جستجوی استاد راهنمای دانشگاهی

John Andersonمشاهده پروفایل

استاد

• Cognitive Modeling
• Computational Neuroscience
• Learning and Memory
• +۳ مورد دیگر

John Anderson is R.K. Mellon University Professor of Psychology and Computer Science at Carnegie Mellon University, jointly appointed in the Department of Psychology and School of Computer Science. He directs the Anderson Lab focused on cognitive architectures and unified theories of cognition through the ACT-R framework. His research integrates computational modeling with neuroimaging to understand knowledge organization, problem-solving, and skill acquisition. Anderson's research examines how humans organize knowledge to produce intelligent behavior, with emphasis on mathematical learning, memory systems, and cognitive skill development. His work combines behavioral experiments, computational modeling (ACT-R), and neural imaging techniques (fMRI, EEG, MEG) to identify cognitive processes and their neural correlates. Recent investigations focus on discovering hidden cognitive stages through brain activation patterns and developing methods to map cognitive models to neural data. His publication trends show consistent focus on: 1) Developing methodologies to link cognitive models with neuroimaging data, 2) Analyzing stages of skill acquisition and problem solving, 3) Investigating the neural basis of mathematical cognition, and 4) Refining the ACT-R cognitive architecture. Recent work emphasizes temporal dynamics of cognition and educational applications of cognitive modeling. Scientific Awards: Heineken Prize for Cognitive Science Franklin Award in Computer and Cognitive Science Anderson advises graduate students including Qiong Zhang (PhD candidate), and leads a research team comprising postdoctoral fellows and research scientists. The lab has secured funding from NSF and other agencies for projects on mathematical learning, cognitive architectures, and neuroimaging methodologies. The Anderson Lab collaborates with researchers in psychology, computer science, and neuroscience to develop cognitive models that simulate human performance across diverse domains. Current projects include modeling inventive mathematical thinking and integrating deep learning with cognitive architectures for explainable AI systems.

• Machine Learning
• Computer Vision
• Structural Health Monitoring
• +۱ مورد دیگر

Senjian An is a Senior Lecturer in the School of Electrical Engineering, Computing and Mathematical Sciences at Curtin University, Faculty of Science and Engineering. His research focuses on machine learning, computer vision, and structural health monitoring, with applications in 3D displacement measurement and anomaly detection. Key interests include physics-guided neural networks, unsupervised learning for time-series data, and vision-based structural analysis. His recent publications emphasize robustness in damage detection, synthetic data generation, and transformer-based models for industrial monitoring. An's work integrates deep learning with engineering challenges, advancing non-contact measurement techniques for civil infrastructure. No students or awards are noted. Collaborative projects involve vibration analysis and maritime surveillance, reflecting a strong applied focus in AI and computer vision.

William J. Turkelمشاهده پروفایل

استاد

• Computational History
• Big History
• Science and Technology Studies (STS)
• +۳ مورد دیگر

William J. Turkel is a Professor of History at The University of Western Ontario, Canada, and a member of the Royal Society of Canada's College of New Scholars. His research focuses on computational history, science and technology studies, and disability studies. He holds a PhD from MIT (2004) and leads the History Department's Fab Lab, equipped with advanced fabrication tools. Turkel's work includes reverse-engineering historical technologies, digital exhibit design, and mentoring over 20 students. He has authored books like Spark from the Deep and The Archive of Place , and his open-source textbook Digital Research Methods with Mathematica is widely used. Awards include the Western Award for Technology-Enhanced Teaching (2021) and SSHRC funding for NiCHE (2004–14). Education: PhD, MIT (2004) Research Interests: Computational methods, big history, modular synthesis, disability studies, and astrobiology. His lab explores tangible computing, 3D printing, and historical experimentation. Articles Overview: Recent work spans digital humanities tool development, historical computing analysis, and music-pattern recognition via machine learning. Key themes include bridging computational methods with historical inquiry. Awards: Royal Society membership (2018), Western University award (2021), and SSHRC leadership (2004–14). Advising & Grants: Supervised over 20 students and postdocs, including Devon Elliott and Ian Milligan. Collaborates with Tim Hitchcock, Edward Jones-Imhotep, and others on projects like MK ULTRA analysis and exoskeleton patent studies. Labs/Teams: The History Department Fab Lab includes 3D printers, CNC tools, and electronics prototyping. His lab designs interactive exhibits and tangible artifacts to explore historical phenomena.

