جست‌وجوی استاد و پژوهشگر

• Statistical Learning
• Molecular Medicine
• Bioinformatics
• +۳ مورد دیگر

Zhi Zhao is a Researcher at the University of Oslo's Faculty of Medicine, affiliated with the department of Statistical learning in molecular medicine. His work focuses on statistical and computational methods applied to molecular medicine, including omics data analysis, pharmacogenomics, and cancer genomics. Research Interests Statistical learning for multi-omics data integration Bayesian variable selection in drug sensitivity studies Biomarker discovery for immune-related diseases Survival analysis modeling in clinical applications Key Contributions Developed R packages like BayesSUR for multivariate Bayesian regression and EnrichIntersect for set enrichment analysis. His recent work includes studies on immune checkpoint inhibitor toxicity biomarkers and pan-cancer drug response prediction. Awards No scientific awards explicitly mentioned in the provided text. Labs/Teams Core member of the Statistical learning in molecular medicine research group.

• Bioinformatics
• Health Informatics
• Affective Computing
• +۳ مورد دیگر

Dr. Stamos Katsigiannis is an Associate Professor in the Department of Computer Science at Durham University. His research focuses on bioinformatics, health informatics, affective computing, and GPU computing. He holds a BSc, MSc, and PhD in Computer Science from the University of Athens and the Athens University of Economics and Business. Before Durham, he was a Postdoctoral Research Fellow and Lecturer at the University of the West of Scotland (2016–2020). His work spans biometric identification via EEG signals, medical image analysis, and deep learning applications in healthcare. Education: BSc (Hons.) in Informatics and Telecommunications from National and Kapodistrian University of Athens MSc in Computer Science from Athens University of Economics and Business PhD in Computer Science from National and Kapodistrian University of Athens Research interests include bioinformatics (e.g., microarray analysis), health informatics (e.g., ECG-based emotion modeling), and GPU computing for real-time applications. He has pioneered studies on EEG-based biometrics and affect recognition in human-horse interaction. Recent articles explore chest X-ray classification using deep learning , AI-generated content detection , and trajectory prediction with graph networks . His research emphasizes interdisciplinary applications of machine learning in healthcare and education. He advises six postgraduate students and collaborates on UK parliamentary AI governance reports. Notable contributions include the BED EEG dataset and the DREAMER emotion recognition database.

• Machine Learning
• Graph Neural Networks
• Algorithmic Reasoning
• +۳ مورد دیگر

Dobrik Georgiev is a Lecturer in the Department of Computer Science and Technology within the School of Technology at the University of Cambridge. His research centers on bridging algorithmic reasoning with neural architectures, focusing on how neural networks can execute and generalize algorithmic processes. His primary research interests include: Neural algorithmic reasoning and its applications to combinatorial problems Graph neural networks and hypergraph learning systems Explainable AI through concept-based interpretability Deep equilibrium models for algorithmic execution Biological data analysis using neural architectures Georgiev's publication record demonstrates consistent innovation in neural execution models, with recent work exploring bottlenecks in algorithmic reasoning (2025), multi-solution reasoning frameworks (2024), and generalization beyond synthetic graph models (2023). His research shows strong interdisciplinary connections between theoretical computer science, machine learning, and computational biology. While no formal awards are documented in available sources, his work has established significant contributions to neural algorithmic reasoning frameworks. Georgiev maintains active research collaborations through the Department of Computer Science and Technology's initiatives, particularly in the areas of machine learning and neural architectures. His technical leadership is evident in software contributions like the LENs library for logic-explained networks.

• Information Theory
• Optimization
• Wireless Communications
• +۱ مورد دیگر

Dr. Teng-Hui Huang is a Postdoctoral Research Associate at the School of Electrical and Information Engineering , University of Sydney. His academic journey includes a Ph.D. in Electrical and Computer Engineering from Purdue University (2022), and prior degrees from National Taiwan University (B.S. 2015, M.S. 2017). Research Interests: Information Theory, Optimization, Wireless Communications, and Machine Learning. Current Projects: Information-Theoretic Multi-Modal Machine Learning, Machine Learning for Next-Generation Wireless Communications, Non-convex Optimization Algorithms for Information Theoretic Privacy. Publications highlight his work on information bottleneck methods, optimization algorithms, and wireless communication systems. He advises research student Abd Al-rahman ODEH on unsupervised multimodal learning.

