جستجوی استاد راهنمای دانشگاهی

Katherine Storrsمشاهده پروفایل

مدرس ارشد

• Visual perception
• Deep learning
• Psychophysics
• +۷ مورد دیگر

Katherine Storrs is a Senior Lecturer in the School of Psychology at the University of Auckland, New Zealand. She leads the Computational Perception Lab, where she combines computational modeling and psychophysical experiments to study visual perception. Her research is supported by a Marsden Fast Start grant, and she is actively involved in teaching and academic service. She earned her PhD in Psychological Science from the University of Queensland in 2015 and has held postdoctoral positions at Justus-Liebig University (Germany) and the MRC Cognition and Brain Sciences Unit (Cambridge, UK). She also worked as a Data Scientist at Twitter in London. Dr. Storrs' research focuses on how the visual system interprets material properties such as gloss, shape, and reflectance. She uses unsupervised deep learning models to simulate and predict human perception, particularly in ambiguous or complex visual environments. Her work bridges cognitive science, neuroscience, and artificial intelligence. Her most recent publications explore topics such as gloss perception, mental rotation, face similarity, and the role of statistical learning in shape encoding. These works frequently appear in high-impact journals like Nature Human Behaviour , PNAS , and Current Biology , demonstrating a strong trend toward using computational models to explain perceptual phenomena. Marsden Fast Start Grant (2021) Humboldt Research Fellowship (2019) UK National Finalist, FameLab (2017) Editorial Board Member, Nature Communications Psychology (2023–) Editorial Board Member, OpenMind (2022–) Social Media Editor, Perception and i-Perception (2020–) She supervises graduate students and teaches courses including PSYCH 306 (Research Methods), PSYCH 775 (Visual Perception in Brains and Machines), and PSYCH 109. She also mentors students in honours, master's, and PhD programs. Her lab is actively involved in interdisciplinary collaborations, particularly with researchers in machine learning and computational neuroscience. The lab is funded by the Marsden Fund and supported by access to high-performance computing resources for training deep neural networks.

Lera Boroditskyمشاهده پروفایل

دانشیار

• Cognitive Science
• Linguistics
• Psychology
• +۳ مورد دیگر

Lera Boroditsky is an Associate Professor of Cognitive Science at the University of California, San Diego (UCSD) and Editor in Chief of Frontiers in Cultural Psychology. She previously held faculty positions at MIT and Stanford University. Her research focuses on the interplay between language, thought, and perception, particularly how linguistic structures shape cognitive processes such as time perception, spatial reasoning, and causal reasoning. She investigates cross-linguistic differences in mental representations, including how speakers of languages like English, Mandarin, and Indonesian conceptualize time and events differently. Her work bridges cognitive psychology, linguistics, and neuroscience, emphasizing the 'experiential basis of meaning.' Notable contributions include demonstrating that grammatical structures (e.g., gender distinctions in nouns) influence object conceptualization and that spatial metaphors for time (e.g., 'the future is ahead') affect behavior and decision-making. She has also explored how metaphor framing influences societal issues like immigration policy and healthcare attitudes. Awards: Searle Scholar, McDonnell Scholar, NSF Career Award, APA Distinguished Scientist Lecturer Research Highlights: Language and Time Perception, Metaphor and Cognition, Cross-linguistic Cognition Boroditsky’s grants include funding from the McDonnell Foundation and NSF, supporting projects on cultural psychology and cognitive modeling. She advises on experimental and theoretical research in cognitive science, though specific student names are not listed here. Her lab explores topics like embodied cognition and the neural correlates of linguistic relativity.

