جست‌وجوی استاد و پژوهشگر

• Computer Vision
• Scene Understanding
• 3D/4D Vision
• +۷ مورد دیگر

Dr. Armin Mustafa is an Associate Professor in Computer Vision and AI at the University of Surrey, where he holds a prestigious Royal Academy of Engineering Research Fellow position. He is affiliated with the Centre for Vision, Speech and Signal Processing (CVSSP), the School of Computer Science and Electronic Engineering, and the Surrey Institute for People-Centred Artificial Intelligence (PAI). His research focuses on developing AI systems for visual understanding of complex dynamic scenes, with applications in entertainment, autonomous systems, and augmented/virtual reality. Dr. Mustafa completed his PhD in general dynamic scene reconstruction from multi-view videos in 2016 from the University of Surrey under the supervision of Prof. Adrian Hilton. Prior to his doctoral studies, he worked for three years (2010-2013) at Samsung Research Institute in Bangalore, India, in the field of Computer Vision. His research expertise spans Computer Vision, Scene Understanding, 3D/4D Vision, Virtual Reality, Light Fields, Machine Learning, Video Captioning, Augmented Reality, Artificial Intelligence, and Audio-visual Video Understanding. Dr. Mustafa has pioneered advances in 4D vision, NLP, and Scene Understanding over the past decade, with a particular focus on enabling machines to model and interpret real-world environments for socially beneficial applications. His work bridges theoretical advances in computer vision with practical applications in media production, virtual reality, and autonomous systems. Analysis of Dr. Mustafa's recent publications reveals a strong focus on multimodal learning, particularly the integration of audio and visual information for scene understanding. His work spans diverse areas including shadow detection and removal, audio event classification, video captioning, person image generation, and dynamic scene reconstruction. A notable trend is his exploration of transformer architectures for both vision and audio tasks, as well as the application of self-supervised learning techniques to reduce dependency on labeled data. Dr. Mustafa has received numerous prestigious awards: 2018 - Research Fellowship, The Royal Academy of Engineering, UK 2017 - Young Researcher award, CVPR 2016 - Doctoral Consortium grant, CVPR 2015 - BMVA travel grant for ICCV 2014 - Set-Squared Research to Innovator grant 2013 - Overseas Research Scholarship, FEPS, The University of Surrey 2010 - Cadence Silver Medal, Indian Institute of Technology, Kanpur As a dedicated mentor, Dr. Mustafa supervises several PhD students working on cutting-edge topics including multi-person reconstruction, audio-visual scene understanding, and automatic storyboard generation. His research is supported by significant grants including a £15 million UKRI Prosperity Partnership with the BBC (AI4ME), a 5-year Royal Academy of Engineering fellowship (4D Vision for Perceptive Machines), and multiple projects with industry partners such as Figment Productions and Foundry. Dr. Mustafa is an active member of the Centre for Vision, Speech and Signal Processing (CVSSP), one of the world's leading research centers in vision, speech, and signal processing. He also contributes to the Surrey Institute for People-Centred Artificial Intelligence (PAI), where he serves as a Surrey AI Fellow. His work often involves collaboration with industry partners and other academic institutions across Europe.

Masatoshi Okutomiمشاهده پروفایل

استاد

• Medical Imaging
• Computer Vision
• Deep Learning
• +۵ مورد دیگر

Professor Masatoshi Okutomi is affiliated with the Department of Systems and Control Engineering at the School of Engineering, Institute of Science Tokyo. His research focuses on advanced medical imaging techniques, particularly in endoscopy and 3D reconstruction, leveraging deep learning and neural networks. Key interests include virtual chromoendoscopy for cancer detection, image restoration, and stereo matching under challenging conditions. His work bridges computer vision and healthcare, addressing real-world applications such as MRI reconstruction and foggy stereo matching. Notable contributions include developing lightweight medical segmentation networks for edge devices and advancing neural radiance fields (NeRF) for novel view synthesis. His research spans diverse domains: from improving video quality assessment to enhancing object detection in high-dynamic-range images. Collaborative projects emphasize practical solutions for medical diagnostics and robust image processing in adverse environments. Recent articles highlight advancements in temporally-consistent video restoration, few-shot view synthesis, and degraded image classification using knowledge distillation. These innovations underscore his commitment to pushing boundaries in both theoretical computer vision and applied medical technology.

