جست‌وجوی استاد و پژوهشگر

• Bayesian Machine Learning
• Medical Image Analysis
• Graph-based Learning
• +۳ مورد دیگر

Raghavendra Selvan, an Assistant Professor (Tenure Track) at the University of Copenhagen, holds joint appointments in the Machine Learning Section (Department of Computer Science), Kiehn Lab (Department of Neuroscience), and the Data Science Laboratory. His academic journey includes a PhD in Medical Image Analysis (2018), MSc in Communication Engineering (2015), and BSc in Electronics and Communication Engineering (2009). PhD - Medical Image Analysis, University of Copenhagen (2018) MSc - Communication Engineering, Chalmers University (2015) BSc - Electronics and Communication Engineering, BMS Institute of Technology, India (2009) His research focuses on Bayesian Machine Learning with emphasis on Medical Image Analysis, Graph-based Learning, Tensor Networks, Approximate Inference, and Multi-Object Tracking Theory. Recent publications highlight his contributions to environmentally sustainable AI practices, efficient deep learning in medical imaging, and novel applications of tensor networks. Key research areas: Green AI and Environmental Sustainability Medical Image Analysis Graph Neural Networks Crystal Structure Prediction Model Compression Materials Science Applications

Andrea Garzelliمشاهده پروفایل

استاد

• Remote Sensing
• Image Processing
• Signal Processing
• +۴ مورد دیگر

Andrea Garzelli is a Full Professor at the University of Siena's Department of Information Engineering and Mathematical Sciences. His research focuses on remote sensing image processing, particularly in optical and SAR sensor technologies, image fusion, and spatial resolution enhancement. He holds teaching roles in 'Fundamentals of Signal Processing and Telecommunications' and 'Statistical Signal Processing.' He earned his Ph.D. in Computer Science and Telecommunication Engineering from the University of Florence. Notably, he was recognized as a World's Top 2% Scientist by Stanford University for 2019–2023 and his career-long contributions. He served as President of the University of Siena's Quality Assurance Committee (2016–2021) and currently coordinates the graduate program in Computer and Information Engineering. Research interests include satellite data analysis (e.g., Sentinel-2, PRISMA), hypersharpening techniques, and environmental monitoring. His work bridges theoretical advancements (e.g., pansharpening algorithms) and practical applications like urban land classification and vegetation index enhancement. Recent articles emphasize reproducibility, meta-analysis, and synthetic data generation through GANs. Awards: World's Top 2% Scientists (2019–2023 & career). Grants/Advising: Supervises remote sensing theses; no specific grants mentioned. Labs/Teams: Leading research in the department's remote sensing and signal processing groups.

Matt J. Rutherfordمشاهده پروفایل

دانشیار

• Autonomous Systems
• Embedded Systems
• Software Engineering
• +۳ مورد دیگر

Matt J. Rutherford is an Associate Professor in the Department of Computer Science at the University of Denver, with a joint appointment in the Department of Electrical and Computer Engineering. He is Deputy Director of the Unmanned Systems Research Institute and a faculty fellow of Project X-ITE. His research focuses on autonomous systems, embedded systems, and software engineering, with extensive contributions to UAV navigation, control systems, and robotics. Rutherford holds a Ph.D. in Computer Science from the University of Colorado Boulder (2006), an MS (2001), and a BS in Civil Engineering from Princeton University (1996). His work emphasizes practical applications of software engineering principles in distributed and embedded systems. Notable projects include radar-based collision avoidance for UAVs, self-leveling landing platforms, and studies on electric vehicle charging impacts on power grids. Rutherford's research bridges theoretical computer science with real-world engineering challenges, particularly in unmanned systems and robotic autonomy. Key publications explore UAV flight control using neural networks, ground/ceiling effects in rotorcraft, and GPU-based real-time pose estimation. His contributions to model-driven systems and distributed testbed automation highlight long-term engagement with software reliability and scalable experimentation frameworks. Rutherford collaborates widely, including with institutions like the University of South Carolina and Politecnico di Torino. His interdisciplinary approach integrates robotics, aerospace engineering, and software engineering to advance autonomous system capabilities.

