جست‌وجوی استاد و پژوهشگر

• Algorithmic Databases
• Data Mining
• Computational Geometry
• +۵ مورد دیگر

Stavros Sintos is an Assistant Professor in the Department of Computer Science at the University of Illinois at Chicago (UIC). He joined UIC after a postdoctoral fellowship at the University of Chicago, where he was part of the ChiData group under Sanjay Krishnan. He earned his Ph.D. in Computer Science from Duke University in 2020, advised by Pankaj K. Agarwal. His research focuses on designing efficient algorithms for databases, data mining, and computational geometry . Key themes include: Theoretical guarantees for practical problems Compact indexing structures for query efficiency Geometric optimization combined with database systems Fairness in algorithmic design (e.g., Fair Set Cover, FairHash) Temporal data analysis and dynamic query processing Notable scientific contributions include a Best Paper Award at ICDT 2024 for work on range entropy queries. His publications span top venues like SIGMOD, PODS, VLDB, and SODA, covering areas such as clustering algorithms, synthetic query witnesses, and temporal join optimizations.

Filippo Menczerمشاهده پروفایل

استاد

• Web Science
• Social Media
• Social Networks
• +۵ مورد دیگر

Filippo Menczer is a Professor of Informatics and Computer Science and Director of the Center for Complex Networks and Systems Research at Indiana University School of Informatics and Computing. He maintains courtesy appointments in Cognitive Science and Physics, and is affiliated with the Center for Data and Search Informatics and the Biocomplexity Institute. Additionally, he holds a Fellowship at the ISI Foundation in Torino, Italy. His research spans computational analysis of digital ecosystems with emphasis on: Web Science: structural and behavioral analysis of internet-scale systems Social Media Dynamics: information diffusion, meme competition, and attention economy modeling Complex Networks: traffic pattern analysis, popularity dynamics, and social link prediction Publications from 2009-2012 reveal consistent focus on social network analytics and information diffusion mechanisms. Key trends include modeling attention-limited meme competition, bursty popularity patterns in social media, and social link prediction through metadata analysis. His work integrates network science, computational social science, and data mining to decode online behavior. His scientific recognition includes: Fellow of ISI Foundation (2013) He leads the NaN research group within the Center for Complex Networks and Systems Research, focusing on interdisciplinary approaches to complex information networks and social media analytics.

• Distributed control
• Autonomy and Control
• Aerospace Systems
• +۶ مورد دیگر

Dengfeng Sun is a Professor and Associate Head of the Gambaro Graduate Program in the School of Aeronautics and Astronautics at Purdue University. His research focuses on distributed control systems, autonomy, resilient networks, and air traffic management. Sun holds a B.Eng. from Tsinghua University, an M.S. from The Ohio State University, and a Ph.D. from UC Berkeley. His work spans advanced air mobility, UAV trajectory planning, and stochastic optimization for large-scale systems. Key contributions include resilient UAV traffic control, distributed state estimation algorithms, and fault detection methods for navigation systems. Sun's research has been published in top journals like IEEE Transactions on Intelligent Transportation Systems and Transportation Research Part E. Education: B.Eng., Tsinghua University (2000) M.S., Ohio State University (2002) Ph.D., UC Berkeley (2008) He advises on cutting-edge projects integrating robotics, autonomous systems, and cloud-based traffic modeling. His lab develops solutions for urban air mobility, emergency medical UAV networks, and next-generation air traffic control systems. Notable collaborations include work with NASA and industry partners on continuous descent approach procedures and metroplex routing paradigms. Sun's work bridges theoretical control systems with practical applications in aviation and infrastructure optimization.

