جست‌وجوی استاد و پژوهشگر

Abani Patraمشاهده پروفایل

استاد

• Computational Sciences
• Data Driven Modeling
• Computer Science
• +۵ مورد دیگر

Abani Patra is a Professor of Computer Science, Mathematics, Mechanical Engineering, and Civil and Environmental Engineering at Tufts University. He also serves as the Center Director for Data Science at the Tufts Institute for Artificial Intelligence (TIAI). His research focuses on computational sciences and data-driven modeling, with applications spanning environmental systems, biomedical imaging, and geophysical hazards. He has directed major initiatives at the National Science Foundation (NSF) and U.S. Department of Energy (DOE), and previously founded the Institute for Computational and Data Sciences at the University at Buffalo. Education: PhD in Mathematics, University of Texas, 1995 MS in Mechanical Engineering, University of Missouri, 1990 BSc in Engineering, Birla Institute of Technology & Science, India Research Interests: Large-scale computational modeling and uncertainty quantification Data-driven approaches for geophysical hazards (e.g., debris flows, volcanic eruptions) Biomedical imaging and metabolic analysis Open science platforms for glaciology and volcanology Key Projects: Developed the Ghub platform for open cryosphere research Launched VICTOR, a cyberinfrastructure for volcanology Advanced AI-driven techniques for postfire debris flow prediction Grants & Leadership: Directed NSF and DOE programs in computational science PI for NSF Cyberinfrastructure grants Former director of the Institute for Computational and Data Sciences

• Biomedical Informatics
• Data Science
• Machine Learning
• +۷ مورد دیگر

Lucila Ohno-Machado, MD, PhD, MBA, is the Waldemar von Zedtwitz Professor of Medicine and Biomedical Informatics and Data Science at Yale University. She serves as Deputy Dean for Biomedical Informatics and Chair of the Department of Biomedical Informatics and Data Science at the Yale School of Medicine. Her leadership roles include overseeing informatics infrastructure for Yale’s academic health system and fostering interdisciplinary collaboration across departments such as Medicine and the Halicioğlu Data Science Institute (previously at UCSD). Ohno-Machado holds an MD from the University of São Paulo (Brazil), an MBA from Fundação Getúlio Vargas (Brazil), and a PhD in Medical Information Sciences and Computer Science from Stanford University. She has held faculty positions at Harvard Medical School, MIT’s Health Sciences and Technology Division, and the UCSD Health Department of Biomedical Informatics, where she pioneered federated learning and privacy-preserving AI methodologies. Her research focuses on predictive analytics, federated learning, quantum computing in healthcare, and blockchain applications to enhance data security. She emphasizes addressing algorithmic bias and promoting health equity through data-driven solutions. Recent work includes developing frameworks for medical device safety evaluation and guiding principles to mitigate disparities in algorithmic healthcare applications. Key achievements include the Inaugural Helen M. Ranney Award (2024), election to the National Academy of Medicine (2024), and the William W. Stead Award (2019). She has led NIH-funded informatics centers and contributed to the first large-scale clinical data-sharing initiative across five UC medical systems. Her grants span AHRQ, PCORI, NSF, and blockchain-related initiatives through the IT/NIST Challenge Award. Ohno-Machado advises on translational research strategies and mentors teams in YBIC (Yale Biomedical Informatics & Computing). Her lab collaborates globally, leveraging federated models and AI to advance personalized medicine while prioritizing patient privacy. She also chairs the OHER Awards for Yale Research Excellence, promoting interdisciplinary health equity research.

