جست‌وجوی استاد و پژوهشگر

Zhibao Mianمشاهده پروفایل

مدرس

• Trustworthy AI
• Machine Learning
• Large Language Models
• +۸ مورد دیگر

Dr. Zhibao Mian is a Lecturer in the School of Computer Science at the University of Hull, UK, and previously held an Associate Professor position at Northwest Normal University. He specializes in trustworthy AI, machine learning, and intelligent maintenance systems. His research integrates AI with IoT, blockchain, and digital twins in Industry 4.0/5.0 contexts. He leads projects on predictive maintenance for offshore wind turbines and AI-driven sustainable energy solutions. Dr. Mian holds a PhD from the University of Hull and an MSc from the University of Nottingham. Research interests include AI ethics, model-based safety analysis, and RCM. He has secured grants such as the CPHC-funded study on AI in software education and oversees multiple PhD scholarships. Notable roles include Editorial Board member of the American Journal of Artificial Intelligence and Reviewer for high-impact journals/conferences like JSS and IEEE. He is a Senior Fellow of the Higher Education Academy and received the Royal Academy of Engineering's 2024 Exceptional Talent designation. Recent publications (2023-2025) focus on ordinal networks, outlier detection, Belt and Road trade analysis, and carbon emissions modeling. He actively advises PhD students on topics like UAV-based anomaly detection and predictive maintenance frameworks.

• Data Center Networking
• Software Defined Networks
• Congestion Control
• +۱ مورد دیگر

Yashar Ganjali is Professor in the Department of Computer Science at the University of Toronto, where he leads research on computer networks and distributed systems. His work focuses on improving efficiency in data center operations through innovative algorithmic approaches. Research areas include: Machine learning applications for network optimization SDN controller architectures and load migration Congestion control mechanisms for high-speed networks Data center traffic engineering and resource allocation Recent projects explore joint time-space scheduling for distributed ML training, network-aware transport protocols, and reinforcement learning for congestion management. His team collaborates with industry partners including Google to develop practical solutions for cloud infrastructure challenges.

Iwona M Jasiukمشاهده پروفایل

استاد

• Composite Materials
• Materials Science
• Biomedical Engineering
• +۴ مورد دیگر

Professor Iwona M. Jasiuk is a multi-disciplinary academic affiliated with the University of Illinois, holding professorships in Mechanical Science and Engineering, Biomedical and Translational Sciences, Bioengineering, Aerospace Engineering, and other departments. She is also affiliated with the National Center for Supercomputing Applications (NCSA), Beckman Institute for Advanced Science and Technology, and the Carl R. Woese Institute for Genomic Biology. Her research focuses on composite materials, bio-inspired structures, additive manufacturing, and computational mechanics, with a strong emphasis on integrating artificial intelligence into materials science. Her work spans topics such as material characterization, metamaterials design, and radiation effects on materials. Notable research areas include thin-ply composites, lattice structures derived from geometric principles, and the mechanical properties of bio-inspired systems like equine hoof walls. She has pioneered the use of deep learning networks for predicting material behavior in complex systems. Professor Jasiuk has received prestigious awards, including the ASME Fellow, SES Fellow, and Vebleo Scientist Award. Her research is supported by collaborations across engineering, biology, and computational fields, leveraging advanced facilities like NCSA for high-performance computing.

Wenhao Dingمشاهده پروفایل

پژوهشگر

• Autonomous Systems
• Reinforcement Learning
• Causal Discovery
• +۳ مورد دیگر

Wenhao Ding is a Research Scientist at NVIDIA's Autonomous Vehicle Group, focusing on enhancing the safety and robustness of physical autonomous systems, particularly autonomous vehicles. His research integrates multi-modal large language models, reinforcement learning, and causal discovery to improve model reasoning capabilities. He holds a Ph.D. from Tsinghua University's Department of Electronic Engineering, with a thesis on 'Generative AI for Critical Digital Twins.' Key research interests include safety-critical scenario generation, causal representation learning, and offline reinforcement learning. His work emphasizes closed-loop simulation for autonomous systems and has led to contributions like the SafeBench benchmarking platform and the RealGen scenario generation framework. He has received the 2022 Qualcomm Innovation Fellowship. Notable collaborations include projects with Prof. Marco Pavone at Stanford and internships at Amazon Lab126 (Astro team) and Bosch Center for AI. He actively reviews for top conferences (ICML, NeurIPS, CVPR) and journals (IEEE T-ITS, RA-L). His recent focus on privacy risks in robotics and causal-aware driving models underscores his commitment to trustworthy AI systems. He organizes conferences like the 2024 IEEE International Automated Vehicle Validation Conference and co-hosted the Secure and Safe Autonomous Driving (SSAD) Workshop at CVPR 2023. His interdisciplinary work bridges theory and practice, addressing critical challenges in autonomous systems' safety and generalization.