Elizabeth S Chenمشاهده پروفایل

دانشیار

• Biomedical Informatics
• Clinical Informatics
• Natural Language Processing
• +۷ مورد دیگر

Elizabeth S. Chen, PhD, FACMI serves as Interim Director of the Brown Center for Biomedical Informatics (BCBI), Associate Professor of Medical Science, and Associate Professor of Health Services, Policy & Practice at Brown University. She leads the Clinical Informatics Innovation and Implementation (CI3) Laboratory and directs the Advance RI-CTR Biomedical Informatics, Bioinformatics, and Cyberinfrastructure Enhancement (BIBCE) Core. Her work focuses on leveraging electronic health data and health information technology for biomedical discovery and healthcare delivery. Education: PhD in Biomedical Informatics, Columbia University MPhil in Biomedical Informatics, Columbia University MA in Biomedical Informatics, Columbia University BS in Computer Science, Tufts University Dr. Chen's research centers on clinical documentation, clinical decision support, health information needs, standards and interoperability, natural language processing (NLP), and data mining and machine learning. Her CI3 Laboratory innovates in EHR use for primary and secondary purposes, implements digital solutions within clinical systems, and inspires the next generation of clinical informaticians. Current projects focus on mental health informatics and pediatric informatics, using data and computational approaches to improve patient care. Analysis of Dr. Chen's recent publications reveals a strong focus on NLP applications in clinical settings, mental health informatics, and EHR data utilization. Her work spans multiple domains including suicide risk assessment, pediatric autism spectrum disorder comorbidities, and clinical note analysis, demonstrating consistent application of machine learning and data mining techniques to solve real-world clinical problems. Scientific Awards: FACMI (Fellow of the American College of Medical Informatics) Dr. Chen actively mentors high school, undergraduate, graduate, and medical students as well as junior faculty. She co-directs the Scholarly Concentration in Biomedical Informatics for medical students and co-instructs biomedical informatics and data science courses. Her grant activities include leadership of the BIBCE Core, which makes EHR and other data accessible for clinical and translational research in Rhode Island. As leader of the CI3 Laboratory, Dr. Chen oversees research focused on innovating EHR use, implementing digital solutions, and training future clinical informaticians. The lab's three-fold mission directly impacts how electronic health data is leveraged for both patient care and research purposes across multiple healthcare domains.

• Machine Learning
• Healthcare Analytics
• Telecommunication Industry Analysis
• +۳ مورد دیگر

Karl Hall is a Lecturer in Computing & Games at the Department of Computing & Games, Teesside University. His research focuses on machine learning applications across diverse domains including healthcare analytics, telecommunication, traffic modeling, and social network analysis. He specializes in developing predictive models for credit risk assessment, disease diagnosis, customer behavior analysis, and infrastructure optimization. Key research interests include machine learning algorithms (e.g., Random Forest, CNNs), explainable AI systems, and their practical implementations in real-world scenarios. He has conducted studies on diabetes prediction, pandemic forecasting, and metaverse risk assessment. His work often bridges theoretical models with practical industry challenges, particularly in finance, healthcare, and transportation sectors. Recent research trends show a focus on interdisciplinary applications of AI, such as biomedical information retrieval frameworks and transformer-based models. He has collaborated on projects analyzing UK traffic datasets and Bitcoin trader networks, highlighting his versatility in data-driven research. No awards or grants are explicitly mentioned in the provided texts. His advising and professional activities are not detailed in the current data, but his publications reflect active engagement with both academic and applied research challenges.

Jelena Schmalzمشاهده پروفایل

مدرس

• Applied Mathematics
• X-ray Imaging
• Tomography
• +۲ مورد دیگر

Jelena Schmalz is a Lecturer in Mathematics at the School of Science and Technology, University of New England. She holds an M.Sc. in Mathematics from Moscow State Lomonossov University and a Ph.D. in Physics from UNE. Her background includes extensive teaching experience in first-year mathematics, number theory, and cryptography courses. Dr. Schmalz's research focuses on applied mathematics with applications to X-ray imaging, tomography, sport science, cryptography, and tertiary education. Her doctoral work investigated direct and inverse problems in X-ray phase-contrast imaging. Her publications demonstrate consistent focus on mathematical applications and educational innovation, with recent work exploring mathematical education during COVID-19 and security protocols in banking systems. Earlier research established foundations in X-ray physics and tomography techniques.

