جست‌وجوی استاد و پژوهشگر

• Federated Learning
• Explainable AI
• Clinical Decision Support Systems
• +۳ مورد دیگر

Christoph Düsing is a researcher at the Semantic Databases Group within the Faculty of Engineering at Universität Bielefeld . He also contributes to the Center for Cognitive Interaction Technology (CITEC) and provides academic advising for the Master Data Science program at the Faculty of Business Administration and Economics . Research Interests: Federated Learning and distributed AI systems Explainable AI (XAI) for clinical applications Clinical Decision Support Systems for sepsis treatment Graph Neural Networks for social influence modeling Data imbalance and client contribution analysis in collaborative AI Impact of AI on labor market dynamics Recent Publications focus on federated learning applications in healthcare, explainability mechanisms, and graph-based AI solutions. Key themes include sepsis management, antibiotic therapy optimization, and social commerce platform analysis. Labs & Collaborations: Semantic Databases Group, Faculty of Engineering Center for Cognitive Interaction Technology (CITEC) Interdisciplinary projects with healthcare institutions

Manuel Schabusمشاهده پروفایل

استاد

• Sleep Research
• Consciousness Studies
• Cognitive Neuroscience
• +۴ مورد دیگر

Manuel Schabus is a University Professor of Psychology at the University of Salzburg, specializing in sleep research, consciousness studies, and cognitive neuroscience. With over 403 publications and numerous ongoing research projects through 2027, he leads significant work in understanding sleep mechanisms and developing innovative sleep interventions. His research spans from fundamental neuroscience to practical applications for sleep disorders. Professor Schabus's work focuses on the intersection of sleep, brain activity, and cognitive functions, with particular expertise in EEG analysis and sleep architecture. His research explores how sleep affects memory consolidation, emotional regulation, and overall mental health. He has pioneered digital approaches to sleep assessment and intervention, including mobile applications and online therapy platforms that make evidence-based sleep treatments accessible to broader populations. His recent publications reveal a strong trend toward digital sleep medicine, with multiple studies on AI-enhanced wearables, mobile CBT-I applications, and personalized sleep optimization strategies. The research spans from fundamental neuroscience (examining prediction-related signals during sleep) to applied interventions (including specialized programs for Ukrainian refugees and shift workers). Franz Gerstenbrand Wachkoma Award 2020 Kurt-Zopf-Förderpreis 2018 START-Preis 2014 Professor Schabus manages numerous research grants totaling millions in funding, with current projects including the Virtual Sleep Laboratory (2024-2027), Sleep2Optimize, and specialized interventions for shift workers and Ukrainian refugees. He actively mentors junior researchers and collaborates internationally across multiple institutions. His work bridges laboratory research with real-world applications, as evidenced by his frequent media appearances discussing practical sleep optimization strategies for the general public. He leads the Center for Cognitive Neuroscience at the University of Salzburg, where his team operates both traditional sleep laboratories and develops digital sleep assessment tools. The research group combines expertise in neuroscience, psychology, computer science, and clinical medicine to tackle sleep-related challenges from multiple angles, with a strong emphasis on translating research findings into practical interventions that improve sleep health in diverse populations.

Libby Kuminمشاهده پروفایل

استاد

• Speech-Language Pathology
• Down Syndrome Research
• Communication Disorders
• +۵ مورد دیگر

Libby Kumin is a Professor at Loyola College in Maryland, specializing in speech-language pathology for individuals with Down syndrome across their lifespan. Her work focuses on communication disorders, speech intelligibility, and phonological development in developmental disabilities. University : Loyola College in Maryland Academic Rank : Professor Kumin's research spans early speech development to adult communication needs, emphasizing parent training, childhood apraxia of speech, and transition planning to adulthood. She has developed evidence-based interventions for understandable speech in Down syndrome populations, including telehealth and classroom strategies. Her publications and presentations (2013-2019) highlight trends in lifespan communication, employment barriers for adults with Down syndrome, and technology accessibility. Key subfields include phonology, narrative language, and inclusive computing. Scientific Awards : Excellence in Education Award (Global Down Syndrome Foundation, 2012) Dean’s Symposium Research Award (Loyola College) Bene Merenti Award (Loyola College) Multiple entries in Who's Who directories (1985-present) Kumin secured grants from the National Science Foundation (EAGER grant for workplace computer interfaces), AccessComputing (job awareness initiatives), and private foundations for speech-language resource development. She has presented nationally on topics like articulation, vocational communication, and family-centered care.

