Find Academic Supervisors

• Turkology
• Turkish Language Education
• Teaching Turkish as a Foreign Language
• +4 more

Prof. Dr. Hayrullah Kahya is a full-time faculty member at the Faculty of Education, Yildiz Technical University , where he has served since 2011. Appointed as an Assistant Professor in 2011, he was promoted to Associate Professor in 2018 and Professor in 2023. He conducted research at Indiana University Bloomington (2014–2015) under a TÜBİTAK scholarship and has coordinated the Erasmus Program for his department from 2015–2024. He is the founder and editor-in-chief of the IBAD Journal of Social Sciences (since 2016). Education : PhD in Turkish Language Education (Marmara University, 2010) Academic Roles : Assistant Professor (2011), Associate Professor (2018), Professor (2023) International Collaboration : Visiting Scholar at Indiana University (2014–2015) His research focuses on Turkology , Turkish Language Education , and Teaching Turkish as a Foreign Language . His work includes discourse analysis of Ottoman Turkish children’s literature, migration studies through Turkmen genealogy texts like Shajara-i Tarākima , and adaptation of Turkish literature for special education (e.g., Braille versions for visually impaired learners). He has explored syntax flexibility in Turkish, historical media (e.g., Angeliaforos Çocuklar İçün ), and cultural integration through language teaching methods. His publications include two books and numerous articles in SSCI (Q1) , AHCI (Q1) , and Scopus databases. Key projects involve the Uyum Çocuk (Harmonization Child) platform , addressing social sustainability through migrant integration, and critical editions of historical Turkic texts like Mûnisû’l-Uşşâk and Firdavsu’l-İkbal . His editorial work on the IBAD Journal of Social Sciences highlights his commitment to academic dissemination.

• Computer Science
• Artificial Intelligence
• Computational Linguistics
• +7 more

Dr. Katerina Zdravkova is a Professor at the Institute of Informatics within the Faculty of Natural Sciences and Mathematics at Ss. Cyril and Methodius University in Skopje. With a Ph.D. in Computer Science and extensive international experience, she has held academic roles since 1989 and authored 4 international journal papers, 2 domestic papers, and 34 conference proceedings. Her work spans e-learning , machine translation , and software engineering , with a focus on Macedonian language technologies. Education : B.Sc. in Mathematics and Informatics (1983), M.Sc. in Computer Engineering (1988), Ph.D. in Artificial Intelligence (1993). Teaching : Delivered 15 undergraduate and graduate courses in informatics, computational linguistics, and software engineering. Research Interests include neural networks for language processing, computational linguistics for Macedonian morphology, and educational technology for cross-border course development. She pioneered machine translation systems and language resource annotation , integrating Web 2.0 into ethics education. Scientific Awards : Special IEEE Award for Exceptional Contribution Projects coordinated include UNESCO IITE initiatives, DAAD collaborations, Tempus curricula, and NATO research on trust management systems. She serves as a reviewer for international ministries and journals and as a program committee member in four conferences.

• Data Science
• Artificial Intelligence
• Legal Technology
• +5 more

Niall McCarroll is a Research Fellow in Data Science at Ulster University's School of Computing, Engineering and Intelligent Systems, based at the Magee Campus in Derry~Londonderry. His research focuses on applying data science and artificial intelligence techniques to legal document analysis and classification, with specific expertise in data augmentation and document classification systems. McCarroll's research interests include: Legal document classification using machine learning Data augmentation techniques for legal text processing Application of shallow and deep learning to non-disclosure agreements Social networking analysis in educational contexts Artificial intelligence for judicial decision-making support His recent publications demonstrate a strong focus on the intersection of artificial intelligence and legal technology, particularly in developing methods for classifying legal documents using both rule-based and generative approaches. McCarroll's work addresses practical challenges in legal text processing, including the classification of clauses in non-disclosure agreements and the application of data augmentation techniques to improve model performance with limited training data. McCarroll is actively involved in significant research projects: "The use of Agentic AI in judicial decision-making" (2025-2026), funded by the Engineering and Physical Sciences Research Council "LFR: Legal Futures Research" (2016-2026) His research has garnered attention, with his 2013 paper on social networking in education accumulating 139 readers on Mendeley and being referenced in 1 Wikipedia page.