Felix Xiaozhu Linمشاهده پروفایل

دانشیار

• Systems Software
• Operating Systems
• Computer Architecture
• +۴ مورد دیگر

Felix Xiaozhu Lin serves as Associate Professor and William Wulf Faculty Fellow in the Department of Computer Science at the University of Virginia's School of Engineering and Applied Science, where he directs the Computer Science Ph.D. Program and MCS/MS Program. Previously a tenured Associate Professor at Purdue University's School of Electrical and Computer Engineering, Lin joined UVA Engineering in August 2020 after completing his doctoral research at Rice University. His educational credentials include: Ph.D. in Computer Science, Rice University (2014) M.S. in Computer Science, Tsinghua University (2008) B.S. in Automation, Tsinghua University (2006) Lin's research centers on systems software at the intersection of operating systems, compilers, and computer architecture, with emphasis on accelerating and safeguarding software systems. His current projects target on-device large language models and speech processing for low-cost hardware ( Analysis of his recent publications reveals a strong trajectory in edge computing and efficient AI systems. His research demonstrates increasing focus on hardware-software co-design for autonomous devices, with significant contributions in video analytics for energy-constrained cameras, kernel virtualization for heterogeneous architectures, and stream processing frameworks leveraging emerging memory technologies. The work consistently addresses real-world constraints like power limitations and network intermittency while maintaining rigorous academic standards. His scientific recognition includes: National Science Foundation CAREER Award (2019) Google Faculty Research Award (2016) NSF CISE Research Initiation Initiative Award (2015) ACM ASPLOS Best Paper Award (2014) Lin leads the XSEL research group mentoring graduate and undergraduate students in systems software development. His educational initiatives include CS4414/CS6456, a modern operating systems course featuring Arm64 baremetal kernel development, multicore systems, trusted execution environments, and filesystem forensics. The course's experiential approach has received strong student feedback for its modern content and practical relevance. His group actively recruits for projects spanning on-device AI, hardware-accelerated speech processing, and next-generation OS development. Based in Charlottesville, Virginia, Lin's research benefits from UVA's proximity to Shenandoah National Park and collaborative opportunities within the university's vibrant computing ecosystem, including the 2024 LLM Workshop he co-organized with Professor Yangfeng Ji.

A.A.J.F. van den Dobbelsteenمشاهده پروفایل

استاد

• Sustainable Architecture
• Climate-Resilient Design
• Urban Climate Adaptation
• +۵ مورد دیگر

Prof. Dr. Ir. Andy van den Dobbelsteen is a Professor of Climate Design & Sustainability at Delft University of Technology's Faculty of Architecture and the Built Environment . His work focuses on integrating sustainable energy systems , climate adaptation strategies , and urban development through innovative design solutions. Department: Architectural Engineering + Technology Expertise: Energy systems, climate-responsive architecture, urban sustainability He actively translates research findings into actionable concepts for heritage building retrofitting , data center energy alternatives , and vertical urban farming , emphasizing interdisciplinary collaboration and knowledge transfer. His recent publications explore parametric modeling for energy efficiency, renewable backup systems , and synergetic urban design . He received prestigious awards including the edX Prize and Knight of the Netherlands Lion . 2020 edX Prize for Zero-Energy Design MOOC 2020 Golden Heron for educational film 'Energy Slaves' 2019 Solar Decathlon Europe 2nd prize As a dedicated educator, he supervises students in user-centred sustainability projects while maintaining active industry partnerships through secondary roles as speaker and columnist.