Lauren Franzمشاهده پروفایل

دانشیار

• Autism Spectrum Disorder
• Child and Adolescent Psychiatry
• Digital Phenotyping
• +۳ مورد دیگر

Dr. Lauren Franz serves as Associate Professor of Psychiatry and Behavioral Sciences and Associate Research Professor of Global Health at Duke University, with Faculty Network membership at the Duke Institute for Brain Sciences. Her clinical expertise centers on child and adolescent psychiatry, specializing in autism spectrum disorder interventions and digital health applications across diverse global contexts. Her academic credentials include: Honorary Associate Professor, Division of Child & Adolescent Psychiatry, University of Cape Town (2023-2028) Residency and Fellowship in Psychiatry and Child and Adolescent Psychiatry, Duke University School of Medicine (2007-2014) M.B.B.Ch., Stellenbosch University, South Africa (2002) Dr. Franz pioneers the integration of artificial intelligence and digital phenotyping to revolutionize autism detection and intervention. Her research spans telehealth adaptation of caregiver coaching in low-resource settings, computational analysis of behavioral markers, and neurodiversity-affirming clinical frameworks. She actively bridges technology development with implementation science to address global disparities in neurodevelopmental care. Analysis of her recent publications reveals dominant trends in machine learning-driven autism screening, cross-cultural adaptation of interventions, and comorbidity mapping between autism and ADHD. Her work consistently leverages electronic health records, mobile applications, and computer vision to identify biomarkers while prioritizing real-world applicability across socioeconomic contexts. Dr. Franz directs significant research initiatives including: Duke-NCCU Interdisciplinary Postdoctoral Training Program (2024-2029) Electrophysiological biomarkers of social engagement in autism (2024-2028) Caregiver Coaching to Improve Outcomes of Young Autistic Children in Africa (2022-2027) ACACIA hybrid effectiveness-implementation trial for autism intervention in Africa She leads the Duke Institute for Brain Sciences' global mental health initiatives, maintaining active collaborations with the University of Cape Town to develop sustainable autism care models. Her international teams integrate psychiatrists, computer scientists, and implementation specialists to create culturally responsive digital tools, with recent work focusing on bias mitigation in AI language models and telehealth delivery in South African communities.

Steffan Wittrup McPhee Christensenمشاهده پروفایل

دانشیار

• Medicine
• Musculoskeletal Pain
• Pain
• +۱۵ مورد دیگر

Steffan Wittrup McPhee Christensen is an Associate Professor at the Department of Medicine and Health Technology , Aalborg University , Denmark. His work bridges musculoskeletal physiotherapy and clinical biomedicine , focusing on neck and back pain , pain sensitivity , and exercise-based interventions . Academic Roles : Board member of the International Association for the Study of Pain (IASP) and Danish Physiotherapists Research Foundation since 2023. Teaching Philosophy : Emphasizes problem-based learning (PBL) and clinical simulation to develop clinical reasoning and rehabilitation planning skills. Research Interests include: Musculoskeletal disorders (neck/shoulder/back pain) Pain sensitivity and movement analysis Exercise interventions and clinical guidelines Global Burden of Disease applications to chronic pain management His scientific contributions span 114 publications (2011–2025) and 19 media appearances , including critiques of postural correction clothing as pseudoscience. Education : PhD in Clinical Science and Biomedicine (2018) Assistant Professor of Education (2016–2017) External Associate Professor at University College of Northern Denmark (2018–present)

Jure Demsarمشاهده پروفایل

استادیار

• Neuroinformatics
• Group Behavior
• Modeling Natural Phenomena
• +۵ مورد دیگر

Jure Demsar is an Assistant Professor and researcher at the Faculty of Computer Science and Informatics, University of Ljubljana . He collaborates internationally with institutions including Washington University (Human Connectome Project), Yale University (Anticevic Lab), Harvard University (Applied Neuroimaging Statistics Lab), and multiple Slovenian faculties. Research visits: Yale University (2 months), Newcastle University (3 months), Groningen University (3 months), University of Houston (1 month) His research bridges neuroinformatics and computational modeling , focusing on: Brain dynamics and behavior EEG and fMRI data analysis Group behavior simulations Computer game development Recent publications highlight interdisciplinary applications of data science in neuroscience and psychology, with tools like bayes4psy for Bayesian analysis. Awards include recognition for pedagogical excellence and research achievements (2017/2018).

