جست‌وجوی استاد و پژوهشگر

Alexander J. Harteminkمشاهده پروفایل

استاد

• Computational Biology
• Machine Learning
• Genomics
• +۵ مورد دیگر

Professor Alexander J. Hartemink holds dual appointments in the Department of Computer Science and Department of Biology at Duke University, Trinity College of Arts & Sciences. He is also a Bass Fellow in Computer Science. His research focuses on computational biology, machine learning, and systems biology, with applications to genomics, epigenomics, and transcriptional regulation. Hartemink leads the Duke Office of University Scholars and Fellows and has directed the Computational Biology and Bioinformatics graduate program. He earned a PhD from MIT (2001), MPhil from the University of Oxford (1996), and BS from Duke (1994). Research Interests His work integrates computational methods to study chromatin dynamics, transcriptional networks, and epigenetic mechanisms. Key areas include modeling chromatin accessibility, predicting transcription factor binding, and understanding cell-cycle regulation. Techniques employed include Bayesian networks, dynamic systems modeling, and machine learning algorithms. Publications & Trends Recent work emphasizes single-cell multi-omics integration, chromatin occupancy modeling (RoboCOP framework), and transcriptional regulation in response to genetic perturbations. Themes include epigenetic plasticity, disease-associated enhancers, and systems-level analysis of gene expression. Awards & Grants Hartemink has received the Sloan Research Fellowship (2005) and NSF CAREER Award (2004). Active grants include NIH funding for chromatin-transcription interplay studies and NSF support for regulatory genome research. He collaborates on projects like the Data+ initiative, promoting interdisciplinary data science. Affiliations & Labs Associated with Duke’s Center for Genomic and Computational Biology and Center for Advanced Genomic Technologies. His lab develops computational tools for genomic analysis, including software for chromatin modeling and epigenetic data integration.

• Applied Mathematics
• Mathematical Physics
• Neurosciences
• +۴ مورد دیگر

Professor Paul C. Bressloff holds the Chair in Applied Mathematics and Stochastic Processes at Imperial College London's Department of Mathematics within the Faculty of Natural Sciences. His research focuses on stochastic and non-equilibrium processes, particularly in molecular and cell biology, utilizing tools from probability theory, statistical physics, and dynamical systems. He authored a seminal textbook Stochastic Processes in Cell Biology (Springer), with a 2nd edition published in 2022. Previously, he led the graduate program in mathematical biology at the University of Utah from 2001 to 2023. Research interests include stochastic multi-particle systems, active particles, phase separation, and diffusion across semi-permeable interfaces. His work spans applications in neural field theory, cytoneme-mediated morphogenesis, and protein trafficking. He is affiliated with the Biomathematics Group and Mathematical Physics Group at Imperial. Recent articles explore stochastic resetting in search processes, narrow-capture problems, and hybrid models of switching diffusions. His advising includes over 20 graduate students, many now faculty in mathematical biology. His contributions bridge applied mathematics and biological systems, emphasizing interdisciplinary approaches to complex stochastic phenomena.

• Dynamic Systems and Control
• Mechatronics
• Digital Printing and Fabrication
• +۷ مورد دیگر

George T. C. Chiu is a Professor in the School of Mechanical Engineering at Purdue University, with courtesy appointments in Electrical and Computer Engineering and Psychological Sciences. He holds a 50% appointment as Assistant Dean for Global Engineering Programs and Partnerships. His research focuses on mechatronics, dynamic systems and control, functional printing, and human-machine interaction, with applications in biomedical engineering, robotics, and advanced manufacturing. Education: PhD (1994), MS (1990) University of California, Berkeley; BS (1985) National Taiwan University. Research interests emphasize application-driven solutions for printing technologies, motion control, and embedded systems. Notable projects include developing inkjet printing for biomedical materials and sensor systems. Awards include ASME Fellowship (2013) and the 2024 ASME Rabins Leadership Award. Publications span topics like inkjet drop dynamics, control systems, and biofabrication. He has led initiatives such as the Purdue FIRST Programs, fostering K-12 STEM education through robotics mentorship. Editorial roles include Editor-in-Chief of IEEE/ASME Transactions on Mechatronics (2017-2019).

