جست‌وجوی استاد و پژوهشگر

Qindan Huangمشاهده پروفایل

دانشیار

• Structural Reliability
• Risk and Life-Cycle Analysis
• Probabilistic Methods in Civil Engineering
• +۴ مورد دیگر

Dr. Qindan Huang is an Associate Professor in the Department of Civil, Construction and Environmental Engineering at Marquette University. His research focuses on structural reliability, risk and life-cycle analysis, probabilistic methods in civil engineering, performance-based design, and decision making under uncertainty. He has contributed extensively to the understanding of corrosion effects on infrastructure systems and probabilistic modeling for pipelines and reinforced concrete structures. Education: Ph.D., Civil Engineering, Texas A&M University (2010) M.S., Civil Engineering, University of Toledo (2004) B.S., Structural Engineering, Tongji University (2001) Research Interests: Structural Reliability Risk and Life-Cycle Analysis Probabilistic Methods in Civil Engineering Performance-Based Design Multi-Hazard Analysis Deteriorating Systems Decision Making Under Uncertainty Recent Publications: Dr. Huang's 2024-2023 work includes probabilistic modeling of pipeline corrosion, life-cycle cost analysis for deteriorating infrastructure, multi-modal NDE frameworks, and seismic performance evaluation of concrete frames. His research emphasizes corrosion management, bond behavior in concrete, and failure prediction under interactive anomalies. Grants: 2020: DOT-PHMSA - Probabilistic Performance Evaluation of Pipelines 2020: DOT-PHMSA - Corrosion Management for Plastic Pipelines 2019: DOT-PHMSA - Multi-Modal NDE for Pipelines 2017: ODOT - Bridge Deck Repair Evaluation 2016: NSF - Bond Behavior in Corroded RC Structures

Marissa Wechslerمشاهده پروفایل

استادیار

• Biomaterials
• Tissue Engineering
• Stem Cell Engineering
• +۵ مورد دیگر

Dr. Marissa Wechsler, a first-generation Hispanic biomedical engineer, returned to her alma mater The University of Texas at San Antonio (UTSA) in 2021 as an Assistant Professor in the Margie and Bill Klesse College of Engineering and Integrated Design. As UTSA's first biomedical engineering undergraduate student (class of 2015), she now leads a 12-member research team in her biomaterials and cell engineering lab while teaching advanced courses like BME 4443: Stem Cell Engineering. Academic Journey: UTSA BME program pioneer (2015) → NSF Graduate Research Fellowship → Ph.D. from UT Austin Leadership Roles: Founding faculty member of UTSA Sigma Xi chapter (2023), SWE faculty advisor, ESTEEMED mentor Research Focus: Specializes in biomaterials engineering with emphasis on: Stimuli-responsive hydrogels for drug delivery and tissue regeneration Stem cell engineering through controlled microenvironments Biosensing platforms using nanoparticle-hydrogel hybrids Regenerative medicine applications for vascular diseases Recent publications demonstrate expertise in RNA-based vaccine delivery systems , nanoparticle engineering , and mitochondrial dysfunction analysis in peripheral artery disease. Her work combines material science innovation with clinical translation potential. Award Highlights: 2023: Sigma Xi Grant-in-Aid of Research 2021: National Science Foundation Graduate Research Fellowship (during studies) Mentorship Impact: As a former participant in federal research programs (RISE, MARC), she now mentors through: Leading 12-member research team (undergraduate to postdoctoral) Sigma Xi leadership (President-elect) Faculty advisor for Society of Women Engineers ESTEEMED program mentor

