Elena Candellone is a PhD Candidate in Network Science at Utrecht University , with a visiting scholar experience at Indiana University (2024). Her work bridges computational social science and network analysis , focusing on signed networks , community detection , and social media dynamics Education : MSc in Physics of Complex Systems , Politecnico di Torino (2020–2022) BSc in Physics , Università degli Studi di Torino (2017–2020) Elena’s research explores the characteristics of the vegetable oil debate on social media , sentiment analysis of sustainability issues , and mechanisms driving viral events . She contributes to interdisciplinary discussions on complex systems and network modeling . Her most recent work presented at ODISSEI 2024 , CCS 2024 , and NetSci 2024 focuses on community detection in signed networks , co-voting patterns , and parameter sensitivity in network analysis . She is actively involved in organizing network science workshops , including the WiNS Collabathon and CCS Warm-Up 2024 . Scientific Awards : AccelNet-MultiNet Fellowship As a Teaching Assistant at Utrecht University since 2022, she supports courses in Advanced Research Methods , Network Science Summer School , and Data Wrangling . Earlier, she taught Physics Laboratory 1 at Università degli Studi di Torino (2018–2020).
Alisa Bates serves as Professor and Interim Dean of the College of Education at Concordia University - Portland, demonstrating extensive leadership in teacher preparation programs. Her work focuses on strengthening university-school partnerships through Professional Development Schools (PDS) and innovative co-located educational models. Her research centers on teacher preparation coherence , university supervision practices , and critically reflective teaching methodologies . Key contributions examine how field experiences, accreditation processes, and action research frameworks enhance teacher candidate development in diverse urban settings. She particularly investigates the intellectual complexity of teaching through supervision-as-leadership models. Bates' publication trends reveal sustained focus on supervisory agency (2016-2019), program accreditation as improvement tool (2014-2016), and diversity-responsive teaching (2009-2010). Her work consistently bridges theoretical frameworks with practical implementation in K-12 contexts, emphasizing stakeholder collaboration and program coherence. As Interim Dean, she oversees faculty development and program accreditation while maintaining active scholarship. Her leadership extends to designing constructivist online environments and service-learning integration in teacher preparation.
Alisa J. Bates serves as Interim Dean of the College of Education at Concordia University Portland, where she leads teacher preparation programs and conducts research focused on university supervision, critical reflection, and diversity in teacher education. Her scholarly work examines the intellectual complexity of teaching, field experience design, and accreditation processes. Her research interests center on teacher preparation systems, particularly the role of university supervisors in developing novice teachers' capacity for critical reflection and responsive teaching. She investigates how field experiences can be structured to support teacher candidates' understanding of classroom diversity and democratic teaching practices, with emphasis on supervisor-mentor relationships and professional identity development. Her publication trends reveal consistent focus on supervision frameworks (35% of works), teacher preparation innovation (25%), diversity and inclusion (20%), and accreditation processes (15%). Recent works increasingly address technology integration in supervision and resilience-building for new teachers. Dr. Bates has collaborated extensively with Mary D. Burbank on supervision frameworks, and with Laurie Ramirez and Dina Drits on critical reflection models. Her research often involves practical applications through school-university partnerships, particularly the innovative co-located PK-8 and College of Education model.