• Machine Learning
• Artificial Intelligence
• Cybersecurity
• +7 more

Prof. Dr. Virginijus Marcinkevičius is a Professor at Vilnius University , serving as the head of the Smart Technologies Research Group and the Artificial Intelligence Laboratory within the Institute of Data Science and Digital Technologies . He is also a Senior Researcher , Project Lead Researcher , and Group Leader . Based in Vilnius, Lithuania, he has been instrumental in advancing research in machine learning , artificial intelligence , cybersecurity , and natural language processing . Research Interests: Machine Learning & AI Cybersecurity & Threat Detection Natural Language Processing Hyperspectral Imaging & Remote Sensing Autonomous Systems & Robotics Big Data & Cloud Computing His work spans both theoretical and applied aspects, including IoT security , visual analytics , and intelligent decision support systems . Recent projects include the development of propaganda detection systems , hyperspectral unmixing algorithms , and autonomous driving agents . Doctoral Supervision: He has supervised 19+ PhD students and 5+ consultants , covering topics from machine learning in cybersecurity to neural machine translation and autonomous UAV navigation . Projects & Grants: He has led or contributed to 15+ national and EU-funded projects , including: CognitiveSTATS – COVID-19 data literacy platform Propaganda and Disinformation Research – ML-based detection DAMIS – Data mining system for national research Raštija 2 – Lithuanian language resource integration Publications: He has authored or co-authored 60+ peer-reviewed publications in journals like IEEE Access , Informatica , Frontiers in Psychology , and Machine Vision and Applications . Professional Memberships: He is a member of the Lithuanian Computer Society , Lithuanian Mathematical Society , and Lithuanian Operations Research Society .

Alexander EckerView profile

Professor

• Machine Learning
• Computational Neuroscience
• Visual Perception
• +3 more

Prof. Dr. Alexander Ecker is Professor of Data Science at the Institute of Computer Science, University of Göttingen, and concurrently holds the prestigious Max Planck Fellow position at the Max Planck Institute for Dynamics and Self-Organization. Since 2020 he also serves on the Executive Board of the Campus Institute Data Science in Göttingen. He leads the Neural Data Science research group, comprising 14 PhD students and 2 postdoctoral researchers, focusing on the interface of machine learning and computational neuroscience. His educational background includes a Dr. rer. nat. in Neuroscience (2014) from the Graduate School of Neural and Behavioral Sciences/IMPRS, University of Tübingen, followed by post-doctoral and group-leader positions at the University of Tübingen and the Max Planck Institute for Biological Cybernetics. Research Interests Machine Learning & Deep Learning: developing novel algorithms for representation learning and generative modeling. Computational Neuroscience: large-scale data-driven modeling of visual cortical circuits. Visual Perception: bridging biological vision and computer vision via biologically inspired architectures. His work has produced a steady stream of influential publications (2019-2025) in leading journals such as Nature Communications , Nature , Nature Methods , PLOS Computational Biology , ICLR , NeurIPS , and CVPR . The publications trend toward integrating high-resolution neural recordings with state-of-the-art machine-learning models to uncover principles of sensory processing, neuron-type classification, and behavior. Scientific Awards & Honors Max Planck Fellow, Max Planck Institute for Dynamics and Self-Organization (ongoing) Executive Board Member, Campus Institute Data Science, Göttingen (since 2020) Teaching, Advising & Grants Regularly teaches advanced courses: “Deep Learning for Image Synthesis”, “Current Topics in Deep Learning”, and “Graph Machine Learning”. Supervises 14 current PhD students and 2 postdocs within the Neural Data Science Group. Offers numerous Bachelor’s and Master’s thesis projects, with topics ranging from neuronal morphology clustering to primate vocalization analysis. Leads or co-leads large collaborative consortia with labs in Göttingen, Tübingen, Baylor College of Medicine, and other institutions across the US and Germany. Labs & Teams The Neural Data Science Group operates at the Institute of Computer Science, University of Göttingen, and is tightly integrated with the Max Planck Institute for Dynamics and Self-Organization. The group maintains active collaborations with over a dozen partner laboratories, including groups led by Fabian Sinz, Andreas Tolias, Thomas Euler, Tim Gollisch, and Viola Priesemann, fostering an interdisciplinary environment that spans computer science, physics, biology, and psychology.

