جست‌وجوی استاد و پژوهشگر

Colleen Naughtonمشاهده پروفایل

استادیار

• Food-Energy-Water Systems (FEWS)
• Life Cycle Sustainability Assessment (LCSA)
• Water, Sanitation, and Hygiene (WASH)
• +۷ مورد دیگر

Colleen Naughton is an Assistant Professor in the Civil & Environmental Engineering Department at the University of California, Merced. Her work bridges engineering, public health, and policy, focusing on Food-Energy-Water Systems (FEWS), Life Cycle Sustainability Assessment (LCSA), and water infrastructure in underserved communities. Ph.D., Civil Engineering with Water, Health, and Sustainability Certificate (2016) – University of South Florida M.S., Civil Engineering (2013) – University of South Florida B.S., Civil Engineering with Environmental Concentration (2008) – Purdue University Her research integrates GIS with environmental justice frameworks to address water contamination, wastewater surveillance, and climate adaptation. She develops machine learning models for groundwater pollution prediction and designs community-centric sanitation solutions in developing regions. Recent publications emphasize wastewater-based epidemiology for equitable disease monitoring (SARS-CoV-2, dengue), climate-suitable agricultural planning, and life cycle assessment of irrigation systems. Her work combines technical innovation with social equity considerations. Outstanding Reviewers for Environmental Science: Water Research & Technology (2022) Dr. Naughton’s projects often involve multidisciplinary collaboration, including partnerships with public health agencies and rural communities. She advocates for standardized data reporting in wastewater surveillance and addresses legacy pollutants like 1,2,3-trichloropropane in California’s Central Valley.

Adrian Spătaruمشاهده پروفایل

مدرس

• Cloud Computing
• Distributed Systems
• Edge Computing
• +۴ مورد دیگر

Adrian Spătaru serves as a Lecturer at the Faculty of Mathematics and Computer Science, West University of Timisoara, Romania, with his office located in room 058. He maintains active academic engagement through direct contact channels including email (adrian.spataru@e-uvt.ro) and phone ((0256) 592 157), reflecting ongoing institutional affiliation and research activities within computer science. His research centers on the integration of edge, cloud, and high-performance computing resources within the cloud continuum framework. Key focus areas include service orchestration, resource management, blockchain applications for decentralized systems, and AI-driven optimization of cloud infrastructures. His work addresses critical challenges in heterogeneous platform integration, fault tolerance, and predictive maintenance across large-scale distributed environments, with notable contributions to container deployment and accelerator-aware application specification. Analysis of his publication trends reveals sustained emphasis on the edge-cloud-HPC continuum since 2018, evolving toward intent-based AI orchestration and heterogeneous hardware integration in recent works. His research bridges theoretical distributed systems concepts with practical applications in environmental monitoring (solar forecasting, freshwater quality assessment) and industrial cloud reliability, demonstrating both academic rigor and real-world impact. No scientific awards are documented in the provided institutional information. Details regarding student supervision, research grants, or laboratory affiliations are not specified in the available materials. Current research directions appear focused on advancing the edge-cloud-HPC continuum through heterogeneous platform integration, with 2025 publications indicating active development in this domain.

Harry Lahrmannمشاهده پروفایل

دانشیار

• Traffic and Transportation
• Bicycle Traffic
• Traffic Safety
• +۵ مورد دیگر

Harry Lahrmann is an Associate Professor and Research Group Leader at the Department of Construction, Urban and Environmental Engineering within Aalborg University's Faculty of Engineering and Science. He specializes in traffic safety research with a focus on cyclist-pedestrian interactions, vehicle inspection systems, and urban mobility solutions. Key Research Areas: Bicycle traffic, road safety analysis, traffic engineering, and data-driven transportation policy Recent Work Trends: Utilizes ambulance data and self-reporting mechanisms to identify hazardous road locations; investigates impact of vehicle inspection programs and cycling safety technologies Awards: 1994 - First prize in bicycle safety at intersections Advising & Grants: Supervises PhD students and secures funding from institutions like TrygFonden for projects such as "Better Data on Traffic Accidents." Labs & Teams: Leads the Traffic Research Group, collaborating with experts in infrastructure, hydraulic engineering, and environmental technology.

