Find Academic Supervisors

James BriscoeView profile

Professor

• Developmental Biology
• Molecular Signaling
• Transcriptional Networks
• +4 more

James Briscoe is a Senior Group Leader at The Francis Crick Institute in London, where he leads a research group focused on developmental biology and morphogen signaling. He previously held positions at the Medical Research Council's National Institute for Medical Research, which later became part of the Francis Crick Institute. Education: BSc in Microbiology and Virology from the University of Warwick, UK PhD from Imperial Cancer Research Fund/King's College London Postdoctoral training at Columbia University with Thomas Jessell Dr. Briscoe's research focuses on the molecular and cellular mechanisms of graded signaling by morphogens and the role of transcriptional networks in cell fate specification. His laboratory employs a range of experimental and computational techniques using model systems including mouse and chick embryos and embryonic stem cells. His work has significant implications for understanding developmental processes and their relationship to disease. His recent publications demonstrate a continued focus on morphogen gradients, neural tube development, and computational approaches to understanding cell fate decisions. His research increasingly integrates single-cell technologies and computational modeling to unravel the complexities of developmental patterning. Scientific Awards and Honors: EMBO Young Investigator (2001) EMBO Gold Medal (2008) Elected to EMBO (2009) Fellow of the Academy of Medical Sciences (2019) Fellow of the Royal Society (2019) As Editor-in-Chief of the journal Development since 2018, Dr. Briscoe plays a significant role in shaping the field of developmental biology. His leadership extends to mentoring researchers and contributing to scientific policy discussions, as evidenced by his recent publication 'Science under siege: protecting scientific progress in turbulent times.' Dr. Briscoe's laboratory at the Crick Institute is well-equipped with access to advanced facilities including light microscopy, flow cytometry, genomics, and computational resources, enabling a multidisciplinary approach to developmental biology questions.

Rebecca SchulmanView profile

Associate Professor

• DNA Self-Assembly
• Smart Biomaterials
• Synthetic Biology
• +7 more

Rebecca Schulman is an Associate Professor in the Department of Chemical and Biomolecular Engineering at the Whiting School of Engineering, Johns Hopkins University. She holds secondary appointments in Chemistry and Computer Science and is affiliated with multiple interdisciplinary institutes, including the Institute for NanoBioTechnology, the Hopkins Extreme Materials Institute, the Chemistry-Biology Interface Program, the Center for Cell Dynamics, and the Laboratory for Computational Sensing and Robotics. She currently co-directs the Passport to Future Technology Leadership program for PhD students. Research Interests: Schulman's research lies at the intersection of DNA nanotechnology, synthetic biology, and smart materials. Her group develops intelligent, adaptive biomolecular materials and nanostructures by integrating concepts from materials science, biochemistry, circuit design, and soft matter physics. The team focuses on engineering dynamic self-assembly processes using DNA to create reconfigurable materials, molecular circuits, and autonomous soft micro-robots. Key themes include self-healing nanostructures, feedback-regulated crystallization, programmable hydrogels, and synthetic genetic networks for materials control. Publication Trends: Her recent publications demonstrate a consistent focus on using DNA-based chemical reaction networks to program spatial and temporal behavior in materials. The work spans from fundamental mechanisms like catalytic polymerization and crystal growth regulation to applications in soft robotics, self-wiring circuits, and synthetic pattern formation. The research is highly interdisciplinary, combining synthetic biology with materials engineering to achieve life-like functionalities in non-living systems. Scientific Awards: AIMBE Fellowship Award Vannevar Bush Faculty Fellowship Award Hartwell Individual Biomolecular Research Award President’s Early Career Award in Science and Engineering (PECASE) DARPA Young Faculty Award DARPA Directors Fellowship NSF CAREER Award Turing Scholar Award DOE Early Career Award Advising and Grants: Schulman mentors graduate students and leads a vibrant research group focused on next-generation biomolecular engineering. Her work is supported by major federal grants, including the NSF CAREER, DOE Early Career, DARPA, and the Vannevar Bush Fellowship—a prestigious Department of Defense award for basic research. She is actively involved in training future leaders through programs like the Passport to Future Technology Leadership. Labs and Teams: The Schulman Lab at Johns Hopkins is a multidisciplinary team working on DNA-powered materials and molecular programming. The lab is embedded within several collaborative centers, enabling strong cross-departmental and cross-institutional research. Their work combines experimental biochemistry with theoretical modeling to design and implement complex molecular systems.

