جست‌وجوی استاد و پژوهشگر

• Computer Vision
• Artwork Analysis
• Cross-depiction Recognition
• +۳ مورد دیگر

Hongping Cai is a Lecturer in the Department of Computer Science at the University of Bath, contributing to the Visual Computing research group. Their work focuses on advancing computer vision techniques for analyzing artistic depictions and cross-domain object recognition. Research interests include computer vision applications in artwork analysis, cross-depiction problem modeling, object recognition across media, and spatial relation modeling. Recent work emphasizes leveraging CNNs for detecting people in artwork, benchmarking cross-depiction systems, and developing graph-based models for visual object analysis. Publications highlight contributions to cross-media recognition challenges, synthesis techniques, and domain adaptation methods. No specific awards or grants are listed, though their research has garnered significant citations and downloads. Advising activities are not documented in the provided materials. Active in the Visual Computing group at the University of Bath, Dr. Cai's work bridges traditional computer vision with artistic depiction analysis, addressing challenges in multi-modal data fusion and cross-domain visual understanding.

• Artificial Intelligence
• Electronic Commerce
• Intelligent Agents and Multi-Agent Systems
• +۶ مورد دیگر

Thomas Tran is a Full Professor at the School of Electrical Engineering and Computer Science (EECS) at the University of Ottawa. He holds a Ph.D. in Computer Science from the University of Waterloo (2004) and a B.Sc. (Double Major in Mathematics and Computer Science) from Brandon University (1999). His research focuses on Artificial Intelligence, Electronic Commerce, Multi-Agent Systems, Trust and Reputation Modeling, and Recommender Systems. He has published over 80 refereed papers and supervised 27 graduate students (4 PhDs and 23 Masters). Education: Ph.D. in Computer Science, University of Waterloo (2004) B.Sc. (Double Major in Mathematics and Computer Science), Brandon University (1999) Research Interests: AI Applications in E-Commerce and Mobile Business Trust Establishment Models in Multi-Agent Systems Recommender Systems and Deep Learning Clinical Data Analysis for Hidradenitis Suppurativa Awards: Governor General's Gold Medal (2004) NSERC Postgraduate Scholarships (PGS A/B) AAAI Doctoral Consortium Participant (2002) Advising and Grants: Supervised 27 graduate students Recipient of multiple research grants (details unspecified) Labs/Teams: Active in AI and E-Commerce research groups within EECS.

Ashish Cherukuriمشاهده پروفایل

دانشیار

• Optimization
• Control Systems
• Multi-agent Systems
• +۷ مورد دیگر

Ashish Cherukuri is an Associate Professor at the University of Groningen's Faculty of Science and Engineering, affiliated with the Optimization and Decision Systems group. His research focuses on optimization-based control, game theory, and multi-agent systems applied to energy, transportation, and robotics. He holds a Ph.D. from UC San Diego and postdoctoral experience at ETH Zurich. Education: Ph.D., University of California, San Diego (2012–2017) M.Sc., ETH Zurich (2008–2010) B.Tech, Indian Institute of Technology Delhi (2004–2008) Research Interests: Data-driven optimization, distributed algorithms, networked cyber-physical systems, and uncertainty handling in energy and transportation systems. Recent work emphasizes stochastic optimization, game-theoretic routing, and risk-aware control. Awards: Robert E. Skelton Dissertation Award (2017) Outstanding Graduate Student Award (2016) Focht-Powell Fellowship (2012–2015) Grants & Service: Editor for the IEEE Control Systems Society, organizer of Energy-Open 2019, and member of professional societies (IEEE, INFORMS, SIAM). Active in conference organization and academic leadership roles. Labs/Teams: Part of the Jan C. Willems Center for Systems and Control and the Engineering and Technology Institute Groningen (ENTEG). Research integrates theoretical advancements with practical applications in energy networks and smart systems.

