Find Academic Supervisors

Ran LeviView profile

Professor

• Algebraic Topology
• Homotopy Theory
• Neuro-Topology
• +7 more

Professor Ran Levi is a Chair in Mathematical Sciences at the University of Aberdeen, affiliated with the School of Natural and Computing Sciences and the Department of Mathematics. His research bridges pure mathematics and neuroscience, focusing on algebraic topology and its applications to neural systems. BSc in Mathematics, Hebrew University, Jerusalem (1988) PhD in Mathematics, University of Rochester (1993) His research interests include Algebraic Topology , Homotopy Theory of classifying spaces , Combinatorial Topology , and Neuro-Topology —the application of topological methods to neuroscience. He leads the Aberdeen Neuro-Topology Research Group and collaborates with the Blue Brain Project at EPFL. His work explores how topological and geometric frameworks can model and predict neural structures, potentially inspiring new mathematical concepts. Recent publications reveal a strong trend in topological data analysis of neural circuits , p-local groups , and combinatorial constructions in topology. His interdisciplinary work appears in journals such as iScience , eLife , PLOS ONE , and Algebraic & Geometric Topology . His research has been supported by grants including: EPSRC: Topological Analysis of Neural Systems (EP/P025072/1) Collaboration with École Polytechnique Fédérale de Lausanne (Blue Brain Project) Professor Levi has advised several researchers, including Dejan Govc, Janis Lazovskis, Henri Riihimaki, and Jason Smith. He is actively involved in international collaborations with mathematicians and neuroscientists across Europe and the US. He is a key member of the Institute of Pure and Applied Mathematics (IPAM) at Aberdeen and contributes to the AberdeenML and Cybersecurity and Privacy research groups. His work exemplifies the growing synergy between mathematics and computational neuroscience.

• Systems biology
• Artificial Intelligence
• Neural Networks
• +7 more

Professor Sandhya Samarasinghe is a leading researcher at Lincoln University's School of Landscape Architecture where she directs the Complex Systems, Big Data and Informatics Initiative (CSBII). With expertise spanning computational biology, AI, and complex systems modeling, she bridges theoretical frameworks with practical applications across biological, agricultural, and environmental domains. PhD, Virginia Tech, Blacksburg, United States MS, Virginia Tech, Blacksburg, United States MSc, International University of Moscow, Moscow, Russia Professor Samarasinghe's research centers on developing advanced computational methodologies for modeling complex systems. Her work explores how soft computing techniques—including neural networks, fuzzy systems, and machine learning—can unravel complexity in biological networks and environmental systems. She advances the view of living organisms as evolved information systems and develops conceptual frameworks for understanding biological complexity from molecular to organismal levels. Her research has significant applications in medical diagnostics, sustainable agriculture, and environmental management. Analysis of her recent publications reveals a strong emphasis on neural network applications across diverse domains, with particular focus on biological systems modeling, Alzheimer's disease research, agricultural technology, and environmental sustainability. Her work consistently integrates theoretical computational approaches with practical problem-solving. Fellow of Modelling and Simulation Society of Australia and New Zealand Senior Member of IEEE Visiting Fellow at Oxford University Visiting Fellow at Princeton University Visiting Fellow at Stanford University Visiting Fellow at CSIRO Professor Samarasinghe has supervised over 25 postgraduate students across diverse research areas including mastitis detection in dairy cattle, neural modeling of cell cycles, autonomous self-repair systems, and computational approaches to vaccine development. She leads the Complex Systems, Big Data and Informatics Initiative which develops cutting-edge computing solutions for complex biological and environmental challenges.

• Neuroscience
• Complex Systems Analysis
• Statistical Physics
• +3 more

Ben Fulcher is an Associate Professor and Senior Lecturer in the School of Physics at The University of Sydney, leading the Dynamics and Neural Systems Group. His research focuses on applying statistical and physics-based methods to study complex systems, with a strong emphasis on neuroscience. He investigates brain structure and function dynamics, particularly exploring the interplay between genetics, neural connectivity, and information processing mechanisms. Fulcher has pioneered tools like hctsa (highly comparative time-series analysis) for analyzing large-scale neuroimaging and physiological data. Research Interests Complex systems analysis Neural dynamics and connectomics Feature-based time-series analysis Gene-brain connectivity relationships Imaging transcriptomics Recent Work Trends Fulcher's recent articles emphasize time-series analysis applications in neuroscience, including studies on anesthesia effects, brain connectivity, and criticality in neural systems. His work bridges computational methods (e.g., generative models, matrix-product states) with biological insights, often involving interdisciplinary collaborations. Key themes include understanding how brain geometry influences dynamics and translating genetic data into connectomic predictions. Grants & Projects 2024: ARC Future Fellowship for sleep research using complex systems methods 2023: Discovery Project on network neuroscience and AI integration Advising & Labs Fulcher supervises research students and directs the Dynamics and Neural Systems Group, which develops open-source tools like the theft R package for time-series analysis. His lab’s work has been featured in media outlets discussing brain structure-function relationships and comparative neurobiology.