• Sports Science
• Football (Soccer) Analytics
• Cognitive Psychology in Sports
• +6 more

Prof. Daniel Memmert is a Professor at the German Sport University Cologne, leading research in Sport Informatics and Sports Games within the Institute of Exercise Training and Sport Informatics. His work focuses on cognitive aspects of sports performance, decision-making, and data-driven analysis in football (soccer) and other sports. He has published extensively on topics like penalty kick strategies, home advantage dynamics, artificial intelligence applications in coaching, and route-setting in climbing. Memmert's research bridges sports science, computer science, and psychology, with over 550 publications and 41 projects to his name. He frequently engages with media, explaining complex sports phenomena to the public. Key Research Areas: Football analytics, cognitive psychology in sports, sports technology, decision-making under pressure Media Contributions: Over 50 media features discussing topics such as AI in coaching, referee bias, and athlete creativity Projects: Includes initiatives on sports data visualization, performance metrics, and prevention of sports betting addiction His work emphasizes translating academic findings into practical tools for athletes, coaches, and sports organizations, combining rigorous data analysis with real-world applications.

Stefan LutherView profile

Professor

• Biomedical Engineering
• Cardiac Physiology
• Nonlinear Dynamics
• +3 more

Prof. Dr. Stefan Luther is a Max Planck Research Group leader (W2, tenured since 2013) at the Max Planck Institute for Dynamics and Self-Organization, Göttingen, and an Honorarprofessor at the Faculty of Physics, University of Göttingen. He holds adjunct roles as Adjunct Associate Professor at Cornell University (2009–2012) and Northeastern University (2016–2018), and serves as DZHK-Professor at the Institute of Pharmacology and Toxicology, University Medical Center Göttingen. His research focuses on nonlinear spatiotemporal dynamics in excitable biological media, particularly cardiac arrhythmias. He pioneered 4D imaging of heart function and developed algorithms for optogenetic and electrical control of arrhythmias. Translational efforts span basic research to preclinical and clinical studies. Education includes a Diplom in Physics (1997) and PhD (2000) from Georg-August-University, Göttingen. Postdoctoral training followed at the University of Twente (2001–2004) and Cornell University’s LASSP (2004–2006). His lab, the Biomedical Physics group, explores electromechanical coupling in cardiac systems and develops novel therapeutic approaches. Collaborations include work on computational modeling, uncertainty quantification in dynamical systems, and fluid dynamics of multiphase flows.

