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Yixuan ZouView profile

Lecturer

• Artificial Intelligence
• Wireless Communications
• Machine Learning
• +7 more

Dr. Yixuan Zou is a Lecturer in Digital Signal Processing at the School of Electronic Engineering and Computer Science, Queen Mary University of London (QMUL). She co-teaches QMUL's Joint Programme with Beijing University of Posts and Telecommunications (BUPT). B.Sc. in Mathematics M.Sc. from Imperial College London Ph.D. in Computer Science, QMUL (2022) Her research focuses on applying artificial intelligence to wireless communications, specifically non-orthogonal multiple access (NOMA), IRSs/RISs-aided systems, and resource allocation for 6G networks. She integrates machine learning, deep reinforcement learning, and meta-learning techniques to optimize network performance. Recent publications highlight advancements in secure IoT node identification, UAV-assisted RIS-NOMA networks, STAR-RIS beamforming, and wideband near-field communication optimization. She contributes to IEEE conferences as a Technical Program Committee member and session chair. Academic Fellow at Digital Environment Research Institute (DERI) Dr. Zou supervises PhD students researching next-generation multiple access, AI-driven near-field communication, and human action recognition, while also serving as a reviewer for top-tier journals like IEEE TWC and IoT-J.

Emmanouil Z. PsarakisView profile

Associate Professor

• Image Processing
• Computer Vision
• Machine Learning
• +7 more

Emmanouil Z. Psarakis is an Associate Professor at the Department of Computer Engineering & Informatics , University of Patras, Greece. Born in 1963, he has been affiliated with the university since 2005 and is a member of the Signal Processing and Communications Lab . His academic journey includes teaching courses such as Signals and Systems Theory , Digital Signal Processing , and Computer Vision and Graphics . PhD in Computer Engineering and Informatics, University of Patras (1991) Dr. Psarakis is renowned for his research in image processing , computer vision , and signal processing , with applications in biomedical imaging , seismology , and robotics . His work spans deep learning , filter design , and 3D reconstruction , reflecting a multidisciplinary approach. His 15 most recent publications (2020–2025) focus on sign language recognition , adversarial defense , 3D shape analysis , and medical imaging . These works integrate machine learning , stochastic modeling , and computer vision to address challenges in video summarization , image inpainting , and seismic signal analysis . Dr. Psarakis has supervised over 10 PhD students , including Fotini Fotopoulou (2016–2019) and Panagiotis Georgantopoulos (2020–2023). He has participated in EU-funded , Hellenic , and bilateral R&D projects related to biomedical signal processing , robot vision , and forest fire monitoring . He leads the Signal Processing and Communications Lab , which collaborates with institutions like the Computer Technology Institute and international universities. His contributions include filter design , image alignment , and stereopsis techniques , with a focus on practical implementations in telecommunications and medical diagnostics .

Engin DanisView profile

Assistant Professor

• Computational Fluid Dynamics
• High-Speed Flows
• Hypersonic Flows
• +3 more

Engin Danis is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the University of Missouri, Columbia. His research focuses on advancing computational methods for complex fluid dynamics, particularly in high-speed and hypersonic regimes. With expertise in high-order discretizations, turbulence modeling, and large-scale numerical simulation, he develops algorithms that capture intricate flow phenomena with high fidelity and computational efficiency. Dr. Danis's research interests include: High-order numerical methods Hypersonic flows Turbulence modeling Tensor networks for fluid dynamics Compressible flow simulation His publication record demonstrates a strong trend toward applying tensor network methodologies to solve challenging problems in computational fluid dynamics. This innovative approach enables more efficient computation of complex fluid phenomena while maintaining high accuracy, particularly for high-speed and hypersonic flows. His research spans both fundamental numerical method development and practical applications in aerospace engineering. Professional affiliations: American Institute of Aeronautics and Astronautics American Physical Society Society for Industrial and Applied Mathematics Prior to joining the University of Missouri, Dr. Danis was a postdoctoral research associate at Los Alamos National Laboratory where he pioneered tensor-train-based solvers for partial differential equations. He also has industry experience from GE Aviation, where he contributed to high-fidelity simulation tools for aircraft engine thermal-fluid systems.

