Find Academic Supervisors

Iain CouzinView profile

Professor

• Collective Behavior
• Animal Behavior
• Neuroscience
• +3 more

Professor Iain Couzin is a leader in the study of collective behavior at the University of Konstanz and director of the Max Planck Institute of Animal Behavior. He holds roles in the Cluster of Excellence Centre for the Advanced Study of Collective Behaviour (CASCB) and the Department of Biology. His research integrates biology, robotics, and computational science to understand how groups of animals (e.g., locusts, fish, birds) coordinate decisions and movements. Key tools include virtual reality for animals, automated tracking systems like TRex, and the Centre for Visual Computing of Collectives (VCC) facility. Research focuses on emergent collective phenomena, such as swarming in locusts, schooling in fish, and cooperative hunting. He emphasizes interdisciplinary approaches, collaborating with neuroscientists and engineers to bridge scales from neural circuits to global migration patterns. Notably, his work on locust swarms has societal implications for predicting agricultural threats. Awards: Highly Cited Researcher (Clarivate Analytics, 2018-2022). Facilities: Imaging Hangar, VCC research building, and novel VR setups for animal studies. Future Goals: Exploring collective memory, cross-species communication, and neuron-to-society-level decision-making. His grants and collaborations support cutting-edge experiments, including multi-scale locust studies and the development of algorithms for collective sensing in robotics. Leadership in bridging theory, experiment, and application positions him at the forefront of this interdisciplinary field.

Luca MazzucatoView profile

Associate Professor

• Neuroscience
• Computational Biology
• Behavioral Science
• +2 more

Luca Mazzucato is an Associate Professor in the Department of Biology at the University of Oregon's College of Arts and Sciences. His research focuses on neuroscience, computational biology, neural networks, and behavioral science, investigating neural circuit dynamics, decision-making, and behavioral organization through computational modeling and experimental approaches. Research interests span theoretical and experimental neuroscience, including probabilistic modeling of behavior, neural ensemble dynamics, sensory processing, and computational approaches to understanding brain function across species from zebrafish to mammals. Recent publications explore neural basis of behavior, decision-making under varying arousal, metastable dynamics in sensory systems, and computational principles of neural network function. Research shows strong emphasis on mathematically modeling biological systems and validating predictions experimentally. Leads the Mazzulab research group exploring neural computation principles underlying adaptive behavior.

Alexander G. Ororbia IIView profile

Assistant Professor

• Predictive Processing
• Active Inference
• Spiking Neural Networks
• +7 more

Alexander G. Ororbia II is an Assistant Professor in the Department of Computer Science at Rochester Institute of Technology (RIT), where he directs the Neural Adaptive Computing (NAC) Laboratory. He is also an affiliate professor in the RIT Department of Psychology, an affiliate faculty member of RIT's Center for Applied Neuroscience, and affiliate faculty in RIT's Cognitive Science program. Ororbia received his Ph.D. in Information Science & Technology from Penn State University and a B.S.E. in Computer Science & Engineering from Bucknell University. His research focuses on developing computational models of neural information processing and synaptic plasticity, with significant contributions to predictive coding, active inference, spiking neural networks, neural-based cognitive modeling, and neurorobotic control. As Director of the NAC Laboratory, he leads research on biomimetic intelligence and brain-inspired computing, specifically developing neurocognitively plausible approaches to adaptation and memory formation in artificial neural systems. His publication trends show a strong focus on brain-inspired computing, with particular emphasis on predictive coding frameworks, active inference models, and spiking neural networks. Recent work has advanced the fields of lifelong machine learning, biomimetic intelligence, and backpropagation alternatives, with applications in robotics, computer vision, and cognitive modeling. His research bridges machine learning, computational neuroscience, and cognitive science to create more robust and adaptive artificial intelligence systems. IGERT Fellow NACME Sloan Scholar Jordan-Rednor Scholarship Bunton-Waller Scholarship Ororbia advises numerous Ph.D. and M.Sc. students working on projects related to spiking neural networks, predictive coding, active inference, and lifelong machine learning. His lab has secured research funding that supports multiple graduate students and postdoctoral researchers. The NAC Laboratory collaborates with institutions including Penn State University and has produced research published in top-tier venues including Nature Communications, Science Advances, NeurIPS, and ICRA. The NAC Laboratory, under Ororbia's direction, focuses on neurocognitively-inspired lifelong machine learning, developing novel learning algorithms and memory architectures for artificial neural systems. The lab explores both non-spiking and spiking neural networks, with applications in robotics, computer vision, and cognitive modeling. Current research directions include predictive processing implementations of cognitive architectures, mortal computation frameworks, and biologically plausible alternatives to backpropagation.

