جست‌وجوی استاد و پژوهشگر

Jehoshua Bruckمشاهده پروفایل

استاد

• Information Theory
• Distributed Storage Systems
• DNA Data Storage
• +۴ مورد دیگر

Jehoshua (Shuki) Bruck is the Gordon and Betty Moore Professor of Computation and Neural Systems & Electrical Engineering at the California Institute of Technology. He leads the Distributed Information Systems Group and serves as the founding director of Caltech's Information Science and Technology (IST) program. His work bridges information theory, storage systems, and computational biology. Bruck holds B.Sc. (Summa cum Laude) and M.Sc. degrees from the Technion-Israel Institute of Technology (1982-1985) and a Ph.D. in Electrical Engineering from Stanford University (1989). He has held academic positions at Caltech since 1994, advancing from Associate Professor to Moore Professor. Research Focus: His dual-track research explores Physical implementations of coding schemes for flash memories (rank modulation, rewriting codes) Biological computation mechanisms, particularly DNA sequence evolution via replication Secure distributed storage systems and RAID schemes Awards & Recognition: Recipient of the Feynman Teaching Prize (2008), Sloan Fellowship, NSF Young Investigator Award, and multiple IEEE best paper awards. Holds over 40 patents and authored 350+ publications. Industry Contributions: Co-founder of storage startups Rainfinity (acquired by EMC) and XtremIO, which pioneered flash storage solutions. Collaborates with NASA-JPL on non-volatile memory for space missions.

Emmanuel Candèsمشاهده پروفایل

استاد

• computational harmonic analysis
• statistics
• information theory
• +۱۲ مورد دیگر

Emmanuel Candès is the Barnum-Simons Chair in Mathematics and Statistics at Stanford University, where he is also a Professor of Statistics and, by courtesy, of Electrical Engineering. He is a member of the Institute of Computational and Mathematical Engineering at Stanford. Previously, he was the Ronald and Maxine Linde Professor of Applied and Computational Mathematics at the California Institute of Technology. His educational background includes: PhD in Statistics from Stanford University (1998) Diplome Ingenieur from Ecole Polytechnique (1993) Candès' research spans computational harmonic analysis, statistics, information theory, signal processing, and mathematical optimization with applications to imaging sciences, scientific computing, and inverse problems. His recent work focuses on conformal prediction, uncertainty quantification, and causal inference, with applications across diverse fields including genetics, machine learning, and artificial intelligence. He has made significant contributions to compressive sensing and mathematical signal processing. His recent publications demonstrate a strong focus on developing statistically rigorous methods for uncertainty quantification, particularly through conformal prediction frameworks. These works address challenges in high-dimensional statistics, machine learning validation, and causal inference across various application domains including genomics, natural language processing, and imaging sciences. His notable scientific achievements include: Alan T. Waterman Award from NSF IEEE Jack S. Kilby Signal Processing Medal (2021) Princess of Asturias Award for Technical and Scientific Research (2020) MacArthur Fellow (2017) Election to the National Academy of Sciences (2014) Election to the American Academy of Arts and Sciences (2014) Candès has served as Chair of the Statistics Department at Stanford (2016-2019) and is currently serving as Director of the Data Science Institute. His research has been supported by numerous grants from the National Science Foundation and other funding agencies. He has given over 60 plenary lectures at major international conferences across mathematics, statistics, biomedical imaging, and physics. As a leading researcher in mathematical statistics and computational mathematics, Candès maintains an active research group focusing on theoretical and applied aspects of statistical learning, signal processing, and optimization. His work bridges theoretical foundations with practical applications across scientific disciplines.

Rick D'Eathمشاهده پروفایل

استاد

• Animal Welfare
• Animal Behavior
• Veterinary Sciences
• +۳ مورد دیگر

Rick D'Eath is a Professor of Animal Welfare at Scotland's Rural College (SRUC), specializing in the intersection of animal behavior and welfare. His work focuses on using behavior to measure welfare, particularly in pigs, addressing issues like social aggression, tail biting, and mounting behavior. He also investigates hunger in ration-fed animals such as sows and broiler breeder chickens, combining behavioral, physiological, and neurobiological approaches. A key area of his research involves precision livestock farming, collaborating on technologies like machine vision and sensors to monitor farm animal welfare. He explores broader policy impacts of trade, regulations, and climate change on animal welfare. Education: PhD in Social Recognition of Flockmates in Laying Hens (University of Oxford, 1996) and MSc in Biological Sciences (University of Oxford, 1993). Key Projects: BBSRC IPA 'Pig ID' (2022-2024): Developing deep learning systems for pig biometrics (£612K). Defra/UKRI Innovate UK 'FarmSense' (2022-2024): Sustainable pig farming using machine vision and sensors (£1.09M). Scottish Government RESAS Projects: EU Exit challenges/opportunities for animal welfare, and laying hen welfare solutions. Research Contributions: Over 110 publications since 1996, with recent focus on tail biting prevention, feed restriction effects, and policy analysis. Active in PhD supervision, including projects on pig genetics and broiler breeder diets. Grants and Collaborations: Funded by BBSRC, Defra, Innovate UK, and the Scottish Government. Collaborates with universities and industry partners like Aviagen.