Michael Krauthammerمشاهده پروفایل

استاد

• Clinical Data Science
• Translational Bioinformatics
• Machine Learning in Healthcare
• +۳ مورد دیگر

Michael Krauthammer is a Professor of Medical Informatics and Chair of the Department of Quantitative Biomedicine at the University of Zurich, affiliated with the University Hospital of Zurich. His lab focuses on Clinical Data Science and Translational Bioinformatics, leveraging AI and machine learning to address healthcare challenges. Key areas include cancer genomics, federated learning, and automated medical imaging analysis. Education and affiliations: Krauthammer leads an interdisciplinary team supported by major funding agencies. His research spans bioinformatics, clinical decision support systems, and multimodal data integration. Notable projects include AI-assisted diagnosis in rheumatology and prime editing efficiency prediction. Recent work emphasizes longitudinal cfDNA analysis, drug interaction modeling, and personalized oncology. The lab collaborates across disciplines, with projects funded by Swiss and international grants. Students and postdocs work on topics like machine learning for radiology reports, longitudinal disease trajectories, and protein design. Key projects include the NTCIR-18 RadNLP challenge, prime editing prediction models (Nature Biotechnology 2024), and vision transformers for capillaroscopy analysis. The lab advocates for reproducible data science and ethical AI in healthcare.

• Radiomics
• Medical Imaging Analysis
• Machine Learning in Healthcare
• +۷ مورد دیگر

Adrien Depeursinge is a Professor at HES-SO Valais-Wallis - Haute Ecole de Gestion, affiliated with the School of Economics and Services and the Management Information Systems department. His research focuses on radiomics, personalized medicine via image-based analysis, and clinical workflow optimization. He leads the development of the QuantImage platform, a physician-centered web-based tool for radiomics research, and contributes to radiomics standardization efforts through initiatives like the Image Biomarker Standardization Initiative (IBSI). His work emphasizes machine learning applications in healthcare, including tumor segmentation, biomarker extraction, and improving diagnostic accuracy through computational models. Key research themes include: 1) Radiomics – developing quantitative imaging features for cancer diagnosis/prognosis; 2) Medical Imaging Analysis – advancing texture-based models, multi-modal fusion, and automated lesion detection; 3) Physician-AI Collaboration – designing user-centric tools for clinical integration. His contributions span neuro-oncology (brain metastases), head-and-neck cancer, and multiple sclerosis imaging. Publications emphasize methodological advancements (e.g., kernel optimization in CNNs, steerable detectors) and clinical validation (e.g., reproducibility of radiomics features across imaging protocols). He collaborates with institutions like the University Hospital of Lausanne (CHUV) and international teams on projects like the HECKTOR challenge for PET/CT tumor segmentation. His work bridges technical innovation with clinical impact, aiming to translate radiomics into actionable clinical tools. QuantImage v2, his flagship tool, enables no-code development of machine learning models using clinical imaging data. Research also includes phantom-based validation of radiomics features and addressing challenges in feature stability across imaging modalities. Current projects explore improving contour quality for radiomics studies and optimizing AI explainability in medical decision-making.

• Biomedical Imaging
• X-ray Computed Tomography
• Spectral Detection
• +۳ مورد دیگر

Prof. Julia Hearts is a Professor at the Technical University of Munich (TUM) , affiliated with the School of Natural Sciences . Her research focuses on biomedical imaging , particularly advancing X-ray computed tomography through phase-contrast and dark-field radiography for clinical and biological applications. Developing spectral detection techniques to enhance diagnostic accuracy Quantitative imaging for element-specific parameter extraction Utilizing synchrotron radiation and standard X-ray tubes Her recent publications demonstrate expertise in dark-field radiography for lung and breast imaging, phase-contrast tomography for tissue characterization, and multi-spectral X-ray analysis for material decomposition. Collaborative work spans oncology , pulmonology , and materials science . Contact: julia.herzen@tum.de