• Statistics
• Data Science
• Bayesian Analysis
• +۳ مورد دیگر

Gudmund Horn Hermansen is an Associate Professor at the University of Oslo, affiliated with the Department of Mathematics within the Faculty of Mathematics and Natural Sciences. His research focuses on advanced statistical methodologies, including Bayesian analysis, time series modeling, and applications in fields such as conflict dynamics, neuroscience, and environmental science. He is a member of the Statistics and Data Science research group and collaborates with interdisciplinary teams on projects involving uncertainty quantification and statistical inference. Key research interests include change-point analysis, hidden Markov models, astrocytic calcium signaling in Alzheimer’s research, and probabilistic forecasting. His work bridges theoretical statistics with practical applications, such as analyzing democratization processes and reservoir parameter interactions. Hermansen has contributed to over 30 peer-reviewed publications, emphasizing methodological innovations and interdisciplinary collaborations. Notable publications include studies on Bayesian hidden Markov models in conflict research, astrocytic signaling mechanisms in sleep regulation, and statistical frameworks for temporal heterogeneity analysis. He maintains an active role in academic service, including editorial contributions and conference participation.

• Medical Imaging
• Positron Emission Tomography (PET)
• Detector Physics
• +۱۲ مورد دیگر

David Schug is a Group Leader in PET detector research at the Chair of Imaging and Image Processing, RWTH Aachen University, Germany. He is also Co-Founder and Co-Director of Hyperion Hybrid Imaging Systems GmbH and serves as a Radiation Safety Officer. His research is centered on advancing positron emission tomography (PET) technology, particularly in detector design, time-of-flight performance, depth-of-interaction encoding, and integration with MRI systems. He is actively involved in both academic and industrial innovation in medical imaging. B.Sc. Physics, RWTH Aachen University (2006–2009) M.Sc. Physics, RWTH Aachen University (2009–2011) Dr.rer.nat., RWTH Aachen University (2012–2015) David Schug's research focuses on medical imaging physics , particularly PET detector development , hybrid PET/MRI systems , and advanced calibration techniques . His work integrates machine learning with physics-based models to enhance timing resolution and image quality. Key areas include ASIC-based readout electronics, RF shielding for MRI compatibility, and real-time data processing. He has made significant contributions to improving coincidence time resolution beyond 200 ps in clinical and preclinical systems. His recent publications demonstrate a strong trend in high-resolution PET detector design , with emphasis on time-of-flight capabilities , depth-of-interaction encoding , and in-system calibration methods . Articles frequently involve machine learning (e.g., gradient tree boosting), ASIC integration (TOFPET), and MRI compatibility. The research spans both preclinical and clinical applications, including breast cancer imaging, brain PET, and proton therapy monitoring. David Schug has not been publicly awarded any scientific prizes or fellowships based on available information. He advises several researchers and students involved in PET detector projects, as evidenced by co-authorship on numerous publications. His team develops cutting-edge detector platforms like the Hyperion series and collaborates on EU projects such as HYPMED. While specific grant details are not listed, his sustained research output and entrepreneurial activity suggest successful funding acquisition. His work bridges academia and industry through Hyperion GmbH. David Schug leads the PET detector research group at the Chair of Imaging and Image Processing, RWTH Aachen. He is part of AG START at Uniklinik RWTH Aachen and collaborates extensively with Prof. Volkmar Schulz. His team focuses on the Hyperion detector platform, developing inserts for simultaneous PET/MRI, with applications in oncology, neurology, and cardiology.

Tam Cai Lianمشاهده پروفایل

دانشیار

• Counselling Psychology
• Adolescent Mental Health
• Drug Abuse Prevention
• +۷ مورد دیگر

