جست‌وجوی استاد و پژوهشگر

• Nephrology
• Chronic Kidney Disease
• Acute Kidney Injury
• +۵ مورد دیگر

Lisbet Brandi is a Clinical Associate Professor in the Department of Clinical Medicine at the University of Copenhagen, specializing in Internal Medicine with a focus on Nephrology. Her dual institutional affiliations at Blegdamsvej 3, 2200 København N and Dyrehavevej 29, 3400 Hillerød reflect her integration of academic research with clinical practice within the Danish healthcare system (Region Hovedstaden). Her research spans multiple dimensions of kidney disease management with particular emphasis on: Chronic Kidney Disease treatment optimization Acute Kidney Injury diagnosis and prevention strategies Implementation of clinical guidelines in real-world settings Mathematical modeling of dialysis processes Healthcare provider perspectives on patient management Analysis of her publication record reveals a strategic focus on translating evidence into practice, particularly through innovative approaches like electronic nudges to improve guideline adherence. Her work bridges clinical medicine, health services research, and biomedical engineering, addressing critical gaps between theoretical recommendations and actual patient care. Dr. Brandi maintains extensive national collaborations across Danish medical institutions, as evidenced by her multi-author publications in high-impact journals including Circulation, American Heart Journal, and PLoS ONE. Her research demonstrates growing influence in both nephrology and cardiology communities, reflecting the significant cardiovascular comorbidities associated with kidney disease.

Riadh Munjyمشاهده پروفایل

استاد

• Photogrammetry
• Unmanned Aircraft Systems (UAS) Mapping
• Remote Sensing
• +۵ مورد دیگر

Riadh Munjy serves as a Professor in Geomatics Engineering at California State University, Fresno's Lyles College of Engineering, where he teaches core courses including CE 205 (Computing in Engineering Analysis), GME 123 (Stereo-Photogrammetry), and GME 125 (Analytical Photogrammetry). His academic foundation includes a Ph.D. in Civil Engineering (1982), M.S. in Applied Mathematics (1981), and M.S.C.E. (1979) from the University of Washington, complemented by a B.S. in Civil Engineering from the University of Baghdad (1976). Ph.D., Civil Engineering, University of Washington (1979-1982) M.S., Applied Mathematics, University of Washington (1980-1981) M.S.C.E., Civil Engineering, University of Washington (1978-1979) B.S., Civil Engineering, University of Baghdad (1972-1976) Professor Munjy's research centers on advancing photogrammetric methodologies with particular emphasis on Unmanned Aircraft Systems (UAS) mapping accuracy, sensor calibration, and novel processing techniques for LIDAR and IFSAR data. His work bridges theoretical mathematics with practical applications in transportation infrastructure, disaster response, and terrain modeling, consistently addressing industry pain points like rolling shutter effects in UAV imagery and LIDAR strip adjustment challenges. He has pioneered standards for GPS-controlled photogrammetry adopted by Caltrans and contributed foundational chapters to the ASPRS Manual of Photogrammetry. Analysis of his 15 most recent publications (2010-2020) reveals a strategic evolution from traditional photogrammetric techniques toward UAS-centric workflows, with increasing focus on automated processing, accuracy validation, and integration of multi-sensor data. His research consistently targets real-world implementation barriers in transportation mapping and corridor projects, demonstrating strong industry relevance through Caltrans collaborations and ASPRS leadership. American Society of Photogrammetry and Remote Sensing Fellow Award (2020) ASPRS Fairchild Award (2014) Caltrans Research Innovation Award (2004) Five School of Engineering Research Excellence Awards (1992, 1996, 1998, 2002, 2003) ASPRS Meritorious Service Awards (1992, 1997) Halliburton Research Award (1992) Professor Munjy's research program has been consistently supported through Caltrans-funded projects including GPS Photogrammetry (2005), UAS Research (2018), and corridor mapping standards development. His advisory impact extends through co-authoring the ASPRS Manual of Photogrammetry and mentoring numerous conference presentations, though formal student lists aren't documented. Current initiatives focus on sUAS data accuracy validation and rolling shutter effect mitigation, positioning his work at the forefront of evolving UAV mapping regulations.