Edgar Vadimovich VatamanitsaView profile

Assistant Professor

• Client-server programming
• Java and Android pattern development
• Application information systems
• +6 more

Edgar Vadimovich Vatamanitsa serves as an Assistant Professor in the Department of Computer Science at Chernivtsi National University, specializing in cross-platform software development and cloud computing applications. His work bridges academic research with industry practice through active engagement in both university teaching and commercial software development. Education: Yuriy Fedkovych Chernivtsi National University (2013): Systems Software, Software Engineer Dr. Vatamanitsa's research centers on Java/Android pattern development, client-server architectures, and distributed systems. He pioneers practical implementations of design patterns in cloud environments, with significant contributions to optical field simulation, semiconductor modeling, and intelligent transportation systems. His methodology emphasizes cross-platform efficiency and real-world applicability of theoretical computing concepts. Analysis of his 2022-2024 publications reveals dominant themes in cloud-based optical computation (AWS infrastructure), Java pattern implementation for Android, and cross-platform solutions for traffic management and semiconductor physics. His work consistently integrates distributed systems with domain-specific challenges in optics, printing technology, and educational analytics, demonstrating exceptional versatility across engineering disciplines. Professional Activities: Member of Bukovina Information Technology Cluster (since 2022) Senior Software Developer at EPAM SYSTEMS Certificate in Cross-platform Programming and Information Security (Ternopil National Technical University, 2023) Tech Summer Bootcamp for Teachers participant (2023)

• computer vision
• computer graphics
• visual perception

Giuseppe Claudio Guarnera serves as a Senior Researcher at the Norwegian University of Science and Technology (NTNU) in the Department of Computer Science, leading the Research Council of Norway-funded "Spectraskin" project on spectral skin modeling. He concurrently holds a Lecturer position in Computer Vision and Graphics at the University of York (UK) and has been a Guest Researcher at Justus-Liebig University Giessen's Department of Psychology since 2017. His academic background includes: Ph.D. in Computer Science from the University of Catania, Italy, with a dissertation in computer vision and pattern recognition Foundational research experience at the USC Institute for Creative Technologies (US) Guarnera's research centers on computer vision, computer graphics, and visual perception applications, with specialized expertise in spectral skin reflectance modeling, BRDF representation, and material acquisition for virtual environments. His work integrates perceptual validation to enhance realism in digital face rendering and fabric modeling, bridging theoretical computer graphics with practical VR applications. Analysis of his 2017-2020 publications reveals a consistent focus on material and skin reflectance measurement techniques, perceptual validation frameworks, and cross-renderer parameter remapping. Key trends include the development of spectral skin modeling for realistic digital faces (notably in the Spectraskin project), practical BRDF acquisition methods, and single-image fabric reconstruction—demonstrating strong interdisciplinary connections between computer vision, graphics, and human perception studies. Guarnera secures competitive research funding as Principal Investigator of the "Spectraskin" young research talents project awarded by the Research Council of Norway, which investigates spectral skin modeling and rendering for realistic digital faces. He directs the Spectraskin project team at NTNU, which specializes in advancing spectral skin modeling techniques through interdisciplinary collaboration between computer vision, graphics, and perceptual psychology researchers.

Xingjie NiView profile

Associate Professor

• Metasurface
• Photonics
• Plasmonics
• +5 more

Xingjie Ni is an Associate Professor in the Electrical Engineering department at the Materials Research Institute (MRI) . With a focus on metasurface physics , photonics , and plasmonics , their research spans advanced optical technologies and computational imaging. Research Trends : Recent work explores metasurface design for achromatic lenses and light manipulation machine learning-enhanced polarimetric imaging with encoding metasurfaces ultrathin optical devices enabling geometric image transformations reconfigurable liquid crystal systems for dynamic photonic applications electrically tunable nonlinear optics for ensemble learning nanoscale fabrication techniques for scalable metalenses Grants & Projects : Active grants include NSF funding for Photonic Integrated Guided-Wave-Driven Metasurfaces NASA collaboration on Metalens Origami Deployable Lidar National Institute of Biomedical Imaging and Bioengineering support for Metasurface-Based Endoscope

