual reality, and computational imaging. Dr. Tang's research interests include developing novel algorithms for multi-view diffusion models, deep recurrent optimizers, and subspace methods. He has contributed to advancements in video depth estimation, point cloud processing, and stabilization of 360-degree videos. His recent work emphasizes generative models and embodied AI, addressing challenges in sparse-view 3D reconstruction and scene-agnostic camera localization. His publications reflect a trend toward integrating deep learning with traditional computer vision problems, such as structure-from-motion and SLAM (Simultaneous Localization and Mapping). The SPINS Lab collaborates on projects involving global bundle adjustment networks and initialization-robust monocular SLAM systems. While no awards or grants are explicitly listed, his active publication record indicates sustained research engagement in these areas. Dr. Tang’s academic profile highlights his contributions to foundational techniques in computer vision and their practical applications in robotics and AI systems. His work bridges theoretical advancements with real-world challenges in imaging and spatial intelligence.

• Multimodal Signal Processing
• Biomedical Signal Processing
• Image and Speech Processing
• +۳ مورد دیگر

Richard Dansereau is a Professor and Associate Dean (Graduate Studies) in the Department of Systems and Computer Engineering at Carleton University, part of the Faculty of Engineering and Design. He holds a Ph.D. from the University of Manitoba and is a Professional Engineer (P.Eng.) and Senior Member of IEEE. His research focuses on signal processing, including biomedical applications, compressive sensing, medical imaging, and fractal complexity analysis. He has led the Signal Processing and Machine Learning Lab and advised numerous PhD students in areas like PET image reconstruction and drone detection through Riemannian geometry. His academic roles include serving as Clerk of Senate and Academic Editor for IET Signal Processing. Key research contributions span deep learning for compressive sensing (e.g., DEQ-based networks), cervical cell segmentation, and biomedical signal processing. Over 150 publications highlight his work on topics like PET-MRI fusion, audio-visual speech enhancement, and radar systems. Collaborations include projects on cardiac PET imaging and drone detection algorithms. Awards include the IEEE Senior Member designation. His teaching spans courses such as Digital Signal Processing, Wavelets, and Biomedical Systems across Carleton and the Georgia Institute of Technology.

Muhammad M. Sherifمشاهده پروفایل

استادیار

• Smart Materials
• Structural Systems
• Machine Learning Applications
• +۴ مورد دیگر

Dr. Muhammad M. Sherif is an Assistant Professor in the Department of Civil, Construction, and Environmental Engineering at the University of Alabama at Birmingham (UAB), part of the School of Engineering. He joined UAB in Fall 2019 after completing his Ph.D. at the University of Virginia and M.S. at Carnegie Mellon University, both in structural engineering. His research focuses on smart materials, structural systems, and machine learning applications in civil infrastructure. He is particularly interested in additive manufacturing for construction and the development of innovative materials like engineered cementitious composites. Education: B.S., United Arab Emirates University M.S., Carnegie Mellon University Ph.D., University of Virginia Research Interests: Dr. Sherif’s work spans material characterization, machine learning models for structural analysis, and the integration of advanced materials into infrastructure systems. He explores topics like crack detection using UAVs, superelastic shape memory alloys, and multi-objective optimization in welding processes. His Advanced Materials and Smart Infrastructure Systems (AMSIS) lab emphasizes interdisciplinary approaches to solving civil engineering challenges. Publications: His recent work includes studies on UAV-based pavement crack detection, machine learning for concrete strength prediction, and optimization of tube-to-tubesheet joints. These reflect trends toward AI-driven solutions and sustainable material innovations. Advising: He actively mentors students in multidisciplinary research projects, emphasizing self-motivation and innovation. His lab collaborates on topics like composite materials, structural health monitoring, and infrastructure resilience. Labs/Teams: His AMSIS lab at UAB focuses on advancing smart materials and infrastructure systems through cutting-edge research and collaboration.

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Dr. Masoud Makrehchi is an Associate Professor in the Department of Electrical, Computer and Software Engineering at Ontario Tech University, part of the Faculty of Engineering and Applied Science. His research focuses on Natural Language Processing, Artificial Intelligence, Machine Learning, and Social Computing, with a strong emphasis on applications in network science and moral AI. He holds a PhD from the University of Waterloo (2007), along with earlier degrees from Shiraz University and Iran University of Science and Technology. Prior to academia, he worked as a Senior Research Scientist at Thomson Reuters (2008–2012) and completed a postdoctoral fellowship at the University of Waterloo (2007–2008). His research expertise includes text mining, social network analysis, and network science, addressing challenges such as signed social network analysis, document classification, and crime trend prediction using social media data. He has received several awards, including the NSERC Postgraduate Scholarship and the SBP Challenge Award (2012). His work spans over 50 peer-reviewed publications in journals like Expert Systems with Applications, Social Network Analysis and Mining, and Web Intelligence, with contributions to conferences such as GECCO and IEEE/ACM. Dr. Makrehchi’s publications highlight innovations in feature selection, text segmentation, and AI-driven frameworks for requirements elicitation. His recent work explores coherence graphs for text segmentation, generative AI in education, and bias detection in medical datasets. He has also contributed to patent applications related to sentiment analysis and text extraction systems.