Wayne HayesView profile

Associate Professor

• Computational Science
• Graph Theory
• Biological Networks
• +5 more

Wayne Hayes is an Associate Professor at the Donald Bren School of Information and Computer Sciences (ICS) at the University of California, Irvine. His research focuses on computational methods applied to biological networks, astrophysical image analysis, and algorithm design. He holds a Ph.D. from the University of Toronto (2001) and has held visiting positions at Oxford University and Imperial College London. His work includes developing the SANA algorithm for cross-species protein function prediction via network alignment and SpArcFiRe for spiral galaxy structure analysis. Hayes' research bridges computer science, biology, and astrophysics, emphasizing interdisciplinary applications like drug targeting through network alignment. His team has advised numerous graduate and undergraduate researchers, contributing to impactful publications in systems biology and computational astrophysics. Education: Ph.D., University of Toronto, 2001; Postdoctoral work at University of Maryland and Mount Sinai Hospital. Visiting Fellowships: Oxford University (2010), Imperial College London (2009–2010). Research Interests: Computational Science, Topological Network Alignment, Biological Network Analysis, Galaxy Morphology, Algorithmic Design. Recent work highlights include SANA's successful cross-species protein function predictions and SpArcFiRe's automated spiral galaxy arm detection. His lab also develops tools like CALFIN for glacial terminus tracking and PoseBench3D for human pose estimation. Hayes emphasizes reproducibility and rigorous validation in computational methods, contributing to both foundational theory and applied tools in his fields.

Kevin J. LiuView profile

Associate Professor

• Bioinformatics
• Computational Biology
• Comparative Genomics
• +3 more

Kevin J. Liu is an Associate Professor in the Department of Computer Science and Engineering at Michigan State University, with affiliations in the Ecology, Evolution, and Behavior (EEB) Program and the Genetics and Genome Sciences (GGS) Program. His research focuses on developing computational methodologies for comparative genomics, particularly under complex evolutionary scenarios, to connect genomic insights to biological function and phenotype. The lab employs big-data-driven approaches to generate hypotheses for biological and biomedical discoveries. Research interests include phylogenetic analysis, evolutionary genomics, computational methods for handling non-tree-like evolutionary histories, and statistical resampling techniques. Liu emphasizes interdisciplinary work at the intersection of bioinformatics and computational biology, addressing challenges in alignment-free phylogenetics and species network inference. Publications reflect a strong focus on statistical methods for phylogenetic reconstruction, error estimation in sequence alignment, and scalable genomic analysis tools like Fastnet and Coal-Miner. His work bridges computational innovation with biological interpretation, aiming to advance our understanding of evolutionary processes and genomic diversity. Prospective students and researchers interested in interdisciplinary bioinformatics are encouraged to contact him via email at kjl@msu.edu .

• Information Theory
• Machine Learning
• Computational Neuroscience
• +3 more

Dr. James Stone is a Visiting Professor at the School of Computer Science, University of Sheffield. His research focuses on interdisciplinary applications of information theory, machine learning, and computational neuroscience. He has authored numerous books and articles on topics such as Bayesian analysis, neural information processing, and deep learning algorithms. His academic contributions span over two decades, with a strong emphasis on bridging theoretical foundations and practical applications. Key areas include the development of mathematical frameworks for understanding neural systems, optimizing machine learning techniques, and advancing educational resources in computational fields. Stone’s work often integrates methodologies from statistics, signal processing, and quantum mechanics, reflecting his broad expertise. Despite extensive publications, no specific awards or grants are explicitly listed in the provided texts. He maintains an active presence in academic outreach through publications and educational materials.

