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Dag HeinemannView profile

Professor

• Biophotonics
• Plant Sciences
• Laser Technologies
• +4 more

Prof. Dr. Dag Heinemann is a Professor at Leibniz University Hannover, affiliated with the Institute of Botany and the Hannover Institute of Optical Technologies (HOT). His research focuses on Phytophotonics, applying optical technologies to plant sciences, including laser-based phytomonitoring and tissue manipulation. He leads the Phytophotonics Group and teaches in programs like 'Molecular and Applied Plant Sciences' and 'Plant Biotechnology.' Education: PhD (2013, Hannover Medical School), MSc (2009), and BSc (2007) in Life Sciences from Leibniz University Hannover. Professional roles include membership in the Faculty Council and leadership in interdisciplinary projects. Research projects include the PhoenixD Cluster of Excellence (2019–2025), advancing precision metrology and photonic technologies. Awards include multiple Excellence in Reviewing Certificates and Otto Science Trophies. Key research areas span laser manipulation techniques, optical sensing in horticulture, and biomechanical analysis of plant tissues. His work bridges optics and plant biology, contributing to both fundamental understanding and applied technologies in agriculture and biomedicine.

Audrey RepettiView profile

Associate Professor

• Optimization
• Fixed Point Algorithms
• Deep Learning
• +4 more

Audrey Repetti is an Associate Professor at Heriot-Watt University, affiliated with both the School of Mathematical and Computer Sciences and the School of Engineering and Physical Sciences. She holds a PhD in optimization from Université Paris-Est Marne-la-Vallée (2015) and an MSc in applied mathematics from Université Pierre et Marie Curie (2011). Her research focuses on optimization (convex/nonconvex/stochastic), deep learning, Bayesian inference, and inverse problems in imaging and graph processing. She received the Royal Society of Edinburgh Fellowship (2022) for her work in optimization for data science. Her teaching includes courses like Bayesian Inference and Computational Methods. She collaborates internationally on projects in radio astronomy imaging, medical imaging, and computational optics. Repetti’s work bridges optimization theory with practical applications, emphasizing scalable algorithms for large-scale data problems.

Eli Renate GrünerView profile

Professor

• Medical Physics
• Neuroimaging
• Biomedical Engineering
• +1 more

Eli Renate Grüner is a Professor at the University of Bergen's Department of Physics and Technology, with joint affiliations at Haukeland University Hospital. Her research focuses on advancing medical imaging techniques, particularly MRI-based methods for studying brain function, perfusion dynamics, and neurodegenerative disorders. She collaborates extensively with clinical researchers to translate imaging innovations into diagnostic applications. Grüner's interdisciplinary work bridges physics, neuroscience, and clinical medicine, developing analytical tools to quantify cerebral blood flow, neurotransmitter systems, and metabolic processes. Her neuroimaging research examines brain network dynamics, alcohol's effects on cognition, and pathological mechanisms in conditions like dementia and Tourette syndrome. Her publications demonstrate expertise in perfusion imaging algorithms, fMRI analysis, and spectroscopic methods. Recent work includes developing visualization tools for spectroscopy data and investigating network switching during cognitive processing.