Tam Cai Lian is an Associate Professor at the Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia. She is a registered counselling psychologist and panel counsellor with the Board of Counsellors Malaysia. Dr. Tam serves as the coordinator for the Master of Professional Counselling (MoPC) program and is the unit coordinator for key courses including Children and Adolescents Counselling and Counselling Skills: Individuals and Couple. Her research focuses on adolescent mental health, drug abuse, counselling effectiveness, and socio-emotional development in Malaysia’s multi-ethnic context. She has led and contributed to numerous research projects, including international collaborations on youth depression, sleep, and anxiety, as well as localized studies on school uniforms and national identity. Her work aligns with UN Sustainable Development Goals related to mental health and well-being. Dr. Tam's recent publications (2019–2024) reflect a strong engagement with psychological distress in gaming and gambling populations, religiosity and marital satisfaction, classroom design and student engagement, and resilience among refugee children. Her research integrates psychological theory with real-world applications in education, public health, and community development. Scientific Awards: Monash-Sunway Decarbonization initiative: Microalgae farming and Native Tree Planting (23 Aug 2018, Other distinction) Dr. Tam has been actively involved in research grants and collaborative projects, including as Chief Investigator in multiple funded studies. She mentors students through the MoPC program and contributes to curriculum development and academic leadership. Her work bridges clinical psychology with public health and education, emphasizing culturally sensitive and evidence-based interventions. She is involved in research teams focusing on youth mental health across continents, neurological disease prediction using AI, and socio-emotional learning in marginalized communities. These collaborations highlight her interdisciplinary approach and commitment to impactful, translational research.

Itamar Jalonمشاهده پروفایل

پژوهشگر ارشد

• Cognitive Neuroscience
• Computational Neuroscience
• Natural Language Processing
• +۳ مورد دیگر

Itamar Jalon is a Postdoctoral Research Associate at the Princeton Neuroscience Institute (PNI) at Princeton University for the 2024-25 academic year, affiliated with both the Hasson Lab and Music Cognition Lab. His research leverages deep learning models to study social and higher-order cognitive processes—including meaning-making in naturalistic interactions, emotion, self- and other-perception, and creativity—in ecologically valid settings. Dr. Jalon earned his PhD (pending approval) in psychology from Tel Aviv University, where his doctoral work focused on computational representations of autobiographical emotions. Prior research spans the neuroscience of pain, creativity, neuromodulation, and psychiatric interventions, reflecting an interdisciplinary approach bridging psychology, neuroscience, and artificial intelligence. His current work applies large language models to decode naturalistic human speech and ECoG neural recordings, targeting real-world cognitive phenomena. This positions him at the intersection of machine learning and cognitive neuroscience, with emphasis on affective and social processes in uncontrolled environments. No scientific awards or honors were documented in the provided materials. Information regarding student mentorship, research grants, or advising activities was not specified in the source text. Dr. Jalon actively contributes to two specialized research units at PNI: the Hasson Lab (investigating neural mechanisms of communication) and the Music Cognition Lab (exploring cognitive foundations of musical experience), utilizing these platforms for naturalistic neuroscience methodologies.

Tony Jebaraمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Graph Theory
• +۳ مورد دیگر

Professor Tony Jebara is a faculty member in the Department of Computer Science at Columbia University, where he chairs the Center on Foundations of Data Science and directs the Columbia Machine Learning Laboratory. His research focuses on machine learning with applications in vision, graphs, and spatio-temporal data. He holds a PhD from MIT (2002) and has advised startups including Sense Networks, Evidation Health, and Agolo. Notable awards include the NSF Career Award (2004), Best Paper at ICML 2009, and recognition as one of Esquire's Best and Brightest (2008). His work has been featured in major media outlets. Jebara's academic contributions span over 100 peer-reviewed papers and a textbook on machine learning. He served as General Chair for ICML 2017 and Program Chair for ICML 2014. His research explores generative and discriminative models, Bayesian inference, and optimization techniques. Current projects include neural ensemble analysis in neuroscience and robust learning algorithms for environmental modeling. Recent publications emphasize scalable methods for collaborative filtering, survival analysis in online experiments, and graphical model applications in neuroscience. His work bridges theoretical advancements with practical systems, including contributions to privacy-preserving algorithms and recommendation systems.