Ehsan Adeliمشاهده پروفایل

استادیار

• Translational Artificial Intelligence
• Medical Imaging
• Computational Neuroscience
• +۳ مورد دیگر

Ehsan Adeli is an Assistant Professor of Psychiatry & Behavioral Sciences at Stanford University, with courtesy appointments in Computer Science and Biomedical Data Science. He leads the Stanford Translational AI (STAI) Lab, focusing on developing AI algorithms for healthcare and mental health. His research integrates 3D motion analysis from video data with neuroimaging techniques like MRI to study neurodegenerative and neuropsychiatric diseases. He holds academic roles including Director of AI/Innovation in Precision Mental Health and membership in institutes like HAI and Wu Tsai Neurosciences. His work spans digital health, computational neuroscience, and explainable machine learning. Education: Ph.D. in Artificial Intelligence & Computer Vision (Carnegie Mellon University), Postdoc in Biomedical Imaging & Computational Neuroscience (UNC Chapel Hill). Research interests include translational AI for mental health, digital humans (3D motion analysis), and human neuroscience (MRI-based brain function exploration). His lab develops tools like the Short Physical Performance Battery (SPPB) app for objective assessments. Notable honors include the Chairman’s Award for Educational Excellence (2023) and Jaswa Innovator Award (2022-2024). He advises over 20 graduate and postdoctoral students across multiple disciplines. Labs/Teams: STAI Lab, Computational Neuroscience Lab, Stanford Vision and Learning Lab.

• Computer Vision
• Deep Learning
• Robotics
• +۵ مورد دیگر

Hao Yang is a Professor at Southeast University's School of Computer Science and Engineering, specializing in computer vision, deep learning, and robotics. His research spans multiple domains including medical imaging, infrastructure inspection, and multimodal AI systems. His research interests focus on advancing computer vision techniques for practical applications. He develops innovative deep learning architectures for object detection, image segmentation, and quality assessment, with particular emphasis on solving challenges in complex real-world scenarios including rotated object detection, ancient character recognition, and 3D point cloud analysis. His work often integrates novel transformer variants and Mamba architectures to address domain-specific challenges. His publication record shows significant contributions across multiple IEEE and ACM journals, with a strong emphasis on practical applications of AI in engineering contexts. His recent work demonstrates expertise in adapting cutting-edge neural network architectures to solve domain-specific problems in civil engineering, medical imaging, and transportation systems. Hao Yang actively collaborates across disciplines, with publications spanning computer science, electrical engineering, civil engineering, and biomedical applications. His work frequently appears in top-tier conferences including CVPR, AAAI, and ACL, demonstrating his broad impact across multiple AI subfields.

Georgios Triantafyllouمشاهده پروفایل

دانشیار

• Criminal Law
• Criminal Procedure
• European Criminal Law
• +۳ مورد دیگر

Georgios Triantafyllou is an Associate Professor of Criminal Law and Criminal Procedure at the University of Athens Law School. He specializes in European and International Criminal Law, with expertise in judicial cooperation mechanisms, human rights in criminal proceedings, and transnational legal frameworks. Education: Bachelor's degree, University of Athens (1987) PhD in Criminal Law, University of Athens (1992) Research focuses on: Criminal procedure reform EU legal harmonization in criminal matters Defense rights in cross-border cases Antitrust criminal liability Key contributions include analysis of European Arrest Warrants, exclusionary rules for illegally obtained evidence, and procedural safeguards under the European Convention on Human Rights. His work bridges national criminal systems with international legal frameworks. Professional activities: Legislative committee member for EU directive transpositions Single point of contact for the EU judicial cooperation study Board member, Hellenic Association of Criminal Law Member, European and International Criminal Law Institute

Kimberly Clevengerمشاهده پروفایل

استادیار

• Exercise Physiology
• Physical Activity Measurement
• Accelerometry
• +۷ مورد دیگر

Kimberly Clevenger is an Assistant Professor in the Department of Kinesiology and Health Science at Utah State University, within the College of Agriculture and Applied Sciences. Her research focuses on advancing the measurement and understanding of physical activity in children and adolescents, particularly through the integration of accelerometry and GPS technologies. She earned her PhD in Kinesiology (Exercise Physiology) from Michigan State University (2019), an MPH in Quantitative Methods from Harvard University (2020), and both her MS and BS in Exercise Physiology from Ohio University. Her research interests center on ensuring valid, reliable, and sensitive measurement of physical activity, the influence of the built environment on children's free-play behavior, and the impact of recess policies on physical activity and health outcomes. She investigates how behavior varies spatially and temporally, and how such variations affect environmental exposures like noise and nature. A key goal is ensuring equitable access to free-play opportunities in schools from pre-K to 12th grade. Her recent publications reveal a strong focus on methodological rigor in physical activity assessment, including calibration of accelerometers, comparison of open-source versus proprietary data processing, fragmentation analysis, and the development of consensus standards for activity intensity classification. Her work often leverages nationally representative datasets such as NHANES and addresses critical public health and policy issues related to youth physical activity. Surveillance and measurement of youth physical activity Recess policy and implementation Accelerometer data processing and validation GPS and spatial analysis of schoolyard activity Childhood obesity and cardiometabolic health Open science and reproducible research methods Dr. Clevenger has mentored several graduate students, including Benjamin Verdejo, Katherine McKee, and Cassie Webster. She teaches KIN 4400 - Measurement and Evaluation in Kinesiology and contributes to the training of future kinesiology researchers. She actively maintains a research blog where she shares technical tutorials on R, accelerometry, NHANES data analysis, and open-source methods, promoting transparency and accessibility in her field.