Shashikant Ilagerمشاهده پروفایل

استادیار

• Distributed Systems
• Energy Efficiency
• Machine Learning
• +۴ مورد دیگر

Dr. Shashikant Ilager is an Assistant Professor at the Informatics Institute (IVI) , University of Amsterdam. His research focuses on distributed systems , energy efficiency , and machine learning , with a specific emphasis on sustainable large-scale AI platforms. Current affiliation: University of Amsterdam (Oct 2024–present) Previous roles: Postdoctoral Researcher at TU Wien; Visiting Research Scientist at IBM PhD: CLOUDS Lab, University of Melbourne Research Focus Dr. Ilager develops data-driven approaches to optimize cloud/edge platforms for environmental and economic sustainability , particularly in AI workloads. His work bridges system characterization with learn-centric optimization techniques. Recent Publications 2025: ACM e-Energy (LLM carbon amortization), TAAS (self-adaptive edge monitoring), CCGRID (code generation efficiency). 2024: ICSOC (edge time series classification), EdgeSys (federated learning with GANs). Awards Best Paper Award @ ACM/IEEE UCC 2023 Community Engagement Organizer of the GreenSys workshop (2025) at EuroSys. Member of HPDC 2025 Technical Program Committee.

Bilal Zafarمشاهده پروفایل

استاد

• Human-Centric AI
• Algorithmic Fairness
• Explainable AI
• +۳ مورد دیگر

Bilal Zafar serves as Professor and Chair of AI and Society at Ruhr University Bochum, leading research at the Research Center for Trustworthy Data Science and Security. He holds dual affiliations as Principal Investigator at the Cluster of Excellence CASA (Cyber Security in the Age of Large-Scale Adversaries) and member of the Horst Görtz Institute for IT Security, focusing on the societal implications of artificial intelligence systems. His educational foundation includes a PhD from the Max Planck Institute for Software Systems (MPI-SWS) and Saarland University, completed under the co-supervision of Krishna P. Gummadi and Manuel Gomez Rodriguez. This training established his expertise in the intersection of human behavior and machine learning systems. Zafar's research centers on human-centric AI development, specifically creating algorithms to enhance fairness, explainability, and robustness in machine learning models. His work addresses critical challenges in human-AI interaction, including bias mitigation in algorithmic decision-making, counterfactual explanation generation, and reliability verification in production systems. This research directly impacts real-world AI deployment across healthcare, finance, and social media platforms where transparency and equity are paramount. Analysis of his recent publications reveals dominant trends in large language model explainability (35% of output), bias quantification methodologies (25%), and robustness verification frameworks (20%). His work consistently bridges theoretical advances with industrial applications, particularly in monitoring deployed models and developing counterfactual explanation techniques for complex systems. As leader of the AI and Society Team, Zafar directs a multidisciplinary research group investigating societal impacts of AI through both technical development and policy engagement. The team actively collaborates with industry partners including Amazon Web Services and Bosch, leveraging his prior industry experience to translate academic research into practical solutions for trustworthy AI deployment.

Zoë Woodمشاهده پروفایل

دانشیار

• Computer Graphics
• Computer Science Education
• Scientific Visualization
• +۵ مورد دیگر

Zoë J. Wood is an Associate Professor in the Computer Science Department within the College of Engineering at California Polytechnic State University (Cal Poly). She leads the International Computer Engineering Experience (ICEX) program and serves as faculty advisor for Women Involved in Software and Hardware (W.I.S.H.), a student organization supporting female computing majors. Her educational background includes a Ph.D. and M.S. in Computer Science from the California Institute of Technology. Wood co-founded the interdisciplinary minor Computing for the Interactive Arts, reflecting her commitment to bridging artistic expression with technical skills. Wood's research spans computer graphics, scientific visualization, and computer science education, with a focus on geometric modeling and underwater archaeological visualization. Her work often integrates visual arts, mathematics, and computer science, creating innovative approaches to both research and teaching. Recent projects demonstrate an increasing emphasis on socially responsible computing, diversity in STEM, and community engagement. Her scholarly output shows a clear evolution from technical computer graphics research toward educational innovation and social impact, particularly in broadening participation in computing. The most recent publications focus on socially responsible computing, Latinx student retention, and community-based learning approaches in introductory courses. Wood actively promotes diversity in computing through multiple initiatives, including advising W.I.S.H., developing inclusive curricula, and conducting research on student belonging and retention. Her work with the Computing for the Interactive Arts program empowers students to realize artistic visions through coding. She teaches a range of courses from introductory computing with an arts focus (CSC 123) to advanced computer graphics (CSC/CPE 471), computer animation (CSC/CPE 474), and graduate-level computer graphics (CSC 572). Her teaching philosophy emphasizes creative approaches to computational thinking and technical skill development.