David Wentzlaffمشاهده پروفایل

استاد

• Computer Architecture
• Green Computing
• Cloud Computing
• +۶ مورد دیگر

David Wentzlaff is a Professor of Electrical and Computer Engineering at Princeton University, with associated faculty roles in Computer Science and the High Meadows Environmental Institute (HMEI). He leads research in computing architecture, green computing, and sustainable system design. As Director of Undergraduate Studies, he shapes educational programs in his field. Education: Ph.D., Electrical Engineering, MIT (2012) M.S., Electrical Engineering and Computer Science, MIT (2002) B.S., Electrical Engineering, University of Illinois at Urbana-Champaign (2000) Research Focus: Future Computing Systems: Designing manycore architectures, cloud computing infrastructure, and chiplet-based systems for exascale computing. Sustainability: Developing energy-efficient hardware, recyclable computing systems, and eco-friendly decommissioning strategies. Hardware-Software Co-Design: Exploring FPGA integration, in-memory computing, and parallel processing frameworks. Advising & Grants: Advises 8 current graduate students, focusing on topics like chiplet design, neural acceleration, and sustainable computing. Recipient of NSF grants for projects like OpenPiton (open-source manycore research platform) and CAREER awards for energy-efficient architectures. Labs & Collaborations: Leads the Wentzlaff Research Group at Princeton. Develops open-source frameworks like PRGA (FPGA prototyping) and OpenPiton (manycore processor).

• Time Series Analysis
• Econometric Theory
• Functional Data Analysis
• +۳ مورد دیگر

Dr Won-Ki Seo is a Senior Lecturer in the School of Economics at the University of Sydney. His research focuses on time series analysis, econometric theory, and functional data analysis. He holds a Ph.D. in Economics from the University of California, San Diego. Research Interests: Dr Seo's work centers on cointegration analysis in functional spaces, Hilbertian processes, and the application of advanced mathematical frameworks to econometric problems. His recent studies explore tail behavior of Lévy processes, functional principal component analysis, and nonlinear time series modeling. Recent work includes analyzing stopped Lévy processes with Markov modulation and developing methodologies for functional time series inference Key contributions to cointegration theory in Banach spaces and functional data econometrics Dr Seo has published extensively in top journals like Econometric Theory and Journal of Time Series Analysis . His research bridges theoretical econometrics and practical applications in financial and environmental economics. Contact: won-ki.seo@sydney.edu.au | Office: A02 Social Sciences Building

Jonathon Hutchinsonمشاهده پروفایل

دانشیار

• Social Media Studies
• Public Service Media
• Cultural Intermediation
• +۴ مورد دیگر

Introduction Jonathon Hutchinson is an Associate Professor and Chair of the Media and Communications Discipline at the University of Sydney. He holds roles as Editor-in-Chief of the Policy & Internet journal and Chief Investigator on major projects like the International Digital Policy Observatory (ARC LIEF) and the eSafety Commission's Emerging Online Safety Issues initiative. His work bridges digital media studies, policy analysis, and cultural theory. Education BComm (Hons), RMIT University PhD, ARC Center of Excellence for Creative Industries and Innovation, QUT Research Interests Hutchinson focuses on digital intermediation processes in cultural production, algorithmic governance, and public service media adaptation. Key areas include: Algorithmic influence on news diversity Social media policy and youth safety Platform politics and regulatory frameworks Recent Article Trends His 2020–2025 work emphasizes global digital policy challenges, generative AI ethics, and Vietnamese media dynamics. Recent projects highlight cross-national comparisons (e.g., China/South Korea news ecosystems) and participatory co-design approaches to online safety education. Awards & Grants 2023 ARC LIEF Grant 2021 eSafety Online Safety Grant Multiple industry partnerships (e.g., eSafety Commissioner, Hans-Bredow-Institut) Advising & Grants Supervised over 15+ graduate students (PhD/Master's/Honours) in media studies and digital culture. Active in interdisciplinary grants addressing cybersecurity, AI governance, and platform regulation. Labs & Networks Member of the Sydney Centre for Healthy Societies, Sydney Southeast Asia Centre, and Charles Perkins Centre. Leads the International Digital Policy Observatory project tracking global regulatory trends.