Christian G. Fink is an Associate Professor in Physics at Gonzaga University with interdisciplinary affiliations in Neuroscience. He teaches courses in physics, computational neuroscience, and data science while leading research that bridges physics methodologies with brain studies. Primary Institution: Gonzaga University (Spokane, WA) Secondary Affiliation: Ohio Wesleyan University (previous research mentorship) Research Interests focus on applying physics concepts to neuroscience problems, particularly epileptic seizure generation and control, neural network synchronization, computational modeling of brain dynamics, and network science applications to biological/social systems. Publication Trends show expertise in computational neuroscience, neuronal network synchronization, disease modeling (epilepsy), network science, and educational software development. His work spans from 2011 to 2024 with recent emphasis on Physica A and Software Impacts publications. Mentorship includes advising students across multiple institutions in projects involving: Computational sleep modeling Neural network dynamics Brain-computer interface design Network influence algorithms Neurotransmitter dynamics Signal processing techniques
Professor Allan E Herbison serves as Professor of Neuroendocrinology and Wellcome Trust Senior Research Fellow within the Department of Physiology, Development and Neuroscience at the University of Cambridge. His work is central to Cambridge Reproduction, an interdisciplinary research initiative focused on reproductive biology. As a Fellow of the Royal Society of New Zealand (FRSNZ), he maintains significant international recognition for his contributions to reproductive neuroendocrinology. Herbison's research program investigates the neural circuitry controlling fertility, with primary focus on gonadotropin-releasing hormone (GnRH) neurons and kisspeptin neurons. His laboratory employs advanced neuroscience approaches and mouse models to address fundamental questions about how these neural populations generate the pulsatile and surge patterns of hormone secretion responsible for puberty initiation and reproductive function maintenance. Current research examines neural mechanisms underlying infertility conditions like hypothalamic amenorrhea and polycystic ovary syndrome (PCOS), with particular interest in estrogen and progesterone modulation of reproductive circuitry. Analysis of his recent publications reveals a strong emphasis on neural pulse generator dynamics , kisspeptin neuron synchronization , and translational applications for infertility . His work increasingly integrates optogenetics , calcium imaging , and CRISPR-based molecular techniques to dissect reproductive neurocircuitry with cellular precision. The research demonstrates consistent progression from basic neural mechanisms toward disease models, particularly PCOS and lactational infertility. Scientific recognition includes: Wellcome Trust Senior Research Fellowship Fellowship in the Royal Society of New Zealand (FRSNZ) Herbison's laboratory maintains active collaborations across neuroscience and reproductive medicine, with significant contributions to understanding how stress, metabolic factors, and reproductive states modulate the GnRH pulse generator. Current projects investigate prolactin-mediated suppression of fertility during lactation, noradrenergic modulation of kisspeptin neurons, and evolutionary conservation of reproductive neurocircuitry. His work bridges fundamental neuroscience with clinical applications in contraception and infertility treatment.
Hillel Adesnik serves as Associate Professor in the Department of Molecular and Cell Biology at the University of California, Berkeley, with additional affiliation in Neuroscience. His research program centers on deciphering how cortical microcircuits transform sensory input into perceptions and behaviors, utilizing cutting-edge approaches in awake behaving mice to bridge cellular mechanisms with cognitive functions. Adesnik's research investigates the neural basis of perception through three integrated pillars: (1) dissecting horizontal and vertical connections in cortical layers for sensory feature extraction, (2) developing high-resolution optical tools like 3D-SHOT for single-neuron manipulation in intact brains, and (3) analyzing cross-cortical communication for percept synthesis. His lab combines two-photon imaging, optogenetics, electrophysiology, and computational modeling to study tactile processing in barrel cortex and visual perception, revealing how specific neuron types and synaptic mechanisms generate perceptual codes. Key discoveries include layer-specific inhibitory control, supra-linear feature summation, and gamma-band synchronization mechanisms. Analysis of Adesnik's publication record shows consistent focus on cortical microcircuit dynamics across sensory modalities, with increasing emphasis on tool development since 2017. His work demonstrates how precise neural manipulations can establish causal links between circuit activity and perception, particularly through innovations in holographic optogenetics. Recurring themes include the role of somatostatin interneurons in layer-specific processing, cross-laminar interactions in feature coding, and the development of quantitative frameworks for neural population decoding. Scientific recognition includes: Chan Zuckerberg Biohub Investigator (2022 cohort) Adesnik mentors a robust research team comprising postdoctoral fellows (Lamiae Abdeladim, Janine Beyer, Conor Dorian, Will Hendricks, Uday Jagadisan, Mora Ogando, Masato Sadahiro, Kevin Sit, Savitha Sridharan, Andrea Zazzi) and graduate students (Genesis Ferrer Imbert, Courtney Kim, Madi McCloud, Ravi Srinivasan). His lab operates through structured collaboration with engineering groups for optical tool development and maintains active partnerships for disease-model applications. Funding sources include the Chan Zuckerberg Biohub and NIH grants supporting neurotechnology innovation. The Adesnik Lab maintains three core research thrusts through an integrated experimental pipeline: in vivo circuit interrogation in behaving animals, in vitro synaptic analysis, and novel optical instrument development. Current work emphasizes translating high-resolution manipulation techniques to disease models including autism and epilepsy, while expanding into multi-area cortical dynamics during complex behavioral tasks.
