Find Academic Supervisors

Lisa Jane MillerView profile

Professor

• Spirituality
• Mental Health
• Neuroscience
• +4 more

Lisa Jane Miller is a Professor of Psychology and Education at Teachers College, Columbia University, and founder of the Spirituality, Mind, Body Institute (SMBI). She holds a B.A. from Yale University, an M.S. and Ph.D. from the University of Pennsylvania, and completed post-doctoral work at Columbia University's College of Physicians & Surgeons. Her research focuses on spirituality's intersection with mental health, neuroscience, and clinical psychology, particularly addressing depression, substance abuse, and wellness interventions across lifespan development. Her academic affiliations include roles in Clinical Psychology and Psychology in Education at TC. Awards include APA Fellow (2011) and recognition for humanitarian contributions to peace-building (2012). She leads TC's clinical psychology doctoral training program and has authored major works like The Spiritual Child and The Awakened Brain . Her work bridges neuroscience, clinical practice, and education to promote holistic mental health strategies. Education B.A., Yale University, Magna Cum Laude (Psychology) M.S./Ph.D., University of Pennsylvania Post-doctoral Scholar, Columbia University College of Physicians & Surgeons Research Focus Neural correlates of spirituality Spirituality's role in depression and addiction prevention Spiritually integrated therapies Spiritual development in K-12 and university contexts Key Contributions Advocated for spirituality's inclusion in psychology Developed interventions for high-risk youth Edited Oxford Handbook of Psychology and Spirituality Labs/Initiatives The Spirituality, Mind, Body Institute (SMBI) under her leadership pioneers research on mind-body-spirit connections in health and education.

Masood KhanView profile

Senior Lecturer

• Affective Computing
• Artificial Intelligence
• Machine Learning
• +3 more

Dr. Masood Khan is a Senior Lecturer in Mechatronics and Mechanical Engineering at Curtin University’s School of Civil and Mechanical Engineering (Faculty of Science and Engineering). He teaches undergraduate and postgraduate courses including Mechatronic Design Projects, Sustainable Energy Systems, and oversees MPhil/PhD training in mechatronics and robotics. His research focuses on human-centered AI applications, including affective computing, responsible AI, and biomedical engineering. Notable projects include developing AI-driven tongue-strengthening systems for dysphagia therapy and thermal imaging for stress diagnosis, supported by a $1.35M CRC grant. He holds the Research Excellence Award (2006) and is a Fellow of the Higher Education Academy (UK) and Pakistan Academy of Engineering. His work spans over 60 peer-reviewed publications, with recent emphases on generative AI in education and explainable machine learning frameworks. Research Highlights: AI-based tongue-strengthening devices for swallowing therapy Thermal imaging for osseous stress pathology detection Facial thermal feature analysis for emotion recognition Intelligent tutoring systems for industrial training Grants & Awards: $1.35M CRC SmartCrete Grant (2016) $440K equipment support for thermography research Fellowships: Higher Education Academy (UK), Pakistan Academy of Engineering Teaching & Supervision: Leads capstone projects and supervises graduate research in mechatronics, AI, and biomedical robotics. Active in curriculum development for technical education in Brunei and SEAMEO.

Tele TanView profile

Professor

• Pattern Recognition
• Computer Vision
• Neurological Signal Analysis
• +5 more

Professor Tele Tan is a Deputy Head of School at Curtin University's School of Electrical Engineering, Computing and Mathematical Sciences (EECMS), affiliated with the Faculty of Science and Engineering. He holds a B.Eng (First Class Hons) and PhD from the University of Surrey, UK. His work focuses on advancing technology for neurodivergent individuals, particularly in physiological signal processing and social robotics. He co-founded the Autism Academy for Software Quality Assurance (AASQA) to support autistic students' transition into tech careers. Key roles include leading initiatives in inclusive education and research programs in STEM and entrepreneurship. Research interests span pattern recognition, computer vision, EEG analysis, and human factors in visual analytics. He actively advises on neurodivergent-friendly technologies and has contributed to over 100 publications. Awards include the 2023 Global QS Reimagined Education Award, Triple E Awards, and Member of the Order of Australia (2021). Teaching includes Engineering Programming and Final Year Projects. Notable achievements include pioneering strength-based programs for autistic adolescents, winning WA IT Awards for collaborative projects, and developing bio-signal sensors for medical applications. His work bridges academia and industry, emphasizing accessibility, innovation, and neurodiversity advocacy.