• Signals and Systems
• Adaptive Systems
• Learning Theory
• +6 more

José C. Príncipe is a Distinguished Professor at the University of Florida , where he holds the Don D. & Ruth Eckis Endowed Distinguished Professor position in the Department of Electrical and Computer Engineering. His primary research area is Signals and Systems , with a focus on adaptive systems and learning theory. Education Agregado, University of Aveiro (1985) Ph.D., Electrical Engineering, University of Florida (1979) M.S., Electrical Engineering, University of Florida (1974) B.S., Electrical Engineering, University of Porto (1972) Príncipe’s research spans machine learning, computational neuroscience, and biomedical signal processing. He has pioneered work in kernel adaptive filtering, information-theoretic learning criteria, and neural network architectures. His recent publications emphasize uncertainty quantification, spiking neural networks, and robust filtering techniques. His 15 most recent articles reflect trends in machine learning (transformers, predictive coding, reinforcement learning), signal processing (kernel methods, entropy-based filtering), and biomedical applications (electrophysiology, pain prediction). Key subfields include neural interfaces, nonlinear system identification, and statistical dependence measures. Scientific Awards : Fellow, American Association for the Advancement of Science (2021) Fellow, National Academy of Inventors (2021) IEEE SP Society Claude Shannon-Harry Nyquist Technical Achievement Award (2021) Life Fellow, IEEE (2017) SEC Faculty Achievement Award Winner (2016) IEEE Neural Network Pioneer Award (2011) Honorary Doctorate, Aalto University (2010) Gabor Award, International Neural Network Society (2006) Príncipe leads the Computational NeuroEngineering Laboratory (CNEL) at the University of Florida, fostering interdisciplinary research at the intersection of neuroscience and engineering. His work includes grants and collaborations in biomedical systems, brain-computer interfaces, and machine learning theory.

Jouke Jan HottengaView profile

Research Fellow

• Genetics
• Genomics
• Molecular Biology
• +5 more

Jouke Jan Hottenga is a Visiting Research Fellow at the Vrije Universiteit Amsterdam, affiliated with the Faculty of Behavioural and Movement Sciences (Biological Psychology department) and the Amsterdam Public Health (APH) research institute's Health Behaviors & Chronic Diseases and Personalized Medicine divisions. His work focuses on genetic and epigenetic mechanisms underlying complex traits, including psychiatric disorders, neurodevelopmental outcomes, and environmental interactions. He has contributed to large-scale genome-wide association studies (GWAS), twin studies, and polygenic risk score analyses. Key research interests include the genetic basis of developmental milestones, mental health, and chronic diseases. Hottenga has co-authored over 498 publications, with recent work emphasizing the role of genetic variants in income-related health disparities and the epigenetic influences of asthma polygenic burden. He teaches courses on genome-wide association methodologies and collaborates internationally on projects like the Netherlands Twin Register. Notable contributions include studies on twin zygosity assessment, ADHD polygenic risk scores, and the genetic architecture of female fertility. His datasets and software contributions highlight expertise in molecular genetics, metabolomics, and epigenetic analyses.

Krzysztof BartoszekView profile

Academic

• Statistics
• Data Science
• Machine Learning
• +3 more

Krzysztof Bartoszek is a Senior Associate Professor at Linköping University's Department of Computer and Information Science (IDA), affiliated with the Division of Statistics and Machine Learning (STIMA). His research focuses on statistical and computational methods in evolutionary biology, stochastic processes, and mathematical modeling. He contributes to interdisciplinary projects combining data science with biological systems analysis. Research interests include phylogenetic comparative methods, trait evolution models, and the application of stochastic operators to population dynamics. Recent work emphasizes multivariate analysis of evolutionary processes, with contributions to understanding bacterial genome organization and trait dynamics across phylogenies. Publications span topics like Ornstein-Uhlenbeck models for large phylogenies, statistical methods for trait evolution, and the interplay between genetics and environmental adaptation. Collaborations involve international teams in computational biology and mathematics.