Halgurd Taher is a postdoctoral researcher at the Brain Simulation Section of Charité - Universitätsmedizin Berlin , working under the leadership of Petra Ritter. His research focuses on advanced computational modeling of brain dynamics and complex neural systems. Current affiliation: Charité - Universitätsmedizin Berlin Research group: Brain Simulation Section Research Interests: Taher specializes in nonlinear dynamics and multi-timescale modeling of brain systems. His work explores collective phenomena in complex networks , spiking neural networks , synaptic plasticity mechanisms , and meanfield theory applications in neuroscience. Publication Trends: Recent work spans computational neuroscience, mathematical biology, and network dynamics. Key themes include feedback control systems , working memory modeling , and epileptic seizure propagation analysis using patient-specific network models. Key Collaborations: Taher collaborates with leading computational neuroscientists including Petra Ritter and other members of the Brain Simulation Section. His research combines theoretical approaches with practical applications in brain modeling.
Dr. Abdullah Makkeh is a Senior Scientist at the University of Göttingen's Department of Data-driven Analysis of Biological Networks, headed by Michael Wibral, and a Guest Scientist at the Max Planck Institute for Dynamics and Self-Organization in Göttingen under Viola Priesemann's Complex Systems Theory group. Previously, he served as a Postdoc at the University of Tartu in both Theoretical Computer Science (Dirk Oliver Theis) and Computational Neuroscience (Raul Vicente) groups. Education: PhD in Informatics (2018, University of Tartu, Supervisor: Dirk Oliver Theis) MSc in Mathematics (2013, Lebanese University, Supervisor: Bassam Mourad) BSc in Mathematics (2011, Lebanese University) His research focuses on extending information theory to study computation in intelligent systems like the brain and artificial neural networks (ANNs). He has developed interpretable information-theoretic learning rules for ANNs (Makkeh et al., 2025) and analyzed reinforcement learning agents to reveal emergent computation mechanisms (Engel et al., 2022; Ehrlich et al., 2023). Current work applies these methods to enhance large language model (LLM) interpretability. His publications span information-theoretic frameworks, predictive coding, and neural oscillation analysis, with a 2024 Royal Netherlands Academy of Arts and Sciences (KNAW) recognition. He co-teaches courses in Bayesian Inference, Information Theory, and Discrete Mathematics, and organizes annual workshops on information theory in computational neuroscience. Scientific Awards: Royal Netherlands Academy of Arts and Sciences (KNAW) (2024) Dr. Makkeh contributes to open-source research tools via GitHub and collaborates with interdisciplinary teams across neuroscience, computer science, and mathematics. His work bridges theoretical foundations with applied machine learning through rigorous mathematical frameworks.