• Computer Graphics
• Machine Learning and Deep Learning
• Geometric Modelling and Processing
• +5 more

Dr. Frederick Li is an Associate Professor in the Department of Computer Science at the University of Durham. He holds a Ph.D. in Computer Graphics from City University of Hong Kong and has held academic roles at The Hong Kong Polytechnic University prior to his current position. His research focuses on Computer Graphics, Machine Learning, Visual Aesthetics, and Educational Technologies. He serves as an Associate Editor for Frontiers in Education and Virtual Reality & Intelligent Hardware, and has organized major conferences such as ICWL 2022 and ISVC 2021. Education: B.A. (Hons.) in Computing Studies from The Hong Kong Polytechnic University, M.Phil. in Computer Science, and Ph.D. in Computer Graphics. Research interests include mesh saliency, human-object interaction recognition, cloud modeling, and student performance analytics. He has published extensively in top venues like IEEE Transactions, ACM SIGGRAPH, and ECCV. Notable awards include Best Paper (ITiCSE 2014) and Outstanding Paper (ICALT 2013). He currently chairs the Undergraduate Board of Examiners and serves as an external examiner for Northumbria University's MSc program. His work spans educational technologies, collaborative virtual environments, and visual aesthetics, with applications in VR therapy, face forgery detection, and artistic style transfer. He supervises numerous Ph.D. students and contributes to Durham’s academic governance, including curriculum development and postgraduate management.

Joana PereiraView profile

Researcher

• Adaptive Deep Brain Stimulation
• Brain-Computer Interfacing
• Deep-Brain Stimulation
• +3 more

Joana Pereira is a postdoctoral researcher at the University Medical Center Freiburg (University of Freiburg) in the Lab of Stereotaxy and Electrophysiology, with concurrent guest researcher status at Radboud University's Donders Institute for Brain, Cognition and Behaviour. Her work centers on developing adaptive deep brain stimulation (aDBS) systems that integrate brain and behavioral signals to treat neuropsychiatric disorders, particularly depression and obsessive-compulsive disorder, while maintaining expertise in brain-computer interfaces for motor restoration. Her educational foundation includes: PhD in Biomedical Engineering (2021) from Graz University of Technology, Austria MSc in Biomedical Engineering and Biophysics (2015) from University of Lisbon, Portugal BSc in Biomedical Engineering and Biophysics (2013) from University of Lisbon, Portugal Dr. Pereira's research spans adaptive deep brain stimulation, brain-computer interfacing, cognitive neuroscience, and movement decoding, with emphasis on closed-loop neurotechnology systems. Current work focuses on identifying cortical neural markers for aDBS in psychiatric disorders and translating BCI frameworks to restore motor function in spinal cord injury through neuroprosthetics. Her methodology integrates electrophysiological data acquisition, real-time signal processing, and machine learning for neural decoding. Analysis of her 15 most recent publications reveals a clear research trajectory: early work concentrated on non-invasive EEG-based BCIs for robotic arm control and grasp neuroprostheses (2016-2021), while recent output pivots toward invasive closed-loop DBS systems for depression (2022-2025). Key thematic clusters include Dareplane software development for closed-loop research, electrophysiological characterization of DBS in depression, and fundamental movement decoding mechanisms. This evolution demonstrates strategic expansion from motor restoration applications to psychiatric neuromodulation. Dr. Pereira secured competitive funding from the Deutsche Forschungsgemeinschaft (DFG) for a project commencing April 2023 to characterize cortical responses to DBS in treatment-resistant depression and OCD patients. While no formal advisees are documented, her collaborative framework involves Prof. Volker A. Coenen's neurosurgery team at Uniklinik Freiburg and Prof. Michael Tangermann's neurotechnology group at Radboud University, forming an interdisciplinary consortium spanning electrophysiology, machine learning, and clinical neurology. Her primary laboratory affiliation is the Lab of Stereotaxy and Electrophysiology at University Medical Center Freiburg, where she investigates DBS mechanisms through intraoperative and chronic electrophysiological recordings. This work synergizes with the Donders Institute's cognitive neuroscience expertise, creating a transnational research pipeline from basic neural decoding to clinical implementation of adaptive neuromodulation therapies.