Isabel Valeraمشاهده پروفایل

استاد

• Machine Learning
• Robust Machine Learning
• Interpretable AI
• +۱۰ مورد دیگر

Isabel Valera is a full Professor in the Department of Computer Science at Saarland University in Saarbrücken, Germany, and an Adjunct Faculty member at the Max Planck Institute for Software Systems (MPI-SWS). She is also a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS), contributing to the Robust Machine Learning Program and the Saarbrücken AI & ML (Sam) Unit. Department of Computer Science, Saarland University Adjunct Faculty, MPI for Software Systems ELLIS Fellow, Robust ML Program Sam Unit, Saarbrücken AI & ML She obtained her PhD and MSc from Universidad Carlos III de Madrid, followed by postdoctoral research at the University of Cambridge and MPI for Software Systems. She previously led an independent research group at MPI for Intelligent Systems in Tübingen and held the Humboldt Post-Doctoral Fellowship and Minerva Fast Track Fellowship. PhD in Machine Learning, Universidad Carlos III de Madrid, 2014 MSc in Multimedia and Communications, Universidad Carlos III de Madrid, 2012 Telecommunications Engineering, Technical University of Cartagena, 2009 Her research centers on developing machine learning methods that are flexible, robust, interpretable, and fair, particularly for heterogeneous, temporal, and high-stakes decision-making systems. She emphasizes applications in medicine, psychiatry, and social domains such as hiring, bail, and lending. Her methodological contributions include Bayesian nonparametric models, latent feature modeling, and temporal point processes. Her recent publications reflect a strong focus on fairness, robustness, and interpretability in machine learning. Key themes include latent feature modeling for mixed data types, clustering temporal event streams, source separation, and fair classification. Her work bridges theoretical innovation with practical applications across healthcare, social networks, and policy-relevant domains. Scientific awards and recognitions include: Humboldt Post-Doctoral Fellowship Minerva Fast Track Fellowship (Max Planck Society) ELLIS Fellow She has been actively involved in teaching and dissemination, delivering tutorials at NIPS and MLSS on temporal point processes and social network analysis. She has also supervised research assistants and mentored junior researchers. Her research has been supported through prestigious fellowships and institutional affiliations. She leads the development of open-source tools such as GLFM, HDHP, and iFDM, promoting reproducibility and accessibility in machine learning research. She is affiliated with the following labs and research groups: Max Planck Institute for Intelligent Systems (former group leader) Max Planck Institute for Software Systems (adjunct, postdoctoral) ELLIS Sam Unit (Saarbrücken AI & ML) Robust Machine Learning Program (ELLIS)

Yingjie Chenمشاهده پروفایل

استاد

• Virtual Reality
• Human-Computer Interaction
• AI in Computer Graphics
• +۳ مورد دیگر

Yingjie Chen is a Professor at Purdue University's Purdue Polytechnic Institute, Department of Computer Graphics Technology. He holds a Ph.D. in Human-Computer Interaction from Simon Fraser University (2011), a Master of Science in Information Technology (2007), and a Bachelor of Engineering from Tsinghua University (1992). His research focuses on interdisciplinary domains including Virtual Reality, AI in Computer Graphics, Human-Computer Interaction, and Information Visualization. He has led multiple grants, including projects on VR training systems for Cryo-EM and precision medicine using Graph AI. Education: Ph.D., Human-Computer Interaction, Simon Fraser University (2011) M.Sc., Information Technology, Simon Fraser University (2007) B.Eng., Thermal Engineering, Tsinghua University (1992) Dr. Chen’s research emphasizes practical implementation and evaluation, blending theory with real-world applications. His work spans VR training systems, autonomous driving perception, and clinical decision support systems. Recent grants include funding for VR-based Cryo-EM training and AI in precision medicine. Research Interests: Virtual Reality (VR) System Design AI-Driven Computer Graphics Visual Analytics for Healthcare Human Factors in Autonomous Systems Grants & Awards: "Virtual Reality Augmented Hands-on Cryo-EM Training" (2020–2023) "Graph AI for Precision Medicine" (2021–2023) VAST Challenge Awards (2017, 2019) His lab, VA.Tech , develops innovative visual systems for interdisciplinary challenges. Collaborations span healthcare, transportation, and environmental science.

