Find Academic Supervisors

• Multiscale Modeling
• Biological Processes
• Physical Processes
• +3 more

Mariya Ptashnyk is an Associate Professor in the School of Mathematical & Computer Sciences at Heriot-Watt University, specializing in Mathematics. She holds an ORCID identifier (0000-0003-4091-5080) and has been affiliated with the university since at least 2015. Her research focuses on multiscale modeling of biological and physical processes, with contributions to plant tissue biomechanics, soil-water interactions, and cell signaling pathways. Education: PhD in Mathematics from the University of Heidelberg, Germany. Postdoctoral research at the University of Oxford and the Centre for Modelling, Simulation in Biosciences (BIOMS), Heidelberg University. Research Interests: Ptashnyk’s work integrates mathematical analysis with biological systems, particularly in understanding root growth mechanics, water transport in soils, and cellular signaling. Her models address challenges in agricultural sustainability, drought mitigation, and plant-microbe interactions. She has pioneered techniques in homogenization theory and multiscale analysis for complex biological systems. Recent Trends in Publications: Her recent work emphasizes mechanotransduction pathways, root-water dynamics, and stochastic modeling of biological processes. Key themes include the role of rhizodeposition in soil rewetting, collective cell migration mechanisms, and non-local chemotaxis models. Awards: Heriot-Watt University Teaching Excellence Award (2019). The Student Union Oscars (2023). Teaching & Activities: Ptashnyk teaches courses like Advanced Mathematical Biology and Applied Analysis. She actively reviews for journals (e.g., Journal of Mathematical Biology ) and organizes conferences (e.g., SciCADE 2026). Her activities include visiting research stints at institutions like the Erwin Schrödinger Institute. Labs/Teams: While no specific lab is named, her collaborations span international teams in plant biology, computational mathematics, and environmental science.

Keith SmithView profile

Lecturer

• Biomedical Network Analysis
• Neuroimaging Data Analysis
• Machine Learning in Neuroscience
• +4 more

Keith Smith is a Lecturer in the Department of Computer and Information Sciences at the University of Strathclyde, where he leads the Digital Health & Wellness Research Group (DHaWG). Previously, he held positions as a lecturer at Nottingham Trent University and as a UKRI Innovations Fellow in Medical Informatics at the University of Edinburgh. He earned his PhD in biomedical signal processing from the University of Edinburgh's IDCOM lab. His research focuses on computational tools for analyzing biomedical networks, including neuroimaging data from structural and functional brain networks, protein interaction networks, and metabolic networks. Key areas include hierarchical complexity of brain networks, machine learning applications for neuroimaging, and theoretical foundations of network geometry and complexity. He collaborates widely with clinical and academic partners to address challenges in neurological and mental health conditions like Alzheimer’s, depression, and schizophrenia. His work contributes to UN Sustainable Development Goals, particularly improving health and well-being through advanced data-driven methodologies. He currently oversees PhD projects exploring brain networks, deep learning for disease prediction, and evolutionary protein biology. Notable achievements include the UKRI fellowship and impactful interdisciplinary collaborations. His talks and presentations, such as on statistical complexity of brain networks, highlight his commitment to advancing theoretical and applied network science.

Sara KellerView profile

Research Fellow

• Biomedical Engineering
• Medical Imaging
• Ultrasound Therapy
• +3 more

Sara Keller is a Research Fellow and Postdoctoral Research Assistant in the Department of Engineering Science. She holds a PhD in Bioengineering from the University of Washington (2021) and a BME from Vanderbilt University (2016). Her research focuses on ultrasound-guided therapies, particularly using microbubbles for targeted drug delivery and biofilm treatment. She joined the BUBBL group in 2021, specializing in cavitation activity analysis and medical acoustics. Her work spans biofilm disruption, cancer treatment monitoring, and preclinical safety studies in porcine models. Key contributions include developing acoustic devices for cell strain quantification and investigating nonlinear echoes from microbubbles. She collaborates on projects like intracranial hemodynamics modeling in sickle cell anemia and optimizing ultrasound-mediated drug delivery systems. Her recent articles emphasize therapeutic efficacy, safety protocols, and translational applications of ultrasound technologies. Research interests include advancing diagnostic tools through cavitation monitoring and enhancing drug penetration in solid tumors. Advising and grants: No formal advisees listed; current role focuses on postdoctoral research. Active in the BUBBL group for collaborative biomedical engineering projects.