Ani EloyanView profile

Associate Professor

• Biostatistics
• Medical Imaging Data Analysis
• Machine Learning
• +3 more

Ani Eloyan is an Associate Professor of Biostatistics and Vice Chair of Biostatistics at Brown University's School of Public Health. She holds a PhD from North Carolina State University (2010). Her research focuses on statistical methods for medical imaging, including matrix decomposition techniques for lesion development modeling, machine learning applications in healthcare data integration, and dimension reduction methods. She has contributed to high-impact studies like the Longitudinal Early-onset Alzheimer's Disease Study (LEADS) and has expertise in neuroimaging analysis for conditions like multiple sclerosis and autism. Teaching responsibilities include courses such as Principles of Biostatistics (PHP 1510/2510), Applied Generalized Linear Models (PHP 2514), and Doctoral Seminars in Public Health (PHP 2950). Her work spans over 29 peer-reviewed articles, with recent publications emphasizing biomarker detection, tumor heterogeneity estimation, and sensorimotor outcome analysis. While no specific awards are listed, her research has been supported through collaborative grants and institutional affiliations. Key contributions include methodological advancements in MRI analysis, functional connectivity studies using fMRI, and the application of statistical frameworks to neurodegenerative diseases. She leads interdisciplinary teams in designing longitudinal studies and integrating complex datasets for clinical insights.

• Neuroscience
• Psychiatry
• Epilepsy
• +5 more

Crystal Marie Cooper is an Assistant Professor at the University of Texas at Arlington, Department of Psychology, and holds adjunct roles at UT Southwestern Medical Center and University of Texas Arlington’s Bioengineering department. She leads research on neuroimaging biomarkers in mental health, particularly focusing on adolescents with epilepsy and major depressive disorder (MDD). Her work integrates electrophysiological, neuroimaging, and clinical data to predict treatment responses and identify psychiatric subtypes. Education: PhD (2012), MS (2009), and BS (2007) in Experimental Psychology from the University of Texas at Arlington. Her postdoctoral training at UT Southwestern Medical Center focused on translational psychiatry research. Research Interests: Neuroimaging (fMRI, MEG, EEG), emotion processing, psychiatric comorbidities in epilepsy, antidepressant treatment response prediction, and biomarker discovery for mood disorders. Her recent studies leverage magnetoencephalography to map spatiotemporal neural dynamics in adolescents with epilepsy and explore reward system dysfunction in depression. Grants: Active proposals include investigations into the brain-heart connection in PTSD, emotion processing in epilepsy patients, and cognitive biomarkers of well-being in youth. Funded projects include a $100k grant from the Jordan Elizabeth Harris Foundation studying neuroimaging biomarkers in epilepsy. Awards: Recognized as a Rising Star by the Society for Biological Psychiatry (2019), New Investigator Award from the American Society of Clinical Psychopharmacology (2015), and multiple top poster awards for translational research. Advising: Supervises graduate students in clinical psychology and neuroscience, with a focus on interdisciplinary approaches combining neuroscience and mental health research. Currently advising Kathryn King (PhD candidate) on multi-modal neuroimaging studies of epilepsy patients. Labs/Teams: Principal Investigator at Cook Children’s Healthcare Systems’ Neuroscience Research department. Collaborates with the EMBARC consortium for biomarker discovery in depression treatment.

Soroush KorivandView profile

Assistant Professor

• Human-Machine Interaction
• Human-Robot Interaction
• Brain-Computer Interface
• +5 more

Soroush Korivand is an Assistant Professor at the Michael W. Hall School of Mechanical Engineering, Mississippi State University. His research focuses on human-machine interaction, brain-computer interfaces, and AI-driven robotics systems. He holds a Ph.D. in Mechanical Engineering from the University of Alabama (2023) and an M.S. in Computer Science (2022), alongside earlier degrees from Iranian institutions. Education: Ph.D., Mechanical Engineering, University of Alabama, 2023 M.S., Computer Science, University of Alabama, 2022 M.S., Mechanical Engineering, Tarbiat Modares University, Iran, 2017 B.S., Mechanical Engineering, Arak University, Iran, 2015 Research Interests: His work integrates robotics, AI, and neurophysiological data to enhance human-robot collaboration in healthcare, manufacturing, and public safety. Key areas include: Reinforcement learning for adaptive systems Brain-computer interfaces for rehabilitation Human performance prediction through physiological signals Publications Trends: Recent work emphasizes multimodal data fusion (EEG/eye tracking), teleoperated robots for law enforcement, and personalized rehabilitation frameworks using machine learning. Awards: Outstanding Teaching Award (2022) Best Graduate Research Exposition (2019 & 2022) Best Student Paper at IEEE/ACM CHASE (2022) Advising & Grants: Currently advising graduate students in robotics and AI, with research supported by institutional grants focusing on human-robot teaming and neurotechnology applications.