Frank G. Schroederمشاهده پروفایل

دانشیار

• Astroparticle Physics
• Cosmic Ray Physics
• Radio Detection Techniques
• +۳ مورد دیگر

Frank G. Schroeder is an Associate Professor in the Department of Physics & Astronomy at the University of Delaware. He holds a Diplom in Physics from TU Darmstadt (2007), a Doctoral Degree (2011), and a Habilitation (2017) from Karlsruhe Institute of Technology (KIT), Germany. He has been at UD since 2018. His research focuses on High-Energy Astroparticle Physics, including cosmic ray detection via radio antennas and computational data analysis. Key collaborations include the IceCube Neutrino Observatory at the South Pole and the Pierre Auger Observatory in Argentina. Recent work emphasizes cosmic-ray anisotropy studies, neutrino oscillations, and detector development for next-generation experiments like IceCube-Gen2 and AugerPrime. His research leverages machine learning for signal identification and analysis, with contributions to understanding ultra-high-energy cosmic rays and neutrino emissions. Schroeder’s teams actively participate in global initiatives such as the IceCube Collaboration and the Global Cosmic-Ray Observatory (GCOS) proposal. His technical innovations include radio antenna arrays and improved data reconstruction algorithms for air showers. He has published extensively on topics including cosmic-ray composition, neutrino point-source searches, and multi-messenger astrophysics.

• Noise Abatement in Machines and Vehicles
• Numerical Modeling in Mechanics
• Automotive Structures and Dynamics
• +۱۲ مورد دیگر

Prof. Wim Desmet is a full professor at the Faculty of Engineering Science and head of the Department of Mechanical Engineering at KU Leuven . His research focuses on advanced modeling techniques for mechanical systems, including: noise and vibration control in automotive and industrial systems computational acoustics and interval field uncertainty modeling metamaterials for broadband vibroacoustic performance AI-driven diagnostic systems in renewable energy and manufacturing Current research projects address challenges in electric vehicle drivetrains, wind turbine monitoring, and multi-physical digital twin development. He actively contributes to academic governance as: Managing Director of KU Leuven Head of Subdivision HIST Chair of multiple executive committees Member of 15+ academic and administrative councils

Laura Barnesمشاهده پروفایل

استاد

• Wireless Health
• Human Machine Interface
• Biomedical Data Sciences
• +۳ مورد دیگر

Laura Barnes is a Professor in the Department of Systems and Information Engineering at the University of Virginia, and Associate Director of the Link Lab. She directs the Sensing Systems for Health Lab, focusing on technology-enabled health solutions. Her academic background includes a B.S. in Computer Science from Texas Tech University (2003), and M.S. (2007) and Ph.D. (2008) in Computer Science from the University of South Florida. Research interests span wireless health , human-machine interfaces , biomedical data sciences , and machine learning applications . Current projects include wearable sensor systems for mental health monitoring, clinician-patient communication frameworks (CommSense), and mobile interventions for anxiety reduction. Funding sources include NIH, National Institute of Aerospace, and the U.S. Army. Recent publications emphasize real-time monitoring , digital phenotyping , and context-aware interventions . Key themes include leveraging large language models for health analysis, developing scalable mobile sensing frameworks, and addressing health equity through technology. Labs/Teams : Sensing Systems for Health Lab, Link Lab Grants : NIH-funded projects on medication adherence and sleep disturbance analysis

Karim Oweissمشاهده پروفایل

استاد

• Neuroscience
• Biomedical Engineering
• Electrical Engineering
• +۵ مورد دیگر

Karim Oweiss is a Pre-eminent Professor at the University of Florida, with joint appointments in the Department of Biomedical Engineering (Herbert Wertheim College of Engineering), Electrical and Computer Engineering, and Neuroscience (McKnight Brain Institute). He holds a Ph.D. in Electrical Engineering and Computer Science from the University of Michigan (2002). His research focuses on neural mechanisms of sensorimotor integration and the development of clinically viable brain-machine interfaces (BMIs) to restore damaged neurological function. His work spans computational neuroscience, neural decoding, optogenetics, and advanced neurotechnology, with a strong emphasis on closed-loop systems and neural plasticity. 2025 : Chemogenetic stimulation of phrenic motor output and diaphragm activity 2024 : Chemogenetic phrenic motoneuron activation enables increased tidal volume 2023 : Compressive sensing of functional connectivity maps from patterned optogenetic stimulation Oweiss has received the NSF Excellence in Neural Engineering Award (2001) and is a Senior Member of the IEEE. He has published extensively on topics including neural decoding, compressive sensing, and multiscale neural interfacing. As editor of Statistical Signal Processing for Neuroscience and Neurotechnology (2010), he has contributed significantly to the field's methodological foundations. His lab develops tools like NeuroQuest for large-scale neural data analysis and implantable neuroprocessors for wireless BMI applications.