Sarah Kösterمشاهده پروفایل

استاد

• Cellular Biophysics
• Cytoskeletal Mechanics
• Optical Microscopy
• +۲ مورد دیگر

Sarah Köster is a Full Professor at the University of Göttingen’s Institute for Cellular and Molecular Physiology of the Brain. She earned her PhD from the University of Göttingen under Prof. Stephan Herminghaus and completed postdoctoral research at Harvard University with Prof. David Weitz. Her career includes appointments as Junior Professor (2008-2011), Associate Professor (2011-2017), and Full Professor (since 2017). Research focuses on cellular biophysics, particularly cytoskeletal mechanics and intermediate filament dynamics. Key investigations include keratin plasticity, vimentin network mechanics, cytoskeletal crosstalk, and DNA organization during cell division. Her publications demonstrate expertise in nanoscale biomechanics, utilizing techniques like nanoindentation, X-ray diffraction, and advanced microscopy to probe cellular structures. Recent work emphasizes multiscale mechanical properties of cytoskeletal networks and their functional implications.

Gabriel A. Silvaمشاهده پروفایل

استاد

• Neuroscience
• Neural Computing
• Quantum Algorithms
• +۷ مورد دیگر

Gabriel A. Silva is a Professor in the Shu Chien-Gene Lay Department of Bioengineering at UC San Diego’s Jacobs School of Engineering, with a joint appointment as Assistant Professor in Ophthalmology. His research bridges neuroscience, theoretical physics, and applied mathematics to explore how the brain encodes and processes information, leveraging quantum logic and algorithms for advanced neural modeling. University: University of California, San Diego School: Jacobs School of Engineering Department: Shu Chien-Gene Lay Department of Bioengineering Academic Rank: Professor Joint Appointment: Assistant Professor in Ophthalmology Research Interests: Silva focuses on neural computation at cellular and network scales, aiming to abstract biological mechanisms into mathematical models that emulate brain-like processing. His work has implications for understanding neurological disorders, developing neural engineering nanotechnologies, and advancing AI systems through emergent complexity. Recent Article Trends: His publications span quantum-enhanced neural modeling, EEG-based disease detection, nonlinear dynamics in brain networks, and interdisciplinary applications of graph theory. Emerging themes include the integration of category theory for network analysis and AI optimization via emergence-promoting schemes. Labs & Teams: Affiliated with UC San Diego’s Institute of Engineering in Medicine, Silva leads research at the intersection of bioengineering, ophthalmology, and neural systems, fostering collaborations with neuroscience and quantum computing domains.

Kunihiko Kanekoمشاهده پروفایل

استاد

• Theoretical Biophysics
• Complex Systems
• Chaos
• +۶ مورد دیگر

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.

• Collective Intelligence
• Temperament and psychobiology of mental illness
• Collective Dynamics
• +۷ مورد دیگر