Payam Barnaghiمشاهده پروفایل

استاد

• Machine Learning
• Neurosciences
• Healthcare Technology
• +۶ مورد دیگر

Payam Barnaghi is a Professor and Chair in Machine Intelligence Applied to Medicine at Imperial College London's Department of Brain Sciences, part of the Faculty of Medicine. He holds multiple leadership roles, including Co-Director of the School of Convergence Science in Human and Artificial Intelligence and Deputy Head of Neurology. His research focuses on AI-driven healthcare solutions, particularly in neurosciences and dementia care. He leads the Translational Machine Intelligence group at the UK Dementia Research Institute (UK DRI) and is a Visiting Professor at University College London's Institute of Child Health. His affiliations include the NVIDIA Deep Learning Institute, the British Heart Foundation Centre for Research Excellence, and the UK DRI Care and Research Technology Centre. He has received awards such as the Wellcome Trust Mental Health Ideathon Award (2023) and the IEEE Outstanding Leadership Award (2017). His work emphasizes remote patient monitoring, digital biomarkers, and explainable AI for early health event detection. Key projects include the TIHM (Technology Integrated Health Management) initiative for dementia care, leveraging wearable sensors and machine learning. He contributes to interdisciplinary efforts in smart care ethics and has published extensively on topics like neural network applications, healthcare data analysis, and clinical decision support systems.

Kyusang Leeمشاهده پروفایل

دانشیار

• Optoelectronics
• Thin-Film and Flexible Devices
• Neuromorphic Computing
• +۵ مورد دیگر

Kyusang Lee is an Associate Professor in the Electrical and Computer Engineering and Materials Science and Engineering departments at the University of Virginia. His research focuses on optoelectronic devices, neuromorphic computing, and smart sensors, emphasizing applications in solar energy conversion and flexible electronics. He holds a B.S. from Korea University (2005), M.S. from Johns Hopkins University (2009), and Ph.D. from the University of Michigan (2014). He conducted postdoctoral research at the University of Michigan and MIT. Education: B.S., Electrical Engineering, Korea University, 2005 M.S., Electrical and Computer Engineering, Johns Hopkins University, 2009 Ph.D., Electrical Engineering and Computer Science, University of Michigan, 2014 Postdoctoral Fellowships: University of Michigan (EECS), MIT (Mechanical Engineering) His research interests span thin-film and flexible optoelectronics, neuromorphic computing architectures, and AIoT-enabled smart sensors. Notable contributions include remote epitaxy techniques for semiconductor membrane integration and solar-tracking concentrator designs. His work bridges materials science and device engineering to advance energy-efficient optoelectronics and bioinspired systems. Key Research Themes: Organic/inorganic optoelectronic devices for solar energy Flexible and stretchable electronics Neuromorphic hardware for edge computing Gas sensing and bioinspired sensor systems Lee’s publications reflect interdisciplinary innovation, with recent work on ferroelectric transistors, neuromorphic vision systems, and high-efficiency photovoltaics. He received the NSF CAREER Award (2020) and AFOSR YIP Award (2023).

Tommy Löfstedtمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +۴ مورد دیگر

Tommy Löfstedt is an Associate Professor at Umeå University , affiliated with the Department of Computing Science and the Department of Mathematics and Mathematical Statistics. His research focuses on machine learning , computer vision , and medical image analysis , with applications in life sciences, radiation therapy, and biomedical imaging. He leads multiple research projects, including AI-driven delineation in radiation therapy, quantitative MRI for radiotherapy, and machine learning for plant nutrient uptake. Current research emphasizes structured regularization methods to improve model interpretability and robustness. Key applications include medical image segmentation , Alzheimer's classification , and uncertainty estimation in MRI . Recent publications highlight his work on morphological regularization , adversarial attack mitigation , and multi-task learning in medical imaging contexts. His projects span 2022–2026 with funding for pediatric oncology automation and gynecological cancer staging. Affiliated with both computing and mathematical departments, he bridges algorithm development with applied mathematical frameworks in medical and life science domains.

