جستجوی استاد راهنمای دانشگاهی

Farzad Mashayekمشاهده پروفایل

استاد

• Turbulent Reacting Flow
• Plasma Flow
• Electrostatic Atomization
• +۹ مورد دیگر

Farzad Mashayek is a Professor and Department Head of Aerospace and Mechanical Engineering at the University of Arizona, College of Engineering. He is a member of the Graduate Faculty and leads the Computational Multiphase Transport Laboratory. His research integrates high-fidelity simulations, machine learning, and experimental validation across diverse domains in fluid dynamics and energy systems. Educational Background: PhD in Mechanical Engineering, State University of New York at Buffalo, Buffalo, NY MS in Mechanical Engineering, Sharif University of Technology, Tehran, Iran BS in Mechanical Engineering, Sharif University of Technology, Tehran, Iran His research interests include turbulent reacting flows, plasma dynamics, electrostatic atomization, solid-ion and lithium batteries, computational fluid dynamics, and machine learning applications in engineering. He employs high-order spectral element methods, phase-field modeling, and deep neural networks to study complex multiphysics phenomena such as drop impact, battery degradation, and turbulence modeling. The recent publications reflect a strong trend toward integrating machine learning with multiphysics simulations, particularly in battery safety (thermal runaway prediction), materials characterization (STEM image analysis), and fluid dynamics (modal analysis of turbulence). His work often involves collaboration with experimental groups to validate models, especially in dental aerosol suppression and electrohydrodynamics. Scientific Awards: Sustained Service Award, American Institute of Aeronautics and Astronautics (AIAA), Spring 2022 Best Presentation Award, The 20th International Conference on Computational Mathematics, Parallel and Distributed Computing, Summer I 2018 Dr. Mashayek has secured funding from NSF (GOALI program) for controlled coating via charged droplet deposition. He advises graduate students and postdoctoral researchers in computational mechanics and energy systems, fostering interdisciplinary research. He has contributed to engineering education, particularly during the pandemic, with active learning strategies in online instruction. He leads a dynamic research team focused on advancing simulation tools and applying them to real-world challenges in energy, manufacturing, and public health.

Hayden Kwok Hay SOمشاهده پروفایل

دانشیار

• Reconfigurable Computing
• FPGA-Based Systems
• Computer Architecture
• +۷ مورد دیگر

Prof. Hayden Kwok Hay SO is an Associate Professor at the University of Hong Kong (HKU), affiliated with the Department of Electrical and Electronic Engineering. He currently serves as Acting Director of the School of Innovation and previously co-directed the Computer Engineering Program. His research focuses on reconfigurable computing systems, FPGA-based architectures, and their applications in AIoT, medical imaging, and high-performance computing. He holds a B.S., M.S., and Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley (1998–2007). Prof. So has been recognized with awards such as the IEEE-HKN Teaching Award (2021), Croucher Innovation Award (2013), and multiple teaching excellence awards. He leads the Computer Architecture & System Research Lab (CASR) and co-founded the Joint Lab on Future Cities (JLFC). His work spans FPGA overlay architectures, graph processing systems, and hardware-software co-design for efficient computing. Key research contributions include advancements in FPGA-based reconfigurable systems, sparse dataflow architectures, and medical imaging accelerators. He has secured grants for projects like 'Advanced machine vision guided aquatic surface vehicles' and 'Efficient and Productive Parallel Data Processing in Hybrid FPGA-CPU Clusters.' Prof. So has advised numerous students and researchers, contributing to over 150 peer-reviewed publications. His current projects explore AI hardware acceleration, neuromorphic computing, and FPGA-driven solutions for big data challenges.

Martin Z. Bazantمشاهده پروفایل

استاد

• Electrochemical Systems
• Transport Phenomena
• Applied Mathematics
• +۳ مورد دیگر

