Nicole Santos Dunn is an Assistant Professor in the Department of Educational and Counselling Psychology, and Special Education at the University of British Columbia's Faculty of Education. As a Registered Psychotherapist in Ontario and a candidate for Clinical Psychologist licensure in British Columbia, she bridges clinical practice and multidisciplinary research grounded in critical suicidology. PhD in Clinical and Counselling Psychology from the Ontario Institute for Studies in Education CPA-accredited residency at the University of British Columbia Research interests include socio-political, cultural, and affective dimensions of health; gendered experiences of livability; and community-driven wellness practices. She employs anti-colonial, queer, and feminist methodologies with ethical emphasis on lived experience and justice-oriented research dissemination. Clinically, she specializes in working with painful and stuck emotions through Process-Experiential and Compassion-Focused approaches. She advocates for anti-oppressive supervision frameworks.
CJ Park is an Assistant Professor in the Department of Educational and Counseling Psychology, and Special Education at the University of British Columbia (UBC), Faculty of Education. A licensed psychologist with international experience, she previously served as Assistant Professor at New Mexico State University and maintains an active clinical and research practice from her Scarfe Library Block 280 office. Her academic credentials include: Bachelor of Psychology from the University of Hong Kong Master of Counseling Psychology from Ewha Womans University (South Korea) PhD in Counseling Psychology from the University of Missouri-Columbia (2021) Dr. Park's research integrates vocational and positive psychology with critical social justice frameworks, examining career development through lenses of diversity, critical consciousness, and cross-cultural identity. Her clinical work employs Interpersonal Psychotherapy and Multicultural-Feminist Therapy to address systemic barriers in educational and career settings. Analysis of her 15 recent publications reveals dominant themes in career psychology (particularly STEM pathways for women of color), microaggression dynamics, and health behavior intersections with sleep/alcohol use. Her work consistently centers marginalized populations while advancing measurement tools like the Career Futures Inventory. Currently accepting graduate students for 2026-27, Dr. Park maintains active research collaborations across counseling psychology, education, and health sciences, with recent publications demonstrating interdisciplinary grant-funded work on social determinants of health and career development.
Bhushan Gopaluni is a Professor in the Department of Chemical and Biological Engineering at the University of British Columbia, where he also serves as Associate Dean for Education and Professional Development in the Faculty of Applied Science. He holds associate faculty positions in multiple interdisciplinary institutes including the Institute of Applied Mathematics, Institute for Computing, Information and Cognitive Systems, Pulp and Paper Center, and Clean Energy Research Center. He previously held the Elizabeth and Leslie Gould Teaching Professorship from 2014 to 2017. Education: Ph.D. in Chemical Engineering, University of Alberta (2003) Bachelor of Technology in Chemical Engineering, Indian Institute of Technology, Madras (1997) Research Interests: Professor Gopaluni's research spans several critical areas at the intersection of chemical engineering, machine learning, and process control. His primary focus includes the development of advanced process control strategies using reinforcement learning and machine learning techniques. He has made significant contributions to battery technology research, particularly in capacity estimation and remaining useful life prediction for lithium-ion batteries. His work also encompasses sustainable energy systems, industrial process monitoring, fault diagnosis, and the application of digital twin technology in chemical processes. His research methodology emphasizes the integration of data-driven approaches with fundamental process understanding, leading to practical solutions for complex industrial challenges. This includes the development of interpretable machine learning models for industrial applications, real-time optimization strategies, and advanced monitoring systems for process industries. Publications and Research Impact: Professor Gopaluni's recent publications demonstrate a strong focus on cutting-edge applications of machine learning in chemical engineering. His work prominently features battery technology and energy systems, with multiple papers addressing lithium-ion battery capacity estimation and management. He has also contributed significantly to process control applications, including drilling process monitoring, greenhouse gas reduction in marine transport, and renewable carbon tracking in biofuel processing. His research extends to advanced computational methods including deep learning, reinforcement learning, and causal discovery in industrial processes. Awards and Recognition: Killam Teaching Prize (University of British Columbia) Dean's Service Medal (University of British Columbia) D.G. Fisher Award in Process Control (Canadian Society for Chemical Engineers) Elizabeth and Leslie Gould Teaching Professor (2014-2017) Professional Service and Editorial Roles: Professor Gopaluni currently serves as Associate Editor for three prestigious journals: Journal of Process Control, The Journal of Franklin Institute, and Results in Control and Optimization. His service to the academic community extends through his role as Associate Dean for Education and Professional Development, where he oversees educational initiatives across the Faculty of Applied Science. Industry Experience: From 2003 to 2005, Professor Gopaluni worked as an engineering consultant at Matrikon Inc. (now Honeywell Process Solutions), where he designed and commissioned multivariable controllers for British Columbia's pulp and paper industry and implemented controller performance monitoring projects across oil & gas and chemical industries.