Nikolaos Koutsoulerisمشاهده پروفایل

استاد

• Predictive Psychiatry
• Machine Learning in Psychiatry
• Psychosis Prediction
• +۵ مورد دیگر

Professor Nikolaos Koutsouleris serves as a Research Group Leader for the Max Planck Fellow Group for Precision Psychiatry at the Max Planck Institute of Psychiatry and holds a position as Senior Physician in the Department of Psychiatry and Psychotherapy at Ludwig Maximilian University (LMU) Munich. His work bridges clinical practice with advanced computational approaches to transform psychiatric diagnostics and treatment. Dr. Koutsouleris specializes in predictive psychiatry, focusing on extracting meaningful patterns from neurobiological, neurocognitive, and clinical data to improve early recognition of functional psychoses. His research employs structural MRI, neuropsychological testing, and clinical evaluations within cross-sectional and longitudinal studies, utilizing advanced machine learning methods to identify and validate biomarkers for single-subject prediction of psychosis. As head of the Early Psychosis Studies and the Workgroup for Neurodiagnostic Applications, he drives initiatives to implement predictive models across healthcare settings for personalized management of high-risk individuals. His publication record reveals a consistent focus on machine learning applications in psychiatry, with recent work addressing critical issues like the generalizability of clinical prediction models, brain aging patterns in large populations, and multimodal approaches to psychosis prediction. His research spans from fundamental methodological challenges to clinical applications, demonstrating how AI and machine learning are transforming psychiatric practice toward precision medicine. Dr. Koutsouleris actively trains pre- and post-doctoral investigators in advanced data analysis techniques, emphasizing comprehensive analysis of complex, high-dimensional datasets using multivariate methods. His leadership in the PRONIA Consortium and other collaborative efforts highlights his commitment to advancing the field through international cooperation and rigorous scientific inquiry.

Nathalia Peixotoمشاهده پروفایل

دانشیار

• Neural Engineering
• Brain-Computer Interfaces
• Biomedical Sensors and Actuators
• +۴ مورد دیگر

Nathalia Peixoto is an Associate Professor in the Department of Electrical and Computer Engineering and Affiliate Faculty in Bioengineering at George Mason University. Her work bridges neural engineering, biomedical applications, and assistive technology development with international collaborations across Israel, Ireland, Peru, and Korea. Educational background: PhD in Electrical Engineering, Universidade de Sao Paulo MS, University of Campinas Research Interests: Dr. Peixoto specializes in neural engineering with focus on brain-computer interfaces using wearable devices. Her lab develops: Neural prosthetics and implantable systems Bioimpedance-based medical sensors Low-cost electrophysiological recording platforms Community-centered engineering design solutions Publication Trends: Her 2022-2025 publications demonstrate strong interdisciplinary convergence between neuroscience, biomedical engineering, and AI. Key trends include machine learning for seizure detection in zebrafish models, electrochemical optimization of neural interfaces, and community-engaged design projects addressing societal challenges through transdisciplinary graduate training. Grants and Projects: Principal investigator for multiple NSF-funded initiatives: NRT-HDR: Transdisciplinary Graduate Training (2019-2024) Smart and Connected Communities: Networked Devices (2017-2019) Bioimpedance for retinal implants (2015-2017) C2MW: Classroom to Makers Week (2015-2016) Additional funding from VA STEM CoNNECT and Longwood University. Laboratory: The Neural Engineering Lab integrates chemistry, physics, and engineering disciplines through team-based projects involving high school to graduate students. Current work includes sustainable food-waste solutions, tremor-capturing robots for low-resource areas, and neural implants with international academic partnerships.