Selma Tekirمشاهده پروفایل

دانشیار

• Natural Language Processing
• Text Mining
• Machine Learning
• +۵ مورد دیگر

Selma Tekir is an Associate Professor in the Computer Engineering Department at Izmir Institute of Technology. She completed her undergraduate studies in Computer Engineering at Ege University in 2001, followed by a master's degree from Izmir Institute of Technology in 2004, and earned her PhD from Ege University in 2010. In 2009, she worked as a visiting researcher at the University of Konstanz, Germany. Her educational background includes: B.Sc.: Computer Engineering, Ege University (2001) M.Sc.: Computer Engineering, Izmir Institute of Technology (2004) Ph.D.: Computer Engineering, Ege University (2010) Dr. Tekir's research spans multiple areas of natural language processing and computational linguistics, with a particular focus on Turkish language processing. Her work explores text mining, news analysis, information warfare, and the application of deep learning techniques to linguistic problems. She has made significant contributions to counterfactual detection in Turkish, multi-modal language models, and the integration of symbolic reasoning with neural approaches. Her research often bridges theoretical advances with practical applications, particularly in the Turkish language context which presents unique challenges as a morphologically rich agglutinative language. Analysis of her recent publications reveals a strong trend toward advancing natural language understanding systems for Turkish, developing methods for counterfactual detection, enhancing question answering systems with knowledge graphs, and applying graph neural networks to biological sequence analysis. Her work demonstrates growing interest in combining symbolic reasoning with neural approaches and exploring self-reflection capabilities in language agents. Dr. Tekir has received research funding for projects including: Consistency and Reliability Assessment in News Chains (TÜBİTAK ARDEB 3501) Application of Data Analysis and Visualization Techniques on Historical Sources (BAP) She teaches courses including CENG 381 - Stochastic Processes, CENG 613 - Scientific Research Methods in Computer Science, and SEDS 501 - Introduction to Data Science.

Michael Inouyeمشاهده پروفایل

استاد

• Systems Genomics
• Cardiovascular Disease
• Population Health
• +۳ مورد دیگر

Professor Michael Inouye is a leading systems genomics and population health researcher at the University of Cambridge (Department of Public Health and Primary Care) and Baker Heart & Diabetes Institute (Melbourne). His work bridges statistical genetics, computational biology, and clinical translation, with a focus on cardiovascular/respiratory diseases and sustainable research practices. Director of Research (Research Professor) since 2021 Honorary Professorial Fellow at University of Melbourne since 2021 Co-leader of Cambridge Baker Systems Genomics Initiative since 2017 Research Interests include: Polygenic risk score development and clinical implementation Multi-omic integration (genomics, proteomics, microbiomics) Host-microbiome interactions in disease Sustainable computational methodologies Cardiometabolic disease prevention Scientific Contributions show leadership in: Developing the Polygenic Score Catalog infrastructure Creating environmentally sustainable algorithmic frameworks Elucidating microbiome contributions to chronic diseases Advancing multi-modal risk prediction models Integrating genetic and metabolic pathway analysis Recent Publications demonstrate expertise across: Polygenic score optimization for blood traits and diabetes Microbiome-disease interaction networks Computational sustainability in biomedical research Cardiovascular risk stratification Multi-omic data harmonization Genetic determinants of molecular phenotypes

Lusi Liمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Wireless Communications
• +۵ مورد دیگر

Lusi Li is an Assistant Professor in the Department of Computer Science at Old Dominion University's Batten College of Engineering & Technology. She earned her Ph.D. from University of Rhode Island in 2021, with prior degrees from Zhongnan University of Economics and Law. Ph.D. in Electrical, Computer, and Biomedical Engineering (URI, 2021) B.S. and M.S. in Computer Science (Zhongnan University, 2014 & 2017) Her research focuses on Computer Vision and Machine Learning , developing scalable algorithms for robust representation learning from large-scale data with applications in industrial processes and wireless communications. Key areas include multi-view/multi-modal learning , transfer learning , few-shot/zero-shot learning , and imbalanced learning . Recent publications reveal expertise in semantic communication , 3D shape understanding , and dynamic spectrum management , with 2025 papers at MILCOM and ACM MM. Her work often integrates graph learning and domain adaptation techniques across wireless systems and industrial fault diagnosis. Awards include multiple federal grants (NSF, ICAR) and institutional honors (FP3, Cheng Fund). She actively mentors graduate students in research and serves on editorial boards for Information Fusion and Pattern Recognition . National Science Foundation (NSF) Grant for dynamic spectrum sharing ICAR grants for coastal community AI systems ODU's FP3 and Cheng Fund awards URI graduate fellowships and awards Her teaching portfolio includes undergraduate courses in theoretical computer science and graduate-level pattern recognition.

