جست‌وجوی استاد و پژوهشگر

• Medical Image Analysis
• Artificial Intelligence
• Healthcare
• +۹ مورد دیگر

Cherise Chen is an Assistant Professor in Computer Vision at the Department of Computer Science, University of Sheffield. She also holds positions as a Visiting Researcher in the Oxford BioMedIA Group at the University of Oxford and an Honorary Research Fellow at Imperial College London. As a core member of the Insigeno Institute and Shef.AI community, Dr. Chen leads research at the intersection of artificial intelligence and healthcare, focusing on translating cutting-edge AI techniques into practical medical applications. Dr. Chen's research program centers on developing robust, data-efficient machine learning algorithms for medical image analysis. Her work spans adversarial data augmentation, robust machine learning frameworks, and data-efficient learning techniques including self-supervised, few-shot, and semi-supervised approaches. She has made significant contributions to multi-task and multi-modal learning, adaptive machine learning systems, and algorithms with built-in considerations for fairness, privacy, robustness, and interpretability. Her research specifically targets clinical applications in cardiac image analysis (including segmentation, registration, and shape remodeling with quality control), prostate image analysis integrated with pathological image analysis, and brain image segmentation for clinical use cases. Analysis of Dr. Chen's recent publications reveals a strong focus on addressing the practical challenges of deploying AI in real-world medical settings. Her work on test-time adaptation methods (2023), adversarial style composition (2022), and cooperative training frameworks (2021) demonstrates her commitment to creating AI systems that maintain performance despite domain shifts and limited labeled data. She has consistently contributed to top-tier medical imaging conferences, with multiple papers accepted to MICCAI from 2018-2023, reflecting her standing in the medical imaging research community. IEEE TMI Gold-level Distinguished Reviewer Award (2022-2023) MICCAI 2023 Outstanding Reviewer Award Winner of the Fetal Tissue Annotation and Segmentation Challenge (FeTA) 2022 Winner of the Multi-sequence Cardiac MR Segmentation Challenge 2019 China National Scholarships (twice, top 0.2%) Dr. Chen actively mentors students and has delivered invited talks at prestigious institutions including Johns Hopkins University, Technical University of Munich, and the German Cancer Research Center. Her laboratory at Sheffield focuses on advancing deep medical image segmentation with particular attention to robustness, reliability, and real-world applicability in clinical workflows.

Kevin Worrallمشاهده پروفایل

مدرس ارشد

• Robotics
• Control Systems
• Mechatronics
• +۵ مورد دیگر

Dr. Kevin Worrall is a Senior Lecturer in Robotics and Control at the University of Glasgow's School of Engineering, Aerospace Sciences division. He holds affiliations with both the Space Engineering and Technology group and the Centre for Medical and Industrial Ultrasonics. His academic journey includes a BEng in Electronics and Electrical Engineering from Glasgow (2003), an MSc in Robotics and Embedded Systems from the University of Essex (2004), and a PhD from Glasgow (2008) focusing on optimization algorithms for mobile robot guidance. Research interests span mechatronic systems for extreme environments (space, underground, Antarctica), precision medical applications, and agricultural robotics. His work integrates control theory, machine learning, and hardware development across: Spacecraft attitude control and satellite systems Ultrasonic drilling and granular material handling Medical ultrasound classification using ML Autonomous planetary exploration technologies Publications demonstrate strong focus on aerospace control systems (inverse simulation, attitude control), planetary drilling technologies, and medical imaging AI. Recent work shows increasing emphasis on machine learning applications in both space systems and healthcare diagnostics. Grant leadership includes: ERC: Interglacial Collapse of Ice Sheets (£339k, CoI) ESA: Drill for Extensive Exploration of Planetary Environments (£253k, CoI) UKSA: Roving with Rosalind (£30k, CoI) EC H2020: Robot for Underground Operations (£477k, CoI) Multiple PI-led industry collaborations in positioning systems and image testing Current PhD supervision covers fault-tolerant space algorithms, planetary rover navigation, spacecraft plume interactions, and infrastructure monitoring. He leads research within the Space Engineering and Medical Ultrasonics research groups.