Maria BrbicView profile

Assistant Professor

• Machine Learning
• Computational Biology
• Biomedical Informatics
• +3 more

Maria Brbic is an Assistant Professor of Computer Science at EPFL, previously a postdoctoral researcher at Stanford University under Jure Leskovec. Her research focuses on developing machine learning methods for biological and biomedical applications, particularly representation learning of high-dimensional datasets, open-world semi-supervised learning, and single-cell genomics. Her work includes the STELLAR method for spatial cell type discovery (Nature Methods 2022), the ORCA framework for open-world learning (ICLR 2022), and contributions to the Fly Cell Atlas (Science 2022). She is involved in the Chan Zuckerberg Biohub and Neuro-omics projects. She received the University of Zagreb's best thesis award, was recognized as a MIT Rising Star in EECS, and won the Basel Computational Biology Conference best poster award. Her research bridges computer science with cutting-edge biomedical discovery.

Aysim TokerView profile

Researcher

• Computer Vision
• Machine Learning
• Deep Learning
• +3 more

Aysim Toker is a Ph.D. candidate at the Chair for Computer Vision and Artificial Intelligence , affiliated with the Technical University of Munich . She works under the supervision of Prof. Dr. Laura Leal-Taixe and Prof. Dr. Xiaoxiang Zhu on interdisciplinary projects. Research Focus: Deep learning, sequence analysis, and remote sensing. Key Contributions: Advancing video object segmentation, Earth observation models, and anonymization techniques. Collaborations: Partnered with international researchers across multiple institutions. Publications span top venues like eLife , ICCV , NeurIPS , and CVPR , with a focus on: Deep learning architectures for multi-modal tasks Image and video analysis in remote sensing and anonymization Tracking and segmentation algorithms Her work bridges foundational computer vision research with applications in Earth science and privacy-preserving technologies.

Sarah F. MuldoonView profile

Associate Professor

• Network Neuroscience
• Complex Systems
• Computational Neuroscience
• +6 more

Sarah F. Muldoon is an Associate Professor in the Department of Mathematics at the University at Buffalo, SUNY. Her research focuses on network neuroscience and complex systems, integrating tools from mathematics, physics, and computational biology. Education: PhD in Physics, University of Michigan (2009) Her research explores network organization in brain function, particularly in health and pathological conditions like epilepsy. Key areas include: Development of novel network statistics (e.g., small-world propensity, multilayer analysis) Computational modeling of brain dynamics and state transitions Experimental data analysis across neuronal networks, ECoG, and fMRI Software tools for seizure detection and community structure quantification She leads a multidisciplinary group affiliated with the Computational and Data-Enabled Sciences (CDES) Program and Neuroscience Program at UB. Her work bridges theoretical and applied network science, with recent publications examining brain states, pandemic modeling, and personalized network diagnostics.

Javier OrlandiView profile

Assistant Professor

• Neuroscience
• Complexity Science
• Statistical Physics
• +5 more

Javier Orlandi is an Assistant Professor in the Department of Physics and Astronomy at the University of Calgary, where he leads the Complex Neural Systems Lab as a Canada Research Chair. His research bridges systems neuroscience, complexity science, and statistical physics, focusing on understanding distributed coding principles in the brain and their impact on perception and learning. He employs computational and experimental approaches, including large-scale calcium imaging and optogenetic manipulation, in collaboration with institutions such as the Hotchkiss Brain Institute and the Computational Neuroscience Platform. Orlandi earned his Ph.D. in Physics from the University of Barcelona (2015), studying spontaneous activity in neuronal cultures. Subsequent postdoctoral research included work at the University of Barcelona (2015–2016) on Huntington’s disease, followed by the University of Calgary (2016–2019) and RIKEN Center for Brain Science in Japan (2019–2022), where he explored cortical processing during decision-making. His research has addressed criticality in neural systems, network inference, and the impact of genetic mutations on brain activity. Key areas of interest include self-organized criticality, neuro-glia communication, and the development of tools like NETCAL for analyzing calcium imaging data. Orlandi has organized challenges to advance connectomics and holds the Eyes High Postdoctoral Fellowship and Canada Research Chair awards. His work integrates theoretical models with experimental data to uncover fundamental principles of neural computation. Affiliations: Complexity Science Group, Hotchkiss Brain Institute, Computational Neuroscience Platform Education: Ph.D. in Physics (University of Barcelona, 2015) Grants: Canada Research Chair, Eyes High Postdoctoral Fellowship Labs: Complex Neural Systems Lab at University of Calgary