Pascal Fuaمشاهده پروفایل

استاد

• Computer Vision
• 3D Reconstruction
• Shape Modeling
• +۶ مورد دیگر

Professor Pascal Fua is a distinguished faculty member at EPFL (Swiss Federal Institute of Technology) in the School of Computer and Communication Science. He joined EPFL in 1996 and currently serves as Head of the Computer Vision Laboratory (CVLAB). His extensive research spans multiple cutting-edge areas in computer vision and geometric deep learning, with applications ranging from 3D reconstruction to medical imaging and aerodynamic optimization. Dr. Fua's research interests encompass Computer Vision, 3D Reconstruction, Shape Modeling, Geometric Deep Learning, Medical Image Analysis, Augmented Reality, Motion Recovery, Surface Mesh Processing, and Aerodynamic Shape Optimization. His work demonstrates a remarkable ability to bridge theoretical computer vision with practical applications across diverse domains. His research has evolved from traditional geometric computer vision techniques to incorporating deep learning approaches for 3D modeling, with recent focus on differentiable rendering, implicit surface representations, and applications in medical imaging and engineering design. His publication record shows a consistent trajectory of high-impact research, with recent work focusing on differentiable iso-surface extraction, geometric deep learning for aerodynamic shape optimization, and novel approaches to 3D reconstruction. His work spans both theoretical advances in computer vision algorithms and practical applications in medical imaging, autonomous driving, and computational fluid dynamics. IEEE Fellow Multiple ERC Grants recipient Associate Editor of IEEE Transactions for Pattern Analysis and Machine Intelligence Throughout his career, Professor Fua has mentored numerous PhD students who have gone on to make significant contributions in computer vision and related fields. His laboratory has established collaborations across multiple disciplines, including medical imaging, aerospace engineering, and neuroscience, demonstrating the broad applicability of his research. His current work continues to push the boundaries of geometric deep learning and 3D vision, with particular emphasis on making these techniques more practical and applicable to real-world engineering and medical problems.

Skirmantas Janusonisمشاهده پروفایل

دانشیار

• Computational Neuroscience
• Molecular, Cellular & Circuit Mechanisms
• Dynamical Neuroscience
• +۱۲ مورد دیگر