Kwon Yun-jeongView profile

Adjunct Associate Professor

• Child Psychology
• Play Therapy
• Multicultural Family Studies
• +3 more

Kwon Yun-jeong serves as an Adjunct Associate Professor in the Department of Child and Family Studies at Seoul National University's College of Human Ecology. She concurrently directs the Korean Association of Applied Behavior Analysts (2022–present) and Mammoth Children and Youth Counseling Center (2019–present), while providing counseling services at the Central Childcare Comprehensive Support Center (2017–present). Her specialty focuses on clinical child psychology with emphasis on therapeutic interventions for diverse family structures. Her academic foundation includes: Ph.D. in Child and Family Studies (Seoul National University, 2007–2016) Master's in Child and Family Studies (Seoul National University, 2003–2007) Bachelor's in Child and Family Studies (Seoul National University, 1998–2002) Research centers on play therapy applications for children with ADHD, trauma, and behavioral issues, alongside multicultural family dynamics including attachment patterns and school adaptation. Her work demonstrates clinical innovation through parent-child play therapy models and culturally responsive interventions for North Korean refugees. Key themes include grief processing in bereavement, metacognitive anxiety strategies, and maternal behavioral control impacts on child development. Publication analysis (2010–2018) reveals evolving expertise from foundational multicultural family studies toward specialized therapeutic techniques. Early work established frameworks for school adaptation programs and language environments, while later research (2015–2018) developed targeted play therapy protocols for obsessive behaviors, ADHD management, and trauma recovery—showcasing clinical refinement through case study methodology. Research leadership includes: National Institute of Korean Language textbook development for multicultural families (2010) Daejeon Western Office of Education school adaptation program (2009) Jeju Provincial Office of Education multicultural family initiatives (2008) Ministry of Health and Welfare Comprehensive Survey on Children and Adolescents (2009) Her Mammoth Children and Youth Counseling Center functions as a clinical hub for evidence-based interventions, while directorship of the Korean Association of Applied Behavior Analysts positions her at the forefront of professional behavior analysis standards in South Korea.

Jun LiuView profile

Professor

• Artificial Intelligence
• Trustworthy AI
• Explainable AI
• +4 more

Professor Jun Liu is a Professor of Artificial Intelligence and Director of the Artificial Intelligence Research Centre (AIRC) at the School of Computing, Ulster University. With over 270 publications and more than £18 million in research funding, he is a leading figure in artificial intelligence, particularly in trust and explainable AI systems and logic-based reasoning methods. Dr. Liu received his BSc and MSc degrees in Applied Mathematics, and PhD degree in Information Engineering from Southwest Jiaotong University, Chengdu, China, in 1993, 1996, and 1999, respectively. Prior to joining Ulster University, he held postdoctoral positions at The University of Manchester, UK (Feb. 2002 - Dec. 2004) and the Belgian Nuclear Research Centre (SCK*CEN) (Mar. 2000 - Feb. 2002). Professor Liu's research focuses on trust and explainable data-knowledge integrated AI decision models with applications in safety and risk analysis, policy decision making, security/disaster management, and healthcare; and logic and automated reasoning methods for intelligent systems, including resolution-based automated reasoning and lattice-valued logics for handling incomparability, inconsistency, and imprecision. His work spans theoretical foundations to practical applications in smart homes, healthcare, and industrial settings. His recent publications demonstrate a strong trend toward developing more trustworthy and explainable AI systems, with particular emphasis on belief rule-based approaches for handling uncertainty in decision-making. The research spans multiple domains including smart home activity recognition, medical imaging, food quality analysis, and environmental monitoring, showing the versatility and applicability of his methodologies. Ulster University best computer science paper award for 2016 IEEE Senior Member including IEEESMC and IEEECI Fellow of the UK Higher Education Academy Associate Editor of IEEE Transaction on Fuzzy Systems Current Chair of IEEE CIS Emergent Technologies Technical Committee As Director of the Artificial Intelligence Research Centre, Professor Liu has secured significant research funding as principal investigator and co-investigator. His current projects include "The use of Agentic AI in judicial decision-making" funded by EPSRC and "Adaptive Modeling Method for Deep Belief Rule Base" for smart home applications. He serves on editorial boards of multiple high-impact journals and organizes international conferences including the 23rd UK Workshop on Computational Intelligence. The Artificial Intelligence Research Centre under Professor Liu's leadership focuses on developing cutting-edge AI methodologies with practical applications. The center collaborates extensively with industry partners including BT through the BTIIC Phase 2 initiative and PwC through their Advanced Engineering and Research Centre, ensuring research has real-world impact across multiple sectors.