Richard Segallمشاهده پروفایل

استاد

• Data Mining
• Text Mining
• Web Mining
• +۶ مورد دیگر

Dr. Richard Segall is a Professor in the Department of Information Systems and Business Analytics at Arkansas State University , affiliated with the Beck College of Sciences & Mathematics . He is also affiliated faculty in the Master of Engineering Management (MEM) Program , the Environmental Sciences Program , and serves on thesis committees at the University of Arkansas at Little Rock (UALR) . Education: Ph.D. in Operations Research, University of Massachusetts at Amherst (1984) M.S. in Operations Research and Statistics, Rensselaer Polytechnic Institute (1975) M.S. in Mathematics, Rensselaer Polytechnic Institute (1973) B.S. in Mathematics, Rensselaer Polytechnic Institute (1971) Dr. Segall's research spans data mining, text mining, web mining, big data analytics, bioinformatics, supercomputing applications, and mathematical modeling . His work bridges business analytics and computational biology , with a focus on transdisciplinary applications in agriculture, healthcare, and space systems. His recent publications emphasize genomic data analysis , plant disease diagnostics , AI-driven healthcare solutions , and space technology forecasting . The integration of machine learning , data visualization , and open-source tools is a recurring theme across domains. Scientific Awards & Grants: Three research awards from the National Research Council (NRC) Software grants from Oracle Corporation and SAS Institute, Inc. Dr. Segall has served on the editorial boards of the International Journal of Data Science , International Journal of Data Mining, Modelling and Management , and International Journal of Fog Computing . He previously contributed to the Arkansas Center for Plant-Powered Production (P3) and currently participates in the Center for No-Boundary Thinking (CNBT) .

Kim Bjergeمشاهده پروفایل

دانشیار

• Computer Vision
• Deep Learning
• Machine Learning
• +۴ مورد دیگر

Kim Bjerge serves as Associate Professor and Group Leader in Aarhus University's Department of Electrical and Computer Engineering, specializing in computer vision and machine learning applications for ecological monitoring. His research bridges engineering and environmental science to develop innovative solutions for insect biodiversity assessment and sustainable agriculture. His core research interests include computer vision, deep learning, and edge computing systems for real-world ecological monitoring. Dr. Bjerge develops time-lapse camera pipelines and deep learning models specifically for insect population tracking in natural environments, with emphasis on agricultural applications like black soldier fly farming and biodiversity conservation. His work integrates signal processing techniques with biological data to create field-deployable monitoring systems. Recent publications reveal a strong trend toward practical implementations of computer vision in entomology, particularly focusing on edge processing for camera traps, automated trait prediction in insect farming, and biodiversity monitoring systems. Key research areas include nocturnal insect monitoring, floral environment analysis, and developing specialized datasets like AMI for insect identification in wild settings. He leads multiple significant research projects funded through competitive grants: MAMBO: Modern Approaches to Monitoring Biodiversity (2022-2026) FLYgene: Sustainable Insect Production for Livestock Feed (2022-2026) Automatisk monitering af nataktive insekter: Automatic nocturnal insect monitoring (2024-2029) Pilotprojekt for automatisk registrering af invasive plantearter: Invasive species monitoring (2020-2021) As head of the Signal Processing and Machine Learning research group, Dr. Bjerge directs interdisciplinary teams developing computer vision solutions for biological monitoring systems. His laboratory focuses on creating robust field-deployable technologies including scanner-based arthropod imaging systems, time-lapse camera networks for floral environments, and edge AI processors for real-time insect monitoring in agricultural settings.