Lucie Tvrznikovaمشاهده پروفایل

پژوهشگر

• Atomic Physics
• Particle Physics
• Dark Matter Detection
• +۳ مورد دیگر

Lucie Tvrznikova is a Postdoctoral Researcher at Lawrence Livermore National Laboratory, specializing in experimental particle physics and detector engineering. Her work focuses on direct dark matter detection, nuclear physics, and cyclotron radiation emission spectroscopy (CRES). She holds a Ph.D. from Yale University (2019), where her dissertation explored sub-GeV dark matter searches and electric field modeling in the LUX and LZ experiments. Her research has advanced understanding of low-mass dark matter particles, detector calibration techniques, and high-voltage behavior in liquid noble gases through projects like XeBrA and the Project 8 collaboration. Key contributions include developing methods to extend LUX's sensitivity using Bremsstrahlung and Migdal effects, creating 3D electric field models for xenon detectors, and advancing CRES technology for neutrino mass measurements. She collaborates on major experiments like LZ and the LUX-ZEPLIN initiative, addressing challenges in next-generation noble liquid detectors. Current work focuses on dielectric breakdown studies in liquid xenon, machine learning applications for data analysis, and neutrino mass measurements using Project 8's Kr and tritium systems.

• Advanced Machining
• Engineering Design and Optimization
• Bio-Engineering
• +۶ مورد دیگر

Robert Heinemann is a Senior Lecturer in the Department of Mechanical and Aerospace Engineering at the University of Manchester, affiliated with the School of MACE. His work focuses on advanced machining, tool condition monitoring, and sustainable manufacturing processes. He holds a PhD from the University of Manchester Institute of Science and Technology (2004) and has extensive research experience in drilling technology, carbon-based coatings, and environmental benign machining. Education: Diplom Ingenieur (Dipl.-Ing. FH) in Mechanical Engineering, University of Paderborn, Germany (1999) MSc in Electronic Engineering and Engineering Management, University of Paderborn/Bolton University (2001) PhD in Mechanical Engineering, University of Manchester Institute of Science and Technology (2004) Research interests include: Drilling and reaming technology for minimally invasive surgery Development of diamond-like carbon coatings for cutting tools Process and tool optimization for aerospace and biomedical applications Environmental sustainability in manufacturing design His research outputs emphasize adaptive drilling strategies, deep learning applications in process monitoring, and sustainable manufacturing practices aligned with UN SDGs. He leads the Laser Processing Research Centre (LPRC), focusing on laser-based machining innovations. Scientific achievements include a Leverhulme Trust Early Career Fellowship (2010) and contributions to over 40 peer-reviewed articles. He advises 9 postgraduate research students and collaborates on multi-disciplinary projects addressing industrial challenges in composites and precision engineering.

Yanchong Karen Zhengمشاهده پروفایل

دانشیار

• Smallholder supply chains
• Supply chain transparency
• Environmentally and socially responsible operations
• +۳ مورد دیگر

Yanchong Karen Zheng is the George M. Bunker Associate Professor of Operations Management at MIT Sloan School of Management. She holds a PhD from Stanford University and degrees from Tsinghua University. Her research focuses on supply chain transparency, smallholder agriculture, and socially responsible operations, employing field-based methods to design implementable solutions. Collaborations include projects with the Karnataka government on agricultural platforms and Fair Trade USA on sustainability. Education: PhD in Management Science & Engineering (Stanford), BS/MS in Automation (Tsinghua). Key research areas include smallholder supply chains in developing countries, environmental responsibility, and behavioral operations. Recent work addresses auction design for farmers’ income improvement and regulatory transparency in China’s food industry. She has won awards such as the NSF Award and MSOM Award, and teaches courses like Operations Strategy and Behavioral Decision Theories.

• Computational Biology
• Molecular Biology
• Cell Biology
• +۵ مورد دیگر

Roy Wollman is a Professor at the University of California, Los Angeles (UCLA) in both the Department of Integrative Biology and Physiology and the Department of Chemistry and Biochemistry within the College of Letters and Science. His work bridges experimental and computational approaches to study dynamic signaling networks and their impact on cellular decisions. Research Focus: Computational and systems biology of signaling pathways Key Techniques: Single-cell analysis, spatial transcriptomics, quantitative modeling Major Themes: Information transmission in biochemical networks, cellular decision-making, epigenetic regulation Recent work has emphasized spatial transcriptomics mapping of brain regions, wound response signaling, and multi-scale analysis from cell biology to physiology. His lab has developed computational tools like scPNMF for gene selection and JSTA for cell segmentation and annotation. Key findings include mechanisms of TNF-induced cell death tradeoffs and laminin scarring effects in stem cell function. Roy Wollman has received continuous NIH funding since 2009, including grants for studying NFκB dynamics (R01GM117134), corneal wound signaling (R01EY024960), and single-cell technologies for traumatic brain injury (R01NS117148). His research combines high-throughput microscopy with computational modeling to understand how cells process dynamic signals.