• Applied Dynamical Systems
• Data Science
• Computational Biology
• +3 more

Bjorn Sandstede is the Alumni-Alumnae University Professor of Applied Mathematics at Brown University. His research focuses on applied dynamical systems, nonlinear waves, pattern formation, and computational biology. He holds a PhD from the University of Stuttgart and has held faculty positions at The Ohio State University and the University of Surrey before joining Brown in 2008. Sandstede has received numerous awards, including the SIAM J.D. Crawford Prize and the Royal Society Wolfson Research Merit Award. He served as Department Chair at Brown and directed the Data Science Initiative. His work involves interdisciplinary collaborations, such as modeling zebrafish stripe formation and developing computational tools like SCOT for single-cell data integration. Sandstede also mentors extensively, advising over 30 PhD students and postdoctoral researchers. He leads the NSF-funded Institute for Computational and Experimental Research in Mathematics (ICERM) and contributes to initiatives promoting diversity and inclusion in STEM. Education: PhD in Mathematics, University of Stuttgart Undergraduate Degree, University of Heidelberg Research Interests: Applied Dynamical Systems Nonlinear Waves and Pattern Formation Computational Biology Data Science PDE Analysis Awards and Recognition: Alfred P. Sloan Research Fellowship SIAM J.D. Crawford Prize Royal Society Wolfson Research Merit Award Elsevier Jack Hale Award Teaching Excellence Awards from Brown University Fellow of the AMS and SIAM Grants and Leadership: Principal Investigator of NSF grant establishing ICERM Director of Brown's Data Science Initiative Member of Research Advisory Board and Tenure Committees Labs and Teams: Leads the Sandstede Lab at Brown, focusing on computational biology and dynamical systems. Collaborates with the Volkening Lab on zebrafish pattern modeling and the Singh Lab on optimal transport methods.

Kunihiko KanekoView profile

Professor

• Theoretical Biophysics
• Complex Systems
• Chaos
• +6 more

Kunihiko Kaneko is a Professor at the Niels Bohr Institute, University of Copenhagen, with a distinguished career in theoretical biophysics and complex systems. He received his PhD and MSc in Physics from the University of Tokyo, and has held leadership roles at the Universal Biology Institute and Center for Complex Systems Biology. PhD Physics, 1984 - University of Tokyo MSc Physics, 1981 - University of Tokyo His research spans five primary areas: Universal Biology, Evolutionary Constraints, Ecosystem Dynamics, Neural Cognition, and Universal Anthropology. He has published extensively on multi-level consistency principles, dimensional reduction in biological systems, and reciprocity between robustness and plasticity across scales. Recent publications show strong focus on microbial ecosystems (2025), evolutionary game theory (2025), neural modular architectures (2024), and dimensional reduction in cellular systems (2024). His work bridges physics and biology through dynamical systems theory applied to diverse phenomena from protocells to human societies.

Gian-Luca OppoView profile

Professor

• Nonlinear Photonics
• Quantum Cavity Solitons
• Bose-Einstein Condensates
• +2 more

Gian-Luca Oppo is Professor of Computational and Nonlinear Physics at the University of Strathclyde and Director of the Institute of Complex Systems. His research spans nonlinear photonics, quantum cavity solitons, Bose-Einstein condensates, and optical pattern formation. Oppo develops theoretical models for laser dynamics, quantum correlations in light sources, and soliton formation in microresonators. Recent work (2024-2025) explores topological photonics applications in frequency combs, polarization symmetry breaking for optical Ising machines, and optomechanical quantum droplet dynamics. He has made fundamental contributions to understanding spontaneous symmetry breaking in Kerr resonators and control of extreme optical events. Oppo's group collaborates internationally on experimental implementations of photonic computing architectures and quantum sensing technologies. Honors include the Occhialini Medal (2011), Royal Society-Leverhulme Senior Research Fellowship (2003), and fellowships from the Royal Society of Edinburgh, OSA, and Institute of Physics.

• Neural Dynamics
• Computational Neuroscience
• Spatiotemporal Patterns
• +3 more

Dr. Pulin Gong is an Associate Professor in the School of Physics at the University of Sydney. His research focuses on understanding the self-organizing mechanisms of neural circuits' spatiotemporal dynamics and their computational principles. He investigates distributed dynamic computation via propagating neural waves, irregular neural activity variability, and coherent spatiotemporal patterns in large-scale neural data. His work combines experimental and computational approaches to unravel neural coding principles. Research interests include: Distributed dynamic computation (e.g., visual feature integration) Irregular neural dynamics and membrane potential fluctuations Coherent spatiotemporal wave patterns (e.g., spiral waves) Recent projects involve analyzing cortical wave patterns in mice and primates, fractional neural sampling, and Lévy walk dynamics in neural systems. Collaborators include institutions like Fudan University and Kyoto University. Current research student: Andrew LY, working on cortico-cortical loop dynamics and AI applications.