Junhong Chenمشاهده پروفایل

استاد

• Hybrid Nanomaterials
• 2D Nanomaterials
• Chemical and Biological Sensors
• +۳ مورد دیگر

Junhong Chen is the Crown Family Professor of Molecular Engineering at the University of Chicago's Pritzker School of Molecular Engineering and Lead Water Strategist at Argonne National Laboratory. His research focuses on hybrid nanomaterials, 2D materials, sensors for chemical/biological molecules, and energy devices. He has pioneered innovations in real-time water sensing and energy storage, with applications in environmental sustainability and healthcare. Chen holds a PhD from the University of Minnesota (2002) and a postdoc from Caltech (2003). He previously directed the NSF Industry-University Cooperative Research Center on Water Equipment & Policy and served as a NSF program director. Education: PhD in Mechanical Engineering (2002, University of Minnesota), Postdoc in Chemical Engineering (2002–2003, Caltech) Research Interests: Nanomaterials, Sensors, Energy Storage, Water Pollution Control Awards: Fellow of National Academy of Inventors, ASME, IAAM Medal, Wisconsin Innovation Award (2016) Chen's lab group develops nanosensors and energy devices using molecular engineering, with a focus on scalable manufacturing and AI integration. Recent work includes graphene-based sensors for real-time water monitoring and novel battery technologies. His research also addresses global challenges like PFAS contamination and sustainable manufacturing.

• Cybersecurity
• Digital Forensics
• AI Security
• +۶ مورد دیگر

Assoc. Prof. Nhien An Le Khac is an Associate Professor at the School of Computer Science, University College Dublin. He serves as Programme Director for the MSc in Forensic Computing & Cybercrime Investigation, which has trained over 1,500 law enforcement officers globally. His research focuses on cybersecurity, digital forensics, AI security, and secure healthcare IT systems. He holds a PhD from Institut National Polytechnique de Grenoble (France) and has supervised 9 PhD students. His work includes pioneering contributions to electromagnetic side-channel analysis (EM-SCA) for IoT forensics, blockchain forensics, and AI-based fraud detection. Education: BSc/MSc: Vietnam National University, Ho Chi Minh City PhD: Institut National Polytechnique de Grenoble, France Professional Certificate in University Teaching & Learning: UCD Research Interests: Cybersecurity, Digital Forensics, AI Security, Machine Learning, Cloud Computing, Big Data Analytics, Healthcare IT Security. Recent Article Trends: Focus on EM-SCA for IoT device forensics, illicit Bitcoin transaction tracking, and cross-device ML portability. His work bridges theoretical AI advancements with practical forensic applications, emphasizing privacy preservation and explainable AI. Awards & Recognition: World’s Top 2% Scientists (2024) UCD Teaching Excellence Awards (2022, 2018) Best Paper Awards at Elsevier, AI-2022, and DFRWS conferences Grants & Advising: Principal Investigator on grants like Cloud Atlas, CERBERUS, and Urban ARK. Advised 9 PhD students who now work in academia/research globally. Active in funding initiatives like ML-Labs (SFI-funded). Labs & Teams: Leads ASEADOS Lab and maintains datasets like EM-SCA and InSDN. Collaborates globally on forensic frameworks and cybersecurity tools.

Sören Laueمشاهده پروفایل

استاد

• Machine Learning
• Optimization Algorithms
• GPU Computing
• +۳ مورد دیگر

Prof. Dr. Sören Laue is a Professor of Machine Learning at the University of Hamburg's Department of Informatics. His research focuses on optimization algorithms, machine learning frameworks, and high-performance computing. He leads the Machine Learning research group and developed the GENO optimization framework and the Matrix Calculus toolset. His work emphasizes GPU acceleration, tensor operations, and scalable solutions for classical machine learning problems. Projects: GENO solver (Python-based optimization), Matrix Calculus (derivative computation), and SQL-based tensor operations. Key Research Themes: Optimization frameworks, GPU computing, neural network scalability, and algorithm design. Selected recent publications highlight contributions to tensor calculus benchmarks, GPU-optimized machine learning pipelines, and novel optimization methods. His work bridges theoretical foundations and practical software tools for the machine learning community.

• Social Physics
• Graph Convolutional Neural Networks
• Large Language Models (LLMs)
• +۴ مورد دیگر

Dr. Alexander Mantzaris is an Associate Professor in the Department of Statistics & Data Science at the University of Central Florida, College of Sciences. His research bridges physics and sociology through Social Physics frameworks, focusing on statistical mechanics and thermodynamic analogies to model social phenomena. Current research explores criticality points in social systems Developing computational tools for NLP and big data Former work on Graph Convolutional Networks in social analysis Specializes in entropy-based modeling of polarization and segregation His publications emphasize interdisciplinary approaches combining network science, computational modeling, and sociological dynamics. Recent articles address thermodynamic formulations of political cycles, energy states in Schelling models, and memory-efficient data processing algorithms. Dr. Mantzaris teaches graduate courses in big data analytics and statistical learning theory. He maintains active research in computational social science with applications to political dynamics, media influence, and complex systems analysis.