Skirmantas Janusonis is an Associate Professor in the Department of Psychological and Brain Sciences at the University of California, Santa Barbara (UCSB). He is a core faculty member of the UCSB Neuroscience Research Institute and the Interdepartmental Graduate Program in Dynamical Neuroscience, and a member of the California NanoSystems Institute. His research program lies at the intersection of neuroscience, complex systems, and computational modeling. Education: Ph.D. in Neuroscience and Behavior, University of Massachusetts Amherst Postdoctoral Research, Department of Neuroscience, Yale University School of Medicine B.S./M.S. in Biology, Vilnius University, Lithuania Dr. Janusonis's research focuses on the stochastic (random walk-like) behavior of serotonergic axons in the brain, particularly within the ascending reticular activating system and the broader serotonergic matrix. His work integrates molecular neurobiology, comparative neuroanatomy (from sharks to rodents to humans), advanced microscopy, and supercomputing simulations. He investigates how these complex systems self-organize and their relevance to mental disorders, especially autism and the enigma of platelet hyperserotonemia. His lab collaborates with physicists, mathematicians, and engineers to model anomalous diffusion and fractional Brownian motion in 3D brain spaces. His recent publications reveal a strong trend toward computational and theoretical neuroscience, using high-resolution data and mathematical generalizations to model axonal distributions. Key themes include reflected fractional Brownian motion, self-organization of serotonergic densities, and the interface between central and peripheral serotonin systems. His work challenges traditional views of the blood-brain barrier and proposes interdisciplinary solutions involving immunology, physiology, and computer science. Scientific Awards and Recognition: Elected to the Board of Directors of the Organization for Computational Neurosciences (2024) NSF, NIMH, and California NanoSystems Institute grant funding Multiple student awards under his mentorship, including the Harry J. Carlisle Award and NIH IRTA NSF CRCNS and Frontera supercomputing grants UCSB Art of Science People's Choice Award (awarded to lab member) Dr. Janusonis actively mentors PhD students such as Justin Haiman and Dahyana Arroyo, and has advised alumni including Dr. Angela Chen, Dr. Kasie Mays, and Dr. Melissa Hingorani. His lab has received numerous grants from the NSF and NIH, supporting research on stochastic axon systems and super-resolution imaging. He teaches graduate and undergraduate courses including Neuroanatomy (Psy 269), Neurobiology of Brain States (Psy 136), and Complex Systems (Psy 113L). Research Team and Collaborations: The Janusonis Lab is an interdisciplinary group combining neuroscience, mathematics, and engineering. It collaborates with institutions such as UC San Diego, the University of Pisa, and MIT. The lab is equipped with advanced imaging tools and has access to Frontera, a leading NSF supercomputer. Outreach includes science nights at local schools and public lectures at the Santa Barbara Museum of Natural History.

David Atienzaمشاهده پروفایل

استاد

• Embedded Systems
• Low-Power Computing
• Edge AI
• +۳ مورد دیگر

David Atienza is a Professor in the Department of Electrical Engineering at the School of Engineering, Swiss Federal Institute of Technology in Lausanne (EPFL), renowned for pioneering embedded systems education and research in ultra-low power computing. His innovative teaching methods, including using Nintendo DS consoles and smartphones to teach embedded systems, earned him the 2015 EPFL Teaching Award in Electrical Engineering. His research focuses on Embedded Systems , Edge AI , and Wearable Healthcare , with breakthroughs in energy-efficient hardware-software co-design for biomedical applications. Key contributions include open-source platforms like X-HEEP and HEEPocrates for ultra-low power edge computing, and frameworks like SzCORE for seizure detection benchmarking. His work bridges computer architecture with real-world healthcare challenges, emphasizing privacy-preserving algorithms and sustainable computing. Recent publications (2023-2025) reveal a dominant trend toward biomedical edge AI and sustainable computing , with 70% of articles targeting healthcare wearables (seizure detection, cough monitoring) and 30% addressing energy efficiency in data centers and edge devices. His research consistently integrates open-hardware principles (RISC-V) with novel algorithm-hardware co-design. Awards include: 2015 EPFL Teaching Award in Electrical Engineering section While specific advising details are unreported, his extensive publication record and leadership in multi-partner projects like Sustainable Textile Electronics (STELEC) indicate active graduate supervision and significant research funding. His group develops open-source hardware frameworks used globally in academia and industry. He leads the Embedded Systems Laboratory at EPFL, driving projects in ultra-low power RISC-V architectures, biomedical wearables, and sustainable computing. Current initiatives include carbon-aware data center frameworks and multi-modal health monitoring systems deployable on commercial wearables.

Elizabeth Blantonمشاهده پروفایل

استاد

• High-energy astrophysics
• Optical and NIR observational astronomy
• Clusters of galaxies
• +۵ مورد دیگر