Ofer Mazorمشاهده پروفایل

مدرس

• Neurobiology
• Sensory Systems
• Biomedical Engineering
• +۳ مورد دیگر

Ofer Mazor, Ph.D., is an Instructor in Neurobiology at Harvard Medical School and Director of the Research Instrumentation Core at Harvard Medical School. His research focuses on sensory systems, biomedical engineering, and neurobiological mechanisms underlying perception and behavior. He has contributed to studies on tactile and auditory processing, gastrointestinal mechanosensation, sleep regulation, and the development of implantable medical devices. His work integrates neurophysiological techniques with engineering innovations, exemplified by his development of wireless implantable devices and novel diagnostic tools. Key research areas include cross-modal sensory processing in the midbrain, gut-brain interactions, and the neural basis of thermosensation. Dr. Mazor’s contributions also extend to pandemic response through respiratory device design and cell phone-based diagnostic technologies. As Director of the Research Instrumentation Core, he supports advanced experimental setups for neurobiological research. His publications span high-impact journals like Cell , Nature , and Science , reflecting interdisciplinary collaborations across neurobiology, engineering, and medicine.

• Healthcare Operations
• Stochastic Optimization
• Medical Decision Making
• +۲ مورد دیگر

Nan Kong is Professor of Biomedical Engineering with a joint appointment in the Edwardson School of Industrial Engineering at Purdue University. His research develops operations research and machine learning methods for healthcare delivery optimization. Education credentials: PhD in Industrial Engineering from University of Pittsburgh (2006) MEng in Operations Research from Cornell University (2000) BS in Automation from Tsinghua University (1999) Research focuses on stochastic optimization for healthcare resource allocation, Markov decision processes for clinical decision support, and machine learning applications in public health. Key application areas include trauma systems, nursing home operations, opioid epidemic response, and pandemic control. Publications demonstrate methodological innovations in stochastic programming, Markov modeling, and hybrid AI-optimization approaches. Recent work addresses EMS drone deployment, nursing home staffing under COVID, and opioid overdose prediction. Laboratory affiliations include Purdue's healthcare engineering initiatives. Research collaborations span clinical institutions and public health agencies.

• Causal Inference
• Machine Learning
• Generative Adversarial Networks
• +۶ مورد دیگر

Murat Kocaoglu is an Assistant Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University. His work focuses on causal inference, machine learning, generative adversarial networks, information theory, and learning theory. He is affiliated with Purdue's West Lafayette campus, with an office in MSEE 362 and maintains a research webpage at muratkocaoglu.com . Research interests span causal discovery methods, fair machine learning, and applications in signal processing and communications. His work bridges theoretical foundations with practical algorithms, emphasizing causal reasoning in complex systems and high-dimensional data. Recent articles explore causal graph learning, counterfactual fairness, and adaptive experimental design. Key contributions include frameworks for efficient causal structure discovery and methods for handling unobserved confounders in bandit problems. His research has been supported by grants such as NSF CAREER and NSF RI Small awards. No awards or student advisees are explicitly listed in the provided materials. His professional activities include contributions to the CausalGAN framework and collaborative work on causal inference in time series and microservices.

• Information Retrieval
• Natural Language Processing
• Machine Learning
• +۴ مورد دیگر

Professor Emine Yilmaz is a distinguished academic at University College London (Department of Computer Science), holding concurrent roles as a Turing Fellow and Amazon Scholar. She leads the Web Intelligence Group at the UCL Centre for Artificial Intelligence and collaborates with Amazon's Alexa Shopping team as an Amazon Scholar. Her research focuses on information retrieval, natural language processing, and machine learning, with over 150 publications (6,250+ citations, H-index 39). Notable awards include the Karen Sparck Jones Award, Google Faculty Research Award, and Bloomberg Data Science Research Award. Her work is funded by EU Horizon 2020, EPSRC, and industry partners like Google and Bloomberg. She has served in leadership roles for journals (e.g., Information Retrieval Journal) and conferences (e.g., ACM SIGIR 2024 PC Chair). Recent projects include organizing the TREC Deep Learning Track and co-founding Humanloop, a UCL spinout company. Research Interests: Machine learning for search systems, conversational AI, educational technology, and ethical AI. Key Contributions: Synthetic test collections (SYNDL), contextual understanding in dialogue systems, and knowledge-enhanced retrieval frameworks. Her articles emphasize robustness in retrieval-augmented generation (RAG), proactive search systems, and bias mitigation in synthetic data. Recent highlights include work on personalization benchmarks (PersonaLens) and trustworthiness in AI systems (TrustRAG). Awards: Karen Sparck Jones Award (2021), Google Faculty Research Award (2020), Bloomberg Data Science Award (2019). She advises over 10 current PhD students and has mentored 9 former students, many now in academia and industry. Her grants span EU, UK, and industry funding, with recent support from Bloomberg’s PhD fellowship program and Google Research Awards. Labs/Teams: Web Intelligence Group (UCL), co-founder of Humanloop, and key contributor to the TREC Deep Learning initiative.