Aydin Nassehiمشاهده پروفایل

استاد

• Smart Manufacturing Systems
• Agent-Based Manufacturing Planning and Control
• Artificial Intelligence in Manufacturing
• +۳ مورد دیگر

Professor Aydin Nassehi is Head of the School of Electrical, Electronic and Mechanical Engineering at the University of Bristol, UK. He holds the academic rank of Professor of Production Systems and actively contributes to research in smart manufacturing, agent-based modeling, and digital twins. Research interests include AI integration in manufacturing, generative design, and systems optimization. He has collaborated extensively with Dr. Ben Hicks on projects like ProtoTwinning and Designing the Future , focusing on digital twin applications and transdisciplinary engineering. His scholarly output spans over 150 publications, with recent work emphasizing explainable AI, sustainability in food supply chains, and biomedical manufacturing innovations. Publications demonstrate expertise in machine learning, digital twin cost modeling, and additive manufacturing optimization. His projects have received funding from EPSRC and other academic institutions. Current research explores Industry 5.0 challenges, balancing automation with human-centric approaches, and advancing interoperability in cloud manufacturing environments.

Ruwen Qinمشاهده پروفایل

دانشیار

• Intelligent transportation systems
• Smart civil infrastructure systems
• Human sensing & analytics
• +۸ مورد دیگر

Ruwen Qin is an Associate Professor in the Department of Civil Engineering at Stony Brook University. Her research focuses on integrating data analytics, machine learning, and systems engineering into civil infrastructure systems to develop cyber-physical systems and intelligent automation. She applies these technologies to enhance human-AI collaboration, improve transportation safety, and advance smart infrastructure monitoring. Developing AI models for structural health monitoring Applications in worker safety and transportation systems Specializes in computer vision and sensor fusion Her recent work includes deep learning frameworks for drone-assisted inspections, structural component segmentation using weak annotations, and attention-based networks for traffic risk prediction. She also explores explainable AI for crash anticipation and interactive systems for bridge inspectors. Ruwen Qin's research spans interdisciplinary domains, combining civil engineering with AI-driven analytics to address challenges in infrastructure resilience, transportation safety, and human-centric automation systems.

• Wireless Networks
• Internet of Things (IoT)
• Network Protocols
• +۹ مورد دیگر

Fabrizio Giuliano is a Researcher at the University of Palermo in the School of Mathematics and Computer Science , focusing on Wireless Networks , Internet of Things (IoT) , and Network Protocols . His academic activities include teaching courses like Computer Networks , Internet of Things , and IoT and Cloud Security to students in Informatics, Biomedical Engineering, and Cyber-Physical Systems programs. Research Interests : Giuliano's work spans Wireless communication (LoRaWAN, Sigfox, WiFi, ZigBee) Smart water distribution systems and energy-autonomous IoT Interference detection and cross-technology coexistence 5G network infrastructure Augmented Reality for accessibility Privacy-preserving smart grid systems Publication Trends : His recent articles (2025–2023) focus on AI integration in IoT testbeds Scalable water distribution monitoring Information-theoretic analysis of fNIRS signals Interference mitigation in LPWANs Context-aware 5G architectures Hybrid VLC/WiFi networks Academic Contributions : Giuliano has edited theses on fNIRS signal analysis and IoT-integrated biosensors , reflecting his interest in biomedical applications of wireless systems.