Dan Edidinمشاهده پروفایل

استاد

• Algebraic Geometry
• Machine Learning
• Topology
• +۱ مورد دیگر

Dr. Dan Edidin is Professor of Mathematics at the University of Missouri's College of Arts and Science, holding a Ph.D. from MIT (1991). His research spans algebraic geometry, machine learning, and topological methods in data science. Current investigations address phase retrieval problems over Lie groups, moduli space invariants, and orbit recovery algorithms. His work connects abstract algebraic geometry with applications in signal processing and cryo-electron microscopy reconstruction. Recent publications develop theoretical frameworks for signal recovery from invariant moments, K-theoretic analyses of moduli stacks, and sample complexity bounds for cryo-EM imaging. This interdisciplinary research bridges pure mathematics with computational imaging challenges.

Anh Nguyenمشاهده پروفایل

دانشیار

• Deep Learning
• Computer Vision
• Generative Modeling
• +۵ مورد دیگر

Anh Nguyen is an Associate Professor in the Department of Computer Science and Software Engineering at Auburn University, affiliated with the Samuel Ginn College of Engineering. His work focuses on deep learning, computer vision, and explainable AI. He holds a Ph.D. from the University of Wyoming and a B.S. from Assumption University. Research highlights include developing methods to improve AI robustness, analyzing biases in large language models, and creating tools for visual correspondence in image processing. He leads the Center for Artificial Intelligence and Cybersecurity Engineering and directs Auburn's first K-6 AI education program through his AI Club after-school initiative. Recipient of a $460,736 NSF CAREER Award for AI innovation Developed the AI@AU lecture series and multiple benchmark datasets (e.g., ImageNet-Hard) Collaborates with industry partners on real-world AI applications Recent projects explore multimodal model limitations (Zerobench), medical imaging (LiteGPT for chest X-rays), and interactive AI systems that incorporate human feedback. His work bridges theoretical advancements with practical implementations in healthcare, wildlife monitoring, and education.

Salam Daherمشاهده پروفایل

استادیار

• Virtual and Augmented Reality
• Healthcare Simulation
• Computer Graphics
• +۴ مورد دیگر

Salam Daher is an Assistant Professor in the Informatics Department at New Jersey Institute of Technology (NJIT). Her research focuses on integrating computer graphics, virtual reality (VR), and augmented reality (AR) into healthcare training, particularly in developing Physical-Virtual Patient (PVP) simulators that provide tactile, auditory, and visual feedback for medical education. She also holds a Courtesy Assistant Professor role at the University of Central Florida's College of Nursing. Education includes a Ph.D. in Modeling and Simulation from the University of Central Florida (2018), M.S. in Modeling and Simulation (2015) and Digital Arts and Sciences (2006), and a B.S. in Computer Science from Lebanese American University (2004). Her research interests span 3D synthetic environments, interactive virtual humans for training, and healthcare simulation innovations. Notable projects include the PVP system, which combines physical and virtual elements to simulate patient conditions, and the VitalHS lab's work on geriatric caregiver training via immersive virtual humans. Dr. Daher has received awards such as the SSH 2021 Technology Innovator of the Year and NCWIT Scholarship. Her work bridges computing, engineering, and arts to address real-world healthcare challenges. She leads the VitalHS NJIT lab and collaborates internationally on interdisciplinary healthcare simulation projects.