• Deep Learning
• Multimodal AI
• Explainable AI
• +6 more

Giorgos Stamou is a Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), and a Visiting Professor at the MIT Sloan School of Management and MIT Open Learning. He directs the Artificial Intelligence and Machine Learning Systems Laboratory (AILS Lab) and has been a senior researcher at the Institute of Communications and Computer Systems (2000-2008) and an academic visitor at Oxford University (2011-2012). His research spans Deep Learning , Explainable AI , and Knowledge Representation , with a focus on Large Language Models and Multimodal Learning . He has coordinated over 60 funded projects and published 150+ papers. Recent work highlights trends in LLM Evaluation , Gender Bias Mitigation , and Multimodal Music Analysis , reflecting his interdisciplinary approach to AI challenges. Stamou has served on steering committees for W3C working groups (Rule Interchange Format, Web Ontology Language) and contributed to cultural heritage metadata enrichment through the CrowdHeritage projects. He founded NTUA's MSc program in Data Science and Machine Learning (2018-2022) and has organized conference tracks on riddle-solving frameworks and hallucination detection.

Hanzhang XuView profile

Associate Professor

• Global Health
• Social Determinants of Health
• Cardiovascular Disease
• +7 more

Hanzhang Xu is an Associate Professor at Duke University School of Nursing, with joint appointments at Duke University School of Medicine's Department of Family Medicine and Community Health and Duke-NUS Medical School's Health Services & Systems Research Signature Program. He is also a Senior Fellow at Duke's Center for the Study of Aging and Human Development and a Faculty Affiliate at the Duke Global Health Institute. Duke University School of Nursing (2024-Present) Duke University School of Medicine, Department of Family Medicine and Community Health (2024-Present) Duke-NUS Medical School, Health Services & Systems Research (2020-Present) Education: Ph.D. in Nursing, Duke University (2018) Xu's research focuses on global health, social determinants of health, cardiovascular disease, Alzheimer's disease and related dementias (ADRD), health services research, and population aging. His work examines racial/ethnic disparities in healthcare outcomes, social frailty indices, and mobile-based interventions for cognitive health. His recent publications highlight trends in racial disparities in cardiovascular care, mobile cognitive training for Chinese Americans, and social determinants' impact on mortality and readmission rates. Xu's work spans the U.S., China, Singapore, and the Philippines, emphasizing cross-cultural health research. Scientific Awards: Fellow, American Academy of Nursing (FAAN) Grants: National Institutes of Health (2025-2030): Characterizing Family Structure, Care Utilization, and Well-Being among ADRD Patients in Asia University of Texas - Southwestern (2024-2029): Poststroke Cognitive Impairment and Dementia Research National Institute on Aging (2022-2027): A Life Course Approach to Heart Failure Hospitalization Risks Xu collaborates internationally with institutions like Duke Kunshan University (China) and Duke-NUS Medical School (Singapore), contributing to cross-national studies on aging, migration impacts, and healthcare systems.

Owen RambowView profile

Professor

• Natural Language Processing
• Computational Linguistics
• Morphology
• +2 more

Owen Rambow is a Professor at Stony Brook University's AI Innovation Institute, specializing in natural language processing and computational linguistics with a focus on formal linguistic analysis. Education and Career: Ph.D. in Computer and Information Sciences, University of Pennsylvania 15-year tenure as Research Scientist at Columbia University Industry experience at AT&T Labs—Research and Elemental Cognition LLC Research Focus: Rambow's work centers on morphology, syntax, and semantics within Tree Adjoining Grammar (TAG) frameworks, bridging phrase structure and dependency representations. His research spans natural language generation/understanding, discourse analysis of belief/sentiment signaling in email/Twitter communications, and sociolinguistic studies of power/gender dynamics in written conversations across Arabic, English, German, and Hindi. Publication Trends: Recent work (2024-2025) heavily explores large language model capabilities in multi-dimensional writing assessment, emotion recognition, theory of mind validation, and morphophonological processing. Key themes include zero-shot learning limitations, cross-dialectal analysis, and pragmatic marker recognition in specialized domains like roadrunner cartoon dialogues.