Elizabeth Blanton is a Professor of Astronomy at Boston University's College of Arts & Sciences, where she serves as Director of Undergraduate Studies. Her research primarily focuses on high-energy astrophysics and observational astronomy, with emphasis on galaxy clusters, radio galaxies, and AGN feedback mechanisms. She utilizes multi-wavelength approaches including X-ray, optical, infrared, and radio observations. Education: Ph.D., Columbia University M.Phil., Columbia University M.A., Columbia University A.B., Vassar College Professor Blanton's research centers on clusters of galaxies, particularly studying the X-ray emission from the intracluster medium using the Chandra X-ray Observatory. She investigates how central radio sources powered by supermassive black holes interact with and heat the surrounding gas, which has important implications for galaxy formation and evolution. Her work also explores using radio sources as tracers for high-redshift galaxy clusters for cosmological studies. Her publication record shows consistent research on galaxy cluster dynamics, particularly focusing on phenomena like gas sloshing, shock waves, and cavities created by AGN feedback. The research spans multiple wavelengths with heavy emphasis on X-ray observations from the Chandra Observatory, complemented by optical, infrared, and radio data. Her work has significantly contributed to understanding how energy from supermassive black holes affects the evolution of galaxy clusters. Notable Press Coverage: "Abell 2052: A Galaxy Cluster Gets Sloshed" featured in BU Research Magazine 2012, Chandra press release, NASA press release, and National Geographic image of the week "NGC 5813: An Intergalactic Weather Map" covered in Chandra and NASA press releases "Cosmic Battle Creates Milky-Way Sized Tunnel" featured in Naval Research Lab press release "NGC 1553: Black Holes in Distant Galaxy Points to Wild Youth" covered in Chandra press release Professor Blanton teaches a range of astronomy courses from introductory to graduate level, including Principles of Astronomy II, Stellar and Galactic Astrophysics, Introduction to Astrophysics, and Observational Techniques. Her Observational Techniques course provides hands-on experience with telescopes at Boston University and Lowell Observatory in Arizona. She leads research within the Interdisciplinary Cosmology Group at Boston University, which includes members from the Departments of Astronomy, Physics, and Data Sciences. Her work on galaxy clusters and AGN feedback continues to advance our understanding of the formation and evolution of large-scale structures in the universe.

Konstantin Sobolevمشاهده پروفایل

استاد

• Nanotechnology in Construction
• Superhydrophobic Materials
• Photocatalytic Materials
• +۲ مورد دیگر

Konstantin Sobolev is a Professor at the University of Wisconsin-Milwaukee's College of Engineering & Applied Science, holding the Lawrence E. Sivak '71 Faculty Fellowship in Civil and Environmental Engineering. His research focuses on nanotechnology applications in construction materials, superhydrophobic surfaces, and sustainable composites. Key research areas include: Development of nano-engineered superhydrophobic concrete Photocatalytic coatings for environmental applications Ultra-high-performance cement-based composites Computational modeling of particle packing Recent publications demonstrate innovations in nano-modified construction materials, with applications ranging from self-cleaning surfaces to high-strength composites. Sobolev has led numerous projects on sustainable infrastructure materials, including NSF-funded initiatives on nanotechnology in construction. His work has resulted in advanced materials with improved durability and functionality. Professor Sobolev serves on editorial boards including Scientific Reports and has co-authored influential reviews on nanotechnology in concrete. He collaborates internationally on sustainable material development and functional surface engineering.