Ao Duمشاهده پروفایل

استادیار

• Probabilistic Seismic Risk Assessment
• Multi-hazard Resilience of Infrastructure
• Surrogate Modeling
• +۵ مورد دیگر

Ao Du, Ph.D. is an Assistant Professor in Structural Engineering at the Klesse College of Engineering and Integrated Design , The University of Texas at San Antonio . His research focuses on enhancing the resilience of infrastructure systems against natural hazards and aging through advanced modeling and data analytics. Education : Ph.D. in Civil Engineering (Rice University, 2020), M.S. in Civil Engineering (Tongji University, 2016), B.S. in Civil Engineering with minor in Mathematics (Tongji University, 2013). Research Interests include probabilistic seismic risk assessment, multi-hazard resilience analysis, surrogate modeling, uncertainty quantification, and structural reliability. His work integrates physics-based modeling , stochastic simulations , and artificial intelligence to address infrastructure risk management. Labs & Teams : Leads the Smart and Sustainable Infrastructure Resilience (S2IR) Lab , focusing on multi-modal sensing, optimization methods, and life-cycle analysis for infrastructure resilience under deep uncertainties.

• Urban Traffic Planning
• Impact Analyses
• Mobility Management
• +۲ مورد دیگر

Dr. Conny Louen serves as Senior Engineer and Head of the Urban Transport Planning research group at RWTH Aachen University's Institute of Urban and Transport Planning (ISB), where she has contributed since 2007. Her work bridges academic research and practical policy development in German and international urban mobility contexts, with emphasis on evidence-based transport planning solutions. She earned her Civil Engineering degree from RWTH Aachen (2000-2006) with specialization in Transport and Urban Planning, completed a diploma thesis on adolescent multi-modal traffic behavior (2006), and received her doctorate in 2013 for research on mobility management's modal shift effects. Her educational journey included an Erasmus year at Universidad Politècnica de València (2005-2006). Louen's research centers on urban traffic systems, impact analyses, and mobility management, investigating how policy interventions like fare-free public transport during pandemics influence travel behavior. She employs simulation models and case studies in cities like Aachen to address parking optimization, electric bus integration, and equitable transport planning, emphasizing actionable outcomes for policymakers through handbooks like 'Planungsinstrumente für eine nachhaltige Mobilität' (2020). Her recent publications (2022-2025) reveal growing focus on automated public transport scenarios (NAIXTransit project), pandemic-era mobility shifts, and transport equity evaluation. She actively contributes to European initiatives like WISE-ACT COST Action while analyzing real-world implementations from Jakarta's BRT to Aachen's electric bus networks. No scientific awards or fellowships were documented in available sources. Louen has secured significant grant funding including the NAIXTransit project (German Federal Ministry grant 01MM20007A, 2020-2021) and participation in European WISE-ACT research. While she teaches Transport Planning I and Public Transport Organization courses, no formal PhD/Master's students were listed in provided materials despite her supervisory role in diploma/doctoral theses. As leader of the Urban Transport Planning research group, she directs applied studies on sustainable mobility solutions, with current work emphasizing automated transit integration, mobility equity frameworks, and charging infrastructure optimization for zero-emission public transport systems.

Hao Xingمشاهده پروفایل

پژوهشگر

• Robot Vision
• Human Action Recognition
• Human-Object Interaction Recognition
• +۴ مورد دیگر

Hao Xing is a scientific researcher and post-doctoral fellow at the Institute for Cognitive Systems (ICS), Technical University of Munich (TUM), working with Prof. Gordon Cheng since May 2024. Previously, he served as a research assistant at the Munich Institute of Robotics and Machine Intelligence (MIRMI) and completed his PhD under Prof. Darius Burschka from 2019. His educational background includes: Master of Science in Mechanical Engineering from Technical University of Munich Bachelor of Engineering in Mechanical Engineering from Hefei University of Technology, China Xing's research focuses on Robot Vision , Human Action Recognition , and Graph Convolutional Networks , with significant contributions to Human-Object Interaction Recognition , Scene Understanding , and Visual Depth Estimation . His work leverages machine learning to develop robust algorithms for human activity analysis and robotic perception, emphasizing spatio-temporal modeling and uncertainty handling in real-world scenarios. Analysis of his 13 recent publications (2019-2025) reveals a dominant focus on graph-based approaches for action recognition and segmentation, with increasing emphasis on open-world applications, uncertainty modeling, and healthcare integration. His research spans computer vision, robotics, and medical applications, demonstrating strong interdisciplinary impact through collaborations in surgical robotics and patient monitoring systems. Xing actively mentors students through master's thesis projects in Stereo Matching, Human Activity Segmentation, and Monocular Depth Estimation, requiring expertise in deep learning frameworks and computer vision algorithms. While no major grants are explicitly mentioned, his thesis topics indicate active research funding in geometric vision and human activity analysis. He operates within the Institute for Cognitive Systems (ICS) at TUM, a key unit in MIRMI's robotics ecosystem focused on advancing human-robot interaction through vision-based scene understanding and motion generation capabilities.