Nicholas C. Jacobsonمشاهده پروفایل

دانشیار

• Digital Phenotyping
• Digital Mental Health
• Anxiety and Depression
• +۶ مورد دیگر

Nicholas C. Jacobson is an Associate Professor of Biomedical Data Science and Psychiatry at the Geisel School of Medicine, Dartmouth College. He serves as the Director of the Treatment Development & Evaluation Core within the Center for Technology and Behavioral Health (CTBH) and leads the AI and Mental Health: Innovation in Technology Guided Healthcare (AIM HIGH) Laboratory. His work bridges computational methods with clinical applications to transform mental healthcare through technology. Dr. Jacobson earned his PhD in Psychology from Pennsylvania State University in 2019, following an MSc in Psychology from the same institution in 2015. He completed his Postdoctoral and Clinical Fellowships in Psychology at Massachusetts General Hospital/Harvard Medical School in 2019. Dr. Jacobson's research focuses on harnessing artificial intelligence and passive sensor data from smartphones and wearable devices to develop scalable, personalized interventions for anxiety and depression. His work has three main pillars: (1) enhancing precision assessment of anxiety and depression using intensive longitudinal data, (2) conducting multimethod assessment utilizing passive sensor data from smartphones and wearable devices, and (3) providing scalable, personalized technology-based treatments utilizing smartphones. As a computational psychologist, he created the Differential Time-Varying Effect Model (DTVEM), an innovative statistical package in R that allows researchers to discover and model optimal lag times in intensive longitudinal data. His methodological expertise encompasses machine learning, structural equation modeling, multilevel modeling, time-series techniques, and dynamical systems modeling. His recent publications demonstrate a strong focus on digital phenotyping, machine learning applications in mental health, and personalized interventions. The research spans multiple domains including depression symptom networks, anxiety disorder assessment, eating disorder prevention, and the use of passive sensing to understand mental health conditions. A notable trend is the application of advanced computational methods to create more precise and personalized mental health assessments and interventions, with increasing emphasis on real-world implementation and accessibility. Principal Investigator of an R01 Award from the National Institute of Mental Health studying personalized deep learning models to predict rapid changes in major depressive disorder symptoms Secured over $6 million in funding as Principal Investigator and over $20 million as a co-Investigator Featured on NBC Nightly News and CBS Morning News for pioneering work in AI-powered mental health applications Dr. Jacobson has developed several impactful digital tools including Therabot, a generative AI therapy chatbot that demonstrated substantial reductions in symptoms of major depressive disorder, generalized anxiety disorder, and feeding and eating disorders in its first randomized controlled trial. He also developed Mood Triggers, a smartphone sensing platform that integrates ecological momentary assessment and intervention to help users identify and manage anxiety and depression triggers. His suite of smartphone applications has reached over 50,000 users in more than 100 countries. Dr. Jacobson is actively recruiting team members and encourages interested individuals to contact him through his personal website. He directs the AIM HIGH Laboratory, which focuses on advancing AI applications in mental healthcare. The lab develops innovative computational approaches to enhance mental health assessment and treatment through technology. Current projects include using passive sensor data to predict symptom changes, developing personalized just-in-time adaptive interventions, and creating quantitative tools that enable precision mental healthcare.

Elaine Katrina Kearneyمشاهده پروفایل

پژوهشگر ارشد

• Motor speech disorders
• Neurological disorders
• Speech therapy development
• +۶ مورد دیگر

Dr. Elaine Katrina Kearney is a Conjoint Research Fellow in Speech Pathology at the School of Health and Rehabilitation Sciences, The University of Queensland, with a secondary affiliation at Princess Alexandra Hospital. She specializes in motor speech disorders and neurological conditions , focusing on brain mechanisms underlying speech changes and evidence-based speech therapies for populations like Parkinson's and Huntington's disease patients. Bachelor (Honours) of Speech Therapy, University College Cork Doctor of Philosophy of Speech Pathology, University of Toronto Her research integrates acoustic analysis , neuroimaging , and computational modeling to study speech processing in neurological disorders. Recent work explores open science practices and sensorimotor integration in speech production. Dr. Kearney's 15 most recent publications (2019–2025) span topics including lexical processing mechanisms , vocal motor control , and clinical implementation science , with methodologies ranging from fMRI studies to meta-analyses and qualitative research . At Princess Alexandra Hospital, she mentors speech pathologists in knowledge translation and implementation science to enhance clinical practice. Current funding includes a Princess Alexandra Hospital Research Salary Support (2024–2027) and past grants for Huntington's disease speech treatment programs (2024–2025).