Pierre SagautView profile

Professor

• Turbulent Flows
• Aerodynamics
• Aeroacoustics
• +5 more

Pierre Sagaut is a Professor at Aix-Marseille Université , leading research in the Instabilities, Turbulence and Couplings team. He serves as Editor-in-Chief of the Journal "Computers and Fluids" and holds editorial roles at Journal of Computational Physics , Journal of Turbulence , and Journal of Scientific Computing . His academic leadership extends to roles in the Scientific Council of the AFM (President) and ERCOFTAC (Vice-President). Research Interests focus on Lattice Boltzmann Method (LBM) for compressible/turbulent flows Aerodynamics, aeroacoustics, and aerothermics Uncertainty quantification and data assimilation Immersed boundary techniques for complex geometries Scientific Awards include the 2024 CNRS Bronze Medal Senior Member, Institut Universitaire de France Article Trends highlight LBM applications in nuclear reactor safety, urban pollutant dispersion, helicopter intake dynamics, and transonic flows. Recent works address mass leakage correction, hybrid RANS/LES turbulence modeling, and quantum-inspired lattice gas algorithms. Grants & Collaborations involve partnerships with CNRS, ERCOFTAC, and AFM, focusing on computational fluid dynamics and turbulence modeling.

• Space Physics
• Radar Remote Sensing
• Radio Remote Sensing
• +6 more

Juha Vierinen is a Professor in Space Physics at the Department of Physics and Technology, UiT The Arctic University of Norway. His research focuses on experimental space physics, specializing in radar and radio remote sensing techniques applied to space plasma dynamics, atmospheric physics, space debris characterization, and planetary science. Develops advanced radar systems for ionospheric and mesospheric studies Works with EISCAT, EISCAT_3D, and GNSS networks Investigates Kelvin-Helmholtz Instabilities, GNSS scintillation, and space debris mapping Recent research includes interferometric imaging of ionospheric structures and machine learning applications in ionogram analysis. He collaborates with international institutions on projects like UNICube, QBDebris, and CASCADE. Based in Tromsø, Norway, he operates from Forskningsparken 1 A220. His publications span topics like solar eclipse effects on ionospheric density, smartphone-based space weather mapping, and multi-static meteor radar turbulence studies. Current work emphasizes volumetric radar analysis and fine-scale plasma irregularity characterization using novel imaging techniques.

Mark IwenView profile

Professor

• Computational Harmonic Analysis
• Mathematical Data Science
• Signal Processing
• +12 more

Mark Iwen is a Full Professor with a dual appointment in the Department of Mathematics and the Department of Computational Mathematics, Science and Engineering (CMSE) at Michigan State University. His research bridges theoretical mathematics and computational science, focusing on high-dimensional data analysis, signal processing, and numerical algorithms. Research Interests: His work centers on computational harmonic analysis, mathematical data science, and algorithms for large-scale data. Key areas include sparse spectral methods, phase retrieval, tensor decomposition, compressive sensing, sublinear-time algorithms, and Johnson-Lindenstrauss embeddings. He develops provably accurate and efficient algorithms for problems in high dimensions, such as solving PDEs, function approximation, and manifold learning. Publications and Trends: His recent publications (2023–2025) emphasize sparse and low-rank tensor methods, phase retrieval, and high-dimensional PDE solvers. There is a strong focus on algorithmic efficiency, provable guarantees, and applications in imaging and data science. Many works involve collaborative efforts and include open-source code. Scientific Awards: Scholarship established in honor of Benjamin (Ben) Segal, reflecting his mentorship and academic impact Advising and Grants: He advises numerous students, including undergraduates and graduate researchers, many of whom are co-authors on publications and code repositories. While specific grants are not listed, his sustained research output and collaborative projects suggest active funding. He co-founded the One World MINDS Seminar, demonstrating leadership in the academic community. Labs and Teams: He leads a research group focused on algorithm development for high-dimensional problems, with strong ties to computational mathematics and data science. His team develops and shares code for sparse FFTs, phase retrieval, tensor methods, and PDE solvers, indicating a collaborative and open research culture.

Timothy H. GfroererView profile

Professor

• Semiconductor Physics
• Optoelectronics
• Sustainable Energy Technology
• +3 more

Timothy H. Gfroerer is a Professor of Physics at Davidson College , where he has been since 1999. His research focuses on semiconductor spectroscopy and defect analysis in optoelectronic devices for solar energy and LED applications. He has mentored over 25 undergraduate researchers who co-authored publications with him. Ph.D. in Physics from Dartmouth College (1996) M.S. in Electrical Engineering from Georgia Institute of Technology (1991) B.S. in Physics from University of the South (1989) His research interests include defect state characterization in semiconductors, carrier diffusion dynamics, and sustainable energy technology . He employs techniques like photoluminescence imaging, thermal diffusivity analysis, and transient capacitance spectroscopy. His work has been funded by the American Chemical Society – Petroleum Research Fund and Research Corporation . The scientific awards he has received include the Duke Energy Distinguished Visiting Faculty Award (2011-2012). His publications demonstrate expertise in defect-related recombination , quantum dot relaxation , and device efficiency optimization . He has also contributed significantly to physics pedagogy through lab development and student-centered teaching methods.