Tian WangView profile

Research Fellow

• Theory and Computation
• Computational Chemistry
• Materials Chemistry
• +4 more

Tian Wang is a Research Fellow in the Department of Chemistry at the University of Washington, College of Arts & Sciences. His research focuses on theoretical and computational approaches to materials design, particularly in electrocatalysis, perovskite materials, quantum dots, and supercapacitors. He holds a Ph.D. from the National University of Singapore (2021), an M.S. from Columbia University (2016), and a B.S. from Nanjing University (2014). His work emphasizes understanding and optimizing material properties through confinement effects, vacancy engineering, and surface reconstruction. Recent studies involve high-entropy alloys for hydrogen evolution reactions and novel lithium recovery methods using hydrogel networks. He has contributed to advancements in perovskite solar cells, fluorescent probes for cellular analysis, and optoelectronic materials. Major Research Areas: Theory and Computation, Materials Chemistry, Electrochemistry Key Projects: Defects passivation in perovskites, supercapacitor performance enhancement via molecular confinement, laser-induced material modifications for oxygen evolution Notable collaborations include projects with Prof. Xiaosong Li and Prof. Zhi-Kuang Tan. His research often intersects computational modeling with experimental synthesis to address energy storage and sustainable materials challenges.

Leon O. ChuaView profile

Professor

• Cellular Neural Networks
• Nonlinear Dynamics
• Chaos Theory
• +3 more

Leon O. Chua is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He joined the faculty in 1970 after serving as an Assistant and Associate Professor at Purdue University. His research focuses on Cellular Neural Networks, Nonlinear Dynamics, and Chaos Theory, with contributions to circuit theory and complexity science. Chua is a Fellow of the IEEE and has received numerous awards, including the IEEE Gustav Kirchhoff Award (2005) and the Neural Networks Pioneer Award (2000). He holds 7 U.S. patents and 8 honorary doctorates from international universities. Education: B.S., Electrical Engineering, Mapúa Institute of Technology, 1959 M.S., Electrical Engineering, Massachusetts Institute of Technology, 1961 Ph.D., Electrical Engineering, University of Illinois, 1964 Research Interests: Cellular Neural Networks and their applications Nonlinear circuits and chaos theory Complex systems and bifurcation analysis Notable Achievements: Coined the term 'memristor' in 1971, later validated experimentally Founded the Cellular Neural Network (CNN) paradigm Recipient of the Top 15 Most Cited Author in Engineering (2002) Lab & Teams: He leads the NOEL (NOnlinear ELectronics) Lab at UC Berkeley, focusing on nonlinear circuits and systems.

Anna VlasitsView profile

Assistant Professor

• Neuroscience
• Ophthalmology
• Neurobiology
• +3 more

Anna Vlasits, PhD is an Assistant Professor in the Department of Ophthalmology and Visual Sciences at the University of Illinois at Chicago. Her research focuses on understanding retinal neural circuits in health and disease, particularly how retinal neurons process visual information and how this is altered in neurodevelopmental disorders like Fragile X syndrome. She directs the Vlasits Lab, which employs advanced microscopy and computational methods to study retinal function. Her work has received awards from the Knights Templar Eye Foundation, L’Oréal/UNESCO, and the Grass Foundation. Education: PhD in Neuroscience from UC Berkeley (2012) Postdoctoral research at University of Tübingen (Germany) and Northwestern University Research interests include motion and color processing in retinal neurons, synaptic interactions, and retinal dysfunction in neurodevelopmental disorders. Recent studies explore GABAergic modulation of chromatic processing and retinal abnormalities in Fragile X syndrome mouse models. Awards: Knights Templar Eye Foundation Award, L’Oréal/UNESCO Prize for Women in Science, Grass Foundation Award Lab activities include hiring postdoctoral researchers and developing novel imaging techniques for deep-tissue neural recording. Collaborations involve advanced optical tools and interdisciplinary neuroscience approaches.