Ashley Thomasمشاهده پروفایل

استادیار

• Developmental Psychology
• Social Cognition
• Infant Cognition
• +۳ مورد دیگر

Ashley Thomas is an Assistant Professor of Psychology at Harvard University , leading the Thomas Lab within the Harvard Laboratory for Developmental Studies . Her research focuses on how infants, toddlers, and children develop naive sociology —understanding social hierarchies, intimacy, and moral judgments in social contexts. Harvard University (2021-present): Assistant Professor University of California, Irvine & MIT (2018-2021): Postdoctoral Fellow (NIH NRSA) Education : PhD in Psychology, University of California, Irvine (2013-2018) BA in Architecture, University of California, Berkeley (2004-2008) Her work reveals how young children interpret social dominance, caregiver interactions, and intimacy through saliva-sharing cues. She has authored key publications in PNAS , Science , and Nature Human Behaviour , showing early sensitivity to hierarchical structures and moral evaluations of parenting decisions. Scientific Awards : NIH National Research Service Award (2018-2021) University of California, Irvine Merit Fellowship (2013-2018) Jean-Claude Falmagne Award (2017) Thomas has taught Introduction to Psychology at UC Irvine and Tools for Robust Science at MIT. She mentors undergraduate researchers through programs like UROP and SURP, emphasizing structural changes in academia and social equity.

• Time Series Analysis
• Deep Learning
• Data-Intensive Systems
• +۶ مورد دیگر

Tomasz Wiktorski is a Professor at the Faculty of Science and Technology , University of Stavanger , where he serves as Study Program Manager for MSc and PhD programs in Computer Science and Data Science. His research integrates conventional time series analysis with deep learning for applications in biomedical data (e.g., wearable devices), oil and gas drilling automation, energy systems prediction, and cloud infrastructure optimization. Education : Not explicitly detailed in the text. Research interests span data-intensive system modeling, focusing on: Biomedical time series analysis (wearables, ECG signal correction) Drilling process optimization via transfer learning and temporal models Energy systems prediction using machine learning Cloud infrastructure monitoring Curriculum development in data science education Scientific trends reveal expertise in recurrent neural networks, support vector machines, and hybrid data modeling for sensor networks across domains like health, petroleum, and cloud computing. Leadership includes designing data science programs and contributing to the EDISON Data Science Framework for global standards.

• Machine Learning
• Quantum Computing
• Natural Language Processing
• +۳ مورد دیگر

William Henrich Due serves as a Lecturer at the Department of Computer Science (DIKU), University of Copenhagen, within the Machine Learning section. His work intersects with the SCIENCE AI Centre and leverages the department's high-performance compute cluster for research in quantum computing, sustainable AI, and medical applications. Research focuses span quantum machine learning (biomolecular simulations, photonic processors), sustainable AI systems (energy efficiency, climate impact), and clinical applications (EEG analysis, medical imaging). His recent publications reveal strong activity in quantum-classical hybrid systems, with 8/15 recent papers addressing quantum computing challenges. The work emphasizes practical implementations in medical imaging and resource-constrained environments. His research aligns with DIKU's Machine Learning section priorities including medical imaging biomarkers and sustainable computing. Key infrastructure includes TreeSense for remote sensing and the department's dedicated compute cluster. No scientific awards were explicitly documented in the provided materials. Due contributes to DIKU's teaching mission as a Lecturer while engaging with the SCIENCE AI Centre's interdisciplinary initiatives. His work connects with medical imaging applications and quantum computing infrastructure development. Active in the Machine Learning section's research ecosystem, his work intersects with medical imaging analysis and quantum computing applications, utilizing specialized resources like TreeSense for environmental monitoring.