• Information Systems
• Operations Management
• Business Strategy
• +۵ مورد دیگر

Henrique O'Neill is an Associate Professor (with Habilitation) in the Department of Marketing, Operations and General Management at ISCTE - University Institute of Lisbon, Portugal. He is an Integrated Researcher at ISTAR-Iscte - Research Center in Information Sciences, Technologies and Architecture. His research focuses on information systems adoption, organizational strategy, and process optimization in healthcare, finance, and public administration. Education: PhD in Business Organization and Management - University of Cranfield (1995) Master's in Electrical and Computer Engineering - Higher Technical Institute (1987) Bachelor's in Electrical Engineering - Higher Technical Institute (1983) Research Interests: His work spans business/IT strategy, systems modeling, process analysis, and technology implementation. Key domains include healthcare informatics, banking systems, and Industry 4.0 applications. He emphasizes practical solutions for organizational performance through technology integration. Publication Trends: Recent articles explore intelligent business systems, telemedicine, design science methodologies, and supply chain innovation. His work consistently bridges theoretical frameworks with sector-specific applications in healthcare, education, and logistics. Professional Engagement: Member: Portuguese Telemedicine Association, Order of Engineers Commissioner: INEM (National Institute of Medical Emergency) reform study Director: Center for IT Development (2010-2014) Advising & Projects: Supervises 7 graduate students (1 PhD, 6 Master's). Leads EU-funded projects including: Atlantic Crossing (2024-2025): US-Portugal academic collaboration in AI/cybersecurity AAL4ALL (2011-2015): Ambient Assisted Living ecosystem UNITE (2000-2002): Ubiquitous teamwork platforms

Ravi Aronمشاهده پروفایل

استاد

• Healthcare IT
• Emergent Technologies
• Healthcare Startups Valuation
• +۶ مورد دیگر

Ravi Aron is a Professor of Healthcare Strategy & Technology at the C. T. Bauer College of Business, University of Houston, and Research Director of the Healthcare Business Institute. He holds a joint appointment in the Department of Health Systems & Population Health Sciences at the Tilman J. Fertitta Family College of Medicine. He earned his Ph.D. in Management Information Systems from New York University's Stern School of Business. His research focuses on healthcare IT, emergent technologies in healthcare operations, valuation of healthcare startups, and AI applications in healthcare. He has published widely in top journals like Management Science and Information Systems Research, and his work bridges information systems, operations management, and technology strategy. Dr. Aron has extensive teaching experience at The Wharton School, Johns Hopkins Carey Business School, and NYU Stern, winning multiple teaching awards. He advises Fortune 500 firms, startups, and policymakers on technology strategy, digital transformation, and risk assessment. His executive education programs address AI, machine learning, and digital business models for global executives. Key awards include the Dean's Faculty Excellence Award (2016), multiple teaching accolades from Wharton and Johns Hopkins, and the Herman E. Kross Best Dissertation Award (1999). His current projects explore healthcare supply chains, predictive models using machine learning, and valuing technology-enabled startups. He regularly participates in global forums like the World Economic Forum, advising on healthcare innovation and technology policy.

Faidra Monachouمشاهده پروفایل

استادیار

• Market Design
• Algorithmic Fairness
• Public Policy Analysis
• +۳ مورد دیگر

Faidra Monachou is an Assistant Professor of Operations Management at the Yale School of Management and a Faculty Fellow at the Center for Algorithms, Data, and Market Design at Yale (CADMY). She specializes in designing market mechanisms and operational interventions that balance efficiency and fairness, particularly in public policy contexts like education. Her research integrates tools from operations research, economics, and data science to address societal challenges. Education: Ph.D. in Operations Research from Stanford University's Department of Management Science and Engineering Postdoctoral Fellowship at Harvard University Bachelor's degree in Electrical and Computer Engineering from the National Technical University of Athens Her research interests focus on market design for public policy , including education systems, labor markets, and algorithmic fairness. Recent work examines human-algorithm trade-offs in college admissions and limitations of diversity interventions in hiring. She frequently collaborates with institutions like the Nokia Bell Labs and the University of Michigan Ross School. Recent Research Trends: Monachou's publications emphasize fairness, equity, and strategic behavior in allocation systems. Key themes include: Algorithmic fairness in education and labor markets Design of simple yet optimal allocation mechanisms Evaluating trade-offs between information and access in policy Awards: INFORMS DEI Best Student Paper Award (2022) ACM EAAMO 2021 Best Paper with a Student Presenter Award Her work has been published in top venues like ACM EC, Management Science, NeurIPS, and AAAI. She actively contributes to interdisciplinary initiatives such as the Mechanism Design for Social Good workshop.