• Artificial Intelligence
• Data Mining
• Data Science
• +۸ مورد دیگر

Richard Andrew Taylor, MD, MHS, is an Adjunct Associate Professor at Yale University's Biomedical Informatics and Data Science department within the Yale School of Public Health. He also holds an Assistant Professor appointment in Emergency Medicine and serves as Director of Artificial Intelligence and Data Science at Yale. Dr. Taylor leads the Yale Interdisciplinary AI & Medicine Lab (Y-IAML), a cross-disciplinary initiative integrating AI, cognitive science, and ethics into healthcare delivery. Education: MD from Emory University (2007), MHS in Informatics from Yale (2015), fellowships in clinical informatics (Yale, 2015) and ultrasound (Yale, 2011). Dr. Taylor's research spans AI applications in emergency medicine, focusing on predictive analytics, data mining, and decision theory. His work addresses cognitive errors in high-pressure clinical environments through machine learning integration, with specific emphasis on electronic health records (EHR) analysis, point-of-care ultrasound outcomes, and health equity in opioid treatment. Recent publications highlight trends in AI-driven triage optimization , EHR-based dementia detection , and language model applications for medical calculations. His lab develops tools for automated clinical scoring , phenotype discovery , and equitable treatment pathways . Scientific Awards: University of St Andrews Global Fellow (2023) Dr. Taylor's grants include NIH funding (NIDA, NIA, NLM), AHRQ, and industry partnerships. He collaborates with experts in biostatistics, ethics, and clinical implementation science. Y-IAML actively engages in multidisciplinary research , combining design, behavioral economics, and computational methods to create implementable AI solutions for emergency care.

Lena Maier-Heinمشاهده پروفایل

استاد

• Machine Learning
• Biomedical Image Analysis
• Surgical Data Science
• +۴ مورد دیگر

Lena Maier-Hein serves as one of Helmholtz Imaging's center coordinators at the German Cancer Research Center (DKFZ), head of the Intelligent Medical Systems division, managing director of the Data Science and Digital Oncology cross-topic program, and is a full professor at Heidelberg University. She also holds the position of managing director at the National Center for Tumor Diseases (NCT) Heidelberg. Her research focuses on machine learning-based biomedical image analysis with specific emphasis on surgical data science, computational biophotonics, and validation of machine learning algorithms. As a fellow of both the Medical Image Computing and Computer Assisted Intervention (MICCAI) society and the European Laboratory for Learning and Intelligent Systems (ELLIS), she leads significant initiatives including presidency of the MICCAI special interest group on challenges and chairing the international surgical data science initiative. Her publication portfolio demonstrates strong focus on algorithm validation, surgical data science, and medical imaging AI, with recent work addressing critical issues in reproducibility, bias in medical AI, and practical clinical implementation. The articles reveal consistent themes around establishing rigorous validation frameworks, developing practical surgical AI tools, and addressing real-world deployment challenges in medical imaging. Her notable scientific recognition includes: 2013 Heinz Maier Leibnitz Award of the German Research Foundation (DFG) 2017/18 Berlin-Brandenburg Academy Prize European Research Council (ERC) starting grant (2015-2020) ERC consolidator grant (2021-2026) She serves on the editorial boards of prestigious journals including Nature Scientific Data, IEEE Transactions on Pattern Analysis and Machine Intelligence, and Medical Image Analysis, demonstrating her leadership in establishing standards for medical imaging research. Her work bridges fundamental AI research with practical clinical applications, particularly in surgical settings where real-time decision support is critical.