• Artificial Intelligence
• Machine Learning
• Anomaly Detection
• +۶ مورد دیگر

Yuantao Fan is an Associate Senior Lecturer at Halmstad University's School of Information Technology, specializing in artificial intelligence and machine learning applications for industrial systems. His academic work bridges theoretical research with practical implementations in predictive maintenance and equipment monitoring. Dr. Fan's research focuses on Artificial Intelligence, Machine Learning, Anomaly Detection, and Data Mining , with particular emphasis on streaming data from real-world applications. His work spans equipment diagnosis and prognosis, predictive maintenance (PdM), and condition-based monitoring systems. His research integrates self-organizing models with expert knowledge to create robust diagnostic frameworks applicable to transportation systems and industrial equipment. Analysis of Dr. Fan's publication record from 2013-2025 reveals consistent specialization in predictive maintenance for transportation systems , particularly bus fleets and heavy vehicles. His work demonstrates progression from basic fault detection methods to sophisticated hybrid approaches combining physics-based models with deep learning. Recent publications increasingly address explainability in predictive maintenance systems and applications in healthcare analytics. Dr. Fan actively contributes to academic instruction as course responsible and examiner for Databases, Introduction to Linux and Python, and Linux administration system courses. He also teaches Artificial Intelligence, Data Mining, Perspectives on Computer Science and Engineering, Machine Learning for Predictive Maintenance, and AI Implementation courses. His technical expertise spans multiple domains including automotive systems diagnostics, battery health monitoring, acoustic signal processing, and healthcare prediction models. Dr. Fan's work demonstrates strong industry collaboration, particularly with Volvo and city bus fleet operators, translating academic research into practical industrial applications.

Tianxing Heمشاهده پروفایل

استادیار

• AI Safety
• AI-driven Simulation
• Large Language Models
• +۵ مورد دیگر

Tianxing He is an Assistant Professor at Tsinghua University's Institute for Interdisciplinary Information Sciences (IIIS), also known as the Yao Class, which he joined in September 2024. He also works part-time at Shanghai Qi Zhi Institute where he recruits for research-oriented positions and video game development roles. His academic journey includes a postdoc at the University of Washington under Yulia Tsvetkov, a PhD at MIT supervised by James Glass, and bachelor's and master's degrees at Shanghai Jiao Tong University in the ACM honored class under Prof. Kai Yu. Dr. He's research spans AI safety and AI-driven simulation with particular focus on large language models. His work examines LLM capabilities in cryptography (AICrypto benchmark), security vulnerabilities (jailbreaking techniques), multi-agent system integrity, and socially impactful applications like vaccine hesitancy simulation (VACSIM framework). He has developed visualization tools like MiniAgents-Pro using Unity for generative agent simulation and created watermarking techniques such as SemStamp and k-SemStamp for detecting machine-generated text. His publication record shows consistent contributions to top-tier conferences including ACL, EMNLP, NeurIPS, and ICLR, with recent work focusing on evaluating LLM capabilities across diverse domains. The research demonstrates sophisticated understanding of both the technical aspects of language models and their broader societal implications, particularly in security and ethical considerations. Scientific Awards: The Best Paper Award at the Efficient Natural Language and Speech Processing Workshop (NeurIPS ENLSP 2022) for "PCFG-based Natural Language Interface Improves Generalization for Controlled Text Generation" Dr. He actively mentors students and recruits research interns, PhD candidates, and post-bachelor researchers. He plans to teach an undergraduate NLP course in Fall 2025 and organizes the IIIS AI Safety Lunch, a monthly gathering in Beijing for those working on AI safety. His recruitment efforts target students interested in LLM social simulation, AI+game development, and AI safety research, emphasizing the need for strong self-motivation and research passion. He leads research initiatives including the IIIS AI Safety Lunch series and has developed multiple visualization and simulation frameworks. His work sits at the intersection of AI safety, language modeling, and practical applications, with team collaborations spanning multiple institutions including MIT, University of Washington, and Shanghai Jiao Tong University.