Martin Z. Bazant is the E. G. Roos (1944) Professor of Chemical Engineering and Professor of Mathematics at the Massachusetts Institute of Technology (MIT), holding the Digital Learning Officer role in the Department of Chemical Engineering. His research focuses on mathematical modeling of electrochemical systems, transport phenomena, and applied mathematics, with significant contributions to battery technology and electrochemical energy storage. He is affiliated with MIT’s Department of Mathematics and the MIT Energy Initiative (MITEI), leading initiatives like the Center for Battery Sustainability and D3BATT. Education: Ph.D. from Harvard University (1997), M.S. and B.S. from the University of Arizona (1993, 1992). His work bridges theory and application, addressing challenges in lithium-ion batteries, solid-state systems, and electrolyte dynamics. Notable achievements include pioneering studies on coupled ion-electron transfer mechanisms and phase separation in battery materials. He is an elected member of the National Academy of Engineering (2025) and a Fellow of the Electrochemical Society (2023). As an educator, he develops MOOCs on transport phenomena and contributes to digital learning initiatives. His research group explores advanced battery diagnostics, machine learning for materials science, and environmental applications of electrochemical processes. Key collaborations include startups like Lithios, Inc., and leadership roles in professional societies such as the International Electrokinetics Society.

Judith Driscollمشاهده پروفایل

استاد

• Functional Materials and Oxides
• Materials for Energy Applications
• Spintronic and Multiferroic Materials
• +۷ مورد دیگر

Judith Driscoll is Professor of Materials Science at the University of Cambridge in the Department of Materials Science & Metallurgy. She holds the prestigious Royal Academy of Engineering Chair in Emerging Technologies and serves as a Visiting Staff Member at Los Alamos National Laboratory. As the founding Editor-in-Chief of APL Materials, she has significantly contributed to the materials science community. Dr. Driscoll's research focuses on Energy Efficient Oxide Materials for Information and Communications Technologies and energy devices. Her work spans the development of non-volatile memory, resistive switching devices, and ferroelectric materials for neuromorphic computing applications. She investigates oxide thin films for applications ranging from data storage to energy generation and conversion, with particular emphasis on creating more energy-efficient device technologies to handle the exponential growth of data-centric applications. Her recent publications demonstrate strong trends in developing novel oxide-based memory devices with improved energy efficiency, particularly for AI applications. The work shows significant progress in hafnium-zirconium oxide ferroelectrics, resistive switching mechanisms, and vertically aligned nanocomposite structures for enhanced device performance. These innovations address critical challenges in reducing the unsustainable energy demands of modern computing, particularly for artificial intelligence systems. Fellow of the Royal Academy of Engineering Fellow of the Materials Research Society Fellow of the American Physical Society Fellow of IOM3, IOP, and Women Engineers Society Fellow of the American Academy of Arts and Sciences Recipient of ERC Advanced Grant Editor-in-Chief of APL Materials Dr. Driscoll leads a vibrant research group that has secured significant funding including her Royal Academy of Engineering Research Chair, an ERC Advanced Grant, and an ECCS-EPSRC grant in collaboration with researchers from the USA. She has founded the Cambridge Centre for Neuromorphic Computing (Neucam) in 2023. Her group operates world-leading growth equipment including pulsed laser deposition with RHEED control, high temperature oxide sputtering, and spatial ALD systems. She collaborates extensively across the University of Cambridge and with international partners to solve complex materials challenges, with her group's role often being to identify optimal materials for functional goals, predict fabrication methods, and then create and characterize these materials.

Amit Singerمشاهده پروفایل

استاد

• Applied Mathematics
• Data Science
• Structural Biology
• +۵ مورد دیگر

Amit Singer is a Professor of Mathematics at Princeton University and a member of the Program in Applied and Computational Mathematics (PACM). He joined Princeton in 2008 after serving as a Gibbs Assistant Professor at Yale University's Department of Mathematics from 2005 to 2008. His research spans theoretical and computational aspects of data science, with a focus on structural biology and methods like cryo-electron microscopy. Education : B.Sc. in Physics and Mathematics (1997), Ph.D. in Applied Mathematics (2005) from Tel Aviv University, Israel. Research Interests : Singer leads advancements in computational methods for structural biology, particularly analyzing noisy data from cryo-EM. His work integrates applied mathematics with disciplines like random graph theory, sensor networks, and diffusion processes. Recent contributions combine representation theory with network construction to extract molecular structures from 2D images. Scientific Awards : Moore Investigator in Data-Driven Discovery Award (2014) Simons Investigator Award (2012) Presidential Early Career Award for Scientists and Engineers (2010) Alfred P. Sloan Research Fellowship (2010) Haim Nessyahu Prize in Mathematics (2007) Singer collaborates extensively with interdisciplinary teams, advancing the application of mathematics to real-world scientific challenges.