Matthew Joseph Kusnerمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Pattern Analysis
• +۵ مورد دیگر

Matt J. Kusner is an Associate Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. He holds additional affiliations as a Senior Academic Member at Mila - Quebec Artificial Intelligence Institute and as a Member at the Institute for Data Valorization (IVADO). Previously, he served as an Associate Professor at University College London and the University of Oxford. Professor Kusner's research spans machine learning, with particular focus on causal inference, fairness in algorithms, representation learning, and domain adaptation. His work bridges theoretical foundations with practical applications in natural language processing, algorithmic fairness, and scientific computing domains including plasma physics and renewable energy systems. His research interests include Pattern Analysis and Artificial Intelligence, Algorithms, and Learning and Inference Theories. His publication record shows consistent output across top machine learning venues including NeurIPS, ICML, and ICLR, with recent work trending toward causal machine learning methods and applications in scientific domains. Professor Kusner has 42 publications spanning from 2014 to 2025, demonstrating sustained research productivity. Turner Dissertation Award for best doctoral dissertation in Computer Science & Engineering Professor Kusner received his PhD in Computer Science from Washington University in St. Louis in 2016 under Kilian Weinberger. His work has been featured in major media outlets including The Guardian, Forbes, and the Harvard Business Review, and he has presented at prestigious institutions including the Federal Reserve Banks, Cambridge Centre for Mathematical Sciences, and the Royal Society.

• Data Mining
• Machine Learning
• Computer Vision
• +۵ مورد دیگر

Dr. Bin Liang is a Senior Lecturer at the University of Technology Sydney (UTS), working within the Faculty of Engineering and Information Technology and the Data Science Institute. He joined UTS in December 2018 as a Lecturer and was promoted to Senior Lecturer in July 2022, following his postdoctoral fellowship at Data61 (CSIRO). PhD in Computer Vision, Pattern Recognition, and Machine Learning from Charles Sturt University, Australia (2012-2015) Masters in Computer Science from Taiyuan University of Technology, China (2009-2012) BEng in Computer Science from Taiyuan University of Technology, China (2005-2009) Dr. Liang's research spans data mining, machine learning, computer vision, pattern recognition, and survival analysis, with a strong focus on practical applications in critical infrastructure. His work develops sophisticated machine learning frameworks for anomaly detection, failure prediction, and optimization in water infrastructure, flood mapping, and real estate appraisal. He has pioneered approaches that integrate graph neural networks with temporal analysis for pipe failure prediction and has made significant contributions to water quality optimization through data-driven methods. His research consistently bridges theoretical advancements with practical implementation in industry settings. His recent publications demonstrate a clear trajectory toward increasingly sophisticated models that capture complex temporal and spatial relationships in infrastructure data. There's a notable emphasis on multimodal learning approaches and domain generalization techniques that enhance model robustness across diverse application scenarios. His work on anomaly detection frameworks like MLAD shows innovation in grouping sensors by temporal characteristics for more accurate monitoring. 2022 – AWA R&D Excellence Award (NSW) 2022 – UTS Medal for Research Impact 2022 – Finalist for 2021-22 AWA Young Water Professional of the Year Award (NSW) 2020 – Finalist for the Best Paper Award of the ICARCV 2020 2019 – Industrial & Primary Industries Merit at the Victorian iAwards 2018 – Australian Museum Eureka Prize for Excellence in Data Science Dr. Liang actively supervises Masters Research and PhD students, focusing on data science applications for infrastructure management. His research has been supported through industry partnerships with water utilities and other infrastructure organizations, translating academic research into practical solutions that reduce water loss, improve infrastructure reliability, and enhance environmental sustainability. His work on pipe failure prediction and leak detection has directly contributed to operational improvements in water distribution networks. As a core member of the Data Science Institute at UTS, Dr. Liang collaborates with interdisciplinary teams working on data-driven solutions for urban infrastructure challenges. His research group focuses on developing scalable machine learning models that can be deployed in real-world settings, often working directly with industry partners to validate and implement their approaches. This industry-academia collaboration ensures that his research remains grounded in practical challenges while pushing the boundaries of data science methodology.

Iosif I. Vaismanمشاهده پروفایل

استاد

• Artificial Intelligence
• Bio-health/Health
• Biology
• +۷ مورد دیگر

Dr. Iosif I. Vaisman serves as Professor and Director of the School of Systems Biology at George Mason University, where he leads academic initiatives at the intersection of computational science and biological research. His leadership encompasses curriculum development, research oversight, and strategic direction for the school's interdisciplinary programs. His educational background includes: PhD from the Russian Academy of Sciences Dr. Vaisman's research spans Artificial Intelligence, Medical Proteomics, and Personalized Medicine with core expertise in machine learning applications for protein structure analysis. His work focuses on decoding sequence-structure-function relationships in biomolecules, developing computational mutagenesis frameworks, and creating predictive models for drug resistance and disease mechanisms. Current projects integrate deep learning with structural biology to address challenges in antimicrobial peptide design and HIV-1 pathogenesis. Analysis of his 2022-2025 publications reveals a pronounced shift toward graph neural networks and transformer models in bioinformatics, with dominant themes in protein classification (32%), antimicrobial peptide prediction (24%), and disease mechanism modeling (20%). His work consistently bridges fundamental computational geometry with translational biomedical applications, particularly in vaccine design and personalized treatment strategies.