Emma TowlsonView profile

Assistant Professor

• Network Neuroscience
• Brain Networks
• Mental Health
• +3 more

Dr. Emma Towlson is an Assistant Professor in the Department of Computer Science at the University of Calgary's Faculty of Science, serving as Assistant Head of Undergraduate Curriculum. She holds a Full Member position at the Hotchkiss Brain Institute and is a Child Health & Wellness Researcher at the Alberta Children's Hospital Research Institute. Her research focuses on network neuroscience, exploring how brain networks' structure-function relationships underpin health and disease. Key areas include network control theory, mental illness vulnerability, and brain connectivity analysis across species. Dr. Towlson's work bridges computer science, physics, neuroscience, and biology. She investigates structural brain networks' topological features (e.g., rich clubs, modules) and their spatial constraints, applying these insights to disorders like schizophrenia, depression, and Alzheimer's. Her lab uses network control principles to identify therapeutic targets for brain stimulation therapies and personalized medicine approaches. Recent grants include a $250,000 New Frontiers in Research Fund (NFRF) Explorations grant (2022) to study preterm birth's impact on brain networks and mental illness risk. She teaches courses on social network analysis (CPSC 572/672) and data visualization (DATA 601). Collaborative efforts include partnerships with Harvard Medical School and Monash University's Turner Institute. Her team’s interdisciplinary projects include analyzing mouse brain connectomes, simulating connectome perturbations, and developing open-source network neuroscience tools. Ongoing work emphasizes translating network science insights into clinical applications for mental health interventions.

Giuseppe VinciView profile

Assistant Professor

• Astrostatistics
• Bayesian Statistics
• Computational Neuroscience
• +8 more

Giuseppe Vinci is an Assistant Professor at the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame, within the College of Science. His research focuses on probabilistic graphical models, particularly in neuroscience and genomics applications. He holds a Ph.D. in Statistics from Carnegie Mellon University (2017), and completed postdoctoral research and lecturing at Rice University (2017–2020). Vinci’s expertise spans astrostatistics, forensic science, and geometric data analysis. Education: Ph.D. in Statistics, Carnegie Mellon University (2017) M.Sc. in Statistics, Carnegie Mellon University (2013) M.Sc. in Economics and Social Sciences, Bocconi University (2012) B.Sc. in Economics, University of Catania (2009) Research Interests: High-dimensional statistical theory of graphical models, matrix completion, astrostatistics, neuroscience, genomics, forensic science, and geometric data analysis. His work addresses challenges in neuronal functional connectivity, genomic networks, and climate science. Awards: NeuroNex Postdoctoral Trainee (NSF) Rice Academy Postdoctoral Fellow Three-minute thesis competition (Top-10, Carnegie Mellon University) Advising & Grants: Vinci mentors multiple Ph.D., MSc, and undergraduate students in projects spanning forensic statistics, genomics, and astrostatistics. He has secured funding for undergraduate research programs and participated in NIH-funded interdisciplinary training. Labs/Teams: Involved in collaborative projects with institutions like Baylor College of Medicine and Rice University, focusing on neurotheory and statistical methods in neuroscience.

Matthew B. DwyerView profile

Professor

• Software Verification and Validation
• Software Engineering
• Program Analysis
• +4 more

Matthew B. Dwyer is the Robert Thomson Distinguished Professor of Computer Science at the University of Virginia, leading research in software verification, program analysis, and autonomous systems. His work focuses on formal methods for ensuring dependable software, particularly in safety-critical domains like autonomous vehicles. He has advised numerous PhD and Master’s students, many of whom hold academic and industry positions globally. Education: Ph.D. in Computer Science from the University of Massachusetts Amherst, M.S. from UMass Boston, and BSEE from the University of Rochester. Research Interests: Software Verification & Validation, Program Analysis, Formal Methods, Neural Network Testing, Safety-Critical Systems. His lab, the Laboratory for Engineering Safe Software, develops tools like DNNV for verifying deep neural networks. Awards: ACM Fellow (2019), IEEE Fellow (2013), multiple test-of-time and impact paper awards from ISSTA, ICSE, and SIGSOFT conferences. Service: Program chair for ICSE (2022), FSE (2004), and other top venues; editorial roles at IEEE TSE, ACM TOPLAS, and Springer STTT. Co-developed property specification patterns for model checking, widely used in industry and academia.