• Materials Science
• Corrosion Engineering
• Alloy Design
• +۳ مورد دیگر

Dr. KN Sasidhar is a Researcher in the Department of Microstructure Physics and Alloy Design at Heinrich Heine University Düsseldorf. His work focuses on advanced materials science, particularly corrosion mechanisms, alloy design, and nanoscale structural analysis. He employs cutting-edge techniques like in situ synchrotron investigations and deep learning frameworks to study material behavior under extreme conditions. Current research emphasizes corrosion resistance in stainless steels, phase transformations during nitriding, and radiation effects on coatings. Key achievements include pioneering studies on nanoscale amorphization in metallic systems, data-centric approaches for materials discovery, and the development of predictive models for alloy performance. His work bridges experimental materials characterization with computational methods, addressing challenges in energy and aerospace applications. Publications span corrosion analysis, microstructural evolution under irradiation, and phase separation phenomena. Collaborative projects involve synchrotron facilities and interdisciplinary teams focusing on materials informatics. No formal awards or grants are explicitly listed in the provided texts, though his prolific publication record indicates active academic engagement.

Dane Morganمشاهده پروفایل

استاد

• Computational materials science
• Ab initio electronic structure
• Multiscale methods
• +۴ مورد دیگر

Dane Morgan is a Professor in the Department of Materials Science & Engineering at the University of Wisconsin-Madison, College of Engineering. His research focuses on computational materials science for materials design, including ab initio electronic structure modeling, multiscale methods, and machine learning applications in materials discovery. His work spans nuclear materials, battery and fuel cell electrodes, and electronic materials. Education : PhD, 1998, University of California, Berkeley MS, 1994, University of California, Berkeley BA, 1992, Swarthmore College Research Interests : Computational materials science, ab initio methods for electronic structure and thermokinetics, machine learning for materials discovery, electrochemical systems modeling, and applications in nuclear materials, batteries, and electronic materials. His work integrates advanced computational techniques with experimental validation. Scientific Awards : 2024 APL Materials, Editors Pick 2023 Microscopy and Microanalysis Best Paper Award (Instrumentation and Software category) 2023 IEEE Transactions on Plasma Science Best Paper Award 2023 Kellet Mid-Career Award 2015 TMS Materials Genome Initiative Ambassador 2006 3M Technical Nontenured Faculty Grant

Daniel Roxburyمشاهده پروفایل

دانشیار

• Nanoscience
• Carbon Nanotubes
• Nanomaterials
• +۴ مورد دیگر

Dr. Daniel Roxbury is an Associate Professor and Graduate Director at the Department of Chemical, Biomolecular and Materials Engineering within the University of Rhode Island's College of Engineering. With expertise in nanoscience and carbon nanomaterials, his research focuses on nano-bio interactions, developing functionalized nanotubes for biomedical applications and environmental monitoring through his NanoBio Engineering Laboratory. His work spans multiple disciplines including: Biomedical nanosensors Smart wearable biomaterials Targeted drug delivery systems Environmental nanotechnology Single-molecule imaging Nanotoxicology Recent publications emphasize machine learning-enhanced spectral analysis, coral reef conservation nanotechnology, and wearable stress monitoring textiles. His 2024 ACS Nano study introduces AI-driven macrophage phenotyping, while 2023 Nature Nanotechnology work explores coral reef restoration strategies using nanomaterials. Awarded the 2019 NSF CAREER grant for cellular nanometrology, he leads multiple NIH-funded projects including: $820,000 NSF CAREER: Spectral Imaging for Sub-Cellular Nanometrology $140,000 Miriam Hospital COBRE: Cortisol Detection Textiles $700,000 NSF EAGER: Multiplexed Wound Biomarker Detection His laboratory houses state-of-the-art equipment including: Near Infrared Hyperspectral Microscope Custom NIR Fluorescence Spectrometer Jasco UV/VIS/NIR Spectrophotometer Biosafety Cabinet Cell Culture Incubator Cryo-Storage System

Amit Singerمشاهده پروفایل

استاد

• Computational Mathematics
• Structural Biology
• Cryo-Electron Microscopy
• +۳ مورد دیگر