Ziquan Liuمشاهده پروفایل

مدرس

• Reliable Machine Learning
• Uncertainty Quantification
• Computer Vision
• +۴ مورد دیگر

Dr. Ziquan Liu is a Lecturer (Teaching & Research) at Queen Mary University of London's School of Electronic Engineering and Computer Science, affiliated with the Centre for Multimodal AI. He holds a PhD from City University of Hong Kong (2023) and dual B.Sc./B.Eng. degrees from Beihang University (2017). His research focuses on trustworthy machine learning, adversarial robustness, and uncertainty quantification in foundation models. He has served as a reviewer for top conferences like NeurIPS, ICLR, and CVPR, earning an Outstanding Reviewer Award in 2021. His teaching includes modules on machine learning for visual data analysis and principles of machine learning. He supervises PhD students in AI safety and reliability, with notable work on conformal prediction, adversarial attacks, and multimodal learning. His research outputs span top venues such as ICML, CVPR, and NeurIPS, addressing challenges in algorithmic fairness, model certification, and cross-modal alignment.

Ina Sarcevicمشاهده پروفایل

استاد

• Astrophysics
• Cosmology
• High Energy Physics
• +۵ مورد دیگر

Ina Sarcevic is a Professor of Physics and Astronomy at the University of Arizona, holding the title of Primary Faculty. Her research focuses on particle astrophysics, dark matter, neutrinos, and collider physics. She has made significant contributions to the DUNE (Deep Underground Neutrino Experiment) collaboration, leading efforts in detector design, software development, and theoretical modeling. Her work bridges experimental and theoretical particle physics, addressing fundamental questions in astrophysics and cosmology. Education: Ph.D. in Physics from the University of Minnesota (1986), B.S. in Physics from the University of Sarajevo (1981), with notable fellowships including the Humboldt Fellowship (1989-1991) and a Doctoral Dissertation Fellowship (1985-1986). Research Interests: Dark matter detection via neutrinos, neutrino interactions at extreme energies, and the role of particle physics in cosmological phenomena. She explores theoretical frameworks for secret interactions of sterile neutrinos and their implications for the diffuse supernova neutrino background. Her work on detector technology for DUNE includes optimizing liquid argon time-projection chambers (TPCs) and developing algorithms for neutrino interaction reconstruction. Selected Honors: 2006 Fellow of the American Physical Society, recipient of the British Council Fellowship for Young Scientists (1980-1981), and Summa cum laude distinction at the University of Sarajevo. Grants and Collaborations: Principal investigator in DUNE-related projects, including detector design, software computing, and data analysis. Involved in international collaborations such as the LHC’s Forward Physics Facility and ProtoDUNE beam tests at CERN. Labs/Teams: Active member of the DUNE Collaboration, focusing on neutrino oscillation physics, supernova neutrino detection, and dark matter signatures in neutrino telescopes. Her group collaborates on developing advanced machine learning techniques for particle identification in large-scale detectors.

Gigliana Melziمشاهده پروفایل

استاد

• Early Childhood Development
• Bilingual Education
• Cultural Studies
• +۳ مورد دیگر

Gigliana Melzi is a Professor of Applied Psychology at New York University’s Steinhardt School of Culture, Education, and Human Development. She holds affiliations with the Center for Latin American and Caribbean Studies and the Latinx Project. As Director of the Culture, Families, and Early Development Lab, her research emphasizes culturally responsive practices in early childhood education, focusing on bilingual and immigrant Latine/x families. Dr. Melzi earned her undergraduate degree in Spanish Literature and Psychology from Clark University and her PhD in Developmental Psychology from Boston University. Her work investigates how cultural and linguistic practices in families influence children’s learning, particularly in STEM and literacy domains. She collaborates closely with Latine/x communities and educational institutions to bridge home and school contexts, advocating for systemic equity in education. Her research highlights family engagement strategies, spatial language in bilingual households, and the role of storytelling in science learning. Funded by organizations like the National Science Foundation and the Spencer Foundation, her projects address disparities faced by marginalized communities. Key areas of exploration include family math talk, code-switching in narratives, and culturally grounded teacher practices. Dr. Melzi’s contributions span over 50 peer-reviewed articles and collaborative grants. Her work is published in journals like Child Development and Developmental Psychology , emphasizing both theoretical advancements and practical applications for early childhood educators.