William Sulis is an Associate Clinical Professor in the Department of Psychiatry and an Associate Member of the Department of Psychology at McMaster University, where he also directs the Collective Intelligence Lab (CILab). With a unique interdisciplinary background spanning mathematics, physics, and psychiatry, Dr. Sulis bridges the gap between theoretical science and clinical practice. His educational journey is exceptionally diverse: B.Sc. (Hon) in Mathematics with minor in Theoretical Physics, Carleton University (1976) M.D., University of Western Ontario (1980) M.A. in Mathematics, University of Western Ontario (1984) Ph.D. in Mathematics, University of Western Ontario (1989) FRCPC in Psychiatry (1984) Ph.D. in Theoretical Physics, University of Waterloo (2014) CRCPC in Geriatric Psychiatry (2015) Dr. Sulis's research explores the intersection of complex systems theory with psychological and psychiatric phenomena. His work on Collective Intelligence investigates how group dynamics emerge from individual interactions, while his research on Temperament and Psychobiology examines the continuum between normal personality variations and mental illness. He has made significant contributions to understanding Synchronization in Complex Systems and developed the concept of Transient Induced Global Response Synchronization (TIGoRS) , which has implications for neural coding and information processing. His theoretical work extends to Quantum Foundations and Process Algebra Theory , where he proposes novel approaches to quantum mechanics. Analysis of his recent publications reveals a consistent thread connecting complex systems theory with psychological and psychiatric applications. His work increasingly focuses on bridging the gap between temperament theory and clinical psychiatry, using mathematical and computational approaches to understand mental illness. Simultaneously, he continues to develop theoretical frameworks in quantum physics through process algebra models, demonstrating remarkable interdisciplinary range. Dr. Sulis has received several prestigious awards including The Governor General's Medal for having the highest overall grade point average in his graduating class, the Henry Marshall Tory Scholarship, and multiple Harry Stevenson Southam Scholarships. Throughout his career, Dr. Sulis has mentored numerous students across disciplines, supervising research projects spanning collective intelligence, semantic space modeling, network dynamics, and temperament studies. His Collective Intelligence Lab has served as a hub for interdisciplinary research connecting computer science, psychology, and psychiatry. Dr. Sulis has also been actively involved in professional organizations, serving as President of The Society for Chaos Theory in Psychology and the Life Sciences (1996-1998) and holding editorial positions for several journals including "Dynamical Psychology" and "Nonlinear Dynamics in Psychology and the Life Sciences." As Director of the Collective Intelligence Lab at McMaster University, Dr. Sulis fosters research exploring how complex adaptive systems can model cognitive and social phenomena. The lab serves as an intellectual nexus where mathematics, computer science, psychology, and psychiatry converge to address fundamental questions about intelligence, both individual and collective.

Gabriel Wainerمشاهده پروفایل

استاد

• Modelling and Simulation Methodologies
• Discrete Event Systems
• Real-Time Modelling
• +۷ مورد دیگر

Dr. Gabriel Wainer is a Professor in the Department of Systems and Computer Engineering at Carleton University's Faculty of Engineering and Design. He leads the Advanced Real-Time Simulation Lab and specializes in modeling and simulation methodologies, particularly focusing on discrete event systems, real-time modeling, cellular automata, and DEVS formalism. Research Interests: Discrete event systems, DEVS formalism, cellular automata, real-time simulation, IoT applications, and parallel/distributed simulation Affiliation: Carleton University Recent publications highlight his work in advanced simulation frameworks, energy-efficient 5G systems using deep reinforcement learning, and pandemic modeling with cellular automata. His lab develops tools like PROMETHEUS and Devsmap for standardized DEVS model representation, while also exploring applications in wireless communication, building energy systems, and behavioral epidemiology.

• Business Analytics
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Adjunct Professor Evgeny Osipov is affiliated with La Trobe University's Business Analytics department. His research spans artificial intelligence, hyperdimensional computing, and neural network architectures. Academic Rank: Adjunct Professor Department: Business Analytics Email: E.Osipov@latrobe.edu.au Research interests include: Hyperdimensional computing and vector symbolic architectures Spiking neural networks and reservoir computing Hardware-efficient AI implementations Causal reasoning in large language models Self-organizing maps and spatiotemporal sequence learning Applications in smart cities and robotic navigation Recent research outputs demonstrate expertise in: Developing unsupervised learning frameworks using hypervectors Optimizing reservoir computing with cellular automata Creating memory-efficient neural network models Advancing hyperdimensional classification techniques Exploring causal graph integration in language models