• Machine Learning
• Computer Vision
• Medical Imaging Analysis
• +۵ مورد دیگر

Almabrok Essa is an Assistant Professor in the Department of Mathematics, Computer Science, and Data Science at John Carroll University. His research focuses on advanced machine learning techniques applied to medical imaging, computer vision, and signal processing. He holds a faculty position at Dolan Science Center E207 and can be reached via email at aessa@jcu.edu. His work bridges theoretical computer science with practical applications in healthcare, environmental monitoring, and security systems. Research interests include deep learning architectures for medical diagnostics (e.g., chest X-ray analysis and skin lesion segmentation), steganography for secure image transmission, and remote sensing technologies for geospatial analysis. He has pioneered methods like Multibit LSB matching for data hiding and developed frameworks like COVID-CLNet for pandemic response. His contributions span over 20 peer-reviewed articles from 2015 to 2025, showcasing innovations in face recognition algorithms, brain signal analysis, and environmental pattern recognition. Notable applied projects include real-time heart/respiration monitoring systems and automated machinery threat detection for infrastructure protection. His research demonstrates strong interdisciplinary potential, combining computational methods with domains like biomedical engineering, environmental science, and cybersecurity.

Lichao Sunمشاهده پروفایل

استادیار

• Artificial Intelligence Security
• AI Privacy
• Machine Learning
• +۷ مورد دیگر

Lichao Sun is an Assistant Professor of Computer Science and Engineering at Lehigh University. He holds a Ph.D. in Computer Science from the University of Illinois at Chicago (2020), and bachelor's and master's degrees in Computer Science and Engineering from the University of Nebraska-Lincoln. His research focuses on AI security and privacy, addressing vulnerabilities in AI systems, particularly in large language models and multimodal systems. He also explores data mining, federated learning, and reinforcement learning applications. His educational background includes: Ph.D., Computer Science, University of Illinois at Chicago (2020) M.S., Computer Science, University of Nebraska-Lincoln B.A., Computer Science and Mathematics, University of Nebraska-Lincoln Research interests emphasize mitigating security threats in AI, including adversarial attacks (backdoors, token-level vulnerabilities), robust watermarking, and privacy-preserving techniques in federated learning. His work integrates vision-language models, multimodal reasoning, and ethical AI safety. Recent studies highlight innovations in secure embedding aggregation, generative AI defenses, and strategic reasoning in LLMs. Publications (2025) reflect a focus on AI security, multimodal systems, and reinforcement learning applications, with contributions to conferences like CCS, NeurIPS, and AAAI. His research bridges theoretical advancements with practical cybersecurity solutions for modern AI architectures. No scientific awards explicitly mentioned in the provided text. His advising and grants details are not detailed here, though his prolific publication record suggests active research funding. No lab/teams explicitly referenced in the input data.

Laura Ferrariمشاهده پروفایل

استادیار

• Multimodal Machine Learning
• Deep Learning on Physiological Signals
• Epidermal Electronics
• +۳ مورد دیگر

Laura Ferrari is an Assistant Professor at Scuola Superiore Sant'Anna, affiliated with the LAMPSE group. Her research focuses on translating innovative technologies into practical applications, particularly in neuroscience and wearable devices. She specializes in developing seamless tattoo-based wearables for biomonitoring and advancing multimodal emotion recognition through collaborations with INRIA (France). Key projects include stress identification systems for autism support and wearable devices analyzing physiological signals combined with video data. Her work bridges data science, materials science, and engineering to enhance quality of life through technology. Research interests span multimodal machine learning, deep learning on physiological signals, epidermal electronics, and tattoo technology. Notable collaborations involve INRIA for emotion analysis and interdisciplinary projects combining wearable sensors with clinical applications. Her contributions address challenges in autism care, stress management, and biomarker detection through novel device designs and algorithm development. Publications emphasize innovations in wearable sensor fabrication, material science for biocompatibility, and multimodal datasets for stress and emotion recognition. These works highlight advancements in both theoretical frameworks (e.g., EEG analysis) and applied technologies like inkjet-printed conductive polymers.

Charlotte Senkpielمشاهده پروفایل

پژوهشگر

• Energy System Modelling
• Social Science Integration in Energy Modelling
• Technology Diffusion Modelling
• +۳ مورد دیگر

Dr. Charlotte Senkpiel is a Senior Scientist in Energy System Analysis at the Fraunhofer Institute for Solar Energy Systems (ISE), Division Power Solutions. Since 2023, she holds dual roles as Deputy Secretary General and Project Lead at Germany's Expert Council on Climate Change (ERK). Her career includes membership in the German Council of Experts on Climate Change (ERK) since 2019, and prior roles as a PhD candidate and researcher at Fraunhofer ISE (2012–2023). She earned her PhD from the University of Freiburg in 2023, focusing on integrating social science with energy system modeling. Research & Projects: Her work centers on energy system modeling, technology diffusion, and policy analysis. Major projects include SOUVERÄN (sustainable supply chains), AutGrid (grid autarky risks/benefits), and Sozio-E2S (socio-cultural influences on energy transitions). Her expertise spans renewable energy integration, decarbonization pathways, and socio-technical systems analysis. Publications: Over 40 peer-reviewed articles and reports since 2013, focusing on energy policy scenarios, decarbonization strategies, and agent-based modeling of technology adoption. Recent work addresses Germany’s 1.5°C carbon budget alignment and industrial electrification pathways. Labs/Teams: Leads energy transition modeling teams at Fraunhofer ISE, collaborating on cross-sectoral decarbonization projects. Active in developing open-source tools for energy system analysis and policy impact assessment.