Timothy McMahanمشاهده پروفایل

استادیار

• Adaptive Virtual Environments
• Neurogaming
• Virtual Reality
• +۷ مورد دیگر

Dr. Timothy McMahan is an Assistant Professor in the Department of Computer Science at the University of North Texas. His research focuses on adaptive virtual environments, neurogaming, and applying VR/AR technologies to neuropsychological assessment and training. He holds a Ph.D. in Computer Science (2016) and multiple advanced degrees from UNT. Education: Ph.D. Computer Science, University of North Texas (2014–2016) M.S. Computer Science, University of North Texas (2007–2013) B.S. Computer Science, University of North Texas (2003–2007) Postdoctorate in Computational Neuropsychology, University of North Texas (2016–2017) Research Interests: Adaptive VR environments for personalized user experiences Neurogaming applications in healthcare and education Machine learning for cognitive assessment and environmental adaptation EEG-based analysis of user engagement and cognitive workload VR tools for neuropsychological disorder diagnosis and treatment Recent work emphasizes VR applications in memory research, adaptive systems leveraging real-time ML predictions, and EEG-driven analyses of subjective experiences. Articles focus on bridging virtual and real-world interactions through computational neuroscience methods. No scientific awards or grants are explicitly listed in the provided materials. No advisees or lab affiliations are documented here.

Luis Ortizمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Computational Game Theory
• +۱۲ مورد دیگر

Luis Ortiz is an Associate Professor at the University of Michigan - Dearborn in the Department of Computer and Information Science , College of Engineering and Computer Science. He previously held faculty positions at Stony Brook University (2008-2015), University of Puerto Rico, Mayaguez (2007-2008), and postdoctoral roles at MIT (2004-2006) and University of Pennsylvania (2001-2004). His research spans Artificial Intelligence , Machine Learning , Game Theory , and Graphical Models , with applications to networks, security, finance, and health. Education: Ph.D., Computer Science, Brown University (2002) B.S., Computer Science, University of Minnesota, Twin Cities His recent publications focus on graphical games, causal inference, and probabilistic modeling, with contributions to Nash equilibrium computation, strategic network analysis, and multi-agent systems. He has received multiple NSF awards , including a CAREER grant. Scientific Awards: 2019 NSF Research Award 2011 NSF CAREER Award 2008 NSF Faculty Diversity Award Ortiz has advised projects in computational game theory , machine learning , and network security . His research grants include NSF funding and industry support from Huawei/FutureWei Technologies. He has led projects on interdependent defense games, wireless localization, and causal strategic inference in networked systems. Labs & Teams: Collaborates with the Stony Brook Game Theory Summer Festival , University of Pennsylvania's Computer and Information Science Department , and industry partners in cellular networking and security.

• Neuroscience
• Robotics
• Computational Biology
• +۴ مورد دیگر

Prof. Pavan Ramdya, the DSM-Firmenich Next Generation Chair in Neuroscience at École Polytechnique Fédérale de Lausanne (EPFL), leads the Neuroengineering Laboratory. His research focuses on reverse-engineering biological intelligence in Drosophila melanogaster to inspire neuroprosthetics, robotics, and AI. He holds a PhD in Neurobiology from Harvard University and completed postdoctoral training in robotics (EPFL), neurogenetics (UNIL), and bioengineering (Caltech). University: École Polytechnique Fédérale de Lausanne (EPFL) School: School of Life Sciences Academic Rank: Professor His lab employs computational, engineering, genetic, and microscopy approaches to study neural population dynamics, biomechanics, and gene expression in limb-dependent behaviors. Key research trends from his publications include neuromechanical modeling of Drosophila , sensory-motor integration, and AI-robotics synergy for biological discovery. HFSP Career Development Award Swiss National Science Foundation Eccellenza Grant UNIL Young Investigator Award in Basic Science FENS-Kavli Network of Excellence member The lab mentors doctoral researchers such as Sibo Wang, Victor Stimpfling, and Femke Hurtak, alongside postdoctoral fellows like Jasper Phelps and alumni including Victor Lobato Rios. Collaborations span robotics (Auke Ijspeert), microrobotics (Sakar), and computational imaging (Fua).