Trung Nghia VuView profile

Researcher

• Bioinformatics
• Computational Biology
• Cancer Genomics
• +5 more

Trung Nghia Vu is a Principal Researcher at the Department of Medical Epidemiology and Biostatistics at Karolinska Institutet in Stockholm, Sweden. His research group focuses on developing and applying statistical and bioinformatics methods for molecular biology and medicine, with particular emphasis on cancer genomics and pharmacogenomics. Dr. Vu received his PhD from the Department of Mathematics and Computer Science at the University of Antwerp, Belgium (2009-2014), following an MSc in Computer Engineering from Korea Aerospace University (2006-2008) and a BSc in Technology from Vietnam National University in Hanoi (2000-2004). His research focuses on developing statistical and bioinformatics methodologies for analyzing high-throughput omics data, with specific interests in single-cell sequencing analysis, characterization of cancer omics, and pharmacogenomics. His group has developed 20 statistical and bioinformatics tools available through Biostat Wiki Primary research areas include gene/isoform quantification, detection of abnormal RNAs, single-cell RNA-seq analysis, and drug response prediction Current projects focus on breast cancer and Acute Myeloid Leukemia (AML) Analysis of Dr. Vu's recent publications reveals a strong focus on cancer genomics, particularly in AML and breast cancer. His work bridges computational biology with clinical applications, developing methods for spatial transcriptomics, drug synergy prediction, and circular RNA analysis. The research demonstrates a clear trajectory toward precision medicine applications, with increasing emphasis on personalized drug response prediction and pathway activation modeling. Dr. Vu actively supervises multiple students and researchers, including both completed PhD students (Quang Thinh Trac, Lu Pan, Wenjiang Deng) and ongoing supervisees (Gulser Caliskan, Meghana Alugunoolla, Yuying Li, Aron Arzoomand). His research is supported by significant grants from the Swedish Research Council, CancerFonden, and Karolinska Institutet. He leads the Computational Medicine and Bioinformatics research group at Karolinska Institutet, which collaborates with international clinical and systems-biology researchers. The group maintains active development of bioinformatics tools and maintains the Biostat Wiki platform for academic use. Dr. Vu also contributes to teaching through master's and doctoral courses in biostatistics and R programming at Karolinska Institutet.

• Hyperspectral Imaging
• Machine Learning
• Low-rank Matrix Approximation
• +2 more

Kinan Abbas is a researcher at the University of Strasbourg's College of Science and Engineering, Department of Computer Science, specializing in hyperspectral imaging and machine learning. His work focuses on spectral image processing techniques including unmixing, demosaicing, and low-rank matrix approximation. His research interests center on hyperspectral imaging and machine learning applications, particularly developing novel methods for snapshot spectral image processing. Key contributions include locally-rank-one-based joint unmixing frameworks, diffusion models for texture synthesis, and entropy-weighted spectral deconvolution techniques. His work bridges theoretical signal processing with practical applications in remote sensing and computational photography. Analysis of his publication trend (2021-2025) shows evolution from foundational spectral unmixing techniques toward advanced generative models, with increasing focus on diffusion-based synthesis and multifractal analysis. His research consistently addresses computational challenges in spectral image reconstruction. Abbas actively collaborates with researchers from ICube laboratory (Strasbourg), including Matthieu Puigt, Gilles Delmaire, and Gilles Roussel, evidenced by consistent co-authorship across 14 publications. His work appears in IEEE Transactions, ICASSP, and French GRETSI conferences, indicating strong institutional support for his research program.

Rahul MouryaView profile

Lecturer

• Machine Learning
• Computer Vision
• Inverse Problems
• +6 more

Rahul Mourya is a Lecturer in Computer Science at the Faculty of Science and Engineering , University of Wolverhampton, UK. He joined in October 2023 and is affiliated with the Digital Innovations and Solution Centre (DISC) , which focuses on fundamental and applied research with societal and economic impact. PhD & MSc in Computer Science (Signal/Image/Vision) from Université Jean Monnet Saint-Etienne, France BEng in Electronics & Telecommunications from University of Pune, India His research develops foundational tools for machine learning, computer vision, inverse problems , and signal/image processing , with applications in autonomous systems, robotics, and sensor networks . Teaching includes computational mathematics, robotics engineering, and deep learning modules. Collaborations span institutions like Heriot-Watt University, Telecom ParisTech, and interdisciplinary projects in underwater acoustics and astronomical imaging. Current research explores measurement-consistent neural networks and optimization algorithms for inverse problems.