Patrick Kluthمشاهده پروفایل

استاد

• Ion Beam Modification of Materials
• Solid-State Nanopores
• Synchrotron Radiation Techniques
• +۴ مورد دیگر

Patrick Kluth is a Professor at the Research School of Physics, Australian National University, leading a research group focused on swift heavy ion-modified materials and nanopore technology. His work bridges materials science, physics, and biomedical applications. Education : Dipl. Phys. from Düsseldorf, Germany; PhD in Physics from RWTH Aachen, Germany (2002, summa cum laude). Research Interests center on: Ion track technology for solid-state nanopore fabrication Advanced materials characterization (SAXS, X-ray absorption spectroscopy) Defect engineering in semiconductors and superconductors Nano-fabrication and semiconductor processing methods Bio-sensor development and ion separation technologies Recent Research Trends show a focus on: Developing affordable microcontroller-assisted nanopore fabrication platforms Enhancing flux pinning in superconductors via ion irradiation Engineering nanomaterials for space applications (carbon-fibre composites) Exploring radiation effects on perovskite solar cells and graphene-enhanced composites Combining machine learning with nanopore sensing for biomarker detection Scientific Awards : Feodor-Lynen Fellowship Borcherts-Medal for PhD excellence Three ARC Fellowships (Postdoctoral, Research, Future) Leadership Roles : Head of Department (2018-2020), Associate Director HDR (2020-2023). His projects include collaborations on Alzheimer's detection sensors and carbon-fibre additive manufacturing for space applications.

Liqiang Wangمشاهده پروفایل

استاد

• Big Data Analytics
• High-Performance Computing (HPC)
• Parallel Computing
• +۳ مورد دیگر

Liqiang Wang is a Professor in the Department of Computer Science at the University of Central Florida (UCF), where he directs the Big Data Lab. Previously, he served as faculty at the University of Wyoming (2006-2015). He holds a Ph.D. in Computer Science from Stony Brook University (2006) and spent a visiting research period at IBM T.J. Watson Research Center (2012-2013). His research focuses on big data analytics, high-performance computing, parallel systems optimization, and applying deep learning to detect programming errors and enhance model robustness. Education: Ph.D., Computer Science, Stony Brook University (2006); Visiting Researcher, IBM Watson (2012-2013). Research Interests: Improving accuracy and security of big data models, optimizing parallel computing systems (HPC, Cloud, GPUs), program analysis for concurrency errors, and deep learning applications in anomaly detection and adversarial robustness. Notable projects include scalable LSQR algorithms for seismic tomography and the OpenMP Analysis Toolkit (OAT) for concurrency error detection. Key Awards: NSF CAREER Award (2011), Castagne Faculty Fellowship (2013-2015), UCF Mid-Career Refresh Award (2020), and grants including a $50K NSF CIVIC-PG grant (2022) and Google/Meta donations. Advising and Grants: Supervises over 20 Ph.D./M.S. students and has secured grants totaling over $100K. Notable collaborations include seismic tomography with NCAR and cloud computing optimization. Labs/Teams: Director of UCF’s Big Data Lab, collaborating on projects like Parallel LSQR and Anti-Neuron Watermarking.

• Environmental Chemistry
• Mass Spectrometry
• Non-Targeted Analysis
• +۵ مورد دیگر

Dr. Saer Samanipour is a Visiting Professor at the Van 't Hoff Institute for Molecular Sciences, part of the Faculty of Science at the University of Amsterdam. His research focuses on advanced analytical techniques for environmental and biomedical applications, with a strong emphasis on non-targeted analysis, mass spectrometry, and machine learning integration. He leads efforts in developing open-source tools like GcDUO and jHRMSToolBox to enhance data interpretation in complex chemical datasets. Key areas include environmental contaminant detection, chemical exposure assessment via wastewater-based epidemiology, and proteomic analysis of snake venoms. His work bridges computational methods with experimental chemistry to address global challenges in environmental health and toxicology. Primary affiliation: Van 't Hoff Institute for Molecular Sciences Research themes: Non-targeted LC-HRMS workflows, machine learning applications in analytical chemistry, PFAS analysis, and exposome research Software contributions: GcDUO (GC×GC-MS), jHRMSToolBox (HRMS data processing) His publications highlight innovations in data-driven approaches for compound prioritization, toxicity prediction, and method optimization. Recent work explores chemical space exploration and chemometric strategies for complex mixture analysis, with applications to environmental monitoring and forensic science.

Nicola Dragoniمشاهده پروفایل

استاد

• Cybersecurity
• Internet of Things (IoT)
• Machine Learning
• +۷ مورد دیگر

Nicola Dragoni is a Professor in Cybersecurity Engineering at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). As Deputy Director and Head of Section, he leads research initiatives focused on securing emerging technologies. Key Research Areas : Internet of Things (IoT) security, machine learning for intrusion detection, cyber-deception techniques, fog computing, malware analysis, blockchain applications, and wireless sensor network security. Supervision : Actively supervising multiple PhD students in projects related to cyber-deception, moving target defense, and bio-inspired security mechanisms. Recent Publications : Contributions to IoT honeypots, drone identification via RF signals, passkey adoption challenges, and cyber range taxonomies.