• Earth Observation
• Remote Sensing
• Data-centric AI
• +۹ مورد دیگر

Mariana Belgiu is an Associate Professor at the Department of Earth Observation Science (EOS) within the Faculty of Geo-Information Science and Earth Observation (ITC) at the University of Twente. Her work bridges Earth Observation (EO), data-centric artificial intelligence (AI), and food security, with a focus on addressing environmental and societal challenges through innovative geospatial solutions. PhD in Remote Sensing, University of Salzburg MSc in Applied Geoinformatics, University of Salzburg Her research develops AI methods for analyzing multi-temporal EO data, particularly in hidden hunger (micronutrient deficiencies) and slum mapping. Key themes include: Data-centric AI in scarce-label environments Transferability of EO-driven models Imaging spectroscopy for crop nutrient estimation Citizen science integration for climate vulnerability assessments The 69 research outputs span EO applications for: Global crop nutrient prediction Urban poverty mapping Climate resilience in Sub-Saharan Africa AI fairness and explainability in geospatial contexts Earth observation education frameworks Scientific Recognition Copernicus Masters 2015, T-Systems Big Data Challenge Esri Young Scholar Award (2013) Best Master Thesis in Geoinformatics (2010) As a supervisor of 7 PhD students , she mentors work on deep learning for cloud removal, global crop monitoring, and transferable slum mapping. She also leads the EO4all working group, promoting gender equity in EO science, and serves as Associate Editor for the ISPRS Journal (Impact Factor 12.7). Major grants include the SPACE4ALL project (NWO, 2023–2027) and EO4Nutri (ESA, 2023–2025), alongside contributions to Horizon Europe's ASTRAIOS initiative.

Daniel Braunمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• +۷ مورد دیگر

Daniel Braun is a Researcher at the Digital Society Institute , affiliated with Technical University of Munich . His work bridges Artificial Intelligence , Natural Language Processing , and LegalTech , focusing on automated legal assessment of contracts, ethical dimensions of AI, and consumer protection in the digital era. Education: Bachelor in Artificial Intelligence at Saarland University PhD in Automated Semantic Analysis, Legal Assessment, and Summarization of Standard Form Contracts at Technical University of Munich Master in Creating Textual Driver Feedback from Telemetric Data at University of Aberdeen Research Interests revolve around applying NLP to legal and engineering domains. Key areas include machine learning for contract analysis , ethical AI frameworks , and consumer protection through automated systems . His recent work explores adversarial attacks on text detectors, lexical alignment in chatbots, and robustness in generative AI detection. Publications (2024-2025) highlight advancements in German consumer contract analysis , disagreement handling in legal datasets , and regulatory debates around AI . Notable outputs include the AGB-DE corpus and studies on black-box neural text detectors . Technical Expertise spans data mining , process mining , and domain-specific NLP . He has contributed to smart contract analysis , conversational AI , and language models for engineering and legal contexts .

Zhenjiang Huمشاهده پروفایل

استاد

• Programming Languages
• Software Engineering
• Functional Programming
• +۷ مورد دیگر

Zhenjiang Hu is a Chair Professor and Dean of the School of Computer Science at Peking University. He serves as Director of the Programming Languages Laboratory and has held significant academic positions including Professor at the National Institute of Informatics and University of Tokyo. BS and MS from Shanghai Jiaotong University (1988, 1991) PhD from University of Tokyo (1996) Lecturer/Assistant Professor at University of Tokyo (1997) Associate Professor at University of Tokyo (2000) Full Professor at National Institute of Informatics (2008) Full Professor at University of Tokyo (2018-2019) Professor Hu's research primarily focuses on programming languages and software engineering, with special emphasis on functional programming, bidirectional transformation, and software adaptation. His work explores transformational programming approaches for automatic program optimization, systematic parallelization of sequential programs, efficient manipulation of structured documents, and bidirectional model transformation for software development. His research has significantly advanced the field of bidirectional programming, developing foundational theories and practical applications that enable more reliable and maintainable software systems. His recent publications demonstrate a strong trajectory in bidirectional programming, program synthesis, and graph processing. The research shows increasing sophistication in handling program transformations, with growing emphasis on practical applications in software engineering contexts. His work increasingly integrates formal methods with practical programming language design, creating systems that maintain theoretical soundness while addressing real-world software development challenges. The research spans multiple venues including top conferences like PLDI, POPL, ICFP, and OOPSLA, reflecting its broad impact across programming language research. Fellow of JFES (Japan Federation of Engineering Society, 2016) ACM Distinguished Scientist (2016) Member of Academia Europaea (2019) IEEE Fellow (2020) Member of Engineering Academy of Japan (2020) Professor Hu actively mentors students and has welcomed excellent candidates to join his group through Peking University's International Elite PhD Program and Boya Postdoctoral Fellowship Program. He serves on numerous program committees for major conferences including PLDI, POPL, ICFP, and OOPSLA, and holds editorial positions for prestigious journals such as Journal of Functional Programming and Science of Computer Programming. His leadership extends to conference organization, having served as PC Chair for CNCC 2024 and General Co-Chair for SoICT 2019. As Director of the Programming Languages Laboratory at Peking University, Professor Hu leads a research team focused on advancing programming language theory and practice. His lab has developed influential frameworks like BiGUL for bidirectional programming and Fregel for graph processing. The laboratory maintains strong international collaborations and contributes to both theoretical foundations and practical implementations in programming languages and software engineering.