Zelalem Negeriمشاهده پروفایل

استادیار

• Biostatistics
• Meta-Analysis
• Diagnostic Test Accuracy
• +۳ مورد دیگر

Zelalem Negeri is an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, affiliated with the Faculty of Mathematics. His work focuses on statistical methods for diagnostic test accuracy, depression screening tools, and meta-analytic techniques. He collaborates with groups like DEPRESSD (Depression Screening Data) to improve mental health assessment methodologies. Research interests include robust meta-analysis models for accommodating outliers, optimal cutoff determination for screening tools (e.g., PHQ-9, EPDS), and evaluating diagnostic accuracy across diverse populations. His studies often use individual participant data (IPD) to enhance precision in estimating depression prevalence and screening tool performance. Recent work emphasizes validating shortened screening tools (e.g., EPDS-Dep-5) and comparing depression assessment methods across modes (e.g., self-administered vs. clinician-administered). He also investigates pandemic-related mental health impacts in populations with systemic sclerosis and explores statistical methods to mitigate biases in data-driven cutoff selection. No scientific awards are explicitly mentioned. His contributions span collaborations with interdisciplinary teams, including the Scleroderma Patient-centered Intervention Network (SPIN), and involve developing robust statistical frameworks to address variability in diagnostic studies.

Susanne Jaeggiمشاهده پروفایل

استاد

• Cognitive Training
• Executive Functions
• Individual Differences in Cognition
• +۴ مورد دیگر

Susanne M. Jaeggi is a Professor of Psychology at Northeastern University, with additional affiliations in the Bouve College of Health Sciences and the College of Arts, Media, and Design. Her research focuses on cognitive training, executive functions, and individual differences in cognition across the lifespan. She holds PhDs in Cognitive Psychology and Neuroscience from the University of Bern (Switzerland), and completed postdoctoral work in Cognitive Neuroscience at the University of Michigan. Her work has been funded by NIH, NSF, IES, ONR, and the Advanced Education Research and Development Fund (AERDF). She leads the Working Memory & Plasticity Lab , which develops interventions to improve working memory and executive functions, and co-leads the Brain Game Center for Mental Fitness and Well-Being , creating evidence-based brain fitness tools. Jaeggi’s research emphasizes understanding mechanisms of cognitive improvement through training, including neuroplasticity and individual variability. Key areas include cognitive aging interventions, gamification, sensory-cognitive interactions, and the impact of socioeconomic factors on academic achievement. Her work integrates behavioral experiments, neuroimaging, and digital health technologies to address real-world challenges in education and healthcare. Recent studies explore music/art-based interventions, brain stimulation (e.g., tDCS), and scalable cognitive assessments. Collaborations span disciplines, with publications addressing topics like neural correlates of training, motivational features in interventions, and cross-modal perception. Her labs emphasize translating findings into public-facing tools, such as freely accessible brain fitness apps. Current projects include optimizing interventions for ADHD populations and leveraging digital platforms for global mental fitness.

Patricia K. Tongمشاهده پروفایل

عضو هیئت علمی

• Economic Policy
• Public Finance
• Labor Economics
• +۱۲ مورد دیگر

Patricia K. Tong is an economist at RAND and a Professor of Policy Analysis at the RAND School of Public Policy. She specializes in public finance and labor economics, with a strong focus on military and civilian personnel policy, compensation, and health economics. Her research addresses critical issues in military personnel retention, family caregiving, retirement benefits, and access to mental and behavioral health services. Research Interests: Tong’s work combines qualitative and quantitative methods to evaluate monetary and nonmonetary compensation policies, labor market outcomes, and social welfare impacts. Her expertise spans economic policy, health economics, military personnel systems, and family support structures. She has led studies on Social Security survivor benefits, tax policy anti-poverty effects, and nurse staffing standards. Publication Trends: Her recent research centers on military compensation, health care access for service members and families, and financial inclusion. Articles reflect a consistent policy-oriented focus, often supporting governmental reviews such as the Quadrennial Review of Military Compensation, and addressing timely issues like pandemic caregiving and unbanked populations. Scientific Contributions: Published in top journals including American Economic Review and Health Economics Key contributor to national policy discussions on military pay, retention, and health care Research used to inform federal and state-level policy decisions Advising and Grants: While specific students are not listed, her leadership in major research projects indicates a supervisory and mentoring role. She has led federally funded studies on military compensation, health care access, and financial inclusion, demonstrating sustained grant support from defense and public policy agencies. Labs and Teams: Tong conducts research within RAND’s interdisciplinary policy teams, particularly those focused on defense, health, and economic security. Her work often involves collaboration with military stakeholders, federal agencies, and public health experts to develop actionable policy solutions.