Amit Singer is a Professor of Mathematics at Princeton University, specializing in computational methods for structural biology and cryo-electron microscopy (cryo-EM). His work focuses on developing mathematical frameworks and algorithms for analyzing large-scale microscopy datasets, particularly in 3D reconstruction and heterogeneity analysis of molecular structures. He leads research in manifold learning, optimal transport, and harmonic analysis, with applications to cryo-EM, signal processing, and inverse problems. Research interests include: (1) Mathematical methods for cryo-EM, including particle alignment, density map analysis, and subspace-based reconstruction techniques; (2) Development of rotation-invariant representations for imaging problems; (3) Application of machine learning and optimization to biomedical imaging challenges. His contributions bridge pure mathematics (e.g., harmonic analysis, manifold theory) with applied computational techniques for real-world microscopy data. Key trends in his recent articles (2023–2025) include advancements in multi-reference alignment methods, Wasserstein distance-based image registration, and overcoming particle detection limitations in cryo-EM. He also explores sparsity constraints, autocorrelation analysis, and novel algorithms for handling heterogeneous datasets. These methods improve resolution and reduce computational costs in analyzing molecular structures at atomic scales. Notable contributions include the ASPiRE software package for steerable PCA, and foundational work on synchronization problems in cryo-EM orientation estimation. His research often addresses algorithmic scalability and robustness to noise in experimental setups.

• Free-Electron Lasers
• Ultrafast X-ray Spectroscopy
• Nanoparticle Dynamics
• +۳ مورد دیگر

Christoph Bostedt holds dual appointments as a Professor of Physical Chemistry at the Ecole Polytechnique Fédérale de Lausanne (EPFL) and as Head of the Laboratory for Synchrotron Radiation and Femtochemistry (LSF) at the Paul Scherrer Institut (PSI). He leads strategic operations for the LSF, managing five research groups and overseeing four beamlines at the Swiss Light Source and the Alvra Endstation at SwissFEL. His research focuses on ultrafast x-ray science, including single-shot imaging, non-linear x-ray spectroscopy, and femtosecond pump-probe techniques. He collaborates globally on initiatives like the Athos project, aiming to advance ultrafast x-ray technologies. Bostedt has over 150 publications and is a Fellow of the American Physical Society, recipient of the Röntgen Prize. Education: Ph.D. from the University of Hamburg with research at Lawrence Livermore and Berkeley National Laboratories. Prior roles include leadership at Argonne National Laboratory and SLAC National Accelerator Laboratory. Research Interests: Single-particle imaging and coherent diffraction X-ray free-electron laser applications Ultrafast dynamics in nanoparticles and molecular systems Non-linear x-ray spectroscopy Time-resolved x-ray pump-probe methods Awards: Fellow of the American Physical Society Röntgen Prize (University of Giessen) Labs & Projects: Spearheads the Athos beamline project at SwissFEL, developing the Maloja endstation for ultrafast x-ray studies. Oversees the Laboratory for Femtochemistry and collaborates on advanced imaging techniques for nanoscale science.

• Quality and Reliability Engineering
• Design of Experiments
• Time Series Analysis
• +۶ مورد دیگر

Rong Pan is a Professor at the School of Computing and Augmented Intelligence, Arizona State University (ASU). He holds a Ph.D. in Industrial Engineering from Pennsylvania State University (2002), an M.S. from Florida A&M University (1999), and a B.S. in Materials Science from Shanghai Jiao Tong University (1995). His research focuses on quality and reliability engineering, design of experiments, time series analysis, and statistical learning theory. Key projects involve NSF-funded research on reliability prediction, accelerated life testing, and degradation modeling. He serves as an Associate Editor for the Journal of Quality Technology and has authored over 80 publications. Courses taught include Reliability Engineering, Design of Experiments, and Statistics for Data Analysts. His academic service includes roles as a referee for IEEE Transactions and IIE journals. Research interests emphasize statistical methods for reliability improvement, with recent work on Bayesian inference models, optimal experimental design, and machine learning applications in industrial systems. Grants include collaborations with the NSF, Arizona Department of Transportation, and Science Foundation Arizona. His work bridges theoretical advancements and practical applications in manufacturing, energy systems, and semiconductor reliability. Education: Ph.D. (2002), M.S. (1999), B.S. (1995) Key Research Areas: Reliability Engineering, Bayesian Methods, Time Series, DOE Active Grants: NSF CMMI, SUNY IT Visiting Scholar Program Teaching: IEE 573 Reliability Engineering, DSE 501 Statistics Service: Journal of Quality Technology (Associate Editor), IEEE Transactions (Referee)