Lijing Xinمشاهده پروفایل

استادیار

• Magnetic Resonance Spectroscopy (MRS)
• Brain Energy Metabolism
• Neuroimaging
• +۳ مورد دیگر

Lijing Xin is an Assistant Professor at the Department of Physics and a research staff scientist at the Center for Biomedical Imaging (CIBM) at Ecole polytechnique fédérale de Lausanne (EPFL), Switzerland. She teaches courses on Biomedical Imaging and Translational MR Neuroimaging, while also contributing to academic administration. PhD in Physics (2010, EPFL) Master's Project (2002-2005) on MRI instrumentation Her research focuses on high-field magnetic resonance spectroscopy (MRS) and MRI for studying brain function and neurological diseases. She develops novel acquisition and quantification methods for 1 H, 13 C, and 31 P nuclei, particularly on 7T clinical platforms . Her work bridges preclinical and clinical research , with collaborations in psychiatry to explore pathophysiology and biomarkers for disorders like schizophrenia and mood disorders. Recent publications include studies on epilepsy , Alzheimer's disease , brain energy metabolism , and neurochemical profiling using advanced MRS and deep learning for psychosis classification. She has contributed to RF coil design , macromolecule suppression , and metabolic pathway analysis across multiple disciplines. She advises PhD students and collaborates on interdisciplinary projects involving neuroimaging hardware , metabolic disease research , and psychiatric biomarker identification . Her lab at EPFL CIBM-AIT develops cutting-edge techniques for high-resolution brain metabolism analysis and clinical translation .

• Biomedical Engineering
• Medical Imaging
• Cancer Research
• +۷ مورد دیگر

Amoon Jamzad is an Adjunct Assistant Professor at the School of Computing, Queen's University, and a postdoctoral fellow at Med-I Lab. He holds a BSc in Electrical Engineering (Electronics) from University of Tehran (2007), an MSc in Biomedical Engineering (2010), and a PhD in Biomedical Engineering (2015). His doctoral research focused on noninvasive ultrasound analysis of kidney stones. He later served as a guest lecturer and lab instructor at University of Tehran for 3 years before joining Queen's University in 2019. BSc: Electrical Engineering (Electronics), University of Tehran (2007) MSc: Biomedical Engineering, University of Tehran (2010) PhD: Biomedical Engineering, University of Tehran (2015) Dr. Jamzad's research centers on cancer cell detection, particularly in developing ultrasonic and optical spectroscopic devices for surgical margin detection. His work integrates advanced machine learning techniques with clinical applications, focusing on breast and prostate cancer surgery. He has pioneered the use of mass spectrometry imaging for cancer margin assessment and developed open-source platforms like ViPRE and MassVision for AI-driven medical data analysis. His recent publications demonstrate expertise in self-supervised learning, domain adaptation, and uncertainty quantification within medical imaging contexts. While no specific awards are mentioned, his contributions to surgical oncology tools and multi-center prostate cancer studies indicate significant clinical impact. He has also explored temporal enhanced ultrasound for composite defect detection and electrosurgical cautery state recognition through deep learning.

Rizza Zahidمشاهده پروفایل

پژوهشگر

• Data Science
• Mental Health
• Public Health Informatics
• +۴ مورد دیگر

Rizza Zahid is a Researcher at the Social, Genetic & Developmental Psychiatry Centre within the School of Mental Health & Psychological Sciences at King’s College London. She joined the Landscaping International Longitudinal Datasets Project in early 2025. Education MSc in Neuroscience (2024), King’s College London BSc in Chemistry, University College London Her research focuses on integrating Data Science, Mental Health, and Machine Learning/Artificial Intelligence to address neurodevelopmental disorders and public health challenges in psychiatry. She has contributed to NHS data quality improvement initiatives across North London and Hertfordshire. The Platforms for Longitudinal Mental Health Data (PALM) team, led by Professor Louise Arseneault, aims to enhance the discoverability of longitudinal mental health datasets for depression, anxiety, and psychosis research. Rizza’s prior work involved applying Natural Language Processing (NLP) to study recurrent falls in dementia patients in collaboration with South London and Maudsley NHS Foundation Trust.