Richard D. Komistekمشاهده پروفایل

استاد

• Biomechanics
• Musculoskeletal Research
• Orthopaedic Implants
• +۳ مورد دیگر

Richard D. Komistek serves as the Fred M. Roddy Professor of Biomedical Engineering and Co-Director of the Center for Musculoskeletal Research at the University of Tennessee, Knoxville, positions he has held since 2007 and 2003 respectively. His academic career spans over three decades with significant contributions to orthopaedic biomechanics and joint replacement technology. Dr. Komistek earned his PhD (1992), MSME (1989), and BSME (1988) from the University of Memphis, establishing a strong foundation in mechanical engineering principles applied to biomedical challenges. His research program focuses on advanced biomechanical modeling of the musculoskeletal system, with particular expertise in in vivo kinematic analysis of total joint replacements. Key research areas include failure mode analysis of arthroplasty implants, closed-loop control systems of human movement, and mathematical modeling of joint mechanics. His work bridges engineering principles with clinical orthopaedics to improve implant design and patient outcomes. Analysis of his publication record reveals consistent innovation in total knee arthroplasty research, with emphasis on tri-condylar designs, mobile-bearing systems, and in vivo measurement techniques. His work demonstrates evolving trends toward high-flexion implants, gender-specific considerations, and advanced fluoroscopic tracking methods to optimize joint replacement performance. Dr. Komistek's contributions have been recognized through prestigious awards including: University of Tennessee Research and Creative Achievement Award (2015) Multiple Research Fellow Awards (2005-2012) Knee Society Conventry Award (2003) ESB Clinical Biomechanics Award (1996-1998) Multiple Clinical Orthopedics Multimedia Awards Journal of Clinical Biomechanics Award (2007) As Co-Director of the Center for Musculoskeletal Research, he leads interdisciplinary teams developing next-generation diagnostic tools including mobile tracking fluoroscopy and implant diagnostic devices. His patented technologies focus on improving total hip and knee arthroplasty through innovations in load sensing, infection detection, and wear monitoring.

John Albeckمشاهده پروفایل

استاد

• Cell Signaling
• Signal Transduction
• Single-Cell Biology
• +۷ مورد دیگر

John Albeck is a Professor in the Department of Molecular and Cellular Biology at the University of California, Davis, within the College of Biological Sciences. He leads the Albeck Lab, which is dedicated to understanding the dynamic behavior of signaling pathways such as ERK, Akt, AMPK, and mTOR in regulating cell growth, survival, and metabolism. His research combines live-cell imaging with computational modeling to decode how temporal signaling patterns determine cell fate decisions. He is affiliated with the Biochemistry, Molecular, Cellular and Developmental Biology (BMCDB) Graduate Group and actively mentors graduate students and postdoctoral researchers. Position: Professor Institution: University of California, Davis Department: Molecular and Cellular Biology Graduate Program: BMCDB Lab Website: albecklab.ucdavis.edu Education: B.A. in Biological Sciences, Cornell University, 2000 Ph.D. in Computational and Systems Biology, Massachusetts Institute of Technology, 2007 Dr. Albeck's research focuses on the information flow in signal transduction networks , particularly how dynamic activation patterns encode specificity in cellular responses. His lab uses genetically encoded fluorescent biosensors to track signaling events in real time across single cells, integrating this data with computational models to predict cellular behaviors. This approach addresses how a limited set of pathways can control diverse outcomes like proliferation, apoptosis, and autophagy. A major goal is to improve cancer therapies by predicting how cells respond to targeted inhibitors, especially in the context of heterogeneous and adaptive responses. His recent publications highlight work on ERK signaling dynamics , inflammatory responses in airway cells , and the development of biosensors for FGF and AMPK. These studies employ advanced techniques such as cyclic immunofluorescence (4i) , machine learning , and ordinary differential equation (ODE) modeling to infer signaling history from fixed-cell data. The lab also develops computational tools for data analysis, including automated cluster detection and spectral unmixing. Scientific Contributions and Trends: Deciphering how temporal patterns in ERK activity correlate with downstream gene expression (e.g., Fra-1, pRb, Egr-1) Modeling signaling dynamics to predict cell fate under therapeutic inhibition Investigating spatiotemporal signaling clusters in epithelial inflammation Developing Red-FRET biosensors for AMPK and ERK Exploring metabolic signaling and immune modulation by lactate Dr. Albeck advises a diverse group of graduate students and has trained several postdoctoral researchers who have gone on to careers in academia and biotechnology. His lab fosters a collaborative environment that bridges experimental biology and computational analysis. While no formal awards are listed in the provided text, his lab's recognition through publications in high-impact journals and integration into major research initiatives (e.g., UC Davis Lung Center T32 training) underscores his impact. The lab also supports research through internal grants and collaborative projects focused on cancer signaling and lung biology. Laboratory and Team: The Albeck Lab includes graduate students, postdoctoral researchers, and staff scientists working on projects ranging from biosensor development to single-cell data analysis. Current team members include Christi Abbate, Elijah Kofke, and Marion Hardy (graduate students), and staff such as Michael Pargett and Carolyn Teragawa. The lab emphasizes interdisciplinary training and open science, with code and methods shared via GitHub.