Christophe Andrieu is a Professor in Statistics within the School of Mathematics at the University of Bristol. His research bridges theoretical probability, computational statistics, and applied mathematics, with significant contributions to Markov Chain Monte Carlo methodologies and Bayesian inference frameworks. He maintains active collaborations across engineering and data science domains. His educational background includes: M.A. from List.Natnl.Scis.App.Lyon Additional M.A. (institution unspecified) Ph.D. from Paris Andrieu's research focuses on Markov Chain Monte Carlo theory , where he develops convergence guarantees and efficiency bounds for complex samplers. His work extends to non-reversible MCMC algorithms , piecewise deterministic processes , and gradient-free optimization techniques. Recent publications demonstrate innovative approaches to state-space models and numerical integration, often addressing high-dimensional statistical challenges through stochastic approximation methods. His fingerprint reveals deep specialization in Markov chain convergence analysis and computational Bayesian statistics. His 15 most recent publications (2021-2025) exhibit consistent focus on theoretical foundations of Monte Carlo methods, particularly convergence analysis of Markov chains and novel sampler designs. Key trends include the application of weak Poincaré inequalities to pseudo-marginal MCMC, development of self-organizing state-space models, and exploration of hypocoercivity in piecewise deterministic processes. The work spans both theoretical advancements and practical implementations for engineering and statistical applications. Andrieu has secured significant research funding including: COmputational Statistical INference for Engineering and Security (COSINES) (2018-2023) New Approaches to Data Science (2018-2023) He has supervised 5 research students and maintains active collaborations in computational statistics and machine learning. His network shows strong connections with probability theory and engineering research groups.
Christopher D. Abraham, MD is an Associate Professor of Radiation Oncology and Associate Professor of Medicine at Washington University School of Medicine in St. Louis. He is affiliated with the Siteman Cancer Center, Brain Tumor Center, and Institute of Clinical and Translational Sciences (ICTS). Dr. Abraham practices at multiple locations including the Center for Advanced Medicine Radiation Oncology Center, Barnes-Jewish West County Hospital, and Siteman Cancer Center – North County. His clinical work focuses on radiation oncology with expertise in treating brain tumors and other cancers. Dr. Abraham completed his Medical Degree at Saint Louis University School of Medicine in 2011 and his Residency in Radiation Oncology at Barnes-Jewish Hospital and Washington University School of Medicine in 2016. He earned his BS in Radiologic Science from the Medical College of Georgia in 2004. Dr. Abraham's research focuses on advancing radiation therapy techniques, particularly in stereotactic radiosurgery for brain metastases, hippocampal-avoidance whole brain radiation therapy, and innovative approaches for glioblastoma treatment. His work demonstrates a strong emphasis on optimizing radiation delivery while minimizing neurocognitive side effects. He has pioneered simulation-free radiation therapy techniques that expedite treatment planning, particularly for palliative care patients. His research also explores the integration of AI and large language models in radiation oncology workflows and insurance appeals processes. Analysis of Dr. Abraham's recent publications reveals a clear research trajectory focused on improving precision in radiation therapy for brain tumors, with particular attention to hippocampal protection, adaptive planning techniques, and combined modality approaches. His work spans clinical trials, technical innovations in treatment planning, and translational research connecting imaging with treatment outcomes. The increasing citation counts of his work, particularly his 2023 paper on simulation-free radiation therapy which has 34 citations, demonstrates growing impact in the field. While specific awards are not listed in the provided information, Dr. Abraham's work has accumulated 536 citations according to Scopus metrics, indicating significant scholarly impact. His research has been referenced in clinical guidelines and policy sources, demonstrating translational relevance to clinical practice. Dr. Abraham actively collaborates with multidisciplinary teams including neurosurgeons, medical oncologists, and physicists. His work on the NRG Oncology/RTOG 0631 trial demonstrates involvement in large cooperative group studies. He has contributed to efforts examining insurance policy adherence to radiation oncology guidelines, showing engagement with healthcare systems issues. As a key member of the Brain Tumor Center at Siteman Cancer Center, Dr. Abraham participates in comprehensive brain tumor care teams that integrate surgical, medical, and radiation oncology approaches. His work with the Institute of Clinical and Translational Sciences highlights his commitment to translating research findings into clinical practice. Current research directions include exploring simulation-free radiation therapy techniques, optimizing hippocampal-sparing approaches, and investigating novel combinations of radiation with immunotherapies.