David Simon DavisView profile

Research Fellow

• Immunology
• Cancer Research
• Glycoproteins
• +6 more

Dr. David Simon Davis is a Postdoctoral Research Fellow at the John Curtin School of Medical Research (JCSMR), Australian National University, where he works within the Division of Genome Sciences and Cancer. His research focuses on the intersection of immunology and cancer biology, with particular expertise in glycoproteins, immune system regulation, and leukocyte biology. He maintains active collaborations across multiple research groups and contributes to translational medical research initiatives at ANU. Dr. Davis' research interests center on understanding how glycoproteins like heparan sulfate and histidine-rich glycoprotein regulate immune responses, particularly in cancer contexts. His work spans immunotherapy development , immune cell profiling , and molecular mechanisms of immune regulation . He employs advanced techniques including spectral flow cytometry, machine learning algorithms for immune signature analysis, and molecular binding studies to investigate cell surface interactions and plasma protein functions. His publication record demonstrates consistent contributions to immunology and cancer research since 2010, with recent work focusing on irradiation-immunity interactions, cancer-associated leukocyte profiles, and B cell selection mechanisms. Dr. Davis' research shows a clear trajectory toward developing novel radio-immunotherapy approaches, particularly for metastatic colorectal cancer. As an active researcher at JCSMR, Dr. Davis contributes to two major projects: A multiparameter platform for novel radio-immunotherapy personalisation in metastatic colorectal cancer (2022-2025) Irradiation Immunity Interaction Lab (ICube) (2020-2028) These projects position him at the forefront of combining radiation therapy with immunological approaches to cancer treatment. His laboratory work in the Irradiation Immunity Interaction Laboratory (I-Cube Lab) focuses on understanding how radiation affects immune responses and how these interactions can be harnessed for therapeutic benefit.

Kartic SubrView profile

Senior Lecturer

• Computer Graphics
• Monte Carlo Rendering
• Physics-based Simulation
• +13 more

Kartic Subr is a Senior Lecturer (equivalent to Associate Professor) in the School of Informatics at the University of Edinburgh, where he conducts research at the intersection of computer graphics, machine learning, and robotics. He is affiliated with the Institute of Perception, Action and Behaviour and leads an active research group in physics-aware simulation and AI for scientific discovery. His research interests span Computer Graphics , Monte Carlo Rendering , Physics-based Simulation , Machine Learning , Robotics , and Computational Biology . He has made significant contributions to rendering theory, light transport analysis, BRDF modeling, and simulation of fluids and fractures. Recently, his group has applied deep learning to protein design, achieving notable success in international competitions. The recent publications reflect a strong trend toward interdisciplinary research, combining computer graphics with AI and biology. Key themes include explainable AI for physics-aware interfaces (e.g., CueTip), neural rendering (NeRF), protein design with CNNs, and reward learning in robotics. His group regularly publishes in top venues such as SIGGRAPH, NeurIPS, and IEEE RA-L. Royal Society University Research Fellowship (2014–2024) Lead, GCRF Impact Accelerator (2019) Lead, EPSRC First Grant (2017) Royal Society Challenge Grant (2016) Royal Society Newton International Fellow (2010) Marie-Curie Visitor (2006) Best Paper Honorable Mention, I3D 2012 Best Paper, I3D 2011 Kartic Subr actively supervises PhD students and postdocs, including Leonardo Castorina, Zhiyuan Zhang, and Kevin Denamganaï. Former students have gone on to positions at DeepMind, University of Edinburgh, and industry. He has secured multiple grants, including from the Royal Society and EPSRC. His research group collaborates across disciplines, particularly with biological sciences on protein design. He leads projects on approximate physics for robotics, protein design via deep learning, and interactive rendering systems. The group has developed frameworks like TIMED for de novo protein design and tools for explainable physics-aware assistants.