Michael HörnquistView profile

Professor

• Systems Biology
• Gene Regulatory Networks
• Computational Biology
• +4 more

Michael Hörnquist serves as Professor and Deputy Head of the Department of Science and Technology (ITN) at Linköping University, concurrently leading the Physics, Electronics and Mathematics (FEM) Division. His responsibilities encompass undergraduate education oversight and management of research activities within FEM, which focuses on communication electronics, mathematics, engineering didactics, physical electronics, and nanotechnology. Hörnquist's research bridges systems biology and computational physics, specializing in gene regulatory network analysis through machine learning techniques including Elastic Net and LASSO regression. His work extends to nonlinear dynamics in aperiodic electronic structures, reflecting FEM's interdisciplinary approach to physics, electronics, and mathematics. This dual focus enables innovative cross-domain applications in biological modeling and electronic systems. Publication trends since 2003 reveal consistent contributions to systems biology, particularly in reverse-engineering biological networks through integrated data approaches. His 2010 DREAM3 challenge-winning work on gene expression prediction exemplifies practical applications of computational methods, while later research (2016) connects molecular oncology with network dynamics. This trajectory demonstrates sustained innovation in translating theoretical models to biological insights across diverse systems. As an academic leader, Hörnquist emphasizes learning as the university's core mission: 'Learning is the essence of what we do as a university, whether it involves teaching, research, or collaboration. It is learning that allows us to grow and mature as individuals.' He actively fosters this philosophy through mentorship and institutional leadership.

Kai ShimagakiView profile

Researcher

• HIV research
• virology
• immunology
• +4 more

Kai Shimagaki is a Postdoctoral Associate at the University of Pittsburgh. His research focuses on HIV-1 evolution, coevolutionary processes between viruses and antibodies, and quantitative analysis of genetic interactions. He holds a Ph.D. from Sorbonne University (formerly Pierre et Marie Curie University), where his work centered on protein structure prediction and pathogen-immune system interactions. Education: Ph.D. in Computational Biology, Sorbonne University Research Interests: Quantitative analysis of viral evolution and immune system dynamics Computational modeling of fitness landscapes and epistasis Development of algorithms for deep mutational scanning and protein sequence analysis His work bridges virology, immunology, and computational methods to understand HIV adaptation mechanisms. Publications Overview: Recent work emphasizes HIV evolution modeling, epistasis detection, and computational tools for genetic fitness prediction. Key methods include Bézier interpolation, Boltzmann machine models, and higher-order covariance analysis. Scientific Awards: None listed. Advising & Funding: Supported by the Honjo International Scholarship Foundation during his Ph.D. No current advising roles noted. Labs/Teams: Affiliation details not specified in text.

Samuel TouchardView profile

Assistant Professor

• Bayesian Statistics
• Statistical Modeling
• Biostatistics
• +3 more

Samuel Touchard is an Assistant Professor (Teaching Focused) at the University of Warwick, Department of Mathematics. He coordinates the IM Dissertation/Project module and teaches courses like Games, Decisions and Behaviour, Applied Statistical Modelling, and Statistical Laboratory. Previously, he worked as a Teaching Associate at the University of Bristol and a Research Associate in the School of Medicine at Cardiff University, focusing on biomarkers in dementia. His PhD (University of Sheffield, 2015) explored Bayesian inference of gene-miRNA regulatory networks. Education: PhD in Statistics from the University of Sheffield (2010–2015), Diplome d’Ingenieur (MSc equivalent) from ENSAI, France (2007–2010), and a Master of Research from University of Rennes 1 (2009–2010). Research interests include Bayesian methodologies, statistical applications in medicine, and data-driven approaches in biological systems. Contact via email or Microsoft Teams.