Andrei Bursucمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Autonomous Driving
• +۳ مورد دیگر

Andrei Bursuc is a Research Scientist and Deputy Scientific Director at Valeo.ai, where he focuses on perception for autonomous driving. Concurrently, he serves as a Research Associate at the Astra Inria project team and teaches the Deep Learning DIY course at Paris Institute of Technology (École Polytechnique) since 2019. He holds a PhD from Mines ParisTech and Telecom SudParis (2012) and a BSc from Polytechnic University of Bucharest (2009). His research spans computer vision and machine learning , with applications in autonomous driving, uncertainty quantification, self-supervised learning, and foundation models. Recent work emphasizes scalable representation learning, open-vocabulary segmentation, and generative modeling for driving scenarios. His publications (2024–2025) cluster around themes of semantic segmentation , depth estimation , and vision-language models , with innovations in unsupervised learning, cross-modal distillation, and efficiency. Trends indicate a focus on robustness, real-world generalization, and multi-sensor integration. He co-organizes workshops (e.g., Uncertainty Quantification at CVPR/ECCV) and serves as Area Chair for NeurIPS/CVPR/ECCV. He contributes to EU projects (e.g., ELLIOT) and open-source initiatives.

Yannick Cornetمشاهده پروفایل

پژوهشگر

• Sustainable Transport Appraisal
• Transportation Systems
• Travel Time Valuation
• +۳ مورد دیگر

Dr. Yannick Cornet serves as a Senior Researcher of Intelligent Transport Systems at the University of Žilina, Slovakia, where he joined the ERAdiate research team in February 2018. His work primarily focuses on sustainable transport appraisal and transition within the H2020 Mobility and Time Value (MoTiV) project. His research expertise centers on sustainable transport indicators, innovative decision-support tools for complex sustainability aspects like equity and long-term environmental impacts, and smartphone-based multi-modal diaries for experiential travel data collection. Cornet bridges quantitative and qualitative methodologies, conducting structured interviews with experts while analyzing diverse transport scales from local cycling to national high-speed rail projects. His work emphasizes traveler perspectives in door-to-door journeys and socio-technical enablers for real-time car-pooling. Analysis of his 15 most recent publications reveals consistent focus on travel time valuation frameworks, multimodal experience assessment, and sustainable urban mobility transitions. Key themes include 'worthwhile travel time' conceptualization, 15-minute city navigation challenges, and paradoxes in smart mobility innovations. His methodology heavily utilizes smartphone-based data collection across European contexts. Cornet actively engages with transport stakeholders including planning agencies, industry CEOs, and civil society. He co-authored the Transport 2050 Strategic Outlook for the TRANSFORuM project, contributing to European White Paper transport goals. His professional background includes 10 years as a project manager in telecommunications prior to academia. His educational background spans multiple continents: PhD in Transport from Technical University of Denmark (2016) with visiting research at University of Oxford; Master's in Technological and Socio-Economic Planning from Roskilde University (2013); and Bachelor's in Software Engineering from Sherbrooke University, Canada (1997).