Mojgan A. Jadidiمشاهده پروفایل

دانشیار

• Geospatial Visual Analytics
• Building Information Modeling (BIM)
• 3D GIS Integration
• +۵ مورد دیگر

Dr. Mojgan A. Jadidi serves as Associate Professor in the Teaching Stream and Director of Common Engineering & BSc Science within the Department of Civil Engineering at York University's Lassonde School of Engineering. A Professional Engineer (P.Eng) and founder of the GeoVA Lab, she leads research at the intersection of geospatial analytics, digital infrastructure, and innovative engineering education aligned with UN Sustainable Development Goals. Education: PhD in Geomatics, Université Laval (2014) MSc in Earthquake and Seismology Engineering, ROSE Center (Italy) & Université Joseph Fourier (France) BSc in Civil-Survey Engineering, Iranian University of Science and Technology Research Focus: Her pioneering work in Geospatial Visual Analytics spans 2D/3D environments, Building Information Modeling (BIM) and 3D GIS integration, and Spatial Quantum Computing applications for smart cities. She develops Infrastructure Digital Twins using sensor data fusion while revolutionizing engineering education through gamification and augmented/virtual reality pedagogies that transform complex spatial concepts into immersive learning experiences. Research Trends: Recent publications (2021-2023) demonstrate convergent innovation across three domains: (1) Building energy optimization through BIM-graph analytics, (2) Transportation safety via AI-driven situational awareness, and (3) Educational technology using VR sandboxes and visual-verbal comics. These works consistently integrate quantum computing principles and UN SDG frameworks to solve urban sustainability challenges. Scientific Recognition: ASEE Zone III Best Paper Award (2023) ASEE Saint Lawrence Best Research Paper & Poster (2022) 3D GeoInfo Conference Best Paper (2018) NSERC Postdoctoral Fellowship (2016) ESRI Student Award (2011) Erasmus Mundus Scholarship (2006) Research Leadership: As Associate Director of York's ESRI Center of Excellence, she manages multi-source funding from NSERC, Mitacs, and York University internal grants. Her professional service spans global organizations including ISPRS Commission IV (Secretary), IEEE Women in Engineering (Member), PEO Etobicoke (Chair), and buildingSMART Canada (Committee Member), driving standards for BIM and digital twin implementation in urban infrastructure. Lab Innovation: The GeoVA Lab develops cutting-edge tools including the TopoSurvey Game for immersive surveying education, PAN-Lassonde XR Sandbox for virtual lab experiences, and quantum computing frameworks for bike-sharing optimization, establishing new paradigms in spatial data interaction and engineering pedagogy.

Abdulrahman Takiddinمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Cyber-Physical Systems
• +۳ مورد دیگر

Abdulrahman Takiddin is an Assistant Professor in the Department of Electrical & Computer Engineering at the Florida A&M University–Florida State University College of Engineering. He holds a Ph.D. in Electrical Engineering from Texas A&M University (2023), an M.S. in Data Analytics from Hamad Bin Khalifa University (2020), and a B.Sc. in Information Systems from Carnegie Mellon University (2014). His research focuses on cybersecurity in smart grids and cyber-physical systems, leveraging machine learning and graph neural networks to detect adversarial attacks such as false data injection and electricity theft. Key areas include resilient power systems, adversarial evasion attack mitigation, and spatio-temporal analysis of power distribution networks. Recent work emphasizes graph-based approaches for enhancing cyber resilience, including eigenvector centrality-enhanced networks and transfer learning solutions for small data scenarios. His publications address both foundational cybersecurity challenges and applied solutions for electrified transportation systems and smart grid infrastructure. Notable trends in his articles include advancements in unsupervised learning for voltage stability protection and recurrent graph networks for replay attack detection. His research also intersects with bioinformatics and artificial intelligence applications in healthcare, though the majority of his work centers on energy systems and cybersecurity.