Giovanni AlbertiView profile

Professor

• Partial Differential Equations
• Inverse Problems
• Applied Harmonic Analysis
• +3 more

Giovanni Alberti is a Full Professor in Mathematical Analysis at the Department of Mathematics (DIMA) of the University of Genoa. He earned his D.Phil. at the University of Oxford and completed postdoctoral positions at École Normale Supérieure (Paris) and ETH Zurich. His research focuses on partial differential equations, applied harmonic analysis, inverse problems, and machine learning. University of Genoa MaLGa Center (Machine Learning Genoa) Mathematical Institute, Oxford Maths Department, ETH Zurich His work bridges mathematical analysis with computational methods, particularly in inverse problems , compressed sensing , and machine learning . He has developed algorithms for real-time geotechnical predictions, sparse optimization for scatterer localization, and continuous generative models. Recent publications emphasize physics-data-driven integration and low-dimensional manifolds. He received the Gioacchino Iapichino Prize (2017), Eurasian Association on Inverse Problems Young Scientist Award (2018), and an ERC Starting Grant (2021). He serves on editorial boards for journals including Inverse Problems and SIAM Journal on Imaging Sciences.

• Cancer Genomics
• Epigenetics
• Bioinformatics
• +2 more

Timothy J Triche is Professor of Pathology at the Keck School of Medicine, University of Southern California. He directs research in cancer genomics, epigenetics, and bioinformatics, with emphasis on pediatric malignancies. His laboratory develops computational tools for genomic analysis and focuses on translating molecular discoveries into clinical diagnostics. Research spans pediatric acute myeloid leukemia, prostate cancer, and sarcoma biology, integrating multi-omics approaches. Key interests include liquid biopsy development, epigenetic dysregulation, and gene fusion mechanisms in oncogenesis. Publications demonstrate consistent focus on molecular diagnostics and bioinformatic innovation, with recent work advancing single-cell analysis methodologies and non-invasive cancer profiling. Earlier foundational studies established mechanisms of fusion oncoproteins in sarcomas.

Tamir GonenView profile

Professor

• Structural Biology
• Membrane Biophysics
• Cryo-Electron Microscopy (Cryo-EM)
• +4 more

Tamir Gonen is a Professor of Biological Chemistry and Physiology at the David Geffen School of Medicine, University of California, Los Angeles (UCLA) , and an Investigator at the Howard Hughes Medical Institute (HHMI) . He is a pioneer in microcrystal electron diffraction (MicroED) , a transformative cryo-EM method for atomic-resolution structure determination. Education: Doctor of Science (DSc), University of Auckland (2025) PhD in Structural Biology, Harvard Medical School (2005) PhD in Biochemistry, University of Auckland (2002) BSc (Hons) in Inorganic Chemistry and Biochemistry, University of Auckland (1998) His research focuses on membrane protein structure and function , particularly those in the blood-brain barrier , using MicroED, X-ray crystallography, NMR, and molecular dynamics. He has determined structures of ion channels, transporters, and drug compounds at resolutions better than 1 Å, advancing drug discovery and understanding disease mechanisms. Recent work includes high-throughput MicroED for ion channel dynamics, energy filtering to enhance resolution, and polymorphic drug characterization . His lab also develops protocols for suspended drop crystallization and focused ion-beam milling of samples. Scientific Awards: Fellow, American Crystallographic Association (2025) Doctor of Science, University of Auckland (2025) Carl Branden Award, The Protein Society (2024) Investigator, HHMI (2017) Member, Royal Society of New Zealand (2017) American Diabetes Association Career Development Award (2009) He leads the Gonen Lab , which emphasizes multidisciplinary approaches and method development in structural biology. His trainees have become faculty at top global institutions, extending his impact on the field.