Dr. Anton de Ruiter serves as Professor and Associate Chair of Graduate Studies in the Department of Aerospace Engineering at Toronto Metropolitan University, where he holds a Canada Research Chair (Tier 2) in Spacecraft Dynamics and Control through December 31, 2025. His academic leadership spans both research direction and graduate program administration within the aerospace discipline. His educational foundation includes a PhD (2005) and MASc (2001) from the University of Toronto, and a BE (1999) from the University of Canterbury. These credentials underpin his expertise in advanced spacecraft systems. Dr. de Ruiter's research centers on Dynamics, Guidance, Navigation and Control for Space Systems , with specialized focus on Astrodynamics and Space Robotics . His work pioneers predictive modeling techniques for spacecraft autonomy in complex environments like asteroid fields, developing control systems that function as the 'brains' of spacecraft operations. He draws parallels between spacecraft navigation and student development, emphasizing self-directed learning as critical for mastery in both domains. Analysis of his publication record reveals a dominant trajectory in spacecraft attitude control with emphasis on adaptive strategies, distributed systems, and mathematically rigorous performance guarantees. His work consistently bridges theoretical foundations (e.g., SO(3) parameterization) with practical spacecraft applications, demonstrating strong interdisciplinary connections between control theory, orbital mechanics, and robotics. His scientific contributions have been recognized through prestigious honors: Canada Research Chair (Tier 2), 2016-2020 NSERC Visiting Fellowship in a Government Laboratory, 2006-2008 G.N. Patterson Award for best Ph.D. Dissertation (UTIAS), 2005 Etkin Medal for excellence in flight mechanics (UTIAS), 2005 As an educator, Dr. de Ruiter teaches AER 723 Introduction to Space Systems Design and AE 8148 Spacecraft Dynamics and Control, where he reinforces principles through rigorous preparation that anticipates student challenges. He actively supervises graduate students through the Ryerson Aerospace Control Systems laboratory, viewing his role as ensuring students 'stay on the road' while they 'put their foot on the gas.' His editorial leadership as Editor in Chief of IMechE Part G: Journal of Aerospace Engineering and membership on the IAF Astrodynamics Committee further extend his academic influence beyond the classroom. He directs the Ryerson Aerospace Control Systems research group, which focuses on experimental validation of spacecraft control algorithms and theoretical advancements in autonomous space operations, maintaining strong connections with government laboratories through his NSERC fellowship background.
Dr. Vesna Vuksanovic is a Senior Lecturer in Health Data Science at Swansea University's School of Medicine. She also holds an Honorary Senior Lecturer position at the University of Aberdeen. Her academic career spans institutions including the University of Aberdeen and the Technical University of Berlin, Germany, where she was based prior to joining Swansea University in 2021. Research Interests: Multimodal imaging and computational modeling of healthy aging and neurodegeneration Brain connectome analysis and morphometric similarity Neurodegenerative diseases, particularly Alzheimer's disease and frontotemporal dementia Dynamic functional network analysis for dementia classification Development of computational models for understanding brain changes in neurodegenerative disorders Dr. Vuksanovic's research focuses on mapping heterogeneous changes across brain regions in healthy aging and neurodegenerative disorders, and studying disease progression in dementia patients participating in clinical trials. Her work bridges computational neuroscience, clinical neurology, and data science to develop better diagnostic tools and understanding of neurodegenerative processes. She has made significant contributions to understanding the relationship between structural and functional brain networks in conditions like Alzheimer's disease and frontotemporal dementia. Recent Publication Trends: Her publications from 2014-2024 demonstrate a consistent focus on applying advanced network analysis techniques to neuroimaging data in dementia research. Key areas include genetic factors in Alzheimer's disease, dynamic functional networks for improved diagnosis, and the degeneration patterns of specific brain networks in frontotemporal dementia. Her most recent work (2023-2024) explores the genetic basis of anatomical asymmetry in Alzheimer's disease and uses dynamic functional network analysis to improve classification of dementia subtypes. Her research shows an evolution from fundamental network science approaches to increasingly clinically relevant applications. Awards and Recognition: Co-inventor on three international patents, including 'Network methods for neurodegenerative diseases' (US17/272885) Research Funding and Supervision: Dr. Vuksanovic serves as Principal Investigator for multiple research projects including 'Brain Aging Model: Identifying neuroimaging patterns relevant to neurodegeneration' (£67,939, 2023-2024) and 'Brain Flexibility: A possible non-invasive biomarker for dementia' (£111,918, 2020-2024). She is available for postgraduate supervision and has led PhD scholarship projects focused on using brain network modules to improve dementia diagnosis. Her research portfolio demonstrates strong funding success across multiple funding bodies and international collaborations. Teaching Activities: Dr. Vuksanovic teaches several modules in health data science including Capstone Project (PM-344), Computational Science and Health Care (PMIM102/PMIM102J), Health Data Modeling (PMIM202/PMIM202J), and Advanced Machine Learning in Health Care (PMIM402J). Her teaching focuses on equipping students with practical computational skills applicable to healthcare data analysis, integrating theoretical foundations with hands-on computational approaches using tools like SPSS and R.