Cheuk Wai Taiمشاهده پروفایل

پژوهشگر

• Structure-Property Relationships
• Functional Materials
• Electron Microscopy
• +۶ مورد دیگر

Cheuk Wai Tai is a Senior Staff Researcher at Stockholm University's Department of Environmental and Materials Chemistry since 2009. He manages the transmission electron microscopes and sample preparation equipment at the Electron Microscopy Center and serves as Section Editor for the Journal of Electronic Materials. His work focuses on quantitative structure characterization in functional materials research, particularly within nanoscience and nanotechnology contexts. Education: Ph.D. in Applied Physics, The Hong Kong Polytechnic University, 2004 M.Phil. in Applied Physics, The Hong Kong Polytechnic University, 2001 M.Sc. in Physics, The Chinese University of Hong Kong, 1998 B.Sc. (Hons) in Engineering Physics, The Hong Kong Polytechnic University, 1997 Dip. in Mechanical Engineering (Computer Aided Engineering), Institute of Vocational Education (formerly Haking Wong Technical Institute), Hong Kong, 1992 His research centers on structure-property relationships in functional materials through advanced electron microscopy techniques. Current specializations include Pair Distribution Function (ePDF) & Diffuse Scattering, Energy Materials characterization, and EM sample preparation methodology development. The group maintains strong focus on translating structural data into functional performance metrics for nanomaterials. Recent publications (2013-2019) demonstrate consistent emphasis on electron microscopy applications for energy storage materials (batteries, photocatalysts) and functional ceramics. Key trends include structural disorder analysis in piezoelectrics, development of quantitative TEM methods like SUePDF, and nanoscale characterization of electrocatalyst surface phases. His work bridges materials chemistry with advanced imaging techniques. Scientific recognition includes: Fellow of The Royal Microscopical Society (U.K.) Senior Member of IEEE Marie Curie Fellowship (2007-2009) from European Commission Sir Edward Youde Memorial Fellowship (2003/2004) from Hong Kong S.A.R. Government He teaches Solid State Chemistry (KZ7003) and leads Introduction to Analytical Electron Microscopy (KZ8009), having previously taught Advanced Transmission Electron Microscopy (KZ8010) before 2011. Major grants supporting his work include: "Quantitative structural characterisation using 3D electron-based pair distribution function" (Swedish Research Council) "A Multidimensional Toolkit for Modern Electron Microscopy" (Swedish Foundation for Strategic Research) "Mitigating Ni-rich Li-ion cathode side-reactions" (Swedish Energy Agency, Co-applicant) He leads the Cheuk-Wai Tai group within Stockholm University's chemistry department and oversees operations at the Electron Microscopy Center, where his team develops and applies advanced characterization techniques for functional materials research.