Antoine GuittonView profile

Adjunct Professor

• Geophysics
• Seismic Imaging
• Full-Waveform Inversion
• +3 more

Antoine Guitton is an Adjunct Professor at Stanford University, specializing in geophysical signal processing and seismic imaging. His research focuses on advanced seismic data processing techniques, including full-waveform inversion, compressive sensing, and the mitigation of artifacts in seismic surveys. He is affiliated with Stanford's geophysics program and can be contacted at aguitton@stanford.edu. Key research interests include wavefield tomography, anisotropic media analysis, and machine learning applications in geophysics. His work addresses challenges such as Doppler effect correction in marine vibrator data, nonstationary statistical modeling, and fault classification using distributed acoustic sensing (DAS). Recent contributions include studies on salami publications in scientific ethics and the optimization of seismic data acquisition systems under field constraints. His publications span over two decades, demonstrating expertise in both theoretical and applied aspects of geophysical exploration.

Tong ZhouView profile

Professor

• Statistical Signal Processing
• Signal Processing for Communications
• Crest Factor Reduction
• +7 more

Dr. Tong Zhou is a Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology. She currently serves as the Director of Georgia Tech-Shenzhen and holds the title of Associate Vice Provost for Academic Affairs. Her research focuses on statistical signal processing , communications signal processing , and nonlinear system analysis . She has been instrumental in establishing Georgia Tech's academic presence in China. Education : B.Sc. from Tianjin University (1989), M.Sc. in Biophysics and Electrical Engineering from the University of Virginia (1992–1995), Ph.D. in Electrical Engineering (1995) with postgraduate research at the Tokyo Institute of Technology. Professional Roles : Editorships in IEEE Transactions on Signal Processing and leadership in IEEE Signal Processing Society committees (2000–2007). Her research interests emphasize crest factor reduction , power amplifier linearization , and OFDM systems , with applications in visible light communication (VLC) and radio interferometric positioning. Notable contributions include PAPR reduction techniques and hardware implementations for VLC systems. Awardee of prestigious honors including IEEE Fellow , NSF CAREER Award , and Steven A. Denning Award for global engagement. Her advising and grants focus on signal processing innovations. She leads the Center for Signal and Image Processing (CSIP) at Georgia Tech and oversees academic initiatives in Shenzhen.

• Medical Imaging
• Neuroimaging
• MRI Reconstruction
• +2 more

Stefan Sunaert is a Professor at KU Leuven's Faculty of Medicine within the Department of Imaging & Pathology. He serves as Head of Radiology and Head of the Division of Medical Imaging (Divisie Medische Beeldvorming), while also holding membership in the LBI - KU Leuven Brain Institute. His institutional roles include senior academic staff positions on the Council of the Faculty of Medicine, Council of the Department of Imaging & Pathology, and Board of the Department of Imaging & Pathology. His research focuses on advanced neuroimaging applications, particularly in MRI reconstruction, brain connectivity analysis, and clinical translation across neurology, oncology, and psychiatry. Key methodologies include diffusion MRI, functional MRI, and multimodal imaging integration for studying conditions ranging from brain tumors to neurodegenerative diseases and psychiatric disorders. Analysis of his 2025 publications reveals a strong emphasis on translational neuroimaging, with recurring themes in brain network analysis (structural/functional connectivity), radiation therapy effects on neural systems, and advanced MRI techniques for clinical applications. His work spans diverse populations including glioma patients, aging adults, children with cerebral palsy, and early Alzheimer's cases. As active promotor or co-promotor on ten major research projects (2023-2029), he leads investigations into neurosurgical planning with dynamic MRI, epilepsy imaging, diabetes-related brain changes, pediatric brain tumor radiotherapy, and neurocognitive decline prediction. These projects demonstrate significant grant acquisition capacity and interdisciplinary collaboration. He leads the Translational MRI unit at UZ Herestraat 49 (box 7003, Leuven) and teaches medical imaging courses including 'Medical Imaging for Physiotherapists' and multiple problem-solving modules in radiology. His ORCID profile (0000-0002-1177-4680) documents extensive publication output in high-impact journals.