• Data Mining
• Data Science
• Data Analytics
• +4 more

Professor Geoff Webb is a world-leading data scientist at Monash University , serving as Director of the Monash University Centre for Data Science within the Faculty of Information Technology . His research focuses on leveraging data science to enable evidence-based decision making and derive actionable insights through artificial intelligence, machine learning, and big data analytics. Core expertise in data mining , bioinformatics , and computational biology Developed Magnum Opus software and contributed to the Weka machine learning workbench Recipient of the Eureka Prize for Excellence in Data Science and leadership roles in major data mining conferences Research Interests Professor Webb's work spans artificial intelligence , machine learning , and data analytics , with a focus on black-box user modelling , interactive data analytics , and statistically-sound pattern discovery . His recent publications highlight advancements in Bayesian networks , time series analysis , and genomic data interpretation , demonstrating his interdisciplinary impact. Scientific Contributions AI in healthcare : EHR-ML framework for clinical records analysis Biological applications : KcatNet for enzyme prediction, PFresGO for protein function Data mining innovations : OPUS search algorithm, proximity forest techniques As a technical adviser to data science company Froomle , he bridges academic research with real-world applications. His work has been recognized through numerous research awards and leadership as Editor-in-Chief of Data Mining and Knowledge Discovery for a decade.

• Machine Learning
• Big Data Analytics
• Computational Intelligence
• +4 more

Vahid Behbood is a Lecturer at the School of Computer Science within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). He belongs to the Decision Systems and e-Service Intelligence Research Laboratory in the Centre for Quantum Computation and Intelligent Systems. Ph.D. in Software Engineering from UTS (2014) His research spans machine learning , big data analytics , computational intelligence , and transfer learning , with a focus on addressing data shortage challenges through fuzzy systems and domain adaptation. His work includes: Advancing fuzzy regression domain adaptation for cross-domain prediction Developing granular computing techniques for financial failure prediction Exploring SQL query error patterns in computer science education Designing IoT architectural frameworks via systematic reviews Publications demonstrate expertise in neural networks , fuzzy logic , Bayesian methods , and financial data analytics . He applies these techniques to banking ecosystems, real estate valuation, and educational data mining.

Feng LiuView profile

Assistant Professor

• Machine Learning
• Hypothesis Testing
• Trustworthy Machine Learning
• +3 more

Feng Liu is an Assistant Professor at the Decision Systems and e-Service Intelligence (DeSI) Lab within the Australian Artificial Intelligence Institute (AAII) at the University of Technology Sydney (UTS). He also serves as a Visiting Scientist at RIKEN-AIP, Japan. His academic journey includes a PhD in Computer Science from UTS (2020), an MSc in Probability and Statistics from Lanzhou University (2015), and a BSc in Mathematics from the same institution (2013). His educational background includes: Ph.D. (2020), Computer Science, University of Technology Sydney, Australia M.Sc. (2015), Probability and Statistics, Lanzhou University, China B.Sc. (2013), Mathematics, Lanzhou University, China Feng Liu's research centers on developing trustworthy intelligent systems through hypothesis testing and reliable knowledge transfer across domains. His work spans two-sample testing for distribution comparison, transfer learning for knowledge adaptation across domains, and defending against adversarial attacks to improve model robustness. His approach combines theoretical foundations with practical applications, particularly in domain adaptation with interval-valued data and secure multi-source learning. His recent publications demonstrate a strong focus on trustworthy machine learning, with significant contributions to interval-valued data processing, novel class discovery under unreliable sampling conditions, and privacy-preserving domain adaptation. His work bridges theoretical machine learning with practical applications in computer vision, bioinformatics, and recommender systems, showing a consistent pattern of addressing fundamental challenges in trustworthy AI. Among his notable recognitions are: Outstanding Reviewer Award of ICLR (2021) AAII Best Student Paper Award (2020) Best Student Paper Award from IEEE International Conference on Fuzzy Systems (2019) UTS-FEIT HDR Research Excellence Award (2019) Publons Peer Review Awards - Top 1% reviewers in Computer Science (2019, 2018) Dr. Liu has actively contributed to the academic community through supervision and service. He has helped supervise four students who collectively produced eight academic papers, three of which were published in CORE Tier A* venues. His service includes program committee roles for major conferences including NeurIPS, ICML, ICLR, and AAAI, as well as reviewing for prestigious journals like IEEE-TPAMI and IEEE-TNNLS. His research has been supported by various grants, including the Australian Laureate postdoctoral fellowship. As part of the AAII at UTS, Dr. Liu contributes to a vibrant research environment focused on advancing artificial intelligence through interdisciplinary collaboration. His work in the Decision Systems and e-Service Intelligence Lab addresses real-world challenges in trustworthy machine learning, with applications spanning healthcare, robotics, and secure information systems.