• Bayesian Statistics
• Nonparametric Methods
• Machine Learning
• +۷ مورد دیگر

David B. Dunson is the Arts and Sciences Distinguished Professor of Statistical Science at Duke University, with a joint appointment in the Department of Mathematics. He is also a Faculty Network Member of the Duke Institute for Brain Sciences. His research bridges theoretical statistics with practical applications across multiple scientific domains, focusing on developing new tools for probabilistic learning from complex data. Dr. Dunson earned his Ph.D. from Emory University in 1997 and his B.S. from Pennsylvania State University in 1994. Dr. Dunson's research focuses on developing statistical methods directly motivated by challenging applications in ecology/biodiversity, neuroscience, environmental health, and criminal justice/fairness. His methodological work spans models for low-dimensional structure in data (latent factors, clustering, geometric and manifold learning), flexible/nonparametric models (neural networks, Gaussian/spatial processes), Bayesian inference frameworks, and models for "object data" (trees, networks, images, spatial processes). His approach emphasizes creating practical tools that scientists and decision makers can use routinely. Dunson's recent publications demonstrate a strong focus on advancing Bayesian methodology for complex data structures across applications in biodiversity mapping, brain connectomics, environmental health, and infectious disease modeling. His work shows consistent innovation in nonparametric Bayesian methods, computational efficiency, and the handling of high-dimensional and structured data, always with an eye toward solving real-world scientific challenges. Dr. Dunson has received numerous prestigious awards including: IMS Medallion Lecturer (2019) Mitchell Prize from the International Society of Bayesian Analysis (2018) Carnegie Centenary Professorship (2018) DeGroot Prize (2017) COPSS Award: President's Award (2010) Fellow of the Institute of Mathematical Statistics (2010) His extensive publication record with numerous co-authors suggests an active research group mentoring graduate students and postdocs. His research on projects like biodiversity mapping (funded by a European Research Council Grant) and brain connectomics indicates well-funded research programs addressing significant scientific challenges across multiple domains. Dr. Dunson's work involves collaborations across multiple labs and teams, particularly through his affiliation with the Duke Institute for Brain Sciences. His research on biodiversity mapping, brain connectomics, and environmental health suggests involvement in large, interdisciplinary teams addressing complex scientific questions that require sophisticated statistical approaches.

Erik Rodnerمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Data Science
• +۵ مورد دیگر

Prof. Dr. Erik Rodner is a faculty member at the University of Applied Sciences Berlin (HTW Berlin), where he serves as a Professor for Machine Learning and Data Science. He also contributes to the School of Engineering Sciences - Technology and Life. His research spans computer vision, machine learning, and biomedical applications, with a focus on learning with limited data, robust visual recognition models, and medical image analysis. He has developed innovative methods for medical diagnostics, industrial classification, and anomaly detection. Recent publications (2025-2016) highlight his expertise in visual in-context learning, semi-weakly segmentation, and active learning frameworks. He has collaborated with institutions such as ZEISS Group, Friedrich Schiller University Jena, and UC Berkeley. Scientific Awards: Award for Excellent Teaching (2023)

Scott Croweمشاهده پروفایل

استاد

• Radiation Oncology
• Medical Physics
• Radiation Dosimetry
• +۳ مورد دیگر

Prof Scott Crowe is a leading academic and clinical researcher in radiation oncology medical physics, affiliated with the Royal Brisbane and Women’s Hospital and the Hudson Institute of Medical Research (HBI) Cancer Care Services. His work bridges clinical practice and advanced research in radiotherapy technologies. Clinical Role: Clinical Lead for Cancer Care Services at HBI, overseeing radiation oncology medical physics. Education: Post-doctoral fellowship at Queensland University of Technology (QUT). Research Interests focus on: 3D Printing: Developing patient-specific phantoms and devices for radiotherapy applications (e.g., lung, vaginal, and oral molds). Dosimetry: Advancing measurement techniques (ionization chambers, Monte Carlo simulations) and addressing challenges like small field dose corrections, skin dose enhancement, and secondary cancer risk assessment. Adaptive Radiotherapy: Real-time motion adaptation systems, including Radixact Synchrony and TomoTherapy, to improve treatment accuracy. Quality Assurance: Statistical process control for beam energy variations, gamma evaluation methods, and machine performance checks. Publication Trends highlight his expertise in integrating 3D printing with dosimetry, optimizing adaptive radiotherapy workflows, and improving quality assurance protocols. His work spans Monte Carlo simulations , proton therapy , and image-guided radiotherapy . Supervision: Mentors higher degree research students in radiation oncology physics. Conferences: Regular presenter at international scientific meetings. Labs & Collaborations: Manages the radiation oncology medical physics research portfolio at Royal Brisbane and Women’s Hospital, collaborating with Hudson Institute on clinical translation projects.