• Information Retrieval
• Machine Learning
• Evaluation
• +۴ مورد دیگر

Maria Maistro is a Tenure Track Assistant Professor at the Department of Computer Science, Faculty of Science, University of Copenhagen. Her research focuses on Information Retrieval (IR) with emphasis on evaluation, reproducibility, click log analysis, learning to rank, expert search, and machine learning applications to IR. She contributes to the AMAOS project aiming to advance expert search methodologies. PhD in Computer Science (IR) from University of Padua (2017) MSc in Mathematics (probability, stochastic methods) from University of Padua (2014) Maria's research addresses critical challenges in IR evaluation through stochastic modeling and user signal analysis. She investigates fairness-relevance tradeoffs in recommender systems, cross-cultural retrieval frameworks, and explainability techniques for language models and recommendation algorithms. Her work combines theoretical foundations with practical applications in healthcare records, insurance domains, and culinary contexts. Recent publications (2024–2025) demonstrate her expertise in: Retrieval-Augmented Generation (RAG) for cross-cultural adaptation Fairness evaluation in recommender systems EEG-based semantic relevance analysis Session-based recommendation explainability Language model pruning consistency Reproducibility frameworks for IR experiments Maria actively participates in academic activities through conference organization roles, including: Organizer, CENTRE@CLEF 2019 Organizer, CENTRE@NTCIR 2019 Organizer, CENTRE@CLEF 2018 Organizer, LEARNER 2017

Sara Mostafaviمشاهده پروفایل

دانشیار

• Computational Biology
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Sara Mostafavi is an Associate Professor at the Paul Allen School of Computer Science & Engineering at the University of Washington (UW), with a focus on Artificial Intelligence and Computational Biology. She is currently on leave (2024-2025) leading the Computational Biology and Translation Organization at Genentech. Mostafavi previously held faculty roles at the University of British Columbia (UBC) and the Vector Institute, where she received prestigious Canada Research Chair (CRC II) and Canada CIFAR Chair in AI (CIFAR-AI) awards. PhD in Computer Science, University of Toronto (2011) Postdoctoral Researcher, Stanford University Her research develops machine learning and statistical methods to study gene regulation, disease susceptibility, and molecular networks, with applications in immunology, genetics, and neuroscience. She has pioneered sequence-to-function models for understanding gene regulatory grammar and predicting disease mechanisms. Her publications highlight expertise in genomic data integration, interpretable AI, and cross-modality learning. Recent work includes analyzing mosaic chromosome loss in aging microglia, immune cell differentiation, and Alzheimer's disease networks. Scientific Awards Canada Research Chair (CRC II) in Computational Biology (2015–2020) Canada CIFAR Chair in Artificial Intelligence (CIFAR-AI) Co-founder of the Machine Learning for Computational Biology (MLCB) Conference Mostafavi has mentored numerous PhD and Master’s students, including Xinming Tu, Anna Spiro, Gherman Novakovsky (PhD 2023), and Elijah Willie (MSc 2020). Her lab collaborates with institutions like the Immunological Genome (ImmGen) Consortium and Canadian Institute for Advanced Research (CIFAR). She leads the Mostafavi Lab, which specializes in combining association evidence across genomics datasets and modeling biological pathways to disentangle spurious correlations. Her team develops tools like CEWAS, AI-TAC, and Brain xQTL web server.

• Machine Learning
• Computer Vision
• Signal Processing
• +۶ مورد دیگر

Giacomo Boracchi is a researcher at the Polytechnic University of Milan , focusing on machine learning, computer vision, and signal processing. His work spans anomaly detection, change detection in data streams, 3D imaging, and biomedical applications. Key Collaborations : Diego Carrera, Luca Magri, Cesare Alippi Industries : Embedded systems, medical imaging, environmental monitoring Recent research explores adaptive Kalman filtering for battery estimation, zero-shot anomaly detection, and explainable AI for vision-language models. Publications highlight applications in waste sorting, histological data generation, and cardiac monitoring. His technical focus includes convolutional networks, ensemble learning, and domain adaptation. Boracchi's work bridges theoretical advancements with real-world systems in fraud detection, semiconductor manufacturing, and wearable health devices.