Yuan-Fang Liمشاهده پروفایل

دانشیار

• Knowledge Graphs
• Natural Language Processing
• Multimodality
• +۵ مورد دیگر

Yuan-Fang Li is an Associate Professor in the Department of Data Science & AI at Monash University's Faculty of Information Technology. He also serves as Associate Dean International. His research focuses on knowledge graphs, natural language processing, multimodality, and graph representation learning. He holds a PhD from National University of Singapore (2006) and a Bachelor of Computing (Honours) from the same institution (2002). Affiliations: Monash University (since 201?), National University of Singapore (PhD 2002-2006) Key Projects: Leading research on neuro-symbolic systems (HARNESS project), large-scale multimodal knowledge management, and maritime knowledge graphs Teaching: Taught courses including FIT4002, FIT4004, and supervised over 20 PhD students Research interests include complex question answering over knowledge graphs, knowledge extraction from text/images, and structural/temporal graph learning. He has published 152+ works with notable contributions to scene graph generation, event extraction, and LLM-based reasoning. Key awards include the 2020 Best Student Paper Award and 2017 Kurzweil Prize. Grants: ARC Discovery Projects, industry collaborations (e.g., Outotec Oy) Labs/Teams: Active in Monash's Data Science & AI research groups, leading neuro-symbolic AI initiatives

Nick Heardمشاهده پروفایل

استاد

• Computational Bayesian Inference
• Clustering
• Changepoint Analysis
• +۶ مورد دیگر

Nick Heard is a Professor and Chair in Statistics at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. His research focuses on computational Bayesian inference, clustering, and changepoint analysis applied to dynamic networks (e.g., computer networks, social networks) and bioinformatics. He leads the EPSRC-funded NeST project on Network Stochastic Processes and Time Series, collaborating with universities including Bristol, Oxford, and LSE. His work bridges statistical theory with applied problems in cyber-security and neuroscience. Research Interests - Modelling large dynamic networks - Changepoint analysis and anomaly detection - Statistical methods for cyber-security - Bayesian computation and inference - Spectral clustering and graph embeddings Grants & Collaborations - Co-leads the NeST project on Dynamic graph embeddings: procedures and inference - EPSRC Programme Grant (EP/T004870/1) supporting Network Stochastic Processes and Time Series research Software & Tools - Developed open-source packages for Bayesian changepoint analysis (e.g., changepoints ) - Code for p-value combination methods ( standardised_partial_product )

Marius Portmannمشاهده پروفایل

استاد

• Cybersecurity
• Computer Networks
• Internet of Things (IoT)
• +۳ مورد دیگر

Professor Marius Portmann is the UQ-Cisco Chair of Network Security at the School of Electrical Engineering and Computer Science (EECS), University of Queensland. His expertise spans Cybersecurity, IoT, and Applied AI. He holds a PhD from ETH Zurich (2003) and has led research in Software Defined Networking (SDN), blockchain, and energy-harvesting IoT systems. Education: PhD in Electrical Engineering from Swiss Federal Institute of Technology (ETH Zurich), 2003. Research focuses on securing IoT networks, AI-driven intrusion detection, and sustainable sensor systems. He has pioneered self-powered IoT systems using energy harvesters and developed frameworks like FlowTransformer for network analysis. His work bridges theoretical advancements with practical applications in smart tourism, energy efficiency, and edge computing. Recent publications highlight innovations in DDoS detection (P4-Secure), sensor-based environmental monitoring (EcoShower), and graph-based anomaly detection (XG-BoT). His datasets (e.g., NF-ToN-IoT-v3) are widely used in ML-based cybersecurity research. Collaborations include industry partners like Cisco and institutions like RMIT. Grants and leadership roles in interdisciplinary projects underscore his impact. He advises on IoT security standards and contributes to open-source tools for network research. Current projects explore edge-AI integration and sustainable sensor networks.