• Statistical Modeling
• Biostatistics
• Biomedicine
• +7 more

Dae-Jin Lee is an Assistant Professor at IE University’s School of Science and Technology, specializing in statistical modeling and data science. Previously, he served as a Research Line Leader at the Basque Centre for Applied Mathematics (BCAM) and coordinated the Knowledge Transfer Unit in Data Science/AI. His academic background includes a Ph.D. in Mathematical Engineering (2010) from Universidad Carlos III de Madrid and postdoctoral research at CSIRO (Australia). His research focuses on statistical methods for complex data, including penalized splines, tensor product smooths, and applications in biomedicine, epidemiology, environmental science, and sports analytics. He has led multidisciplinary projects funded by public and industry grants, collaborating globally with experts across fields like engineering, medicine, and biology. Key research themes include predictive modeling for health outcomes (e.g., SARS-CoV-2 pneumonia severity), sports injury prevention, and AI in healthcare. His work integrates machine learning with traditional statistical techniques, addressing real-world challenges like pedestrian dynamics simulations and automated medical diagnostics. He is actively involved in scientific organizations, including the Spanish Biostatistics Society and the Statistical Modelling Society. His recent publications highlight innovations in growth curve modeling, AI ethics, and spatiotemporal data analysis, reflecting his commitment to advancing both theoretical and applied statistics.

Alex ArenasView profile

Professor

• Complex Systems
• Network Science
• Multilayer Networks
• +12 more

Alex Arenas is a Full Professor in the Department of Computer Engineering and Mathematics at Universitat Rovira i Virgili (URV), Tarragona, Spain. He is also an External Faculty member at the Complexity Science Hub in Vienna and Chief of Complex Systems Science at the Pacific Northwest National Laboratory, USA. His research spans complex systems, network science, computational epidemiology, and multilayer dynamics, with applications in public health, neuroscience, and social systems. Research Interests: His work focuses on the physics of multilayer networked systems, particularly the interplay between structure and function in complex networks. Key areas include synchronization, epidemic modeling, network medicine, the physics of the microbiome, and higher-order interactions in spreading processes. He investigates dynamic transitions using functional multilayer frameworks and develops models for real-world systems like urban mobility and misinformation diffusion. The recent articles highlight a strong trend in computational epidemiology, especially post-COVID modeling of vaccination strategies, rebound dynamics, and wastewater surveillance. There is also significant work on synchronization in oscillator networks, chimera states, and higher-order network effects, reflecting a deep engagement with nonlinear dynamics and theoretical network science. Applications span medicine, urban planning, and social systems. Scientific Awards: Fellow, American Physical Society (2018) Fellow, Network Science Society (2020) ICREA Academia (2011, 2017, 2022) Narcís Monturiol Medal (2022) Web Science Trust Test of Time Award (2024) Complex Systems Society Senior Award (2024) Advising and Grants: Arenas has supervised numerous PhD students and postdoctoral researchers, though specific names are not listed. He has been Principal Investigator on 47 research projects, including EU FP7 projects, a James S. McDonnell Foundation grant, and Horizon Europe's CREXDATA project. He has served as an editor for Physical Review E , Journal of Complex Networks , and Network Neuroscience , and has reviewed for major funding agencies including ERC, MINECO, and international bodies. Labs and Teams: He leads the Alephsys Lab at URV, which develops tools like Radatools for network analysis and community detection. His team focuses on interdisciplinary modeling of real-world complex systems using data-driven and theoretical approaches.