• Computer Vision
• Machine Learning
• Robotics
• +۷ مورد دیگر

Yuxiong Wang is an Assistant Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC). His research focuses on computer vision, machine learning, and robotics, particularly in open-world perception, meta-learning, generative modeling, and agent learning. He previously held a postdoctoral position at Carnegie Mellon University and obtained his Ph.D. from the Robotics Institute under Prof. Martial Hebert. He has received notable awards including the Best Paper Honorable Mention at ECCV 2020 and Best Paper Finalist awards at CVPR 2019 and 2022. His work bridges generative and discriminative AI, with contributions to 3D vision, human-object interaction, and vision-language models. Recent research highlights include RandAR (decoder-based visual generation), InterMimic (physics-based human-object control), and Argus (vision-centric reasoning). He teaches courses such as CS 445 (Computational Photography) and CS 598 LTL (Learning to Learn). His group actively collaborates with NVIDIA and Electronic Arts, and his work has been funded by grants from NASA and the NCSA. Current projects explore meta-learning, open-world learning, and applications in robotics, healthcare, and materials science.

J. Derek Tuckerمشاهده پروفایل

عضو هیئت علمی

• Functional Data Analysis
• Geometric Methods
• Statistical Image Understanding
• +۴ مورد دیگر

J. Derek Tucker is an Adjunct Clinical Associate Professor in the Department of Statistics at the University of Illinois and a Distinguished Member of the Technical Staff at Sandia National Laboratories. He holds a Ph.D. in Statistics from Florida State University (2014) and degrees in Electrical Engineering from Colorado State University (B.S. 2007, M.S. 2009). His research focuses on functional data analysis, geometric methods, unaligned data modeling, statistical image understanding, and Bayesian model calibration. At Illinois, he teaches a topics course on functional data analysis. Education: Ph.D. in Statistics, Florida State University (2014) M.S. in Electrical Engineering, Colorado State University (2009) B.S. in Electrical Engineering, Colorado State University (2007) Research Interests: Functional data analysis with geometric methods Statistical image understanding Bayesian model calibration Point processes and spatio-temporal modeling Uncertainty quantification His recent work includes advancements in elastic distance metrics for anomaly detection and functional data registration. He has received the Director of National Intelligence Team Award for contributions to SLiCE team projects. Publications span statistical methodology, climate impact analysis, and computational statistics. Awards: Director of National Intelligence Team Award (2020) Grants and collaborations involve Sandia National Labs and interdisciplinary projects in climate science, materials science, and healthcare analytics. He actively contributes to R packages like 'fdasrvf' for elastic functional data analysis.

• Network Analysis
• Statistical Learning
• Neuroimaging Data Analysis
• +۳ مورد دیگر

Subhadeep Paul is an Associate Professor of Statistics at The Ohio State University, within the Department of Statistics, College of Arts and Sciences. He joined the faculty in 2017 and is affiliated with the Translational Data Analytics Institute (TDAI). His research focuses on statistical inference in complex networks, neuroimaging data analysis, and high-dimensional data. Education: PhD in Statistics from the University of Illinois, Urbana-Champaign (2017). Research interests include network analysis, statistical learning, neuroimaging data analysis, and high-dimensional data analysis. He develops computationally feasible methods such as spectral clustering and matrix factorization for analyzing networks, with applications in neuroimaging and other complex systems. Recent work emphasizes community detection in multi-layer networks, Hawkes process models for temporal dynamics, and spatial transcriptomics analysis. His publications explore topics like peer influence modeling in therapeutic networks, spatial autoregressive frameworks, and virocell metabolic reprogramming. No scientific awards explicitly mentioned. Advising/Grants: No student advisees or grant information provided in the text. Affiliated with TDAI for interdisciplinary data science collaboration.