Yangming Liمشاهده پروفایل

پژوهشگر ارشد

• Image Analysis
• Machine Learning
• Mathematical Modeling
• +۷ مورد دیگر

Yangming Li is a Research Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP) at the University of Cambridge, affiliated with the Cambridge Image Analysis research group. His work bridges applied mathematics, theoretical physics, and machine learning, with a focus on developing innovative models for image analysis, generative processes, and natural language understanding. Research interests include Fourier Neural Operators, diffusion models, generative adversarial networks (GANs), and their applications in solving complex problems across scientific computing and data-driven domains. His recent contributions explore operator learning for PDEs, robust diffusion models under noisy conditions, and adversarial attacks in text watermarking systems. Publications highlight advancements in operator-based neural networks, risk-sensitive generative modeling, and domain-aware NLP frameworks. His methodologies emphasize mathematical rigor while addressing practical challenges like missing data and model expressivity limitations. Active collaborations span interdisciplinary teams at DAMTP and the broader University of Cambridge research community.

Ali Abdollahzadehمشاهده پروفایل

پژوهشگر ارشد

• Medical Image Analysis
• Biophysics
• Diffusion MRI
• +۶ مورد دیگر

Ali Abdollahzadeh is an Academy Research Fellow at the A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, within the Faculty of Health Sciences. His research focuses on medical image analysis and biophysics, particularly in neuroimaging techniques like diffusion MRI and electron microscopy. He develops advanced computational tools for analyzing white matter ultrastructures and neurovascular interactions. Key projects include studying the role of meningeal lymphatics in neuroinflammation and migraine mechanisms, as well as creating open-source software like SproutAngio and gACSON for bioimage informatics. His work bridges biophysics, computational neuroscience, and clinical applications. Recent studies explore axonal remodeling under stress, neurovascular dynamics, and deep learning applications in neurological disease analysis. He collaborates with the Computational Neuroanatomy Lab and Multiscale Imaging Group, advancing interdisciplinary research in brain imaging and neurodegenerative disorders. Publications highlight innovations in segmentation algorithms, 3D imaging techniques, and translational studies linking microscopic findings to clinical outcomes. His research aims to enhance understanding of CNS structure-function relationships and improve diagnostic tools through advanced imaging methodologies.

Zervakis Michalisمشاهده پروفایل

استاد

• Digital Image Processing
• Signal Processing
• Biomedical Engineering
• +۳ مورد دیگر

Zervakis Michalis is a full Professor at the Technical University of Crete (TUC), serving as Rector and Director of the Digital Image and Signal Processing Laboratory (DISPLAY). He holds a Ph.D. in Electrical Engineering from the University of Toronto (1990) and has been a faculty member at TUC since 1995. His expertise spans Digital Image/Signal Processing, biomedical applications, and neural network implementations in automation. Education: Ph.D., Electrical Engineering, University of Toronto (1990) M.Sc., Electrical Engineering, University of Toronto (1985) B.Sc., Electrical Engineering, Aristotle University of Thessaloniki (1983) Research Interests: Signal/image processing for biomedical applications Machine learning for healthcare diagnostics EEG/ECG analysis and seizure detection Neural network architectures for automation Multi-sensor systems and aerial surveillance His research emphasizes clinical applications like epilepsy monitoring and cardiovascular diagnostics, leveraging deep learning and multimodal data fusion. He has led over 20 international projects and published >90 papers in image/signal processing. Current work focuses on AI-driven medical systems and UAV-based infrastructure monitoring. Labs/Teams: Director of the DISPLAY Lab, collaborating on projects like the BorderUAS semiautonomous surveillance platform and the Pulsense cardiovascular monitoring system. Grants/Contributions: Extensive involvement in EU-funded initiatives and industry partnerships, advancing smart farming, power line inspection, and emergency response systems.