• Organoid Engineering
• Systems Biology
• Developmental Biology
• +3 more

Keisuke Ishihara is an Assistant Professor in the Department of Computational and Systems Biology at the University of Pittsburgh School of Medicine. His research focuses on engineering human brain and cardiac organoids using genetic, chemical, and computational approaches to uncover novel regulatory mechanisms and physical principles underlying tissue development. His lab is located at Biomedical Science Tower 3, with an office in room 10020A. Dr. Ishihara holds a PhD in Systems Biology from Harvard University. His work bridges synthetic biology, developmental biology, and biophysics to address fundamental questions in organogenesis and cellular morphogenesis. Recent research highlights include studies on BMP-mediated neural tube patterning in organoids and the biophysical dynamics of microtubule assemblies in large cells. Publications from his lab emphasize interdisciplinary approaches to understand cell size scaling, mitotic spindle dynamics, and self-organization in synthetic tissues. His team has contributed to advancements in organoid technology, uncovering dormant genetic programs and physical principles governing tissue architecture. Laboratory activities are centered at the University of Pittsburgh, collaborating with the School of Medicine's computational and systems biology initiatives. For more details, visit his lab website linked below.

David B. BensimonView profile

Professor

• Biophysics
• Single-Molecule Studies
• DNA Mechanics
• +7 more

David B. Bensimon is a world-leading biophysicist and Professor in the Department of Chemistry and Biochemistry at the University of California, Los Angeles, holding the prestigious Regent's Professor title since 2007. He maintains a dual academic position, serving as Directeur de Recherche at the French National Center for Scientific Research (CNRS) at the Ecole Normale Supérieure (ENS) in Paris while teaching and conducting research at UCLA for one quarter each year. His academic journey began with a Ph.D. from the University of Chicago in 1986 under Leo Kadanoff, followed by postdoctoral research at Bell Laboratories and ENS Paris. Professor Bensimon's research spans multiple frontiers in biophysics and molecular biology, with particular expertise in single-molecule studies of nucleic acids and their proteins. His laboratory pioneered the Magnetic Trap technique for manipulating individual DNA molecules, enabling groundbreaking investigations into DNA mechanics, topoisomerase interactions, and molecular combing. His recent work has expanded into optogenetics, developmental biology using zebrafish models, and cancer research, with significant contributions to understanding how single-cell oncogene activation leads to tumorigenesis. His research output shows remarkable breadth across disciplines, with recent publications spanning biophysics, developmental biology, cancer research, and genomic technology development. Bensimon's work on opto-chemical tools has particularly transformed how researchers can control biological processes with unprecedented spatiotemporal precision, especially in zebrafish models. His laboratory has developed photoactivatable versions of key molecular tools including Cas9 (OptoCas9) and cyclofen systems that allow precise control of protein activity at the single-cell level. 2007 Regent's Professor at UCLA 1997 Vinci of Excellence Award for phospholipid vesicle research 1994 Jacques Monod Prize for Molecular Combing discovery Special Prize of the French Physical Society for DNA mechanics work ICAM Fellow KITP-UCSB Representative Bensimon has made significant contributions to both basic science and translational applications, co-founding Depixus for nucleic acid sequencing and epigenetic analysis. His laboratory continues to push boundaries in single-molecule biophysics while expanding into developmental biology and cancer research, with recent work demonstrating that activation of kRas in dedifferentiated cells increases tumorigenesis probability by two orders of magnitude. His mentorship has produced notable researchers including X. Michalet, and his theoretical work extends to the philosophical unification of scientific disciplines as evidenced by his book "The Unity of Science".