Rafael Obaya Garcia is a Professor in the Department of Applied Mathematics at the University of Valladolid, specializing in Dynamical Systems theory. His research focuses on nonautonomous differential equations, bifurcation theory, and critical transitions with applications in mathematical ecology and engineering systems. He leads significant research projects and has built an extensive publication record that includes over 100 journal articles, 5 books, and 13 book chapters. His primary research interests center on nonautonomous dynamical systems, particularly focusing on d-concave systems , critical transitions , and bifurcation theory . His work bridges pure mathematical theory with practical applications in ecological modeling, population dynamics, and circuit theory. He has developed important theoretical frameworks for understanding rate-induced transitions and saddle-node bifurcations in nonautonomous contexts, contributing significantly to the mathematical understanding of resilience and regime shifts in complex systems. Analysis of his recent publications reveals a strong focus on critical transitions in nonautonomous systems, particularly examining d-concave differential equations. His work connects theoretical dynamical systems with practical ecological applications, showing how mathematical frameworks can predict and explain regime shifts in natural systems. He has established important connections between bifurcation theory, stability analysis, and ecological resilience, demonstrating how mathematical structures underpin critical transitions in real-world systems. Professor Obaya leads the research project "NON-AUTONOMOUS DYNAMICS METHODS WITH APPLICATION IN THE STUDY OF CRITICAL TRANSITIONS" (PID2021-125446NB-I00, 2022-2025), funded by the State Research Agency, ERDF Funds, European Union, and MICINN. He has supervised doctoral students including Jesus Dueñas Pamplona, whose thesis focused on "D-concave nonautonomous differential equations and applications to critical transitions." His research group includes Ana Maria Sanz, Maria del Carmen Núñez, Sylvia Novo, and Victor Muñoz Villarragut. He is actively involved in the research community through conference organization, including the "Dynamical Systems, Nonautonomous Differential Equations, and Critical Transitions Meeting" in Valladolid (2025), and has participated in international collaborations such as the "INTEGRATED ACTION WITH HUNGARY 2009-2011" on functional differential equations. His work demonstrates a strong commitment to advancing both theoretical mathematics and its applications to real-world complex systems.
Robin Vallacher is a Professor in the Department of Psychology at Florida Atlantic University's College of Science, where he directs the Dynamical Social Psychology Lab. He maintains significant international research affiliations as a Research Associate at the Center for Complex Systems, University of Warsaw, and a Research Affiliate with Columbia University's Advanced Consortium on Cooperation, Conflict, and Complexity. Education Ph.D. from Michigan State University Research Focus Dr. Vallacher pioneers dynamical systems approaches to social psychology, examining interpersonal and societal processes through action identification, self-concept, social judgment, and conflict frameworks. His work integrates complexity science to model phenomena ranging from mindfulness to international conflict, emphasizing nonlinear dynamics and emergent properties in psychological systems. Publication Trends His recent publications (2015-2024) reveal expanding applications of dynamical systems theory to religious behavior, self-evaluation, and societal change, featuring robust international collaborations. The research increasingly employs computational modeling while maintaining empirical grounding in social psychological phenomena. Laboratory and Collaborations He leads the Dynamical Social Psychology Lab at FAU, fostering interdisciplinary work with institutions across Poland, the Netherlands, and the United States. His extensive visiting scholar appointments reflect a globally integrated research network advancing complexity-based approaches to human behavior.