Jason XuView profile

Associate Professor

• Biostatistics
• Artificial Intelligence/Machine Learning
• Epidemiology
• +3 more

Jason Xu is an Associate Professor of Biostatistics at the University of California Los Angeles (UCLA), with a courtesy appointment in Duke University's Statistical Science department. He holds a Ph.D. in Statistics from the University of Washington (2016) and a BS in Mathematics from the University of Arizona (2012). His research focuses on statistical methodologies for stochastic processes, with applications in epidemiology, cancer biology, and machine learning. Xu has led NSF-funded projects investigating epidemic dynamics on adaptive networks and cancer latency modeling through semi-Markov processes. He teaches advanced courses in Bayesian inference, time series analysis, and statistical computing. Notable recognitions include the NSF Postdoctoral Fellowship and NDSEG Fellowship. Research Interests: Stochastic modeling of biological systems Bayesian computational methods Epidemiological network analysis Cancer stem cell dynamics Optimization algorithms for statistical inference Grants & Awards: RAISE: IHBEM Grant (NSF, $2M, 2022-2026) NSF Postdoctoral Research Fellowship (2018-2021) NDSEG Fellowship (2013-2016) Labs/Teams: Core member of UCLA's Biostatistics Computational Biology Group, collaborating with Duke's Statistical Science group on multi-institutional projects.

Jehoshua BruckView profile

Professor

• Information Theory
• Distributed Storage Systems
• DNA Data Storage
• +4 more

Jehoshua (Shuki) Bruck is the Gordon and Betty Moore Professor of Computation and Neural Systems & Electrical Engineering at the California Institute of Technology. He leads the Distributed Information Systems Group and serves as the founding director of Caltech's Information Science and Technology (IST) program. His work bridges information theory, storage systems, and computational biology. Bruck holds B.Sc. (Summa cum Laude) and M.Sc. degrees from the Technion-Israel Institute of Technology (1982-1985) and a Ph.D. in Electrical Engineering from Stanford University (1989). He has held academic positions at Caltech since 1994, advancing from Associate Professor to Moore Professor. Research Focus: His dual-track research explores Physical implementations of coding schemes for flash memories (rank modulation, rewriting codes) Biological computation mechanisms, particularly DNA sequence evolution via replication Secure distributed storage systems and RAID schemes Awards & Recognition: Recipient of the Feynman Teaching Prize (2008), Sloan Fellowship, NSF Young Investigator Award, and multiple IEEE best paper awards. Holds over 40 patents and authored 350+ publications. Industry Contributions: Co-founder of storage startups Rainfinity (acquired by EMC) and XtremIO, which pioneered flash storage solutions. Collaborates with NASA-JPL on non-volatile memory for space missions.

• Computational Neuroscience
• Statistics
• Machine Learning
• +4 more

Robert E. Kass is the Maurice Falk University Professor of Statistics and Computational Neuroscience at Carnegie Mellon University, with affiliations in the Department of Statistics & Data Science, Machine Learning Department, and Neuroscience Institute. His career spans statistics, machine learning, and neuroscience, focusing on statistical methods for analyzing neural data, particularly spike trains and brain connectivity. Research highlights include: Developing statistical frameworks for neural data analysis Investigating neural oscillations and cross-regional interactions Contributing to neuroscience education through Neuromatch Academy Exploring intersections between statistics, machine learning, and scientific inference Scientific awards and recognitions include: Outstanding Statistical Application Award (ASA) Distinguished Achievement Award (COPSS, 2017) Election to National Academy of Sciences (2023) Fellowships in ASA, IMS, AAAS His recent publications focus on: Neural circuit oscillations and phase-amplitude analysis Latent dynamic modeling of high-dimensional neural recordings Population-level neural interactions and connectivity Educational frameworks for computational neuroscience training Methodological bridges between statistics and machine learning Advanced graphical models for neural synchronization studies