Subodha Kumarمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Blockchain
• +۱۱ مورد دیگر

Subodha Kumar is the Paul R. Anderson Distinguished Chair Professor of Statistics, Operations, and Data Science at Temple University’s Fox School of Business. He also serves as Founding Director of the Center for Business Analytics and Disruptive Technologies and holds a secondary appointment in Information Systems. With prior faculty roles at the University of Washington and Texas A&M University, he has been recognized with prestigious awards including the INFORMS ISS Distinguished Fellow Award (2023) and POMS Fellow (2019). His research focuses on artificial intelligence, machine learning, blockchain, fintech, and cybersecurity, with over 240 publications in top journals and two books. He actively contributes to academic leadership as Deputy Editor of the Production and Operations Management Journal and Founding Executive Editor of Management and Business Review. Research Interests: Artificial Intelligence Machine Learning Blockchain Fintech Healthcare Analytics Social Media Analytics Supply Chain Analytics Awards: INFORMS ISS Distinguished Fellow Award (2023) POMS Fellow (2019) Changjiang Scholars Chair Professorship (China) Namesake Award: The Subodha Kumar and Geoffrey G. Parker Award for Digital Transformation Research Grants & Advising: Secured NIH grants, advises PhD students in Operations & Supply Chain Management, and oversees the Center for Business Analytics. His editorial roles include multiple top journals and conference leadership (e.g., POMS Annual Conference 2018). Labs/Teams: Leads the Center for Business Analytics and Disruptive Technologies, fostering innovation in analytics and tech-driven business strategies.

Xavier Alameda-Pinedaمشاهده پروفایل

پژوهشگر

• Machine Learning
• Computer Vision
• Audio Processing
• +۷ مورد دیگر

Xavier Alameda-Pineda is a Research Director at Inria Grenoble Rhône-Alpes, where he leads the RobotLearn Team. He is affiliated with Université Grenoble Alpes and has been a key member of the Perception team. His work integrates machine learning, computer vision, and audio processing for scene understanding and human-robot interaction. Research Interests: His research lies at the intersection of multimodal machine learning and social behavior analysis. He focuses on developing algorithms for understanding human behavior in natural settings using audio-visual signals, with applications in robotics and AI companions. His work emphasizes real-world challenges such as noisy data, missing modalities, and dynamic environments. Publication Trends: His recent publications reflect a consistent focus on multimodal fusion, particularly combining vision and audio for social scene analysis. Themes include group behavior recognition, sound source separation, and cross-modal learning, often applied in robotics contexts. Scientific Awards: SIGMM Rising Star Award 2018 IEEE TMM Outstanding Associate Editor Award 2022 ACM TOMM Best Paper Award 2020 Best Paper Award, ACM MM 2015 Best Scientific Paper Award, ICPR 2016 Best Student Paper Award, IEEE WASPAA 2015 Outstanding Paper Award, ICMI 2011 Novel Technology Paper Award Finalist, IROS 2017 Advising and Grants: Xavier has mentored students and early-career researchers, evidenced by co-authored student papers. He coordinated the H2020 SPRING project on socially pertinent robots in gerontological healthcare and co-leads an AI chair on audio-visual perception for companion robots, indicating leadership in funded research initiatives. Labs and Teams: He is the leader of the RobotLearn Team at Inria and was previously part of the Perception team. He has also collaborated with the Multimodal and Human Understanding Group at the University of Trento.

Chao Huمشاهده پروفایل

دانشیار

• Engineering Design under Uncertainty
• Battery Health Diagnostics and Prognostics
• Machine and Structural Health Monitoring
• +۳ مورد دیگر

Dr. Chao Hu is the Collins Aerospace Professor in Engineering Innovation and Associate Professor at the University of Connecticut's Department of Mechanical Engineering within the College of Engineering. His research focuses on engineering design under uncertainty, battery health diagnostics, and structural health monitoring. He holds a B.E. from Tsinghua University (2007) and a Ph.D. from the University of Maryland (2011), with prior roles at Medtronic and Iowa State University. Research interests emphasize physics-informed machine learning for prognostics, battery degradation modeling, and reliability-based design optimization. Key publications include work on digital twin models for lithium-ion batteries, federated learning for fleet-wide fault diagnosis, and probabilistic machine learning pipelines for real-time state estimation. He has received awards like the ASME Design Automation Young Investigator Award and highly cited paper recognitions. Dr. Hu serves as Senior Editor for Engineering Optimization and Review Editor for Structural and Multidisciplinary Optimization . His work spans academic leadership in journals and industrial collaborations. Current projects include battery aging datasets (UConn-ILCC and UConn-ISU-ILCC), design for remanufacturing frameworks, and high-rate structural health monitoring techniques.