Steven Seth Harrodمشاهده پروفایل

دانشیار

• Railway Engineering
• Operations Management
• Transportation Systems
• +۴ مورد دیگر

Steven Seth Harrod is an Associate Professor at the Department of Engineering Technology and Didactics Production, Transportation and Planning at the Technical University of Denmark (DTU). His work focuses on railway engineering, operations management, and sustainable transportation systems contributing to UN SDGs related to infrastructure and education. He holds a PhD in Operations Management from the University of Cincinnati (2007), MSc degrees in Quantitative Analysis (University of Cincinnati) and Transportation (MIT), and a BSc in Mechanical Engineering from Trinity College Hartford. Research interests include railway scheduling, delay propagation modeling, predictive maintenance of rail infrastructure, and Industry 4.0 applications in transportation. His recent work explores covariate-dependent Markov processes for rail defect prediction and digitalization challenges in European rail systems. He supervises PhD projects on data-driven maintenance strategies and leads initiatives like the Green Mobility Shift (GMS) project. Recipient of the 2016 INFORMS Railway Application Section Student Paper Award for contributions to operations research in rail systems. Active in professional networks including INFORMS and collaborates on projects funded by Danish industry partners. Advises on railway dispatch systems (ODIN project) and mega-project accountability frameworks (CAMP initiative).

Zhiyao Duanمشاهده پروفایل

استاد

• Computer Audition
• Audio-Visual Scene Understanding
• Machine Learning
• +۳ مورد دیگر

Zhiyao Duan is a Professor in the Department of Electrical and Computer Engineering and Computer Science at the University of Rochester, with affiliations to the Goergen Institute for Data Science. He holds a B.S. and M.S. from Tsinghua University and a Ph.D. from Northwestern University. Primary: Department of Electrical and Computer Engineering Secondary: Department of Computer Science Affiliated: Goergen Institute for Data Science His research focuses on Computer Audition, exploring audio-visual scene understanding, source separation, and human-computer collaborative music systems. He has received prestigious awards including an NSF CAREER Award and best paper honors at SMC 2017. His work is funded by NSF, NIH, Adobe, Kwai, and others. Recent publications emphasize audio-visual integration, speech synthesis, and deep learning architectures. He actively participates in academic service, serving on editorial boards and technical committees. The Audio Information Research (AIR) lab, which he leads, has hosted numerous students and collaborated with institutions like Northwestern University, Tsinghua University, and Stanford University. Scientific Awards: NSF CAREER Award Best Paper Award at SMC 2017 Best Paper Nomination at ISMIR 2017 Grants: NSF BIGDATA Grant No. 1741472 NSF Grant No. 1846174 for Human-Computer Collaborative Music Making University of Rochester AR/VR and Health Analytics Grants Students: Yapeng Tian Lele Chen Sefik Emre Eskimez Bochen Li Rui Lu Yujia Yan

• Continual Learning
• Few-Shot Learning
• Domain Adaptation
• +۷ مورد دیگر

Mahardhika Pratama is a Professor in the Department of Computer Science at Universitas Indonesia's Faculty of Computer Science, where he leads research in machine learning and artificial intelligence. His work bridges theoretical advancements with practical applications across various domains including time series analysis, autonomous systems, and resource-constrained environments. Pratama's research focuses on overcoming fundamental challenges in machine learning systems, particularly in the areas of continual learning and few-shot learning. His work addresses the critical problem of catastrophic forgetting in neural networks while developing efficient learning systems that can adapt to new tasks with minimal data. His publications reveal a strong emphasis on developing practical solutions for real-world applications where data is scarce or constantly evolving. Analysis of his recent publications (2023-2025) shows a clear research trajectory toward more sophisticated approaches for continual learning under data scarcity, with increasing focus on transformer architectures, multi-agent reinforcement learning, and cross-domain adaptation techniques. His work demonstrates a consistent pattern of addressing both theoretical foundations and practical implementations of adaptive learning systems. His research has been supported through numerous collaborative projects with international institutions including UNSW Canberra, Swinburne University of Technology, and University of Alberta, reflecting the global impact of his work in the machine learning community.

Anai N Kothariمشاهده پروفایل

استادیار

• Surgical Oncology
• Gastrointestinal Cancer
• Machine Learning in Healthcare
• +۲ مورد دیگر

Dr. Anai N. Kothari serves as Assistant Professor in the Division of Surgical Oncology at the Medical College of Wisconsin, specializing in surgical treatment of upper gastrointestinal and advanced GI tract malignancies. His educational background includes an MD from University of Wisconsin-Madison and surgical oncology fellowship at MD Anderson Cancer Center. Research focuses on transforming cancer care through computational approaches, including predictive analytics for surgical outcomes, AI-enabled clinical trial matching systems, and federated learning for oncology data. Current investigations examine health equity in algorithmic deployment and synthetic data applications. Recent publications demonstrate expertise in developing healthcare AI systems, including natural language processing for clinical documentation analysis and privacy-preserving risk calculators. Emerging work explores federated learning architectures for multi-institutional collaboration while preserving data security. Educational leadership includes studies on residency selection predictors and international medical education pathways. Professional memberships include Society of Surgical Oncology and Society of Asian Academic Surgeons.