Brian Ingallsمشاهده پروفایل

استاد

• Systems Biology
• Synthetic Biology
• Mathematical Modeling
• +۳ مورد دیگر

Brian Ingalls is a Professor in the Department of Applied Mathematics and cross-appointed to Biology at the University of Waterloo. His research applies mathematical and control-theoretic approaches to biological systems, including genetic regulatory networks, microbial communities, and cellular metabolism. Institutional Affiliation: Faculty of Mathematics, University of Waterloo Contact: bingalls@uwaterloo.ca His work focuses on systems biology and synthetic biology , particularly sensitivity analysis of biochemical networks, optimal experimental design, and mathematical modeling of cellular processes. Research funding comes from NSERC and CIHR . Notable contributions include the textbook Mathematical Modeling in Systems Biology (MIT Press, 2013) and the Ingalls Quantitative Cell Biology Lab , which investigates intracellular and intercellular network dynamics through computational and experimental methods. Key Collaborations: iGEM Waterloo, Chemical Engineering, and international synthetic biology networks Advising: Mentored 15+ graduate students and postdocs across applied math, biology, and engineering fields

Sapun Parekhمشاهده پروفایل

دانشیار

• Nonlinear Microscopy
• Biomolecular Condensates
• Medical Imaging
• +۳ مورد دیگر

Sapun Parekh is an Associate Professor in the Department of Biomedical Engineering at the University of Texas at Austin, supported by the Cockrell Family Fellowship. His research focuses on developing label-free imaging and analytical tools using nonlinear chemical microscopy to diagnose pathologies such as type 2 diabetes. The Parekh Lab, operating at UT Austin and the Max Planck Institute, investigates molecular basis of pathology, microscopy instrumentation, and mechano-chemical coupling in cancer. Key research areas include chemical and nonlinear microscopy, molecular physics of biomaterials, and imaging molecular structure under mechanical deformation. Recent work emphasizes biomolecular condensates, blood clot mechanics, and metabolic defense mechanisms in cancer cells. The lab actively recruits graduate students and postdoctoral researchers in topics like nonlinear microscopy and neurodegeneration imaging. Notable students include Jacob, Sam, Nick, and Advika, who have passed exams or defended theses. Collaborations with institutions like Brown University and EMBL advance interdisciplinary projects. The lab’s innovations bridge fundamental biophysics with clinical applications, emphasizing inclusion and innovation. Education Background: Not explicitly stated in provided texts. Affiliations: UT Austin Biomedical Engineering, Max Planck Institute. Research interests span imaging technologies, biomaterials, and disease mechanisms, with recent articles addressing biomolecular condensates, clot mechanics, and nanotechnology. The lab’s work is published in high-impact journals, reflecting its commitment to advancing biomedical diagnostics and therapies.

José Alvaradoمشاهده پروفایل

استادیار

• Biophysics
• Soft Matter
• Fluid Mechanics
• +۴ مورد دیگر

José Alvarado is an Assistant Professor of Physics at the University of Texas at Austin, affiliated with the College of Natural Sciences. His research focuses on biophysics, soft matter, and active matter, particularly exploring mechanical design principles in biological systems. He investigates topics such as planar cell polarity (PCP), actomyosin networks, and morphogenetic processes. Alvarado’s work integrates experimental and theoretical approaches, often involving collaborations with centers like the Center for Nonlinear Dynamics and Texas Robotics. His studies address questions about how biological systems achieve mechanical efficiency and how active matter principles apply to biological actuation and control. Key themes in his research include the nonlinear mechanics of actomyosin gels, the role of PCP in tissue shaping during convergent extension, and the design of biomimetic actuators for robotics. He has also contributed to understanding fluid dynamics in microscale systems, such as hairy surfaces and colloidal liquid crystals.