• Diversity Management
• Higher Education
• Labor Studies
• +۳ مورد دیگر

Dr. Jennie O'Connor is a Senior Lecturer in the Strategy, Leadership & Operations department at Middlesex University . Her research focuses on Diversity Management , Inclusive Teaching , and Labor Studies , with particular emphasis on organizational behavior and educational equity. Research Trends: Her publications (2003-2006) examine diversity implementation challenges in educational and workplace contexts, emphasizing practical barriers to policy execution. Key themes include skill development for diversity management, cross-cultural pedagogy, and labor studies curriculum reform. Academic Contributions: Notable works include analyzing diversity domains in student learning (2006), exploring direct vs. indirect diversity training approaches (2005), and critiquing diversity policy-to-practice transitions in labor studies (2003).

• Behavior Genetics
• Family and Twin Studies
• Causal Modeling
• +۷ مورد دیگر

Miriam A. Mosing, PhD, is currently the Head of the Behavior Genetics unit at the Max Planck Institute for Empirical Aesthetics in Frankfurt am Main, Germany, a position she has held since August 2021. She also serves as an Associate Professor at the Department of Medical Epidemiology and Biostatistics and the Neuroscience Department at the Karolinska Institutet in Stockholm, Sweden, and holds a Develop Research Momentum (DRM) Senior Research Fellowship at the Melbourne School of Psychological Sciences, University of Melbourne, Australia. Dr. Mosing's primary research focuses on behavior genetics, specifically examining how genetic and environmental factors interact to influence individual differences in quality of life throughout the lifespan and expertise acquisition. Her work frequently uses musical expertise as a model domain to study these interactions. She investigates the complex relationships between cultural engagement (particularly musical and creative activities), mental health, social isolation, loneliness, stress, flow experiences, and overall well-being. Her research employs advanced methodologies including twin studies, causal modeling, and genome-wide association studies to disentangle the relative contributions of genetic and environmental factors. Her publication record demonstrates consistent contributions to understanding the genetic architecture of human behavior, with recent work examining dancer personalities, musical ability, cognitive aging, mental health outcomes, and gene-environment interactions in expertise development. Her research has significant implications for identifying causal pathways that could inform interventions to improve quality of life and mental health outcomes. Develop Research Momentum (DRM) Fellowship from the University of Melbourne (2020-2025) Marcus och Amalia Wallenbergs Foundation grant for 'Healthy learning' research (2018-2022) National Institute of Aging research project grant (2018-2022) Multiple Karolinska Institutet Fellowships for research on gene-environment interplay Dr. Mosing leads a research program that integrates large-scale twin studies with advanced genetic methodologies to understand the complex pathways through which genetic predispositions and environmental exposures jointly shape human development, health, and expertise. Her Behavior Genetics unit at the Max Planck Institute serves as a hub for international collaboration on these questions, working with twin registries and research groups across multiple countries.

Guido Zucconمشاهده پروفایل

استاد

• Information Retrieval
• Health Search
• Formal Models of Search
• +۱۵ مورد دیگر

Guido Zuccon is a Professorial Research Fellow at the School of Electrical Engineering and Computer Science , The University of Queensland (UQ), where he leads the Information Engineering Lab (ielab) . He serves as the AI Director for the Queensland Digital Health Centre (QDHeC) and is an Affiliate Professor at the UQ Centre for Health Services Research . He was previously a Lecturer and Senior Lecturer at Queensland University of Technology and a Postdoctoral Fellow at CSIRO. His research spans Information Retrieval , Health Search , Formal Models of Search , and Health Data Science , with a strong focus on consumer health search, cohort identification, clinical decision support, and systematic review automation. He has pioneered work on search interaction, semantic models, and the evaluation of retrieval systems in health contexts. His recent publications highlight a strong trend toward leveraging large language models (LLMs) for zero-shot retrieval, federated search, dense retrieval, and query formulation. His work integrates advanced neural methods with practical applications in healthcare, including systematic review automation and clinical AI. He frequently publishes at top venues such as SIGIR, ECIR, and WSDM, often in collaboration with key researchers like Bevan Koopman, Shengyao Zhuang, and Harry Scells. ARC DECRA Fellow (2018–2020) Best Paper Awards at AIRS 2017, CLEF 2016, ALTA 2015, ECIR 2012 Best Reviewer Award at ECIR 2014 Principal Investigator on ARC Discovery Projects and MRFF grants Guido Zuccon actively supervises a large cohort of PhD students, primarily in areas related to neural information retrieval, health search, and systematic review automation. He has led significant research projects funded by the ARC, Google, Microsoft, GRDC, and CSIRO. He is a key organizer of international evaluation labs such as the CLEF eHealth Consumer Health Search task and the TREC 2019 Decision Track. He leads the ielab , a vibrant research group focused on information retrieval and data science, and contributes to major open-source initiatives like Big Brother , a tool for logging user interactions in web studies.