Archie Singh Khumanمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Computational Intelligence
• Machine Learning
• +۵ مورد دیگر

Dr. Archie Singh Khuman serves as Associate Head of Education and Senior Lecturer at De Montfort University's School of Computer Science and Informatics. An award-winning academic and proud DMU alumnus (BSc 2009, MSc 2011, PhD 2017), he specializes in Artificial Intelligence, Computational Intelligence, and Uncertainty Modelling with extensive leadership in curriculum development and Universal Design for Learning. His educational background demonstrates deep institutional commitment: PhD in Computer Science (2017, De Montfort University) MSc in Intelligent Systems (2011, De Montfort University) BSc (Hons) in Computer Science (2009, De Montfort University) Dr. Khuman's research pioneers hybrid uncertainty frameworks, notably the R-fuzzy grey analysis framework (RfGAf), bridging Grey Theory, Fuzzy Logic, and Rough Sets. His work focuses on quantifying perception-based uncertainty for applications in healthcare diagnostics, robotic swarm intelligence, and risk assessment systems, emphasizing real-world implementation through industry partnerships. His publication trajectory reveals increasing interdisciplinary convergence, particularly in healthcare AI applications since 2021. Recent outputs integrate Grey-Fuzzy methodologies for medical diagnostics (diabetes, heart disease), sentiment analysis of pandemic communications, and adaptive systems in automotive/industrial contexts, demonstrating strong translation from theoretical foundations to societal impact. Major recognitions include: Five consecutive Students' Choice Teaching Awards (2018-2024) De Montfort Teacher Fellowship (2022) Senior Fellow of the Higher Education Academy (2022) Leverhulme Trust-funded international Grey Systems network (£124,997) Best Paper Award at Grey Systems and Uncertainty Analysis (2016) As academic lead for the Data Analytics Project, he mentored students in developing machine learning solutions that generated £2.5m savings for retail partners. His grant portfolio includes Royal Society funding for IoT-based scheduling (£12,000) and leadership of the Race Equality Network. Current initiatives focus on transnational education partnerships and SI-PASS peer learning programs. Dr. Khuman co-leads DMU's Institute of Artificial Intelligence research group and serves as School Governor for Inglehurst Junior School, maintaining active collaborations with institutions in China, Canada, Romania, and Spain through the International Network on Grey Systems.

Isabelle Préfontaineمشاهده پروفایل

استادیار

• Autism Research
• Intellectual Disability
• Behavioral Intervention
• +۲ مورد دیگر

Dr. Isabelle Préfontaine is an Assistant Professor in the Department of Foundations and Practices in Education at Université Laval's Faculty of Education. Her academic journey includes a Postdoctoral Fellowship in Sociology at the University of Quebec at Montreal (2023) focused on social inclusion of autistic individuals, a PhD in Psychoeducation from the University of Montreal (2022) examining differential effectiveness of autism interventions, and a Master's in Psychoeducation (2017) validating technology-assisted behavioral tools. Research Focus Her research program centers on neurodevelopmental disorders with emphasis on: Early behavioral intervention efficacy School transition support systems Social inclusion mechanisms Technology-assisted therapies Longitudinal outcomes analysis She leads a significant FRQSC-funded project (2024-2027) investigating school exclusion pathways for neurodiverse students. Publication Trends Her 15 most recent publications demonstrate consistent focus on autism intervention research, featuring longitudinal methodologies, machine learning applications for treatment personalization, mobile health solutions for behavior management, and socio-educational inclusion frameworks. Recent works show increased emphasis on community implementation and predictive modeling. Teaching & Supervision She teaches undergraduate psychoeducation courses including: PSE-1012: Psychoeducational observation PSE-2101: Intervention in Intellectual Disability PSE-2103: Intervention with ASD PSE-3001: Psychoeducational Intervention Planning