• Contemplative Psychology
• Mindfulness
• Meditation
• +7 more

William Van Gordon is an Associate Professor in Contemplative Psychology at the College of Health, Psychology and Social Care. His research focuses on mindfulness, meditation, and their applications in mental health, addiction theory, and philosophy. He explores intersections between Buddhist practices, Stoicism, and contemporary psychological frameworks. Key research interests include ontological addiction theory, nonattachment, compassion, and nature connectedness. His work spans mindfulness-based interventions for addiction recovery, meditation neuroscience, and the psychological effects of philosophical practices like Stoic death contemplation. His recent articles analyze the validation of addiction scales, Buddhist-derived therapies for gambling harm, and interdisciplinary studies merging Buddhism and quantum mechanics. Collaborations with international scholars highlight his global academic network. While no awards are listed, his contributions to contemplative psychology and mindfulness research are substantial. He has co-authored studies on mindfulness in education, healthcare, and workplace well-being, emphasizing practical applications of contemplative practices.

• Neurolinguistics
• Language Development
• Educational Neuroscience
• +5 more

Dr. Jodi M Tommerdahl is an Associate Professor at The University of Texas at Arlington, affiliated with the Department of Higher Education Adult Learning and Organizational Studies under the School of Educational Leadership and Policy Studies. Her research focuses on neurolinguistics, language development, reasoning, and the intersection of neuroscience with education. She holds a PhD in Neurolinguistics from the University of Paris-Sorbonne, an MA in European Languages and Literature from the University of London, and a BA in Scandinavian Languages and Literature from the University of Minnesota Twin Cities. Dr. Tommerdahl’s teaching spans courses like Neuroscience and Language, Educational Neuroscience, and Special Education. She has received awards including the Spotlight Award from Tech Fort Worth (2016) and a Faculty Development Leave (2016). Her research projects include grants funded by NSF and private partnerships, focusing on topics like neuro-feedback anger management, assistive technologies for disabilities, and mobile learning innovations. Her articles explore diverse topics such as cryptocurrency, language learning apps, and cognitive aging. She actively collaborates on interdisciplinary projects, including studies on emotion recognition via EEG and the efficacy of handwriting apps for children. Dr. Tommerdahl is a member of the International Clinical Phonetics and Linguistics Association and has presented at conferences globally, including the International Conference on Linguistics and Literature in Paris.

• Biomedical Signal Processing
• Machine Learning
• EMG Pattern Recognition
• +3 more

Oluwarotimi Samuel is a Senior Lecturer in Computer Science at the University of Derby's College of Science and Engineering. His multidisciplinary research integrates biomedical engineering, machine learning, and sensor technologies for healthcare applications. Primary research domains include myoelectric pattern recognition systems for prosthetics control, EEG-based neurological disorder detection, and sensor fusion techniques for movement analysis. Recent work demonstrates strong focus on deep learning architectures for biomedical signal interpretation and rehabilitation technologies. Publication trends show increasing emphasis on multimodal sensing approaches, with 2024-2025 works incorporating attention mechanisms and federated learning frameworks. Over 70% of recent publications address clinical applications of machine learning.

Loukas G AstrakasView profile

Professor

• Medical Physics
• Magnetic Resonance Spectroscopy (MRS)
• Functional Magnetic Resonance Imaging (fMRI)
• +7 more

Loukas G. Astrakas is Professor of Medical Physics at the University of Ioannina, School of Medicine , Greece, where he also serves as Director of the Medical Physics Laboratory at the University Hospital of Ioannina. After obtaining a B.Sc. in Physics from the University of Athens (1989), he earned a Ph.D. in Magnetic Resonance Spectroscopy from the Technical University of Athens (1999), followed by an M.Sc. in Medical Physics from the University of Patras (2014). His post-doctoral training took him to the National Centre of Scientific Research “Demokritos”, Harvard University, the Athinoula A. Martinos Center for Biomedical Imaging and Massachusetts General Hospital, Boston. He was appointed Lecturer (2009), then Assistant Professor (2013), Associate Professor (2020) and finally full Professor (2024) at the University of Ioannina. Research Focus: Professor Astrakas’ work centres on the development and clinical translation of advanced magnetic-resonance techniques—particularly magnetic-resonance spectroscopy (MRS), functional MRI (fMRI), and quantitative MRI—for non-invasive characterisation of paediatric brain tumours, epilepsy, neuro-degenerative diseases, testicular pathologies and rheumatological disorders. His research bridges fundamental physics, biomedical engineering and clinical radiology, with emphasis on metabolic imaging, voxel-based morphometry and machine-learning-assisted analysis. Publications & Impact: His 105+ peer-reviewed publications (2001–2025) map an evolving trajectory from methodological innovation in MRS and fMRI to large-scale clinical validation studies. Recurring themes include paediatric neuroradiology, neuro-oncology, and integration of imaging biomarkers with genomic data to enable precision medicine. Awards & Funding: Marie Curie Fellowship, European Commission (2004–2007) Leadership & Service: Director, Medical Physics Laboratory, University Hospital of Ioannina (2019–present) Member, NMR Surgical Laboratory, Massachusetts General Hospital (2001–present) Professor Astrakas teaches core and advanced courses in medical physics, supervises graduate research, and maintains active collaborations with Harvard Medical School, MGH, and European imaging networks, driving forward next-generation MR technologies for routine clinical care.