Jan Bergström is Assistant Professor of Clinical Psychology at Stockholm University's Department of Psychology, where he serves as Director of Studies for the Postgraduate Psychotherapist Program and Head of the Stockholm University Psychology Clinic. His research focuses on self-help based CBT and digital psychological interventions, with particular emphasis on clinical behavior analysis, psychotherapy supervision, Behavioral Activation for depression, exposure-based interventions for anxiety/OCD, and Functional Analytic Psychotherapy (FAP). He also investigates metatheoretical aspects of psychotherapy and mental health treatment mechanisms. Research group: Clinical psychology, neuroscience and health Key clinical interests: Mental health problems in neurodiverse populations (autism, ADHD) His publications demonstrate extensive work on internet-delivered therapies for panic disorder, OCD, and depression, including comparisons of blended vs full treatment modalities. He has developed non-heteronormative psychometric instruments like the revised Social Interaction Anxiety Scale (SIAS). Current projects include ZeroOCD smartphone app development for OCD treatment and iMERAT emotion recognition training for adolescents. The academic literature shows his focus on treatment efficacy, therapist time optimization (47% reduction in blended models), and functional analytic approaches to mental health classification. His cross-disciplinary work combines psychological theory with technology-mediated solutions, bridging clinical practice, neuroscience, and digital health innovations.
Beat Rechsteiner is a Senior Lecturer and Postdoctoral Researcher at the Institute of Education, University of Zurich, specializing in educational processes within schools. He serves as Project Lead for the SNSF Research Project R2 (Regulation of Routines in Teaching Development) and contributes to theoretical and empirical research on teacher collaboration, school improvement, and social network analysis. His work emphasizes self-regulated learning, instructional capacity, and adaptive strategies for educational challenges. Doctoral Program in Education (University of Zurich, 2018–2022) Master’s in Educational Science (University of Zurich, 2014–2018) Secondary School Teacher Training (Zurich University of Teacher Education, 2005–2009) His research focuses on: Teacher collaboration networks and their impact on school improvement Professional development dynamics through experience sampling Brokerage mechanisms in educational change Adaptation of routines during crises (e.g., pandemic effects on math competencies) Recent publications highlight trends in social network analysis, school reform, and collective regulation. Key themes include boundary-crossing activities, data-driven school development, and stress management in collaborative environments. Awards include the GRC Travel Grant (2022). He actively reviews for journals like Teaching and Teacher Education and Journal of Educational Change , participates in international exchanges (University of Antwerp), and teaches graduate courses on systematic reviews, school improvement routines, and educational research.
Sebastian Hensel is a Professor of Pure Mathematics at the Mathematical Institute of Ludwig Maximilian University of Munich (LMU), where he also serves as the Dean of Studies. His research focuses on the intersection of low-dimensional topology and geometric group theory, with emphasis on mapping class groups, handlebody groups, and diffeomorphism groups of surfaces. He leads the Geometry and Topology Working Group and is actively involved in teaching advanced seminars. Hensel received his PhD from the University of Bonn in 2011 under Ursula Hamenstädt. Before joining LMU, he held positions as a Dickson Instructor at the University of Chicago and as a temporary academic councilor in Bonn. His research employs geometric methods to study algebraic structures in topological spaces, particularly surfaces and 3-manifolds. Scientific Awards: Dickson Instructor Fellowship, University of Chicago Teaching & Advising: Hensel regularly teaches courses on Riemannian geometry, geometric group theory, and manifold topology. He currently advises bachelor and master's theses in geometry/topology and organizes block seminars. As Dean of Studies, he oversees academic programs at the Mathematical Institute. Affiliations: Member of the Geometry and Topology Group at LMU, with collaborations spanning multiple institutions including TUM and international partners.