Amir-Homayoun Javadiمشاهده پروفایل

مدرس ارشد

• Cognitive Neuroscience
• Memory Enhancement
• Neuroimaging
• +۷ مورد دیگر

Dr. Amir-Homayoun Javadi is a Senior Lecturer in Cognitive Neuroscience at the School of Psychology, University of Kent . He holds additional roles as an Honorary Research Associate at the Institute of Behavioural Neuroscience, University College London and a Visiting Professor at the School of Rehabilitation, Tehran University of Medical Sciences . His research focuses on non-pharmacological interventions to enhance memory, learning, and mental health through methods like physical exercise, brain stimulation, music, and sleep studies. Key Research Themes: Memory consolidation and enhancement Neurostimulation (tDCS, tACS) Sleep and circadian rhythm effects Emotion processing Computational modeling The 15 most recent publications highlight his work in neurostimulation during memory tasks, music-induced emotional states, exercise neuroscience, and Alzheimer’s disease diagnostics. Scientific awards are not explicitly mentioned. He supervises PhD students in areas including memory enhancement, declarative memory, and procedural skill learning, while maintaining memberships in the Federation of European Neuroscience Societies (FENS) and British Neuroscience Association (BNA). The Javadi Lab investigates cognitive mapping, neural oscillations, and brain-body interactions using EEG, fMRI, and AI.

Ryan Gallagherمشاهده پروفایل

مدرس ارشد

• Physiotherapy
• Data Science
• Machine Learning
• +۶ مورد دیگر

Ryan Gallagher is a Conjoint Senior Lecturer at the School of Health Sciences, College of Health, Medicine and Wellbeing at the University of Newcastle. He is also a Senior Physiotherapist and Data Scientist with expertise in leveraging healthcare data to improve patient outcomes. His work bridges clinical practice and data science to address healthcare challenges. Dr. Gallagher's educational background includes: Doctor of Philosophy in Physiotherapy from the University of New South Wales Bachelor of Physiotherapy from the University of Newcastle Master in Data Science majoring in Computational Intelligence His research focuses on applying data science and machine learning to healthcare challenges, particularly in the areas of stroke rehabilitation, telehealth, and advanced practice clinical roles. Dr. Gallagher is particularly interested in how E-Health and technology can transform healthcare delivery and the physiotherapy profession. His work on idiopathic Normal Pressure Hydrocephalus (iNPH) has made significant contributions to understanding how gait and balance assessments can predict surgical outcomes following CSF tap tests. He has extensively researched the application of machine learning to analyze clinical trends and outcomes on a large scale. Analysis of Dr. Gallagher's publication record reveals a strong focus on translational research that bridges clinical practice with data science. His work spans multiple domains including stroke rehabilitation, telehealth implementation, musculoskeletal care, and neurological conditions like iNPH. A significant thread throughout his research is the application of data-driven approaches to improve healthcare efficiency, patient outcomes, and access to services, particularly in rural settings. His more recent work shows increasing emphasis on healthcare system redesign and value-based care. Dr. Gallagher has received notable recognition for his research: Best HDR Publication for 2019 from the School of Health Sciences at the University of Newcastle Currently, Dr. Gallagher serves as co-supervisor for a PhD candidate researching "Understanding the Drivers of Telehealth Usage among Patients in Australia and how it Reduces Costs over In-person Healthcare Services." His supervisory work reflects his dual expertise in healthcare delivery and data science. While specific grant information isn't detailed in the provided text, his publication record suggests involvement in multiple research projects related to stroke care, telehealth implementation, and neurological rehabilitation. Dr. Gallagher's work appears to be closely connected with clinical teams at John Hunter Hospital and involves collaboration with multiple healthcare professionals across various specialties. His research on rural healthcare models suggests engagement with regional healthcare networks in New South Wales. His recent work on practice-based research networks indicates leadership in developing collaborative research infrastructure among physiotherapists in regional Australia.