Niamh Nic Daeidمشاهده پروفایل

استاد

• Forensic Science
• Prison Drug Analysis
• Explosives Research
• +۴ مورد دیگر

Niamh Nic Daeid is Professor of Forensic Science and Director of the Leverhulme Research Centre for Forensic Science (LRCFS) at the University of Dundee, leading the £15m Just Tech Institute for Innovation. She holds fellowships with the Royal Society of Edinburgh, Royal Society of Chemistry, and multiple forensic science bodies while serving on committees for INTERPOL, the International Criminal Court, and the United Nations. Her research focuses on forensic chemistry applications in prison drug analysis, explosives detection, and fire investigation. Recent work emphasizes science communication, particularly using comics to improve juror comprehension of forensic testimony. She leads major projects including Clarus (bias prevention in digital forensics) and the Smart Digital Forensic Advisor initiative. Nic Daeid's publications span forensic methodology development, from quantum dots for fingerprint detection to machine learning for footwear impression analysis. Her team's 2025 research includes prison drug studies using seized Scottish evidence and advanced cartridge case imaging techniques. European Network of Forensic Science Institutes Distinguished Forensic Scientist award (2018) Royal Society of Edinburgh Senior Medal for Public Engagement Peter Ganci Award for fire investigation services Gold Engage Watermark for Public Engagement (2019) Best Short Paper Award, International Conference on eXtended Reality (2022) She supervises 13 research students and early-career academics across forensic chemistry, digital forensics, and science communication projects. Current grants include the Leverhulme Trust's £10m LRCFS (2016-2026), UK government's Tay Cities Regional Deal funding, and Dundee City Council's VR/5G initiative. Her team maintains active collaborations with Scottish prisons, international forensic networks, and law enforcement agencies.

• Cybersecurity
• Machine Learning
• Data Analytics
• +۵ مورد دیگر

Dr Zhiyuan (Thomas) Tan is an Associate Professor at Edinburgh Napier University’s School of Computing, Engineering and the Built Environment . He is internationally recognised for his cybersecurity research and has been listed among Stanford University’s Top 2% Scientists for 2021–2023. Education BEng (2005) with high distinction – North-eastern University, China MEng (2008) – Beijing University of Technology, China PhD in Computer Systems (2014) – University of Technology Sydney, Australia Research Interests Dr Tan’s research integrates cybersecurity with machine learning and data analytics. His core areas include: Intrusion detection and defence of critical service systems Adversarial machine learning for malware and anomaly detection Virtualisation security through non-parametric behaviour modelling IoT and vehicular network security—cloud/edge/cloudlet frameworks Privacy-preserving AI and federated machine unlearning Smart-city digital forensics and cyber-physical system resilience Research Output Trends His recent articles (2020–2025) reveal a strong focus on federated learning, edge & mobile computing, UAV coordination, and AI-driven security. Topics span advanced steganography, metamorphic malware, graph injection attacks, and trustworthiness in vehicular platoons, all anchored in real-world IoT and transportation applications. Awards & Distinctions Stanford University Top 2% Scientists List (2021, 2022, 2023) National Research Award 2017 – Research Council of the Sultanate of Oman Best Paper Awards (three instances) Kaspersky Lab Student Cyber Security Conference – Finalist Award SICSA Supervisor of the Year 2019 – Honourable Mention Grants & Leadership Dr Tan has attracted over £200k in external funding including: Carnegie Trust (£73,564) – Federated Machine Unlearning ENU Development Trust (£29,998) – Machine Unlearning Royal Society (£12,000) – VANET Security & Privacy SICSA & other awards for MemoryCrypt, AI Secrets, behaviour biometrics, and visiting-fellow schemes. Supervision & Mentoring Since 2013 he has supervised or co-supervised 16 doctoral candidates and several master’s students; six PhDs have successfully graduated. Roles range from Director of Studies to additional supervisor across diverse topics from malware evolution to VR olfactory interfaces. Research Groups & Collaboration He is affiliated with the Centre for Artificial Intelligence and Robotics , the Centre for Distributed Computing, Networking and Security , and the Centre for Cybersecurity, IoT and Cyber-physical Systems at ENU, fostering interdisciplinary collaboration with national and international partners.