John KubieView profile

Associate Professor

• Hippocampal function
• Spatial cognition
• Navigation behavior
• +5 more

John Kubie is an Associate Professor in the Department of Cell Biology at SUNY Downstate Medical Center's School of Medicine. His research career spans several decades, with publications dating from the 1980s to the present day. Dr. Kubie has maintained a consistent focus on understanding the neural mechanisms underlying spatial cognition and navigation. Dr. Kubie's primary research interest centers on hippocampal place cells and their role in spatial navigation and cognitive mapping. His work combines experimental neuroscience with computational modeling to understand how populations of neurons represent space and contribute to navigation behavior. He has extensively studied how place cells—pyramidal neurons that fire only when an animal is in specific locations—form the basis of cognitive maps that help rats navigate their environment. His research has explored how place cells adapt to novel environments, how they're affected by hippocampal lesions, and how their firing patterns relate to spatial memory. His publication record shows a consistent trajectory from early foundational work on place cell properties in the 1990s to more recent investigations into hippocampal remapping, neural network dynamics, and computational models of spatial cognition. Recent publications indicate continued active research in computational neuroscience, including studies on theta-resonant pyramidal neurons and the relationship between grid cells and hippocampal function. Dr. Kubie has maintained a long-standing collaboration with Dr. Robert U. Muller, as evidenced by numerous co-authored publications spanning decades. After Dr. Muller's passing, Dr. Kubie co-authored a memorial piece honoring his colleague's contributions to neuroscience. His laboratory has developed sophisticated techniques for recording from multiple neurons simultaneously and has created behavioral tasks to assess spatial capabilities in rats. The work bridges experimental neuroscience with computational approaches, seeking to develop computer models that can solve navigational problems similar to those solved by the biological hippocampus.

Laure SoulierView profile

Lecturer

• Design of language models for Information Retrieval (IR) and Natural Language Processing (NLP)
• Data-to-Text Generation
• Search-Oriented Conversational Systems
• +3 more

Laure Soulier is a HDR (Habilitation à Diriger des Recherches) Lecturer at Sorbonne University within the MLIA team at the ISIR (Institute for Intelligent Systems and Robotics) laboratory. Her research focuses on the design of language models for Information Retrieval (IR) and Natural Language Processing (NLP) applications, including data-to-text generation , search-oriented conversational systems , language models for robotics , and continual learning with domain adaptation . Recent publications highlight her work on cross-encoders, co-speech gesture generation, and latent space metrics. She supervises an ANR-funded postdoctoral researcher position (SCAI/BnF program) and collaborates on projects involving multimodal techniques, user interaction analysis, and document vectorization. Her scientific contributions include best paper awards at CORIA 2021, SCAI@EMNLP 2019, CORIA 2015, and AIRS 2013. Laure Soulier’s work spans collaborative information retrieval models, entity ranking in heterogeneous networks, and neural approaches for knowledge-based IR. She has contributed to evaluation frameworks for LLMs in IR and co-speech gesture generation, with applications in e-commerce search, medical information retrieval, and social media-based collaboration. Her research integrates user roles, document representations, and reinforcement learning techniques.

Susanne GerberView profile

Professor

• Bioinformatics
• Computational Genomics
• Systems Biology
• +2 more

Susanne Gerber is a Professor at iDNA and Adjunct Director at the Institute of Molecular Biology (IMB), Johannes Gutenberg University Mainz (JGU), affiliated with the Faculty of Biology's Bioinformatics department. Her academic journey includes an Assistant Professorship in Bioinformatics at JGU (2015-2020) and postdoctoral research at Università della Svizzera italiana. Her educational background comprises a PhD in Biophysics from Humboldt University of Berlin (2011), an M.Sc. in Bioinformatics from Free University of Berlin and Konrad Zuse Institute (2007), and a B.Sc. in Bioinformatics from Free University of Berlin and Max Planck Institute (2004). Dr. Gerber's research spans Bioinformatics, Computational Genomics, Systems Biology, Molecular Evolution, and Neuroinformatics , focusing on developing computational frameworks for genomic analysis, neurodegenerative disease modeling, and microbiome interactions. Her work integrates machine learning with multi-omics data to address complex biological questions in molecular evolution and neural systems. Analysis of her 15 most recent publications (2024-2025) reveals a strong emphasis on nanopore sequencing applications for RNA modification detection, deep learning frameworks for genomic data enhancement, and neurobehavioral modeling using AI-driven approaches. Key thematic clusters include epitranscriptomics, chromatin dynamics, and computational psychiatry with ethical AI considerations. Her methodological innovations include tools like COMET for network analysis, CCUT for chromatin data enhancement, and ModiDeC for RNA modification classification, demonstrating translational impact across genomics and neuroscience. Dr. Gerber leads research groups at IMB and iDNA focusing on computational genomics, advising students in bioinformatics and securing grants for AI-driven genomic analysis. Her labs develop open-source tools for nanopore data processing and neuroimaging analysis, fostering collaboration between computational and experimental biologists.