• Text Mining
• Computer Science
• Radiobiology
• +5 more

Privatdozent Dr. Dr. Ingo Feinerer serves as the Head of Faculty of Engineering at the University of Applied Sciences Wiener Neustadt in Austria. With two doctoral degrees and the Habilitation qualification (indicated by Privatdozent), he holds a senior academic position equivalent to Associate Professor in many systems. His research interests span multiple disciplines: Text Mining and Computer Science (class diagrams, schema mapping, domain constraints) Radiobiology and Ion Therapy for advanced cancer treatment Social Network Analysis and digital media History of Psychology and academic journal evolution Prof. Feinerer's work demonstrates exceptional interdisciplinary breadth, connecting technical computer science with medical physics applications and historical analysis. His current major research initiative is the PAIR project (2022-2026), which focuses on expanding radiobiological understanding of ion beams for cancer treatment. This project recognizes ion therapy using protons and carbon ions as the most advanced radiation-based cancer treatment due to its physical and biological advantages over conventional photon beams. His publication record spans from 2007 to 2025, with 36 publications including 18 journal articles and 15 conference papers. Recent publications show a shift toward medical physics applications while maintaining connections to his computer science expertise, as evidenced by the 2025 paper on an open-source framework for pre-clinical ion-beam research. As Head of Faculty, Prof. Feinerer oversees academic programs and research activities within the Faculty of Engineering, contributing to both educational leadership and active research in his diverse fields of expertise. His ORCID identifier (0000-0001-7656-8338) provides a persistent link to his scholarly work across these interdisciplinary domains.

Joshua S. LongView profile

Assistant Professor

• Prison Violence
• Prison Classification
• Restrictive Housing
• +8 more

Joshua S. Long is an Assistant Professor in the School of Criminology and Justice Studies at the University of Massachusetts Lowell, housed within the College of Fine Arts, Humanities and Social Sciences. Based in the Health and Social Sciences Building (Room 467), he teaches correctional policy and criminological theory while maintaining an active research agenda. Recognized with the 2025 Award for Excellence in Undergraduate Teaching, Dr. Long bridges academic rigor with practical correctional applications through collaborations with MIT's Educational Justice Initiative and the Global Community Corrections Initiative. Dr. Long's educational foundation includes comprehensive training from the University of Cincinnati: Ph.D. in Criminal Justice Graduate Certificate in Applied Behavioral Analysis M.S. in Criminal Justice B.S. in Criminal Justice His research program centers on violence prevention and risk management within correctional systems. Dr. Long investigates prison classification efficacy, restrictive housing impacts, and sex offender management through both quantitative analysis of large datasets and qualitative case studies. His work on vigilantism and prison homicides reveals complex social dynamics within carceral environments, while his evaluation of in-prison educational programs demonstrates tangible pathways for rehabilitation. Current projects examine historical prison homicide trends and biographical studies of lifers, contributing to evidence-based correctional reforms. Dr. Long's publication trajectory shows consistent high-impact contributions, including his 2025 book Prison Homicide: Killing and Dying in Prison Today and the co-authored Avenging Child Sex Abuse (2024). His research spans prison violence mechanisms, risk assessment validity, and educational interventions, with recent work increasingly incorporating technology-based solutions for rehabilitation. The meta-analysis on HOPE programs (2024) exemplifies his policy-relevant scholarship that directly informs correctional practice. Dr. Long's scholarly excellence is recognized through: 2025 Award for Excellence in Undergraduate Teaching (UMass Lowell) 2024 Wiley Top Cited Article recognition for correctional meta-analysis He maintains active research funding through collaborations with MIT's Educational Justice Initiative and the Global Community Corrections Initiative. Current projects include longitudinal studies of prison homicides, evaluations of educational programming in correctional facilities, and biographical analyses of long-term incarcerated populations. Dr. Long regularly presents findings at national conferences including the American Society of Criminology and has contributed to Nordic correctional research through the 30th Nordic Sociological Association. Through the Educational Justice Initiative at MIT, Dr. Long expands his impact beyond traditional academia into practical correctional reform. His work with the Global Community Corrections Initiative connects UMass Lowell to international best practices in community-based supervision, creating unique opportunities for student involvement in cross-cultural correctional research.