Random Difference Equations Professors and Supervisors

Ivan Kryvenمشاهده پروفایل

دانشیار

Utrecht UniversityNetherlands

Dr. Ivan Kryven is an Associate Professor in Mathematical Modeling at the Mathematical Institute, Faculty of Science, Utrecht University. His research spans graph theory, probability, numerical methods for dynamical systems, partial differential equations, stochastic analysis, and randomized algorithms, with applications primarily in chemistry and materials science. His expertise includes: Mathematical Modeling Stochastic Modeling Discrete Mathematics Graph Theory Differential Equations Statistical Physics Dr. Kryven's research focuses on the mathematical foundations of complex systems, with particular interest in network science, random graphs, and their applications to polymer science and materials engineering. His work often bridges discrete and continuous mathematical objects, revealing fundamental connections across different domains. Much of his research has practical applications in chemistry and materials science, with recent efforts expanding into complex societal systems and epidemiology. His scientific contributions demonstrate strong trends in percolation theory, random graph representations of physical processes, and computational methods for analyzing complex networks. His work connects theoretical mathematics with practical applications in materials science, epidemiology, and complex systems analysis. His notable scientific contributions include publications in Nature Communications, Physical Review E, and Scientific Reports, with research spanning from polymer networks to epidemic modeling and complex systems analysis. Dr. Kryven has supervised numerous students through their academic journey: PhD Students: Ruben Hendriks, Mike de Vries, Jochem Hoogedijk, Niek Mooij, Verena Schamboeck (now at Austrian Power Grid), Yuliia Orlova (now postdoc at University of Amsterdam) Master Students: Yeyang Hu, Josanne Verheule, Hannah Onverwagt, Niels Scholte, Fleur Slegers, Mike de Vries, Niek Mooij, Camillo Schenone, Andi Lin, Thomas Bakx, Nynke Brouwer, Yaïr Hein, Frits Verhagen, Femke Ieperen Postdoc: Rik Versendaal (now faculty at TU Delft) He is actively involved in the Mathematics of Complexity research theme at Utrecht University and participates in outreach activities through µ-Open, providing opportunities for student engagement in mathematical research.

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Pål Ullebergمشاهده پروفایل

استاد

University of OsloNorway

Pål Ulleberg is a Professor at the Department of Psychology, University of Oslo, specializing in quantitative research methods and safety psychology. His academic appointment focuses on methodological approaches within psychology, particularly in the areas of transport safety and organizational behavior. As a principal investigator at the Department of Psychology and a member of the National Research Ethics Committee for Social Sciences and Humanities (NESH), he plays a significant role in shaping research standards and ethics in Norwegian academia. Professor Ulleberg's research interests primarily center on quantitative research methods, with emphasis on multivariate methods, structural equation modeling, and meta-analyses. Within safety psychology, his work focuses on accident prevention, particularly in transport and industry settings. His research applies models and theories from social psychology, personality psychology, and cognitive psychology to safety-relevant behaviors, aiming to understand, explain, and ultimately influence behavior to prevent accidents. His work spans organizational psychology, work psychology, and methodological innovations in psychological research. His recent publications reveal a strong focus on transportation psychology, particularly regarding autonomous vehicles and public acceptance of new transport technologies. His work also extends to mental health interventions, peer review processes, and organizational behavior. The breadth of his research demonstrates consistent application of rigorous quantitative methods across diverse psychological domains, from traffic safety to clinical interventions and organizational effectiveness. Professor Ulleberg received the Teaching Award at the Department of Psychology, University of Oslo, in 2008, recognizing his excellence in education. His commitment to teaching is evident through his instruction of advanced quantitative methods courses including PSYC2104 – Quantitative Methods A and PSY4020 – Applied quantitative research methods. Professor Ulleberg earned his doctorate in psychology from the Norwegian University of Science and Technology (NTNU) in 2002. Before joining the University of Oslo as an associate professor in 2007 (later promoted to Professor), he worked in psychiatric health services research, accident prevention, and served as an associate professor of psychometrics at NTNU. His current research projects include CAST (Campaign and awareness-raising strategies in traffic), CyWalk (Internet survey about the means of transport of the future), and research on The use of Expert Reports as evidence in Child Protection Decision-Making.