• Cybersecurity
• Privacy
• Networking for Big Data
• +4 more

Shui Yu is a Professor of the School of Computer Science in the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS), where he also serves as the Deputy Chair of the UTS Research Committee. His academic career spans over 20 years in Australia and 7 years in China, with additional teaching experience in Hong Kong and Indonesia. He has developed more than 10 units in cybersecurity, computer science, data analytics, and computer games, serving as the Course Director for Computer Science undergraduate programs. Professor Yu's research interests center on cybersecurity, privacy, networking aspects of Big Data, and applied mathematics for computer science. He pioneered the field of 'networking for big data' in 2013 and edited the seminal book 'Networking for Big Data' published in 2015. His work has practical applications in industry, including Amazon Cloud's auto-scale strategy against distributed denial-of-service attacks. Current research focuses include privacy and security concerns associated with big data, security issues in smart grids, anonymous transactions on Blockchain, and anonymous communication for web browsing privacy. Analysis of his recent publications reveals a strong research trajectory spanning cybersecurity, privacy-preserving technologies, networking for big data, and applied mathematics. His work shows increasing focus on quantum-resistant cryptography, federated learning security, and adversarial robustness in AI systems. The interdisciplinary nature of his research bridges theoretical foundations with practical applications in IoT, blockchain, and cloud environments. Fellow of IEEE (2023) Distinguished Lecturer of IEEE Communications Society (2018-2021) Distinguished Visitor of IEEE Computer Society (2022-2024) Professor Yu has secured numerous research grants from the Australian Research Council, including current projects on privacy and fairness in high intelligence models (DP240100955), improved security and privacy for online platforms (LP220200808), and secure blockchain for financial applications (LP220100453). He has served on editorial boards of multiple IEEE journals including IEEE Communications Surveys and Tutorials, IEEE Communications Magazine, and IEEE Internet of Things Journal. His service extends to organizing major conferences such as IEEE Globecom 2015 and IEEE INFOCOM 2016-2017.

Jason SmithView profile

Senior Lecturer

• Combinatorics
• Topology
• Topological Data Analysis
• +12 more

Jason Smith is a Senior Lecturer in Mathematics at the Department of Physics and Mathematics, Nottingham Trent University. His work bridges pure mathematics and neuroscience, focusing on combinatorial and topological methods for analyzing brain networks and functional data. His educational background includes: PhD in Computer and Information Science, University of Strathclyde (2015) MMath, University of Bath (2012) His research lies at the intersection of combinatorics, topology, and neuroscience. He applies tools from graph theory, topological data analysis (TDA), and algebraic topology to study the structure and function of neural systems. Key areas include the analysis of connectomes (such as C. Elegans and Drosophila), modeling brain network dynamics, and developing theoretical frameworks for understanding neural coding through combinatorial and topological lenses. His work often involves collaborations with neuroscientists and computational biologists. His recent publications demonstrate a strong trend toward interdisciplinary research, combining deep mathematical theory with applications in neuroscience. The articles span topics from the topology of directed flag complexes and tournaplexes to the combinatorics of permutation patterns and the Abelian sandpile model, all unified by their relevance to understanding complex biological networks. There is a clear progression from theoretical combinatorics toward computational and applied neurotopology. He has collaborated extensively with researchers from the University of Aberdeen, EPFL (including the Laboratory for Topology and Neuroscience and the Blue Brain Project), and other institutions. He has developed several open-source software tools such as Flagser-count, Tournser, and Deltser for computing persistent homology in directed networks, reflecting his commitment to computational reproducibility and tool development. He has presented his work in various venues, with talks and posters on topics including permutation posets, tournaplexes, and functional brain data classification. His research is supported by collaborative projects focusing on the topological analysis of neural systems and the mathematical foundations of pattern posets.