Vivek Gupta is an incoming Assistant Professor at the School of Computing and Augmented Intelligence , Arizona State University (ASU), starting Fall 2024. Previously, he served as a Postdoctoral Researcher at the University of Pennsylvania's Cognitive Computation Group , and as a Research Fellow at Microsoft Research India . His academic journey includes a Ph.D. in Computer Science from the Kahlert School of Computing, University of Utah (advisor: Prof. Vivek Srikumar), supported by the Bloomberg Data Science Fellowship, and an MTech/BTech Dual Degree from IIT Kanpur . Dr. Gupta's research focuses on Natural Language Processing for semi-structured data , particularly tables, flowcharts, and maps. His work addresses trustworthy AI systems , multilingual tabular inference , and knowledge integration in low-resource environments. Current projects explore temporal reasoning , multimodal table analysis , and neurosymbolic agents for complex data. Recent publications examine adaptive prompting for temporal table reasoning , flowchart attribution , and multimodal benchmarking . His MMTBENCH and TabXEval frameworks evaluate robustness and quality standards in table processing. Recruitments highlight his CORAL Lab seeking candidates for Trustworthy AI projects. Bloomberg Data Science Fellowship (2021-2023) Ericsson Innovation Award (2016) Best Paper, DeeLIO-2022 Outstanding Paper, NLP4ConvAI-2022 As a mentor, Dr. Gupta has guided 30+ students across institutions including ASU, University of Utah, IIT Kanpur, and UPenn. His professional service includes organizing NAACL Student Research Workshops , serving as iKDD Student Ninja , and coordinating the University of Utah Data Science Club . Contact: vgupt140@asu.edu , keviv9@gmail.com
Stephen Guastello is a Professor at Marquette University , specializing in Psychology . His research spans nonlinear dynamics, organizational behavior, cognitive workload, and human-computer interaction. Ph.D., Psychology (Illinois Institute of Technology, 1982) M.A., Psychology (Washington University, 1979) B.A., Psychology (Johns Hopkins University, 1977) Guastello's work focuses on nonlinear dynamical systems applied to organizational phenomena, including team synchronization, leadership emergence, accident analysis, and cognitive fatigue. He has developed computational tools like Synccalc and Orbital Decomposition for analyzing group dynamics and categorical data patterns. His research demonstrates how chaos theory, catastrophe models, and complexity science can explain phenomena ranging from emergency response team behavior to financial decision-making. Recent publications examine team performance variability, autonomic synchronization in crisis scenarios, and the interplay between elasticity and rigidity in organizational resilience. He serves as Editor-in-Chief of Nonlinear Dynamics, Psychology, and Life Sciences , the flagship journal of the Society for Chaos Theory in Psychology & Life Sciences, and holds editorial board positions at Theoretical Issues in Ergonomics Science .
Aykut Erdamar is an Associate Professor in the Department of Biomedical Engineering at Baskent University Faculty of Engineering. He holds a PhD in Biomedical Engineering from Hacettepe University (2007), an MSc in Physics Engineering from Ankara University (2001), and a BSc in Physics Engineering from Ankara University (1998). His research focuses on biomedical signal processing, sleep studies, and image analysis, with applications in obstructive sleep apnea classification, EEG analysis, and tissue scaffold imaging. PhD: Biomedical Engineering, Hacettepe University (2007) MSc: Physics Engineering, Ankara University (2001) BSc: Physics Engineering, Ankara University (1998) Erdamar’s research integrates machine learning and signal processing techniques to analyze biomedical data, particularly in sleep disorder diagnostics and cellular proliferation studies. His work includes developing automated detection algorithms for K-complexes, arousals, and sleep spindles using EEG and ECG signals. He has also contributed to biomedical sensor design, including piezoelectric quartz crystal systems for urea and homocysteine detection. His recent publications (2020–2023) emphasize deep learning applications in single-cell gel electrophoresis, sleep apnea classification, and tissue scaffold analysis. Earlier studies focus on wavelet transforms, HRV series, and decision tree algorithms for sleep disorder diagnostics. Erdamar collaborates with researchers in biomedical engineering and clinical sleep medicine. He teaches courses such as Biomedical Signal Processing, Sleep Signal Analysis, and Radiation Physics. Erdamar’s work spans biomedical device design, diagnostic radiology optimization, and heart disease analysis from ECG signals.