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Kyle Nickodemمشاهده پروفایل

پژوهشگر

University of Minnesota Twin CitiesUnited States

Educational and psychological measurement
Study design for causal inferencing
Tools for promoting reproducible research
+۱ مورد دیگر

Kyle Nickodem serves as the Director of the Research Methodology Consulting Center at the University of Minnesota-Twin Cities , specializing in quantitative methods in education . He holds a PhD in Quantitative Methods in Education and a BA in Psychology. Degrees : PhD (University of Minnesota-Twin Cities), BA (University of Notre Dame) Research Interests span educational and psychological measurement , causal inferencing study design , and reproducible research tools . His work focuses on positive youth development through collaborations with school districts and educational organizations. Contributions include developing R packages like DIFreport and kfa for methodological rigor. He provides consultations on multilevel modeling , propensity scores , and study design , emphasizing data reproducibility and statistical programming . Current Projects involve analyzing social-emotional learning metrics , gender differences in violence perpetration , and measurement invariance across large-scale educational datasets. Contact: nicko013@umn.edu

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Nicholas C. Jacobsonمشاهده پروفایل

دانشیار

Dartmouth CollegeUnited States

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.

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Andreas Buttenschönمشاهده پروفایل

استادیار

Northwestern UniversityUnited States

Andreas Buttenschön is an Assistant Professor in Applied Mathematics at the University of Massachusetts Amherst, affiliated with the Department of Mathematics and Statistics. His research bridges mathematical analysis with biological systems, focusing on cellular dynamics, non-local modeling, and computational methods. Key research areas: Applied Mathematics, Mathematical Biology, Computational Biology, and Non-local Modeling Teaches advanced courses like Math 456 (Mathematical Modeling) , Math 725 (Functional Analysis) , and Math 545 (Applied Linear Algebra) His publications explore interdisciplinary topics such as cell polarization, collective migration, and cytoskeletal dynamics. Notably, his work integrates numerical methods with biological applications, including finite difference schemes for partial differential equations and stochastic simulations for cellular processes. Recent projects emphasize continuum models for cell clusters and bifurcation analysis in polarized systems.

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Stephen Tripodiمشاهده پروفایل

استاد

Florida State UniversityUnited States

Stephen Tripodi is a Professor and Doctoral Program Director at Florida State University's College of Social Work, where he also serves as Faculty Director of Academic Dissemination at the Institute for Justice Research and Development. He holds a PhD in Social Work from the University of Texas and a BA in Psychology from Michigan State University. His research examines: Trauma histories of incarcerated populations Mental health and substance use interventions in correctional settings Recidivism pathways for women and youth Reentry program efficacy Quality of life in prison healthcare systems Dr. Tripodi's publications (2013-2019) demonstrate strong focus on gender-specific trauma interventions, recidivism correlates, and randomized trials of prison-based therapies. Key thematic clusters include: Trauma-informed care adaptations for incarcerated women Intersections of mental health, substance use, and reoffending Health disparities in correctional settings Methodological rigor through RCTs and latent variable modeling He leads significant grants including: A $1.2M NIJ trial on trauma reentry services for youth $480K Arnold Ventures project on therapeutic communities $250K jail-based trauma care evaluation Co-PI on $7.5M multistate reentry initiative Through the Institute for Justice Research and Development, he advances translational research on evidence-based criminal justice reforms.

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Joran Jongerlingمشاهده پروفایل

استادیار

Tilburg UniversityNetherlands

Dr. Joran Jongerling is an Assistant Professor in the Department of Methodology and Statistics at Tilburg University's Tilburg School of Social and Behavioral Sciences. His academic work focuses on developing and applying advanced statistical methods for analyzing longitudinal and intensive longitudinal data, with particular expertise in multilevel AR(1) models and Bayesian statistics. Dr. Jongerling's research interests span several interconnected areas within quantitative methodology. He specializes in Bayesian Statistics , Multilevel Analysis , Dynamic Modeling , and the analysis of Longitudinal Data collected through Experience Sampling and diary methods. His methodological work addresses critical challenges in modeling individual differences in dynamic processes, measurement invariance across time, and appropriate statistical techniques for intensive longitudinal datasets. Analysis of his publication trends shows a significant increase in research output in recent years, with 11 publications in 2025 alone. His work spans both methodological development and substantive applications across psychology, education, and health sciences. His methodological contributions often focus on refining techniques for analyzing intensive longitudinal data, while his applied work demonstrates these methods in contexts ranging from multicultural personality assessment to motor development in children with Down syndrome. Dr. Jongerling actively contributes to advancing methodological standards in psychological research through his work on measurement invariance, simulation studies evaluating statistical techniques, and development of models that appropriately handle the complexities of real-world longitudinal data. His research has practical implications for researchers designing and analyzing intensive longitudinal studies across multiple domains of psychological science.

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