• Topological Data Analysis
• Machine Learning
• Biomedical Engineering
• +3 more

Dhananjay Bhaskar is a Research Fellow in the Department of Genetics at Yale School of Medicine, Yale University. His research integrates topological data analysis, machine learning, and mathematical modeling to address problems in biophysics and biomedical research. He holds a PhD in Biomedical Engineering and Sc.M. in Data Science from Brown University, and degrees in Computer Science and Applied Mathematics from the University of British Columbia. His work spans applications such as immune response analysis, epidermal regeneration, IVF embryo selection, and cancer cell migration. Education: PhD Brown University (2021), ScM Data Science (2021), MSc Applied Mathematics (2017, UBC), BSc Computer Science (2015, UBC). Research interests focus on leveraging interdisciplinary methods to understand complex biological systems. Key areas include topological analysis of single-cell data, machine learning for medical diagnostics, and computational modeling of cellular dynamics. His work on glial scar formation and calcium signaling in epidermis regeneration has advanced understanding of tissue repair mechanisms. Notable recognition includes the 2024 Kavli Postdoctoral Fellowship for his project on neuroenergetic principles in C. elegans neural circuitry. Collaborations include work with Smita Krishnaswamy on immune response prediction and Valentina Greco on epithelial regeneration. Publications highlight contributions across neuroscience, immunology, and cell biology, with recent focus on multimodal AI for psychiatry and geometric scattering methods for molecular analysis.

Ümmü Seleme Nizam BayrakView profile

Researcher

• Hyperuniformity
• Nonlinear dynamics
• Random matrix theory
• +3 more

Ümmü Seleme Nizam Bayrak is a Researcher at the Faculty of Electrical Engineering and Information Technology, Ruhr University Bochum, affiliated with the Simply Complex Lab. She holds a Ph.D. in Electrical Engineering and Information Technology from Ruhr University (2019–2025), an M.Sc. in Physics from Boğaziçi University (2014–2019), and a B.Sc. in Mathematics from the same institution (2009–2014). Her research focuses on hyperuniformity , nonlinear dynamics , and complex systems , with applications in materials science, biophysics, and interdisciplinary studies. Recent work explores self-organization in dissipative systems and universality across scales, from quantum dots to biological cells. Her publications highlight advancements in laser-based synthesis techniques and nonlinear systems, reflecting a blend of experimental and theoretical approaches. She actively contributes to the Simply Complex Lab’s projects, investigating emergent phenomena in driven systems and complex networks.

Keith BlowView profile

Professor

• Photonics
• Optical Networks
• Optical Processing
• +4 more

Keith Blow is a Professor in Electronic Engineering at Aston University, leading the Photonics Research Group and the Adaptive Networks Communications Research Group. He holds a BA (First Class Honours) in Physics and Theoretical Physics from Cambridge University (1978) and a PhD in Solid State Physics from the Cavendish Laboratory (1981). Prior to joining Aston in 1999, he worked at BT Research Laboratories, focusing on optical fiber technologies and nonlinear effects. His research spans photonics, optical networks, adaptive communication systems, and energy-efficient protocols. Key areas include soliton-based transmission, nonlinear optical processing, and wireless sensor networks. He has supervised 6 students and contributed to over 99 publications, with notable work on soliton crystals, optical frequency combs, and FSO channel optimization. Blow serves on the editorial board of the Journal of Modern Optics and reviews for conferences like the Advanced Photonics Congress. His labs focus on advancing optical communication systems, network efficiency, and sensor network applications, emphasizing practical implementations of theoretical findings.

• Computational Mathematics
• Mathematical Biology
• Numerical Analysis
• +3 more

Dr. Andreas Aristotelous is an Associate Professor in the Department of Mathematics at the University of Akron's College of Engineering and Polymer Science. His research focuses on computational mathematics, mathematical biology, and numerical analysis, with applications in biomedical modeling, fluid dynamics, and materials science. He specializes in developing and analyzing numerical methods for partial differential equations, particularly those modeling complex biological systems and physical phenomena. His work integrates advanced computational techniques like discontinuous Galerkin methods, hybrid discrete-continuum models, and deep learning approaches to study problems such as biofilm dynamics, SARS-CoV-2 infection mechanisms, and developmental biology processes like dorsal closure in Drosophila. Recent studies emphasize heterogeneity in medical data, immune response modeling, and microgravity biofilm growth. Dr. Aristotelous's publications span computational epidemiology, fluid dynamics, and interdisciplinary applications of mathematical modeling. He actively contributes to the scientific community through peer-reviewed articles and maintains a research website at https://blogs.uakron.edu/aaristot/ .