Chixiang ChenView profile

Associate Professor

• Biostatistics
• Public Health
• Neuroscience
• +8 more

Chixiang Chen is an Associate Professor in Biostatistics at the Department of Epidemiology & Public Health, University of Maryland School of Medicine, with joint appointments in the Department of Neurosurgery and the University of Maryland Institute for Health Computing. He is a NIH-funded principal investigator whose research spans theoretical and applied statistics, focusing on causal inference, data integration, machine learning, and single-cell analysis for aging, Alzheimer’s disease, and clinical trial design. Education: PhD in Biostatistics (2020), Pennsylvania State University Postdoctoral Researcher (2021), University of Pennsylvania Research Interests: Developing robust statistical frameworks for real-world data integration (e.g., Medicare claims, UK Biobank). Innovative causal inference methods for observational studies and clinical trials. Single-cell RNA-seq and bulk data deconvolution for aging and disease studies. Machine learning in multi-modal biomedical data and federated learning. Grants: NIH/NIA R01 (2024-2029) for post-fracture recovery analysis. Johns Hopkins ICTR grant (2024-2025) for Alzheimer’s disease risk factors. OAIC CC Flexible Early Career Faculty Award (2023-2024). NIH R01 collaborations in aging, neuroscience, and surgery. Scientific Awards: 2024: Honorable Mention (ICSA), Early Career (ACTStat), Pepper Pilot Grant. 2023: RCCN Travel Award, OAIC CC Flexible Early Career Faculty Award. 2021: Dean’s Scholarly Achievement (Penn State). 2019: Alumni Society Award (Penn State), JSM Student Paper Award. Labs & Collaborations: Director of the SILVER Lab (Statistical Information Integration and Learning for Health Data). Collaborations in gerontology, cardiology, neuroscience, and oncology. Developed R packages: DRDNet, InteRD, MultiRD, ELCIC, eMultiMR.

Nur YazdaniView profile

Professor

• Structural Engineering
• Bridge Engineering
• Fiber-Reinforced Polymer (FRP) Composites
• +6 more

Nur Yazdani is a Professor of Civil Engineering at the University of Texas at Arlington , affiliated with the College of Engineering. Her research focuses on structural engineering, non-destructive evaluation (NDE), fiber-reinforced polymer (FRP) composites for infrastructure, and hazard mitigation in coastal and wind/flood-prone regions. Bridge Engineering Concrete Durability Fire and Corrosion Resilience Machine Learning for Structural Prediction Her recent work emphasizes machine learning models for concrete strength prediction, flood mitigation in residential coastal buildings, and hybrid applications of CFRP/GFRP laminates for retrofitting bridges and concrete components. She leads the NSRI Lab , addressing infrastructure resilience through experimental and numerical studies. Scientific Awards : NSF RET Program Evaluation She integrates interdisciplinary education into her research, including collaborations with biology and architecture in classroom implementations for bridge deck evaluation and coastal resilience.

• Radio Astronomy
• Cosmology
• 21-cm Signal
• +5 more

Dr. Vishambhar Pandey is a researcher in the Astronomy department at the Faculty of Science and Engineering, University of Groningen. With a substantial publication record spanning multiple years, his work focuses on radio astronomy using the LOFAR (Low-Frequency Array) telescope, particularly in the areas of 21-cm signal detection, cosmic reionization, and radio interferometric techniques. His research interests center around Radio Astronomy , Cosmology , and 21-cm Signal observations, with specific expertise in LOFAR data analysis, power spectrum measurements, and calibration techniques for low-frequency radio observations. His work often addresses challenges in detecting the faint cosmological 21-cm signal against bright foregrounds and systematic effects. Dr. Pandey's recent publications show a consistent focus on advancing techniques for 21-cm cosmology with LOFAR, particularly in addressing radio frequency interference, calibration challenges, and systematic effects that impact the detection of the cosmological signal. His work spans observational studies, data analysis methods, and theoretical interpretations of results in the context of cosmic reionization. As a contributor to numerous collaborative projects including the LOFAR Long-Baseline Calibrator Survey and various LOFAR data products, Dr. Pandey has played significant roles in developing calibration strategies and analyzing complex radio astronomy datasets.