Rastko SknepnekView profile

Professor

• Pattern formation in complex geometries
• Physics of biological membranes
• Physics of artificial membranes
• +5 more

Rastko Sknepnek is a Chair of Biological Physics at the University of Dundee , affiliated with both the School of Science and Engineering (Physics department) and the School of Life Sciences (Computational Biology). His research focuses on pattern formation in complex geometries, physics of biological/artificial membranes, active matter systems, and computational biophysics. PhD in Physics (2004, Missouri S&T) Postdoctoral training at McMaster, Iowa State/Ames Lab, Northwestern, and Syracuse University Joined University of Dundee in 2013 as Lecturer and Dundee Fellow Current work explores cellular homeostasis , actomyosin dynamics , and collective cell behavior , with recent publications analyzing epithelial monolayers, active matter models, and developmental mechanics. His 2025 projects include AI applications for drug resistance and cell shape quantification. Scientific Awards : Distinguished University Postdoctoral Fellowship (Syracuse, 2012) Dundee Fellow (2013) He leads the Computational Soft Condensed Matter and Biophysics Group , collaborating with institutions like University of Oxford, UCL, and University of Bristol. Grants include £2.1 million from UKRI for embryonic self-organization research and BBSRC funding for cell dynamics studies.

Behrooz YousefzadehView profile

Associate Professor

• Mechanical Vibration
• Nonlinear Dynamics
• Elastic Wave Propagation
• +5 more

Behrooz Yousefzadeh is an Associate Professor in the Department of Mechanical, Industrial and Aerospace Engineering at Concordia University, Montreal. He leads the Wave and Vibration Engineering (WAVE) Lab, affiliated with the Applied Mathematics Lab of Quebec’s Centre de Recherches Mathématiques (CRM) and the Concordia Institute of Aerospace Design and Innovation (CIADI). His research focuses on nonlinear dynamics, mechanical metamaterials, architectural acoustics, and elastic wave propagation in periodic systems. His work bridges engineering, applied physics, and mathematics, with applications in vibration analysis of turbomachinery and novel wave-steering materials. Research Interests Mechanical vibrations and nonlinear dynamics Elastic wave propagation and metamaterials Stability analysis and architectural acoustics Nonreciprocal wave phenomena in spatiotemporally modulated systems Publications & Trends Recent work emphasizes nonreciprocal dynamics in modulated materials, phase-preserved wave steering, and defect engineering in periodic systems. Key contributions include experimental validation of nonreciprocal wave propagation and computational methods for nonlinear system analysis. Over 25 peer-reviewed articles highlight advancements in metamaterial design, parametric instability, and coiling fluid dynamics. Scientific Awards Best Paper Award at the International Symposium on Optomechatronic Systems (2014) Advising & Grants Supervised 6 students to completion (PhD/MASc). Active in securing research funding through collaborative projects with CRM, CIADI, and industry partners. Organized sessions at major conferences like SIAM, ICTAM, and Phononics. Labs & Collaborations WAVE Lab explores cutting-edge topics including: nonlinear wave steering, acoustic black holes in timber structures, and coiling patterns in fluid mechanics. Collaborations span applied mathematics, materials science, and aerospace engineering.

Stefan KlusView profile

Lecturer

• Data-driven model reduction
• Transfer operator approximation
• Machine learning
• +8 more

Dr. Stefan Klus is a Lecturer at the School of Mathematics and Physics, University of Surrey. His research focuses on data-driven model reduction, transfer operator approximation, and kernel-based machine learning applied to dynamical systems. He specializes in interdisciplinary applications across quantum physics, fluid dynamics, and computational biology. Education: PhD in Industrial Mathematics (2011, Paderborn University) and Habilitation (2020, Freie Universität Berlin). Research Interests : Data-driven modeling and reduced-order methods Koopman operator theory and transfer operators Machine learning for dynamical systems (e.g., Deeptime library) Tensor decompositions and quantum systems analysis Graph-based analysis (e.g., microbiome dynamics) Publications : Klus has contributed to over 50 peer-reviewed articles, with recent work emphasizing: Kernel methods for quantum chemistry and physics Tensor-based approaches for high-dimensional systems Applications in climate science (e.g., Pacific SST modeling) Agent-based modeling and social systems Technical Contributions : Co-developer of the Deeptime Python library for dynamical modeling Pioneer in Koopman operator-based model reduction Advanced graph kernel methods for microbiome analysis