Hamed Rahimianمشاهده پروفایل

استادیار

• Stochastic Optimization
• Data-Driven Optimization
• Risk-Averse Optimization
• +۳ مورد دیگر

Dr. Hamed Rahimian is an Assistant Professor in the Department of Industrial Engineering at Clemson University. He holds a Ph.D. in Industrial and Systems Engineering from The Ohio State University (2018), an M.Sc. from The University of Arizona (2012), and B.Sc./M.Sc. degrees from Sharif University of Technology (2008/2011). Prior to Clemson, he was a Postdoctoral Research Fellow at Northwestern University under Prof. Sanjay Mehrotra. His research focuses on data-driven decision-making under uncertainty, including stochastic optimization, distributionally robust optimization, and risk-averse methodologies. He has published in top journals such as Mathematical Programming , SIAM Journal on Optimization , and Operations Research . Education: Ph.D. Industrial and Systems Engineering, The Ohio State University (2018) M.Sc. Industrial Engineering, The University of Arizona (2012) B.Sc./M.Sc. Industrial Engineering, Sharif University of Technology (2008/2011) His research has been recognized with awards including the Harold W. Kuhn Award (2022), Runner-Up INFORMS Computing Society Student Paper Award (2017), and 2nd Place IISE Pristker Dissertation Award (2019). He serves as an Associate Editor for INFORMS Journal on Computing and Sharif Journal of Industrial Engineering & Management . Grants & Advising: He secured an Air Force grant (2024) on multistage stochastic programming. His research group focuses on advancing optimization under uncertainty with applications in healthcare, energy, and supply chains. Labs/Teams: Leads a research group in Clemson’s Industrial Engineering department, collaborating on projects in distributionally robust optimization and data-driven decision-making.

• Data Science
• Machine Learning
• Harmonic Analysis
• +۷ مورد دیگر

Mauro Maggioni is a Professor in the Departments of Mathematics and Applied Mathematics and Statistics at Johns Hopkins University. His research focuses on the mathematical foundations of Data Science, with applications in molecular dynamics, hyperspectral imaging, and reinforcement learning. He employs techniques from Harmonic Analysis, Approximation Theory, and Probability to develop scalable algorithms, particularly multiscale methods for analyzing high-dimensional data. Maggioni’s work bridges theoretical mathematics and practical applications, including cardiac electrophysiology modeling, unsupervised segmentation of hyperspectral images, and reduced-order modeling of complex systems. Education: B.S. in Mathematics from Università degli Studi in Milan, Italy; Ph.D. in Mathematics from Washington University in St. Louis. He held a Gibbs Assistant Professorship at Yale before moving to Duke University and later becoming a Bloomberg Distinguished Professor at JHU. Research Interests: Mathematical foundations of Data Science, Machine Learning, Partial Differential Equations, and their applications in physical and biological systems. Notable contributions include diffusion wavelets, interaction kernel learning, and multiscale geometric analysis of molecular dynamics data. Scientific Awards: Popov Prize in Approximation Theory (2007), NSF CAREER Award and Sloan Fellowship (2008), Fellow of the American Mathematical Society (2013), Simons Fellowship (2020). Advising & Grants: Maggioni mentors postdocs and students in areas like stochastic systems and signal processing. His group’s work is supported by Simons Foundation grants, NSF funding, and collaborations with institutions like MINDS and CIS at JHU. Labs/Teams: Leads a research group focused on data-driven discovery in mathematics and applied sciences, emphasizing interdisciplinary collaboration across computational methods, statistics, and domain-specific applications.

Pankaj Mehtaمشاهده پروفایل

استاد

• Statistical Physics
• Systems Biology
• Ecological Dynamics
• +۳ مورد دیگر

Pankaj Mehta is a Professor in the Department of Physics at Boston University, with additional affiliations in the Department of Biomedical Engineering. His research bridges statistical physics, theoretical biology, and interdisciplinary systems approaches. Key areas include ecological dynamics, synthetic biology, and the application of machine learning principles to biological systems. His work focuses on understanding emergent phenomena in biological systems, such as cell fate decisions, ecosystem stability, and signal processing in cellular networks. He has pioneered methods combining physics-based modeling with computational tools to study complex systems, including gene circuits, microbial communities, and cancer dynamics. Recent contributions highlight the use of order parameters for interpreting cellular states, geometric frameworks for ecological niches, and machine learning analogies to ecological principles. His interdisciplinary approach integrates experimental data with theoretical models to address questions in biomedicine, environmental science, and fundamental physics.