• Computer Networks
• Network Security
• Multimedia Communication
• +۴ مورد دیگر

Christos Papadopoulos is a Professor and Sparks Family Chair of Excellence in Global Research Leadership in the Department of Computer Science at the University of Memphis. He joined the university in Fall 2020, coming from Colorado State University where he held a professorial position. His expertise spans computer networks, network security, multimedia communication, and distributed systems. He holds a DSc in Computer Science from Washington University in St. Louis (1999). Dr. Papadopoulos' research focuses on advancing secure and efficient network architectures, particularly in vehicular networks and Named Data Networking (NDN). He has led collaborative projects to create open platforms for sharing vehicle telematics data and improving cybersecurity in automotive systems. His work often bridges theoretical contributions with practical implementations, addressing challenges in data privacy, network scalability, and real-time communication. His recent publications reflect a strong emphasis on autonomous systems, cybersecurity datasets, and innovative applications of NDN in scientific data management. He has contributed to foundational work in internet measurement, traffic analysis, and privacy-preserving techniques for wide-area networks. Notable grants include collaborative NSF-funded initiatives on privacy in internet measurements, open telematics data platforms, and NDN-based solutions for big science projects. His research has implications for transportation safety, environmental monitoring, and secure communication protocols. Dr. Papadopoulos is actively involved in academic leadership, including editorial roles and conference organization. His interdisciplinary approach integrates computer science with environmental and automotive engineering, fostering cross-sector innovation.

• Machine Learning
• Deep Learning
• eHealth
• +۴ مورد دیگر

Dr. Gaetano Manzo is a Researcher at the HES-SO Valais-Wallis - School of Management , focusing on Machine Learning , eHealth , and Recommender Systems . He holds a BSc in Management Information Systems and leads research in agent-based systems for healthcare support, data-driven clinical decision frameworks, and vehicular networking optimization. His research interests include: Digital transformation in healthcare Explainable AI for medical decision support Personalized health-assistant chatbots Privacy-preserving data sharing Context-aware recommendation systems Key publication trends (2020-2025) show expertise in: Agent-based modeling for cancer survivor support Rule extraction from neural networks Floating content optimization in vehicular networks Weak supervision techniques for clinical datasets Multicultural streaming media recommendations He has contributed to projects like: The Ark (REPS-CITI Real Estate PaaS) Regional Development Axis (2019) for local innovation support

Zico Kolterمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Adversarial Machine Learning
• +۵ مورد دیگر

Zico Kolter is a Professor and Department Head of the Machine Learning Department at Carnegie Mellon University's School of Computer Science. He also serves on the Board of Directors at OpenAI, chairs its safety and security committee, and holds roles at Gray Swan AI and Robert Bosch, LLC. His research focuses on AI safety, adversarial robustness, LLM security, and computational methods for energy systems. Kolter holds a PhD in Computer Science from Stanford University (2010) and has received accolades including the DARPA Young Faculty Award and Sloan Fellowship. His work addresses challenges in machine learning, optimization, and control systems, with applications to sustainability, robotics, and energy grids. Notable contributions include advancements in adversarial defense mechanisms, implicit models, and energy disaggregation. Kolter teaches courses such as Graduate Artificial Intelligence and Deep Learning Systems, and leads a research group producing influential work in NeurIPS, ICML, and other top venues. Scientific awards include multiple best paper recognitions at NeurIPS, ICML, AISTATS, IJCAI, KDD, and PESGM. His advising spans students like Victor Akinwande and Andy Zou, focusing on topics from causal discovery to LLM security. Kolter's contributions extend to software tools like TorchDEQ and frameworks for computational sustainability, emphasizing both theoretical rigor and real-world impact.

• Uncertainty Quantification
• Ensemble Kalman Methods
• Bayesian Inverse Problems
• +۳ مورد دیگر

Xin T. Tong is Associate Professor in the Department of Mathematics at the National University of Singapore, specializing in uncertainty quantification, machine learning, and operations research. His research develops theoretical foundations and methodologies for structured problem-solving in high-dimensional settings. Current investigations focus on ensemble Kalman methods, Bayesian inverse problems, sampling algorithms, and stochastic optimization with non-i.i.d. data. Research emphasizes mathematical analysis of algorithm efficiency and structure-aware computational methods. Professional background includes postdoctoral work at NYU's Courant Institute and Ph.D. from Princeton University.