Dr.-Ing. Ullrich Mönich is a Senior Researcher and Lecturer at the Technical University of Munich (TUM) , affiliated with the Chair of Theoretical Information Technology and leading research activities at the Advanced Communication Systems and Embedded Security Lab (ACES Lab) . Since 2019, he has been instrumental in shaping experimental and theoretical research in 6G communications, physical layer security, and signal processing. Education: Dr.-Ing. in Electrical Engineering, Technische Universität München (2011) – supervised by Prof. Holger Boche Previous affiliations include MIT (2012–2015) and TU Berlin Research Focus: His research spans signal processing, wireless communications, machine learning, and sampling theory , with a strong emphasis on physical layer security , computability in signal processing , and 6G communications . He explores theoretical foundations and practical implementations, including neuromorphic computing, digital twinning, and secure modular coding schemes. Publications & Trends: His recent publications (2023–2025) are heavily concentrated in 6G communications , integrated sensing and communications (ISAC) , semantic physical layer security , and digital twinning . These works often combine theoretical analysis with experimental validation using 5G/6G testbeds and neuromorphic hardware. Teaching & Supervision: Regularly teaches "Foundations of Analog, Digital, and Quantum Computers" (tutorials since 2018) Previously taught "Applied Functional Analysis" and "Advanced Signal Theory" Involved in practical courses like "Software Defined Radio Laboratory" Labs & Teams: He leads the ACES Lab at TUM, which focuses on experimental validation of advanced communication systems, including physical layer security, neuromorphic computing, and 6G testbeds. The lab collaborates with national and international partners, including MIT, and is supported by major funding bodies such as the German Federal Ministry of Education and Research (BMBF) and the German Research Foundation (DFG).
Prof. Dr. Lubomir Banas is a full-time Professor at the Faculty of Mathematics , University of Bielefeld. His research focuses on numerical analysis of stochastic partial differential equations (SPDEs) , particularly in micromagnetism, phase field models, and stochastic games. He leads Subproject B03 in the SFB 1283 project 'Taming Uncertainty and Profiting from Randomness and Low Regularity in Analysis, Stochastics and Their Applications.' Research Interests: Numerical methods for SPDEs and singular-degenerate PDEs Adaptive finite element techniques and a posteriori estimates Phase field models (Cahn-Hilliard, obstacle potentials) Stochastic games with asymmetric information Computational micromagnetism and magnetostriction Self-organized criticality and nonlinear stochastic flows Recent work includes: 2025: Numerical approximation of biharmonic wave maps and stochastic games 2024: Sharp interface limits for stochastic Cahn-Hilliard equations 2023: Singular-degenerate SPDEs and a posteriori estimates 2022: Stochastic total variation flow and Hamilton-Jacobi-Bellman equations 2021: Nematic electrolytes and homogenization of two-phase flows He serves on examination boards for Bachelor's and Master's programs in Mathematics and Mathematical Physics, and supervises graduate students within the Bielefeld Graduate School in Theoretical Sciences . His publications (over 30) address convergence analysis, error estimation, and computational modeling in applied mathematics.
Dr. Steven Schiff is the Harvey and Kate Cushing Professor of Neurosurgery and Professor of Epidemiology at Yale University, serving as Vice Chair for Global Health in the Department of Neurosurgery. He is affiliated with multiple Yale institutions including the Yale Institute for Global Health, Yale Program for Biomedical Ethics, Yale Interdepartmental Program in Neuroscience, and the Yale Center for Brain and Mind Health. Dr. Schiff's educational background includes an MD and PhD in Physiology from Duke University, followed by General Surgery Internship, Neurosurgery Residency, and Pediatric Neurosurgery Fellowship at Children's Hospital of Philadelphia. His training established the foundation for his dual expertise in clinical neurosurgery and biomedical engineering. Dr. Schiff is a pioneer in neural control engineering and sustainable health engineering with a strong focus on global health applications. His research spans neural engineering, infectious disease modeling, hydrocephalus treatment in low-resource settings, and the development of low-cost medical imaging technologies. He has made significant contributions to understanding neonatal infections in Africa, particularly the discovery of Neonatal Paenibacilliosis. His work on Predictive Personalized Public Health (P3H) integrates engineering principles with public health practice. His recent publications demonstrate a strong focus on applying AI and engineering solutions to global health challenges, particularly in neurosurgery and infectious disease. Key themes include the development of low-field MRI technology for resource-limited settings, understanding Paenibacillus infections causing hydrocephalus in African infants, and applying computational modeling to infectious disease dynamics. NIH Director's Pioneer Award (2015) NIH Director's Transformative Award (2018) Fellow of American Association for the Advancement of Science (2012) Fellow of American Epilepsy Society (2016) Honorary African Name: Musaazi, Fumbe Clan (2018) Dr. Schiff leads the Center for Global Neurosurgery at Yale University, building on his previous work founding the Center for Neural Engineering at Penn State. His NIH-funded research has enabled significant advances in understanding and treating infant infections in the developing world. He serves on multiple editorial boards including the Journal of Computational Neuroscience and Journal of Neural Engineering, and has contributed to FDA scientific committees. His work bridges engineering, neuroscience, and global public health to address critical healthcare disparities worldwide.