Sylvain SenéView profile

Professor

• Computer Science
• Theoretical Computer Science
• Discrete Mathematics
• +5 more

Sylvain Sené is a Professor of Computer Science at Aix-Marseille University (AMU), affiliated with the Department of Computer Science and Interactions (DII) and the Computer Science and Systems Laboratory (LIS). He leads the ANR-funded project FANs (Foundations of Automata Networks) and focuses on discrete mathematics, theoretical computer science, and computational properties of automaton networks. His research bridges abstract computational models with applications in biology, particularly gene regulatory networks and cellular reprogramming. Research Interests: Automaton networks, Boolean networks, and cellular automata Computational complexity and dynamical systems Discrete mathematics and theoretical computer science Applications in systems biology and genetic networks Key Projects: Principal investigator of the ANR project FANs (2019–2023), focusing on advancing automata network theory Collaborations with CNRS, Chilean universities, and international institutions Advising & Grants: Directed PhD students including Pacôme Perrotin and Martín Ríos Wilson Secured funding through ANR and other national/international grants Labs & Teams: LIS (Computer Science and Systems Laboratory), collaborating with interdisciplinary teams in biology and mathematics.

Yohan JohnView profile

Academic

• Dynamical Systems Theory
• Computational Neuroscience
• Cognitive Neuroscience
• +7 more

Yohan John is a Research Assistant Professor in the Neural Systems Laboratory at Sargent College, Boston University, specializing in computational and systems neuroscience. He holds a PhD from Boston University’s Department of Cognitive and Neural Systems, where he studied under Profs. Dan Bullock and Frank Guenther, focusing on reinforcement learning and timing in the basal ganglia. His postdoctoral work with Prof. Helen Barbas explored cognitive-emotional interactions using computational modeling. Yohan’s research centers on dynamical systems theory as a unifying framework for understanding brain dynamics and psychiatric disorders, particularly how disruptions in neural networks manifest as symptoms. Education: BSc in Physics, St Stephen's College, Delhi (2004) MSc in Physics, Indian Institute of Technology Bombay (2006) PhD in Cognitive and Neural Systems, Boston University (2011) His research interests span dynamical systems theory , computational neuroscience , cognitive-emotional interactions , and psychiatric disorder modeling . He is particularly interested in how brain networks generate stable cognitive and behavioral states (attractors) and how their pathology leads to mental illness. He also investigates broader philosophical and epistemological questions in science, especially the role of metaphor, naming, and boundaries in cognition. Yohan’s recent publications and essays reflect a strong interdisciplinary approach, bridging neuroscience, philosophy, AI, and science communication. His work on semiotic ghosts in LLMs, the power of naming, and visual metaphors in science demonstrates his interest in the intersection of cognition, language, and computation. These writings reveal a consistent theme: understanding complex systems through dynamic, emergent, and metaphorical lenses. Scientific Awards: No scientific awards mentioned in the provided texts. Yohan is actively involved in science outreach, writing for the public on Quora (where he is a top writer in neuroscience) and co-organizing the NeuroLogos YouTube channel, which discusses recent developments in cognitive neuroscience. He has also led discussion groups on dynamical systems, neural representations, and computational theories of mind. While no formal students or grants are listed, his advisory or mentoring roles may exist informally through these educational initiatives. Labs and Teams: Neural Systems Laboratory, Sargent College, Boston University (current affiliation) Emergence Journal Club (organized by Yohan) NeuroLogos YouTube channel (co-organizer and contributor)