David A. Mullerمشاهده پروفایل

استاد

• Electron Microscopy
• Materials Science
• Nanoscale Science
• +۳ مورد دیگر

David A. Muller serves as the Samuel B. Eckert Professor of Engineering in the School of Applied and Engineering Physics at Cornell University and co-directs the Kavli Institute at Cornell for Nanoscale Science. His research group focuses on developing quantitative electron microscopy methods to understand materials properties at the atomic scale, with particular emphasis on sustainable energy applications and quantum materials. Muller's laboratory utilizes some of the world's highest resolution electron microscopes housed in specially designed, environmentally isolated rooms. Muller received his undergraduate education at the University of Sydney and earned his Ph.D. in Physics from Cornell University in 1996. Between 1997 and 2003, he was a member of the technical staff at Bell Laboratories, where he applied his expertise in imaging single atoms and atomic-scale spectroscopy to determine the physical limits of transistor miniaturization. In 2003, he returned to Cornell as a faculty member, where he has since established himself as a leader in advanced electron microscopy techniques. Muller's research spans multiple frontiers in materials science, with particular focus on understanding how electronic-structure changes at the atomic scale control macroscopic behavior in diverse systems like turbine blades, fuel cells, and transistors. His current work emphasizes the physics of renewable energy materials, atomic-scale control of materials to create electronic phases that cannot exist in bulk, and developing hardware and algorithms for 'big data' acquisition from high-bandwidth pixelated electron microscope detectors. His group's work bridges theoretical physics and experimental techniques, requiring researchers who can think in both real and reciprocal space while considering both fundamental principles and practical applications. Analysis of Muller's recent publications reveals a strong trend toward advancing electron ptychography and 4D-STEM techniques for atomic-scale imaging. His group has pioneered methods for 3D atomic-scale metrology, strain mapping, and imaging of radiation-sensitive materials. The research spans applications from semiconductor technology to quantum materials and energy storage systems, demonstrating the versatility of his microscopy approaches across multiple scientific domains. Top 100 Young Innovator by Tech Review Magazine (2003) Burton Medal from Microscopy Society of America (2006) Ernst Ruska Prize of German Society for Electron Microscopy (2021) John Cowley Medal from International Federation of Societies for Microscopy (2023) Fellow of American Physical Society Fellow of American Association for the Advancement of Science Fellow of Microscopy Society of America Muller has mentored an extensive group of students and postdocs who have gone on to successful careers in academia and industry. His former students hold faculty positions at institutions including Rice University, University of Southern California, Seoul National University, Colorado School of Mines, and the University of Michigan, among others. His research has been supported by substantial grants, including a $22.5M NSF grant that accelerates materials discovery. The Muller lab maintains close collaborations with the Kavli Institute at Cornell and PARADIM (Platform for the Accelerated Realization, Analysis, and Discovery of Interface Materials). The Muller lab operates at the forefront of electron microscopy, housing specialized instrumentation including high-resolution transmission electron microscopes in environmentally isolated rooms. The group collaborates extensively with other research teams at Cornell and worldwide, focusing on understanding materials atom by atom. Current research directions include applying machine learning to electron microscopy data analysis, developing cryogenic techniques for studying low-melting-point materials, and exploring quantum phenomena in engineered materials systems.

Thomas Grenierمشاهده پروفایل

دانشیار

• Medical Image Segmentation
• Clustering
• Filtering
• +۳ مورد دیگر

Thomas Grenier is an Associate Professor in the Department of Electrical Engineering at INSA Lyon and a member of the CREATIS laboratory (CNRS UMR 5220, INSERM U1294). He obtained his HDR (Habilitation à Diriger des Recherches) in 2023 and his Ph.D. in Image Processing from INSA Lyon in 2005. His research focuses on medical image segmentation, clustering, and filtering using feature space, scale-space, and deep learning approaches. Doctoral School: EEA (Electronics, Energy, and Automatics) Research Affiliation: CREATIS Lab (CNRS/INSERM/INSA Lyon/Université Lyon 1/Université Jean Monnet Saint-Etienne) He has contributed to 20 papers and co-supervised 5 PhD students, including Léo Dumortier and Florent Guépin. Grenier leads the annual Deep Learning for Medical Imaging (DLMI) school, which he co-founded, and has organized five editions across Lyon and Montreal since 2019. The school emphasizes practical deep learning applications in medical imaging for participants of all expertise levels. His work spans interdisciplinary domains such as medical imaging , deep learning , and image processing , with recent publications on generative AI for MRI synthesis, explainable networks, and segmentation of neurological pathologies in preclinical models. He manages pedagogical platforms, coordinates LabEx PRIMES project activities, and oversees lab room infrastructure for 200 hours/year across 10 training programs. Grenier also leads the MUSIC transversal project on Multiple Sclerosis since 2019.

Aaditya Ranganمشاهده پروفایل

دانشیار

• Numerical Analysis
• Scientific Computing
• Neuroscience
• +۴ مورد دیگر

Aaditya Rangan is an Associate Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He holds a Ph.D. from UC Berkeley (2003) and a B.A. from Dartmouth College (1999). His research focuses on applying numerical analysis and scientific computing to biological systems, including neuronal network dynamics in the insect olfactory system and mammalian visual cortex. He also develops computational tools for genomic data analysis, particularly biclustering methods for gene expression and SNP datasets. Rangan currently directs NYU's master's program in mathematics. Key contributions include models of synaptic depression in neural systems and algorithms for cryo-EM data processing. His work is published in journals like the Journal of Computational Neuroscience and PLoS Computational Biology , and his software tools are available on GitHub.