Jocelyn Chanussotمشاهده پروفایل

استاد

• Remote Sensing
• Hyperspectral Imaging
• Signal and Image Processing
• +۴ مورد دیگر

Jocelyn Chanussot is a Professor at Grenoble Institute of Technology, holding the AXA Chair of Remote Sensing. He is affiliated with GIPSA-Lab (Laboratoire de traitement du Signal et des Images) at Ense3 (École Nationale Supérieure de l'Énergie, l'Eau et l'Environnement) and maintains a connection with the Chinese Academy of Sciences through his AXA Chair position. His research focuses on advancing remote sensing technologies, particularly in hyperspectral imaging and artificial intelligence applications for environmental monitoring. Professor Chanussot specializes in hyperspectral imaging, which captures information across several hundred wavelengths, allowing for detailed characterization of physical properties in observed scenes. His work develops algorithms to extract meaningful information from complex remote sensing data, with applications spanning natural disaster monitoring, environmental observation, biodiversity assessment, and urban planning. He has pioneered approaches that leverage artificial intelligence, particularly deep learning techniques, to process and analyze large-scale remote sensing datasets. His scholarly output shows a clear trend toward integrating advanced AI techniques with remote sensing data. Recent publications demonstrate growing emphasis on transformer architectures, contrastive learning, generative models, and foundation models specifically adapted for hyperspectral data. His work increasingly addresses multimodal data fusion, anomaly detection, and real-time processing for applications in natural disaster response. Ranked among the 157 most cited French researchers in 2019 by Clarivate Analytics Professor Chanussot actively serves on numerous conference program committees, particularly for SPIE's Image and Signal Processing for Remote Sensing conferences. His research is supported by the AXA Research Fund through his AXA Chair in Remote Sensing, which focuses on developing algorithms for natural disaster monitoring and emergency response. He collaborates extensively with international institutions including UCLA and Stanford University on applications ranging from toxic gas detection to tropical forest biodiversity assessment. He leads research at GIPSA-Lab, focusing on developing advanced tools and algorithms for extracting information from complex remote sensing data. His team works on processing heterogeneous data including aerial photos, multispectral and hyperspectral images, and other environmental measurements to improve natural disaster prediction and response capabilities.

• Language Acquisition
• Cognitive Science
• Computational Modeling
• +۷ مورد دیگر

Emmanuel Dupoux is a Professor at École des Hautes Études en Sciences Sociales (EHESS), affiliated with the School of Advanced Studies in Social Sciences and the Laboratory of Cognitive Science and Psycholinguistics (LSCP). His work bridges cognitive science, computational linguistics, and machine learning to study infant language acquisition and social cognition. Co-creator and director of EHESS Cognitive Science Master program Former LSCP laboratory director (1998-2009) Current research focuses on textless speech modeling and ecological audio analysis Research areas include: Modeling early language acquisition mechanisms using Bayesian models and HMM Investigating phonological 'deafness' and critical age plasticity Studying social cognition development through infant-toddler experiments His technical contributions span speech processing toolkits like Shennong, emergent communication frameworks (STOP dataset), and innovative approaches to self-supervised speech modeling. Recent publications demonstrate breakthroughs in: Topography-inspired CNN designs for better memory efficiency Prosody-aware generative spoken language models (pGSLM) Textless emotion conversion systems He actively explores how machine learning can reverse-engineer infant language learning processes from ecological audio data, with applications in neurodegenerative disease diagnostics. Key collaborations include: INRIA's Cognitive Machine Learning (CoML) team Facebook AI Research (FAIR) partnerships VoxPopuli multilingual speech corpus development

Søren Knudsenمشاهده پروفایل

استادیار

• Information Visualization
• Human-Computer Interaction
• Data Visualization
• +۳ مورد دیگر

Søren Knudsen is an Assistant Professor at the Digital Design Department, IT University of Copenhagen and holds a Marie Curie Fellowship for postdoctoral work at the University of Copenhagen. He serves as Academic Director of the Human Data Interaction Lab. Positions: Assistant Professor, Marie Curie Postdoctoral Scholar Affiliations: IT University of Copenhagen, University of Copenhagen Key Roles: Academic Director, Human Data Interaction Lab His research centers on information visualization and human-computer interaction , particularly in face-to-face and online contexts. He explores how large-area displays and social media can enhance data comprehension and collaborative discussion. Notable work includes surveys on interactive visualization techniques for large displays, frameworks for data visualization education , and EMR-based algorithms for healthcare performance evaluation. His publications span journals like IEEE Transactions on Visualization and Computer Graphics and PLOS ONE . Awards and honors: Marie Curie Fellowship He collaborates across disciplines, with ties to healthcare analytics, educational technology, and computer science departments. His recent studies address challenges in visualization design handoff , proxemics , and ubiquitous data interaction .