Saeed Piriمشاهده پروفایل

استادیار

• Healthcare Analytics
• Machine Learning
• Decision Support Systems
• +۲ مورد دیگر

Saeed Piri is Assistant Professor of Operations and Business Analytics at the University of Oregon's Lundquist College of Business. His research develops data mining methodologies for healthcare applications, including clinical decision support systems and value-based payment models. Piri employs machine learning techniques to address challenges in predictive modeling, particularly imbalanced data and missing values in medical datasets. Research areas include healthcare operations analytics, ensemble modeling, and association analysis. Recent work examines impacts of ridesharing platforms on emergency department utilization and evaluates pay-for-performance programs in dialysis care. Methodological innovations include SIMO (Synthetic Informative Minority Over-Sampling) algorithms for imbalanced classification. Awards include the John and Emiko Kageyama Award (2021) and Gilbreth Memorial Fellowship. Piri holds PhD/MS degrees in Industrial Engineering and serves on editorial boards for Journal of Business Analytics and Decision Support Systems.

• Internet of Things (IoT)
• Cybersecurity
• Machine Learning
• +۵ مورد دیگر

Dr. Aftab Ali is a Lecturer in Computing (specializing in Internet of Things) at the School of Computing, Ulster University, Belfast. His research focuses on IoT security, cybersecurity, machine learning applications in smart systems, and healthcare informatics. He contributes to secure IoT architectures, federated learning for threat detection, and blockchain-based solutions for IoT environments. His work spans diverse areas including malware detection across platforms, software defect prediction using advanced models like transformers, and trust management systems for IoT devices. Recent projects include SHIELD (a blockchain-enabled IoT security framework) and FL-DSFA (federated learning for secure IoT routing). Aftab's research also bridges healthcare and IoT through digital twin technologies and wearable device security. He has published extensively in top-tier venues, demonstrating expertise in both theoretical advancements and practical implementations of secure IoT ecosystems.

Mircea Nicolescuمشاهده پروفایل

استاد

• Computer Vision
• Surveillance Systems
• Robotic Vision
• +۳ مورد دیگر

Mircea Nicolescu is a Professor in the Department of Computer Science and Engineering at the University of Nevada, Reno. He holds a Ph.D. (2003) and M.S. (1999) from the University of Southern California, and a B.S. (1995) from Polytechnic University Bucharest. He co-directs the Computer Vision Laboratory . His research focuses on computer vision , particularly in video content understanding , surveillance systems , robotic vision , and human-computer interaction . Key areas include robust tracking in dynamic scenes, autonomous navigation, and gesture/face recognition. Recent work addresses maritime safety, cryptocurrency forecasting, and robotic task parameter estimation via multimodal data fusion. He has published over 100 peer-reviewed articles and holds a U.S. patent for 'Fast Digital Pan-Tilt-Zoom Video'. His work has earned a Best Paper Award Finalist distinction (2021). Teaching responsibilities include Programming Languages and Advanced Computer Vision courses. Collaborative projects involve multi-agent systems, intent recognition, and spiking neural networks for pattern analysis.

José R. Zubizarretaمشاهده پروفایل

استاد

• Causal Inference
• Biostatistics
• Health Policy
• +۳ مورد دیگر

José R. Zubizarreta is a Professor in the Department of Health Care Policy at Harvard Medical School, a Professor in the Department of Biostatistics at Harvard School of Public Health, and a Faculty Affiliate in the Department of Statistics at Harvard University's Faculty of Arts and Sciences. His research focuses on developing statistical methods for causal inference, particularly in healthcare and public policy contexts. He has received multiple awards, including the Kenneth Rothman Award and the Tom Ten Have Memorial Award, and is a Fellow of the American Statistical Association. He holds editorial roles at prominent journals such as the Annals of Applied Statistics and the Journal of the American Statistical Association . His work includes co-editing the Handbook of Matching and Weighting Adjustments for Causal Inference , a seminal reference in the field. Zubizarreta’s research addresses challenges in observational studies, including methods for bias reduction, generalization of results, and applications in healthcare quality measurement and policy evaluation. Notable contributions include advancements in optimal matching techniques, balancing weights, and scalable methods for analyzing large observational datasets. His collaborative projects span interdisciplinary areas such as healthcare economics, epidemiology, and machine learning applications in causal inference. Zubizarreta actively contributes to software development, including R packages like mipmatch and designmatch , which are widely used in statistical research. His academic service includes roles in editorial boards and grant review panels, reflecting his leadership in advancing statistical methodology and its applications. Zubizarreta’s work bridges theoretical rigor with practical impact, addressing pressing questions in healthcare systems and public policy.