• Green and Sustainable Computing
• High-Performance Computing
• AI-Driven Assistive Technologies
• +9 more

Dr. Ishfaq Ahmad is a Full Professor of Computer Science and Engineering at The University of Texas at Arlington (UTA) since 2002, with prior roles as Associate Professor and Director of the Multimedia Technology Research Center at the Hong Kong University of Science and Technology. He leads the Institute of Research in Security (IRIS) at UTA and has extensive industry collaboration, overseeing over $15M in research funding. His research focuses on sustainable computing, AI-driven assistive technologies, IoT, and high-performance computing. Dr. Ahmad has authored over 290 publications and holds prestigious awards including IEEE Fellow and IDES Distinguished Life Fellow. Education: PhD in Computer Science (Syracuse University, 1992), M.S. in Computer Engineering (Syracuse University, 1987), B.S. in Electrical Engineering (University of Engineering and Technology, Lahore, 1985). Research Interests: Green Computing, Exascale Systems, Assistive Technologies, Smart Grids, and STEM Education. His work emphasizes sustainability and interdisciplinary innovation, with contributions to video compression, parallel algorithms, and technology transfer. Awards & Grants: Over 40 international awards, including Google Scholar top rankings in multiple fields. Key grants include NSF-funded projects on assistive technologies and sustainable computing, along with industry partnerships like those with Adobe and Sun Microsystems. Labs & Leadership: Founded Journal of Sustainable Computing and the International Green and Sustainable Computing (IGSC) conference. Serves as Editor-in-Chief of Springer’s Discover Internet-of-Things and on editorial boards of IEEE journals.

Praveen MadirajuView profile

Professor

• Data Science
• Text Analytics
• Healthcare Informatics
• +2 more

Dr. Praveen Madiraju is a Professor and Director of the DATA Lab at Marquette University's Computer Science Department, where he also serves as Graduate Chair. His research spans data science, text analytics, and healthcare informatics, with active projects in veteran mental health, COVID-19 outcomes, and EHR optimization funded by organizations like Northwestern Mutual and Advancing Healthier Wisconsin Endowment. He leads an NSF REU Site on 'Data Science Across Disciplines' and teaches courses including Big Data Systems and Database Principles. His publications demonstrate consistent focus on applying machine learning to healthcare challenges, particularly in mental health crisis prediction and clinical text analysis. Dr. Madiraju has supervised PhD student Megan Flint and collaborates extensively with medical institutions. His work integrates transformer models, multimodal learning, and mobile health interventions to address real-world problems.

Alexandra PiryatinskaView profile

Professor

• Time Series Analysis
• EEG Signal Processing
• Complexity Theory
• +3 more

Professor Alexandra Piryatinska is a faculty member in the Department of Statistics at San Francisco State University, located at TH 942. Her research focuses on time series analysis, EEG signal processing, complexity theory, and applications in medical diagnostics and biomarker identification. She holds a Ph.D. in Mathematics and has published extensively in interdisciplinary areas combining statistics with neuroscience and oncology. Her work emphasizes novel methodologies for change-point detection in multidimensional time series, classification of EEG records using ϵ-complexity theory, and statistical modeling of cancer drug treatment success. Notable contributions include applications in Parkinson’s disease postural balance analysis, neonatal EEG sleep stage detection, and glycoprotein profiling in breast cancer subtypes. Her research trends span algorithmic complexity-based classification, biomedical signal processing, and data-driven medical decision-making. She collaborates across disciplines to address challenges in clinical diagnostics and pharmacogenomics. Her advising and grant activities are not explicitly detailed in the provided text, though her publications suggest involvement in collaborative research projects. She is affiliated with the SF State Faculty site and maintains an active research program in statistical methods for complex biomedical data.