• Data Science
• Bioinformatics
• Open Science
• +۴ مورد دیگر

Philippe Rocca-Serra is a Researcher at the Oxford e-Research Centre (OeRC), University of Oxford, and an Associate Member of the Engineering Science Department. Affiliated with Kellogg College, his work focuses on advancing open science through FAIR data principles, interoperable metadata standards, and translational biomedical data systems. He holds a DPhil in Molecular Genetics from the University of Bordeaux, supported by an EMBO Fellowship, and has contributed to major initiatives like the ISA-Tab format, the FAIR Cookbook, and the Translational Data Catalog. His research spans data standards for omics technologies, semantic validation frameworks (e.g., BioValidator), and infrastructure for reproducible research. Key contributions include the ISA API platform, metabolomics standards (nmrML, mzTab-M), and FAIRification frameworks. He actively collaborates with global initiatives such as ELIXIR, the Common Fund, and the Precision Toxicology Initiative. Rocca-Serra’s work emphasizes bridging data producers and consumers through machine-actionable metadata, fostering interdisciplinary research and policy compliance. He leads projects on clinical trial metadata profiling and has published extensively on data governance, computational workflows, and the role of FAIR principles in drug discovery and pandemic preparedness. Education: Ecole Nationale Supérieure d'Agronomie de Rennes (Diplôme d'Ingénieur), University of Bordeaux (PhD, Molecular Genetics) Key Roles: Group Coordinator at OeRC, Co-Investigator on international grants Tools Developed: ISAcreator, COPO, FAIR Cookbook, Data Tags Suite (DATS) Awards: EMBO Fellowship (2001) Labs/Teams: Oxford Data Readiness Initiative, FAIR Implementation Network

Ed Chienمشاهده پروفایل

استادیار

• Computer Science
• Graphics
• Computational Engineering
• +۳ مورد دیگر

Ed Chien is an Assistant Professor at Boston University's Department of Computer Science within the College of Arts & Sciences. He specializes in applying differential geometry and topology to graphics, computational engineering, and machine learning. Previously, he was a postdoctoral researcher at MIT's CSAIL and Bar-Ilan University. His work focuses on mathematically rigorous solutions to problems in geometric data processing and optimal transport. Education PhD in Mathematics, Rutgers University (2015) A.B. in Mathematics & Physics, Dartmouth College (2009) Research Highlights Dr. Chien's research includes fundamental studies on hexahedral mesh topology for Finite Element Modeling, optimal transport applications in machine learning, and singularity-free geometric algorithms. His work bridges theoretical mathematics with computational tools for engineering and graphics. Publications span top venues like NeurIPS, Eurographics, and SIGGRAPH, reflecting contributions to geometry processing and machine learning intersections. Program committee roles include Eurographics SGP (2019) and AAAI (2020). Awards & Recognition No specific awards listed, but notable service includes program committee memberships and peer-reviewed contributions to leading conferences. Grants & Advising Active in mentoring through academic positions but no explicit grant details provided. Research focuses on advancing geometric algorithms and optimal transport methodologies.

David Danksمشاهده پروفایل

استاد

• AI Ethics
• Causal Inference
• Healthcare Technology
• +۴ مورد دیگر

David Danks is a Professor of Data Science, Philosophy, and Policy at the University of California, San Diego. His work bridges AI ethics, causal inference, and policy, focusing on governance frameworks for emerging technologies. He leads research on trustworthy AI systems, healthcare technology applications, and sociotechnical risks. Danks is affiliated with the DIVER Lab, exploring interdisciplinary approaches to AI's societal impact. His research spans causal discovery algorithms, ethical AI design, and the intersection of science and policy. Notable themes include mitigating bias in quantum machine learning, dynamic certification for autonomous systems, and addressing unforeseen technological harms. He has contributed to national AI policy through roles like the National Artificial Intelligence Advisory Committee. Publications emphasize ethical challenges in AI development, such as algorithmic fairness, epistemic utility, and moral responsibilities in dual-use technologies. His work frequently intersects with healthcare innovation, including personalized hemodynamic models for surgical risk reduction. While no formal awards or grants are listed, Danks' involvement in high-profile initiatives like the CCC Whitepaper on pandemic prevention underscores his leadership in translational ethics and policy.