Aamir Younis RajaView profile

Assistant Professor

• Medical Physics
• Radiation Physics
• Medical Imaging Physics
• +8 more

Dr. Aamir Younis Raja is an Assistant Professor in the Physics Department at Khalifa University, UAE, where he co-founded the Medical Physics wing and co-developed the accredited M.Sc program in medical physics. He has held academic roles including Senior Research Fellow at the University of Otago, Visiting Research Fellow at the University of Canterbury, and Visiting Academic Teaching Staff at ARA Institute of Canterbury. His research focuses on radiation physics , medical imaging physics , and spectral photon-counting CT applications in bone/cartilage health , metal implant characterization , cancer imaging , atherosclerosis , and arthritis . Education: PhD in Medical Physics (University of Canterbury, 2013), M.Sc in Applied Physics (UET Pakistan, 2006), B.Sc in Physics & Mathematics (University of the Punjab, 2004) Dr. Raja’s work combines nanoparticle technology with low-dose multi-energy CT to identify non-toxic contrast agents, develops machine learning-based radiation monitoring tools , and pioneers AI-driven artefact reduction in CT imaging. His projects include collaborations with international institutions on material decomposition algorithms and biomedical applications of spectral CT. Notable scientific recognition includes being a Fellow of the Union for International Cancer Control and securing the Khalifa University Faculty Startup Grant . He has supervised over 20 thesis students across University of Otago, University of Canterbury, and Khalifa University, including PhD candidates working on material reconstruction software , parametric color imaging , and machine learning for CT artefact reduction .

Iacopo MochiView profile

Researcher

• EUV Lithography
• Nano-imaging
• X-Ray Metrology
• +6 more

Iacopo Mochi is a Group Leader for Advanced Lithography and Metrology and beamline scientist at the Paul Scherrer Institute (PSI), specifically working at the Laboratory for X-ray Nanoscience and Technologies within the PSI Center for Photon Science. He has extensive experience in EUV lithography, X-ray optics, and semiconductor metrology, with a career spanning multiple prestigious research institutions including Lawrence Berkeley National Laboratory and imec. Dr. Mochi received his physics degree from the University of Florence and earned a PhD in Methods and Technologies for Environmental Monitoring. His career path has included significant contributions to LIDAR systems, astrophysical instrumentation, and ultimately EUV technologies for semiconductor manufacturing. At PSI since 2016, he has established himself as a leading researcher in advanced lithography techniques. His primary research focuses on the development of EUV instrumentation for semiconductor metrology, particularly the XIL-II metrology end station - a lensless microscope dedicated to EUV photomask inspection. His work spans nano-imaging, interferometry techniques in the X-Ray spectrum, and the characterization of advanced materials for semiconductor manufacturing. Dr. Mochi has made significant contributions to the field through the development of RESCAN, an actinic pattern inspection platform based on coherent diffraction imaging. Analysis of his publication record reveals a strong trajectory of innovation in EUV lithography, with recent work pushing resolution limits to 5 nm, developing novel metrology techniques, and applying machine learning approaches to improve image reconstruction and defect detection. His research directly addresses critical challenges in semiconductor manufacturing as the industry moves toward smaller technology nodes. Dr. Mochi's scientific contributions include groundbreaking work on EUV pellicles for mask protection, characterization of absorber and phase defects on EUV reticles, and advancements in interference lithography techniques. His collaborative research demonstrates strong partnerships with industry and academic institutions worldwide. As the beamline scientist for the XIL-II beamline, Dr. Mochi coordinates and supervises experiments that support cutting-edge research in semiconductor manufacturing and nanotechnology. His leadership extends to mentoring junior researchers and contributing to the development of next-generation scientists in the field of advanced lithography.

Jarmo MalinenView profile

Lecturer

• Applied Mathematics
• Numerical Analysis
• Mathematical Systems Theory
• +3 more

Jarmo Malinen is a Lecturer at the Department of Mathematics and Systems Analysis, Aalto University , School of Science. He leads the speech modeling group and participates in the Comspeech consortium, collaborating with institutions like the University of Helsinki and Turku University Hospital. His research spans mathematical systems theory, numerical analysis, and computational speech science, focusing on MRI-based vocal tract modeling, glottal flow dynamics, and speech acoustics. He develops physical models for speech production and contributes to educational technologies like the STACK system. Key publication trends include applications of finite element methods to vowel formants, inverse filtering for glottal flow estimation, and eigenvalue problem solutions for coupled systems. His work intersects applied mathematics, biomedical imaging, and speech physiology. Malinen advises master's and PhD students in mathematical modeling and speech research, though specific student names are not listed in the provided texts. He has developed tools for MRI data post-processing and vocal tract resonance optimization.