Markos Mentzelopoulosمشاهده پروفایل

مدرس ارشد

• Computer Vision
• Augmented Reality
• Serious Gaming
• +۷ مورد دیگر

Markos Mentzelopoulos is a Senior Lecturer and Course Leader for the BSc Computer Games Development program at the University of Westminster's Faculty of Science and Technology. He holds a BEng in Electronics Systems Engineering (2000) and an MSc in Broadband and Mobile Communication Networks (2002) from the University of Kent. His research focuses on computer vision, augmented reality, serious gaming, and AI-driven game design, with contributions to visual information retrieval, pervasive computing, and immersive learning environments. He has published extensively in venues like IEEE IJCNN, ACM SIGDOC, and CIVR, and organized workshops such as the IEEE Virtual Reality and Advanced Interfaces Workshop. Teaching responsibilities include modules on game programming (C#/C++), game AI, unity3D/UE4 development, and mobile game design. He integrates cutting-edge technologies like Oculus Rift and Microsoft Kinect into his courses. As a member of the Computational Vision and Imaging Technology Cluster, he explores applications of machine learning and gesture recognition in educational and gaming contexts. Industrial experience includes roles at GlaxoSmithKline (2006) as a program analyst and developer. His work bridges academia and industry through projects like the Westminster Serious Games Platform (wmin-SGP), enabling real-time authoring of roleplay simulations for education. Current research emphasizes immersive learning applications, gamification in criminal psychology training, and conversational AI for environmental education. He actively reviews for IEEE and ACM conferences and serves on program committees for multimedia and neural network events.

Saeed Amalمشاهده پروفایل

استاد پژوهش

• Artificial Intelligence
• Healthcare Applications
• Deep Learning
• +۷ مورد دیگر

Saeed Amal is an Assistant Research Professor at Northeastern University's College of Engineering and Department of Bioengineering. He leads the Amal Lab for Precision Medicine, focusing on AI-driven healthcare solutions. His primary affiliation is with Northeastern's Roux Institute and he holds academic leadership roles in cardiovascular and cancer research. Previously, he was a Postdoctoral Fellow at Stanford University School of Medicine and held industry roles as VP of R&D at Cardiacx Medical (cardiology startup) and Data Scientist positions at companies like General Electric and Dynamic Yield. Education: B.Sc. in Computer Science, Technion - Israel Institute of Technology (2005) M.Sc. in Computer Science (Information Retrieval), University of Haifa (2009) Ph.D. in Computer Science (Machine Learning), University of Haifa (2019) Postdoctoral Research, Stanford University (2021–Present) Research Interests: AI applications in healthcare, including deep learning for medical imaging, NLP for clinical text analysis, and multimodal data fusion for cardiovascular and cancer care. His work emphasizes precision medicine, automated disease diagnosis (e.g., prostate and breast cancer), and improving patient outcomes through AI-driven tools. Key Contributions: Developed AI tools for cardiovascular disease prediction using multimodal data fusion, led initiatives in digital pathology for cancer grading, and pioneered the use of generative AI in Gleason pattern segmentation. His lab has published extensively on machine learning models for adverse event prediction in surgery and healthcare decision-making visualization. Awards: Emerging Visionary Award (Northeastern University NAI, 2025) Grants and Collaborations: Partnered with Santovia Path AI and Prima CARE on AI tools for cancer diagnosis. His work is supported by Northeastern's research initiatives and collaborations with institutions like Stanford University School of Medicine. Labs/Teams: Director of The Amal Lab for Precision Medicine, focusing on AI and multimodal data integration for disease care.

• Artificial Intelligence
• Data Analytics
• Machine Learning
• +۳ مورد دیگر

Professor Xiaohui Tao is a distinguished academic at the University of Southern Queensland's School of Mathematics, Physics and Computing, leading the Computing Discipline Team and chairing the ICT Programs Governance Committee. He holds a PhD in Information Technology from Queensland University of Technology (2009). His research focuses on artificial intelligence, machine learning, and health informatics, with over 200 publications in top journals like IEEE TKDE and conferences such as AAAI and IJCAI. He has mentored 10 doctoral students and leads a research group developing real-world AI applications. Education: PhD in Information Technology, Queensland University of Technology, 2009 Research Interests: Dr. Tao's work spans AI-driven health informatics, machine learning algorithms, natural language processing, and privacy-preserving technologies. His projects address critical challenges like mental health monitoring, smart healthcare systems, and privacy in 5G/IoT environments. Recent advancements include federated learning for unlearning mechanisms and multimodal fusion for medical decision support. Grants & Awards: Awarded Australia Research Council grants (DP220101360), Australian Endeavour Fellowships, and multiple best paper awards at conferences like BESC’22 and WI-IAT’20. Recognized for contributions to remote patient monitoring and AI in depression treatment. Labs & Teams: Leads a research group focused on AI applications in healthcare and data science, collaborating on projects like computational social science for mental health and privacy-preserving IoT systems.