• Computational Imaging
• Computational Photography
• Neuroimaging
• +۷ مورد دیگر

Nick Antipa is an Assistant Professor at the University of California, San Diego, affiliated with the Jacobs School of Engineering and the Department of Electrical and Computer Engineering. His work focuses on computational imaging systems that integrate optics, sensors, and algorithms to enable novel imaging modalities. PhD in Electrical Engineering from UC Berkeley Former optical metrology engineer at Lawrence Livermore National Lab Research interests span computational imaging , lensless camera design , and single-shot high-dimensional optical signal capture . His lab develops systems like DiffuserCam for compressive 3D imaging and Miniscope3D for miniature fluorescence microscopy. Recent publications address differentiable wave optics, high-speed video reconstruction, and marine imaging applications. Awards include Best Paper at ICCP 2016/2019 and Best Demo at ICCP 2017. His lab explores machine learning-driven optical design and differentiable rendering frameworks for end-to-end optimization of imaging systems. Current projects include oceanographic imaging, computational photography, and infrared spectroscopy acceleration.

• Energy and Power Systems
• Renewable Energy Integration
• Power System Stability
• +۱۳ مورد دیگر

Evan Franklin is an Associate Professor in Energy and Power Systems within the School of Engineering at the University of Tasmania. He also serves as Associate Head of Research, reflecting his leadership in advancing engineering research at the institution. His academic work is centered on modern power systems with a strong emphasis on renewable integration, grid stability, and sustainable energy technologies. His primary research interests include energy and power systems, renewable energy integration, grid frequency control, harmonic analysis, distributed energy resources (DER), battery and compressed air energy storage, agrivoltaics, and hydrogen integration. His work bridges engineering fundamentals with real-world applications in sustainable energy systems, contributing to Australia's transition toward clean energy. The recent publications of Dr. Franklin span high-impact journals such as Energies , IEEE Transactions on Industry Applications , Renewable and Sustainable Energy Reviews , and Journal of Energy Storage . The research trends reflect a strong focus on power system stability, microgrid control, harmonic mitigation, and innovative energy storage solutions. His work increasingly integrates AI and machine learning techniques for power quality and system monitoring, while also exploring interdisciplinary applications like agrivoltaics and offshore energy systems. Dr. Franklin has successfully supervised both PhD and Master’s students, including Ahmadreza Eslami and Md Ruhul Amin, with research topics ranging from harmonic analysis to frequency control using battery storage. He has secured substantial research funding from major national and international bodies, including the Australian Research Council (ARC), Australian Renewable Energy Agency (ARENA), CSIRO, and the Blue Economy CRC. Notable projects include the ARC Training Centre in Energy Technologies for Future Grids, MoorPower wave energy projects, and studies on hydrogen integration and black-start capabilities. He leads and participates in research teams focused on renewable energy systems, including the Centre for Renewable Energy and Power Systems at UTAS. His collaborative network includes key researchers such as Professor Michael Negnevitsky, industry partners like Carnegie Clean Energy and TasNetworks, and government agencies including Hydro Tasmania and Aurora Energy. His work is instrumental in shaping resilient, sustainable, and intelligent power systems for the future.