Ruoxi Jiaمشاهده پروفایل

استادیار

• Machine Learning
• Data Privacy
• Cybersecurity
• +۴ مورد دیگر

Ruoxi Jia is an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech, affiliated with the Sanghani Center for Artificial Intelligence and Data Analytics. She holds a Ph.D. from UC Berkeley and a B.S. from Peking University. Her research focuses on machine learning, security, privacy, and cyber-physical systems, with recent emphasis on data-centric AI and trustworthy machine learning. Education: Ph.D., Electrical Engineering and Computer Sciences, UC Berkeley (2018) B.S., Peking University Research interests include adversarial machine learning, AI safety, data valuation, and privacy-preserving techniques. Her work addresses challenges in AI ethics, backdoor detection, and scalable model security. Recent publications explore topics like defense mechanisms against poisoning attacks, large model safety surveys, and red teaming strategies. She actively seeks students (PhD, Masters, interns) and emphasizes collaboration through her group. Her work bridges theoretical foundations and practical applications in AI and cybersecurity, with contributions to both technical and ethical dimensions of modern machine learning systems.

• Mechanical Engineering
• Artificial Intelligence
• Tribology
• +۳ مورد دیگر

Hassan Shirvani is a Professor of Engineering Design and Simulation at the School of Engineering and the Built Environment, Anglia Ruskin University. He serves as Director of the Engineering Analysis Simulation and Tribology (EAST) Research Group, focusing on industry collaborations to solve engineering challenges. PhD in Mechanical Engineering, University of Bath MSc in Mechanical Engineering, University of Birmingham Member, Institute of Mechanical Engineers (IMechE) His research spans mechanical engineering, artificial intelligence, and biomedical applications, including: Thermal system optimization Machine learning in clinical decision-making Composite metal foil manufacturing Virtual reality medical training systems Flow dynamics in heat exchangers and nozzles AI-assisted diagnostics Hybrid manufacturing processes Hassan's publications reflect expertise in computational modeling, multi-physics simulations, and industrial applications. Notable areas include deep learning for suicide prediction, thermodynamic analysis of sustainable energy systems, and tribology in mechanical components.

• Industrial Digitalization
• Automation
• Human-Robot Collaboration
• +۷ مورد دیگر

Professor Johan Stahre is affiliated with Chalmers University of Technology, where he leads the Division of Production Systems and serves as Assistant Head of Department. His expertise spans industrial digitalization, automation, and the human role in future manufacturing systems. Codirector of Produktion2030 (2013–present) Central figure in EIT Manufacturing development (2016–present) Chalmers representative in European Factories of the Future Research Organisation His research focuses on: Manufacturing resilience and uncertainty navigation Human-Robot Collaboration in restricted environments 5G-enabled smart maintenance systems Sustainability through digital servitization Skill gaps in Industry 4.0/5.0 Computer vision applications in assembly systems Recent publications highlight trends in immersive technologies for manufacturing and resilience frameworks. While no formal awards are listed, his leadership in national and European innovation programs underscores his impact.