Xi Chen is an Associate Professor in the Grado Department of Industrial & Systems Engineering at Virginia Tech. He holds a Ph.D. in Industrial Engineering and Management Sciences from Northwestern University, an M.S. in Industrial Engineering & Systems Engineering from NC State, and a B.Sc. in Automation from Huazhong University of Science & Technology. His research focuses on stochastic modeling, simulation optimization, and power electronics, particularly in high-frequency converter design using GaN devices. Key research interests include planar transformer optimization, EMI reduction in power systems, and GaN-based high-efficiency converters. His work spans applications in adapters, PFC converters, and high-density power modules. Notable contributions include ultra-high efficiency 140W PD3.1 adapters and LLC modules using GaN power ICs. Education: Ph.D., Industrial Engineering & Management Sciences, Northwestern University M.S., Industrial Engineering & Systems Engineering, NC State B.Sc., Automation, Huazhong University of Science & Technology Awards: 2013 Nemhauser Dissertation Prize Student Scholarship Award (Spring Research Conference) Arthur P. Hurter Award for Academic Excellence Professional Activities: INFORMS Simulation Society ACM SIGSIM Society for Industrial and Applied Mathematics Recent Courses: ISE 5414: Random Process ISE 5424: Simulation Labs/Teams: Active in power electronics research with a focus on GaN devices and high-frequency converter design.
Rudie P.J. Kunnen is an Associate Professor at the Faculty of Applied Physics and Science Education , Eindhoven University of Technology, leading the Turbulent and Multiphase Flows group. His research focuses on heat, mass, and particulate transport in turbulent flows, with applications in geophysics and industry. Active in UN Sustainable Development Goals related to environmental protection Collaborator in projects like Active Contamination Control for Equipment and SubstrateS Research Interests : Turbulent flow dynamics, rotating convection, vortex structures, thermophoresis, plasma-liquid interactions, and geostrophic turbulence. His work combines experimental and numerical approaches (e.g., direct numerical simulation, particle image velocimetry). Scientific Awards : NWO Vici Prize (2024) Advising and Collaborations : Supervised multiple BSc and MSc theses at TU/e. Collaborates with researchers like F. Toschi and H.J.H. Clercx on turbulence projects.
Professor Adam Dunn is a leading academic in Biomedical Informatics and Digital Health at The University of Sydney , where he established the Discipline of Biomedical Informatics and Digital Health in 2020. With nearly 20 years of experience, his work integrates machine learning , natural language processing , and computational social science to address challenges in public health , clinical epidemiology , and evidence synthesis . His research programs focus on: (1) improving health information access and trust, (2) analyzing misinformation uptake via digital traces, and (3) developing AI tools for systematic review efficiency. Current projects include generative AI applications in patient discharge instructions , fairness in multimodal health AI , and infodemic burden measurement toolkits for WHO. Recent publications span clinical NLP , vaccine credibility , and social media surveillance . Awards include global recognition in medical informatics and editorial leadership roles at npj Digital Public Health and npj Digital Medicine. He has supervised over 15 PhD scholars and served on NHMRC and MRFF grant review panels. Key Projects: WHO Infodemic Toolkit, NLM R01 grant on ClinicalTrials.gov integration Expertise: AI in health, systematic review methodology, health information trust
David Castañón is a Professor of Electrical and Computer Engineering (ECE) and Systems Engineering (SE) at Boston University. He holds a PhD from MIT (1976) and has held leadership roles including Department Chair of BU ECE (2010-2014) and President of the IEEE Control Systems Society (2008). His research focuses on stochastic control, optimization, game theory, and distributed computing, with applications in sensor management, inverse problems, and autonomous systems. Education: PhD, Massachusetts Institute of Technology (1976). Key affiliations include the Center for Information and Systems Engineering, the Rafik B. Hariri Institute for Computing, and the ALERT Department of Homeland Security Center of Excellence. He teaches courses such as EC702 Recursive Estimation and EC719 Statistical Learning Theory. Research interests span stochastic control, estimation theory, optimization algorithms, and multi-agent systems. Notable contributions include work on sensor management, cooperative operations, and inverse problem solutions for medical and security imaging. His work often integrates theoretical frameworks with practical applications in autonomous systems and distributed computing. Scientific achievements include IEEE Fellow status (2006), CSS Distinguished Member Award, and leadership roles in major conferences like the IEEE Conference on Decision and Control (2007 as General Chair). He has also served on the Air Force Advisory Board and the IEEE Society Review Committee. Grants and lab affiliations include the NSF Engineering Research Center for Subsurface Sensing (2001-2013) and the SENTRY DHS Center of Excellence (2021-present). His interdisciplinary collaborations bridge robotics, medical imaging, and security systems.