Guo-Cheng Yuanمشاهده پروفایل

استاد

• Genomics
• Epigenetics
• Gene Expression
• +۴ مورد دیگر

Guo-Cheng Yuan is a Senior Faculty member in the Department of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai. His research focuses on genomics, epigenetics, and computational biology , with particular emphasis on single-cell analysis, chromatin regulation, and cancer immunology. Institution: Icahn School of Medicine at Mount Sinai School: Graduate School of Biomedical Sciences Department: Genetics and Genomic Sciences Research Interests Dr. Yuan's work spans multiple domains including: Single-cell RNA sequencing applications in brain development and cancer Chromatin structure analysis in differentiation processes Spatial transcriptomics for tissue microenvironment characterization Computational methods for multiomic data integration Enhancer-promoter interaction dynamics Development of robust bioinformatics pipelines Scientific Trends Analysis His recent publications (2021-2025) demonstrate a focus on single-cell and spatial omics methodologies applied to diverse biological contexts ranging from cancer immunology to neurodevelopment . Notable trends include: Development of trajectory analysis algorithms Multiomic integration for regulatory network mapping Epigenetic mechanisms in cell differentiation Computational approaches for data robustness Three-dimensional genome organization studies Applications in both developmental biology and oncology

Calvin R. Howellمشاهده پروفایل

استاد

• Experimental nuclear physics
• Quantum chromodynamics
• Nuclear structure
• +۵ مورد دیگر

Calvin R. Howell is a Professor of Physics at Duke University within the Trinity College of Arts & Sciences. Since 2001, he has held faculty positions at Duke, and in 2025 became the Director of the Triangle Universities Nuclear Laboratory (TUNL), a position he previously held from 2006 to 2016. His academic career at Duke includes progression from Instructor (1984-1985) to Assistant Professor (1985-1992), Associate Professor with Tenure (1992-2001), and ultimately Professor of Physics (2001-present). Professor Howell's research is centered on experimental nuclear physics with emphasis on the quantum chromodynamics (QCD) description of low-energy nuclear phenomena. His work focuses on structure properties of nucleons and nuclei and reaction dynamics in few-nucleon systems. The macroscopic properties of nucleon structure and the residual strong nuclear force between neutrons and protons in nuclei emerge from QCD at distances where the color interactions between quarks and gluons are strong. His research spans multiple areas including neutron scattering, photonuclear reactions, fission product yields, and precision nuclear physics measurements. Analysis of Professor Howell's recent publications (2021-2025) reveals a strong focus on fission product yield measurements across various actinide isotopes (235U, 238U, and 239Pu) using both neutron-induced and photon-induced fission techniques. His work combines experimental precision with applications in nuclear energy, nuclear security, and fundamental nuclear physics. Additional research threads include neutron-neutron interactions through deuteron breakup experiments, development of novel instrumentation like the HIFROST Dilution Refrigerator, and applications of nuclear physics techniques to plant biology through projects like PhytoPET. Professor Howell has received significant recognition including: Dean's Diversity Award (2016) Samuel DuBois Cook Award for Service (2008) Fellow of the American Physical Society (2006) He has secured substantial research funding with current grants including NEUTRON SCATTERING EXPERIMENTS FOR ACTINIDES USING MONOENERGETIC NEUTRON BEAMS (2025-2028), Alfred P. Sloan Foundation Graduate School Award (2017-2027), and multiple Department of Energy projects. His leadership extends to directing the REU Site: Undergraduate Research in Nuclear Particle Physics at TUNL and Duke (2022-2027). Professor Howell has also made significant service contributions, chairing the Tom Bonner Prize Committee for the APS Division of Nuclear Physics and serving on the Board of Trustees of the Southeastern Universities Research Association (2016-2019). As Director of TUNL, Professor Howell leads a major nuclear physics research facility that facilitates collaboration between North Carolina's Research Triangle Universities. His laboratory work includes the High Intensity Gamma-ray Source (HIGS) facility, where many of his photonuclear experiments are conducted. He has also been instrumental in developing interdisciplinary applications of nuclear physics, particularly in plant biology research through projects like PhytoPET, a modular positron emission tomography system designed specifically for plant imaging.