Fons van der Sommenمشاهده پروفایل

دانشیار

• Medical Image Analysis
• Computer-Aided Diagnosis
• Artificial Intelligence
• +۲ مورد دیگر

Fons van der Sommen is an Associate Professor in Electrical Engineering at Eindhoven University of Technology, specializing in Video Coding & Architectures. He leads research on computer-aided detection systems for early cancer diagnosis, particularly focusing on esophageal and colorectal neoplasia through advanced AI and computer vision techniques. His research interests span medical image analysis, AI-assisted diagnostics, and developing robust systems for clinical deployment. Recent publications focus on overcoming real-world implementation challenges of AI in endoscopy and enhancing the trustworthiness of diagnostic systems. Recent research trends show strong emphasis on surgical AI applications (robot-assisted procedures), generative models for medical data augmentation, and quality assurance frameworks for clinical AI deployment. His work integrates deep learning with clinical validation across gastrointestinal and pulmonary oncology. TU/e Best PhD Thesis Award (2018) Best Poster Presentation (2017, 2013) He coordinates multiple research projects including TASTI-XECS221002 (Advanced AR for AI-based Servitization) and XL-ARGOS (extended reality solutions). Manages collaborations with medical centers on AI implementation for cancer screening.

Hung Laمشاهده پروفایل

دانشیار

• Robotics
• Computer Vision
• Control Systems
• +۲ مورد دیگر

Hung La is an Associate Professor in the Department of Computer Science and Engineering at the University of Nevada, Reno's College of Engineering. He directs the Advanced Robotics and Automation Lab, focusing on robotic systems for infrastructure inspection (culverts, bridges), autonomous navigation, and control systems. His research integrates computer vision, machine learning, and multi-agent systems to develop field-deployable robotic solutions. La's publications demonstrate expertise in robotic perception (3D registration, SLAM), control systems (quadrotors, climbing robots), and secure machine learning. Recent work emphasizes practical applications in civil infrastructure inspection, warehouse automation, and medical robotics. His research consistently combines theoretical advances with hardware implementations for real-world deployment.

• Biometric Recognition
• Presentation Attack Detection
• Deepfake Forensics
• +۵ مورد دیگر

Giulia Orrù is an Assistant Professor of Computer Engineering at the University of Cagliari, Italy, affiliated with the Department of Electrical and Electronic Engineering. She holds an M.S. and Ph.D. in Electronic Engineering and Computer Science from the same institution. Her research focuses on biometric recognition, presentation attack detection, deepfake forensics, and cybersecurity. She is a core member of the Pattern Recognition and Applications Laboratory (PRA lab), contributing to projects like BullyBuster (anti-bullying detection systems) and LivDet competitions (fingerprint liveness detection). Dr. Orrù has authored numerous publications in top-tier conferences and journals, with a strong emphasis on advancing secure authentication systems and combating adversarial attacks in biometrics. She actively serves as a referee for pattern recognition and cybersecurity venues. Teaching activities include courses such as Technologies for Information Security and Artificial Intelligence and Security . Research interests span multimodal biometrics, 3D face reconstruction for surveillance, AI-driven anti-bullying systems, and deepfake detection algorithms. Her work bridges theoretical advancements (e.g., diffusion models for anomaly detection) with real-world applications like forensic recognition and crowd analysis. Notable projects include the Synthetic Data for Face Recognition (SdFR) initiative and contributions to the Face Deepfake Detection Challenge. Dr. Orrù's lab explores cutting-edge techniques in EEG-based personal recognition and vulnerability analysis of medical AI systems. She maintains active participation in international research networks and has developed open-source tools for evaluating biometric system resilience against spoofing attacks.