James Salamyمشاهده پروفایل

مدرس

• Photonics
• Optical Engineering
• Laser Technology
• +۳ مورد دیگر

James Salamy is a part-time Lecturer in the Department of Electrical and Computer Systems Engineering at the College of Engineering, Monash University. He actively contributes to both research and teaching initiatives, with a focus on photonics and engineering education. His work bridges technical innovation with pedagogical advancements, ensuring holistic development of educational strategies. Current Affiliation: Monash University, College of Engineering Academic Role: Lecturer Status: Part-time faculty member His research spans two primary domains: photonics and engineering education . In photonics, he investigates laser stabilization techniques using microring resonators for dense wavelength-division multiplexing (WDM) systems, aiming to improve optical network scalability and reliability. In education, he explores factors influencing student success, including mental health literacy, onboarding experiences, and the role of failure in academic growth. Additionally, he has contributed to cloud-based machine learning infrastructure optimization. Recent research trends emphasize interdisciplinary collaboration, particularly between Monash University and the University of Warwick, focusing on scalable automated feedback systems for cross-campus education. His technical work on self-injection locking mechanisms has implications for temperature-stable laser systems, while his educational studies address equity and pedagogical innovation. While no scientific awards are documented in the provided material, his 2024-2025 projects include research on Research Cross Campus Peer Instructions and scalable feedback systems, demonstrating his commitment to enhancing engagement in information engineering education.

• Automated reasoning
• Formal methods
• Autonomous systems
• +۵ مورد دیگر

Zhe Hou is a Senior Lecturer at the School of Information and Communication Technology , Griffith University, Australia. His academic journey includes a PhD in automated reasoning for separation logic from the Australian National University (2015) and prior research roles at Nanyang Technological University, Singapore (2015-2017). He joined Griffith University in 2017 and became permanent faculty in late 2019. Research Interests : Formal methods for software verification Automated reasoning with logical frameworks Blockchain technology and security Quantum computing verification Integration of LLMs with rigorous reasoning Sports analytics via model checking Recent Publications demonstrate expertise in neural-symbolic reasoning, blockchain security, quantum SAT solvers, and runtime verification frameworks. His work combines formal logic with machine learning for applications in cybersecurity and AI trustworthiness. Scientific Awards : ACM SIGSOFT Distinguished Paper Award (2025) Supervision Roles : Principal/Associate Supervisor for 6+ doctoral projects in blockchain security, AI verification, and network security. Professional Activities : Editor for Springer-Nature and Formal Aspects of Computing special issues, conference chair for ICFEM, ICECCS, and ISACE symposia.

• Artificial Intelligence
• Knowledge Graphs
• Semantic Technologies
• +۵ مورد دیگر

Dr Zhe Wang is a Senior Lecturer at the School of Information and Communication Technology, Griffith University, focusing on artificial intelligence, knowledge graphs, and semantic technologies. He earned his PhD in Computer Science from Griffith University (2011) and previously worked as a Research Fellow at the University of Oxford (2011-2013) on ontology-based systems. Research: Specializes in knowledge graph construction, rule mining for explainable AI, and integrating machine learning with logical reasoning. Led development of the scalable RLvLR rule-mining system and contributed to the HermiT ontology reasoner. Teaching: Instructs undergraduate and postgraduate courses including Introduction to Artificial Intelligence, Secure Development Operations, and Software Engineering Fundamentals. Grants: Funded by Australia's Economic Accelerator Ignite Grant (2025) for AI-driven marine life survey systems and Office of National Intelligence projects (2021-2022). Publications: Active in top venues like AAAI, ICASSP, and ISWC, with recent work on temporal knowledge graph reasoning, auction design algorithms, and neurosymbolic AI systems.