• Mathematical Biology
• Biochemical Kinetics
• Systems Biology
• +3 more

Santiago Schnell is the Provost and Professor of Mathematics at Dartmouth College, with adjunct roles in Biochemistry & Cell Biology and Biomedical Data Science. His research integrates mathematical, computational, and statistical approaches to address complex problems in biomedical sciences, focusing on rigor, reproducibility, and robustness. Key areas include enzyme kinetics, systems biology, and scientometrics. Affiliations: Provost, Dartmouth College Professor of Mathematics, Dartmouth College Adjunct Professor of Biochemistry & Cell Biology Adjunct Professor of Biomedical Data Science Research Interests: Schnell's work bridges theoretical and experimental biology through models for enzyme kinetics, reaction networks, and biomedical data analysis. He pioneers methods to standardize enzyme data reporting (e.g., EnzymeML) and evaluates research university performance metrics. His team explores macromolecular crowding effects, protein aggregation, and interdisciplinary applications like medicine-art interfaces. Publications: Recent work emphasizes re-evaluating quasi-steady-state approximations, analyzing ER stress sensors, and modeling neuroblastoma. His articles span mathematical biology, biochemistry, and systems biology, reflecting expertise in both theory and application. Labs/Teams: Leads a multidisciplinary group at Dartmouth, collaborating globally. Current team includes postdoc Abhishek Mallela and graduate student Isabella Gimon, advancing projects on biochemical kinetics and biomedical data science.

• Network Science
• Nonlinear Dynamics
• Machine Learning
• +4 more

Michelle Girvan is a Professor in the Department of Physics at the University of Maryland, College Park (UMD), with affiliations in the Institute for Physical Science and Technology and the Institute for Research in Electronics and Applied Physics. Her research focuses on network science applications in biological, social, and technological systems, integrating machine learning and nonlinear dynamics. She holds a B.S. from MIT (1999) and a Ph.D. from Cornell University (2003), followed by a postdoctoral fellowship at the Santa Fe Institute. Notable awards include the UMD Distinguished Scholar-Teacher (2022) and Fellow of the American Physical Society. Her work spans interdisciplinary topics such as information cascades, epidemiology, and genetic regulatory networks. Recent projects emphasize hybrid forecasting models combining machine learning with knowledge-based systems. Girvan leads the Girvan Networks Lab, advising over 20 graduate students and postdocs. She teaches courses like Physics 165 (Programming for Physical Sciences) and Physics 615 (Nonlinear Dynamics). Her $3M NSF grant supports training in network biology, leveraging computational methods to address complex systems challenges. Scientific contributions include studies on reservoir computing for dynamical systems prediction, jet lag asymmetry modeling, and network robustness in neuronal systems. She serves on the Santa Fe Institute's External Faculty and Science Steering Committee, promoting interdisciplinary collaboration in network science.

• Mathematical Biology
• Systems Biology
• Biochemical Networks
• +11 more

Robyn Araujo is a prominent researcher in mathematical and systems biology, with a focus on the design principles of biochemical networks, particularly those enabling robust perfect adaptation and homeostasis. Her interdisciplinary work bridges mathematics, biology, and medicine, with applications in cancer, immunology, and metabolic regulation. Her research interests center on the topological and dynamical properties of biological networks. She investigates how network structure enables robustness and adaptation in systems such as cholesterol regulation, T-cell dynamics, and protein-protein interactions. Her work often employs control theory, algebraic methods, and computational modeling to uncover universal design principles in complex biological systems. The analysis of her recent publications reveals a strong trend toward understanding adaptation mechanisms in biochemical networks using topological and control-theoretic approaches. She frequently collaborates with Lance A. Liotta, focusing on the application of mathematical models to cancer biology and biomarker discovery. Her work spans theoretical foundations and practical biomedical applications, including immunovirotherapy and ecological resilience. Robyn Araujo has made significant contributions to the field through high-impact publications in journals such as Nature Communications , PLoS Computational Biology , and Bulletin of Mathematical Biology . Her research has been widely cited, indicating strong influence in mathematical biology. She has advised or collaborated with numerous researchers across disciplines, contributing to projects in cancer modeling, tuberculosis biomarkers, and opinion dynamics. While specific grant information is not provided, her sustained publication output suggests successful funding and collaborative research programs. Her work often involves interdisciplinary teams and computational modeling environments, though specific lab or team names are not mentioned in the provided text.