Edward Cazalasمشاهده پروفایل

استادیار

• Radiation Detection
• Nuclear Engineering
• Radiation Effects on Materials
• +۴ مورد دیگر

Edward Cazalas serves as Assistant Professor in the Civil & Environmental Engineering Department within the University of Utah's College of Engineering. His primary academic appointment began August 2018 following a brief Visiting Assistant Professor role (May-July 2018). He directs the CAZ-RAD research group focused on radiation detection, nuclear security, and radiation effects. His research spans radiation detector development, quantum materials response to radiation, neutron spectrometry, and radiation effects on microelectronics. Key projects include quantum dot radiation response studies, neutron spectrometer development, and FLASH radiotherapy dosimetry. His group operates three specialized laboratories: East Lab (radiation detection electronics), West Lab (reactor-adjacent irradiation testing), and Computational Lab (modeling/simulation). Analysis of his recent publications reveals strong emphasis on quantum materials for radiation detection (perovskite-graphene devices), neutron detection alternatives to helium-3, and radiation effects in nanoscale systems. His work bridges fundamental physics with practical applications in nuclear security, reactor technology, and medical physics. Best Student Paper Award (ANS Radiation Protection Division, 2021) Cazalas has secured over $800,000 in external funding from DOE, DoD, and NIH for radiation effects research, including NNSA Consortium grants and STTR awards with InnoSys Inc. He advises multiple PhD and MS students, with recent graduates including Teancum Quist, Codey Olson, and Jesse Snow. His group maintains strong industry and national laboratory collaborations while operating within the University of Utah's TRIGA Reactor facility and Nanofab Labs. The CAZ-RAD group recently completed major infrastructure projects including installation of a pre-WWII steel shielding cave and PuBe neutron irradiation facility. Current initiatives focus on quantum dot radiation response, radiation-hardened stepper motors, and electron/photon emulation machines.

Suzanne Littleمشاهده پروفایل

استاد

• Computer Vision
• Media Analytics
• Machine Learning
• +۲ مورد دیگر

Professor Suzanne Little is a Full Professor at Dublin City University's School of Computing and SFI Principal Investigator at the Insight Centre for Data Analytics. She holds a PhD from the University of Queensland and leads research in media analytics, computer vision, and machine learning. Her work develops methods for analyzing multimedia content including video analysis for surveillance, sports analytics, transportation, and medical imaging. Research focuses on: Content-based multimedia indexing and retrieval Computer vision for object/event detection in video Bias analysis in deep learning models Edge computing for video analytics Optimization of neural network architectures Her recent publications address bias mitigation in AI systems, efficient object detection methods, and novel applications of event cameras. Professor Little co-directs the SFI Centre for Research Training in Artificial Intelligence and contributes to the iForm Advanced Manufacturing Centre. She has led multiple EU projects in multimedia and big data, including the Smart Stadium IoT initiative with Croke Park. Her work has been featured in media outlets including RTE, Silicon Republic, and The Irish Times.

• Snow Hydrology
• Remote Sensing
• Climate Science
• +۳ مورد دیگر

Dr. McKenzie Skiles is an Associate Professor at the University of Utah's School of Environment, Society & Sustainability, leading the Snow Hydrology Research-to-Operations Laboratory (Snow HydRO Lab). Her work focuses on snow hydrology, dust-on-snow radiative forcing, and remote sensing applications to assess snowpack dynamics and water security in semi-arid mountain regions. She holds a PhD in Geography from UCLA (2014), alongside multiple degrees from the University of Utah, including dual BS in Geography and Environmental Studies (2008) and an MS in Geography (2010). Her academic journey includes a postdoc at Caltech/JPL (2015–2016) and prior roles as an Assistant Professor at the University of Utah (2017–2023) and Utah Valley University (2016–2017). Her research integrates field observations, numerical modeling, and remote sensing to quantify how light-absorbing particles (dust, black carbon) accelerate snowmelt, impacting water resources in the Western U.S. Key projects include the Dust^2 initiative and the Surface Atmosphere Integrated Field Laboratory (SAIL). Recent work highlights the shrinking Great Salt Lake's role in increasing dust-on-snow events in the Wasatch Mountains. Publications emphasize snow albedo modeling, dust radiative forcing, lidar applications, and machine learning for snow water equivalent estimates. Collaborations span federal agencies (NASA, NOAA) and universities, with a focus on bridging science and operational water management.