Xiaocheng Liمشاهده پروفایل

استادیار

• Operations Research
• Online Optimization
• Revenue Management
• +۲ مورد دیگر

Xiaocheng Li is an Assistant Professor in the Department of Analytics, Marketing and Operations at Imperial College Business School. He holds a B.S. in mathematics and statistics from Peking University and a PhD in Management Science and Engineering from Stanford University. His research develops algorithms for online decision-making under uncertainty, with applications in revenue management, dynamic pricing, and machine learning. Current work focuses on optimization under non-stationary environments, large language model alignment, and incentive design for human annotation. Dr. Li has received multiple awards including the INFORMS Decision Analysis Society Best Student Paper Award (2019) and World Bank Big Data Innovation Challenge (2016). His publications appear in leading journals including Operations Research, Management Science, and proceedings of NeurIPS and ICML. He serves on program committees for major operations research conferences and maintains international collaborations through academic exchanges and research visits.

Jiwei Zhaoمشاهده پروفایل

دانشیار

• Semiparametric Statistics
• Missing Data Analysis
• Causal Inference
• +۱۰ مورد دیگر

Jiwei Zhao is an Associate Professor at the University of Wisconsin-Madison, jointly appointed in the Department of Statistics (School of Computer, Data & Information Sciences) and the Department of Biostatistics & Medical Informatics (School of Medicine & Public Health). He leads the Zhao Research Group, focusing on statistical and machine learning methods for biomedical data analysis. His work is supported by multiple NIH and NSF grants, including an NIH R01 project on patient-reported outcomes in vocal fold paralysis and a Distinguished Research Fellows Program at UW-Madison. Dr. Zhao earned his PhD in Statistics from UW-Madison in 2012 under Dr. Jun Shao. Prior to his current position, he was a Postdoctoral Researcher at Yale University (2012-2013) and held Assistant and Associate Professor roles at SUNY Buffalo (2014-2020). His academic leadership includes serving as Associate Editor for journals like the Annals of Applied Statistics and Scandinavian Journal of Statistics , and as Area Chair for AISTATS (2024-2025). His research interests span semiparametric methodologies, missing data analysis, causal inference, and domain adaptation. Key applications include patient-reported outcomes, electronic health records, clinical trials, and studies on aging, mental health, and cancer. His group collaborates with institutions like the Data Science Institute, Institute on Aging, and Center for Demography of Health and Aging. Recent work emphasizes efficient parameter estimation under privacy constraints, robust learning in nonignorable missing data scenarios, and leveraging machine learning for medical imaging and genetic studies. He actively recruits researchers across postdocs, PhD, and undergraduate levels to join his team.

• Machine Learning
• Computer-Aided Design (CAD)
• Embedded Systems
• +۳ مورد دیگر

Nicolo' Bellarmino is a Research Fellow and Research Assistant at the Department of Automatic Control and Computer Science (DAUIN) of Politecnico di Torino. His roles include teaching and collaborating in courses such as System Programming, Computer Science, and Future of Work across Computer Engineering and Aerospace Engineering programs. He is affiliated with the CAD - Electronic CAD & Reliability Group (DAUIN), focusing on research in machine learning, embedded systems, and hardware testing. His research interests center on computational intelligence applied to computer-aided design, particularly in machine learning techniques for microcontroller performance screening. He has also explored neural network resiliency, fault detection in CNNs, and device-aware testing methodologies. Recent work emphasizes compressed sensing, feature selection, and transfer learning in resource-constrained systems. Bellarmino's publications span conference proceedings and journals, addressing challenges in embedded systems testing, neural network optimization, and biomedical applications like COVID-19 detection via exhaled breath analysis. His contributions bridge theoretical machine learning advancements with practical hardware validation and optimization. He actively contributes to academic teaching, maintaining an email contact at nicolo.bellarmino@polito.it for professional inquiries.

Fergus Imrieمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Drug Discovery
• Healthcare Informatics
• +۷ مورد دیگر

Fergus Imrie is a Florence Nightingale Bicentenary Fellow at the University of Oxford's Department of Statistics, affiliated with the Oxford Protein Informatics Group (OPIG) and the Computational Statistics and Machine Learning group. His research focuses on advancing machine learning methodologies for healthcare applications, drug discovery, and predictive modeling with an emphasis on explainability and causal inference. He holds a DPhil from Oxford, supervised by Charlotte Deane, and completed postdoctoral work at UCLA under Mihaela van der Schaar. His work spans generative models for molecular design, multimodal healthcare AI systems, and data-centric methodologies for robust machine learning. Education Doctor of Philosophy (DPhil), University of Oxford (Department of Statistics) Postdoctoral Scholar, University of California, Los Angeles (UCLA) Research Interests Imrie's methodological contributions include causal inference frameworks, explainable AI techniques, and generative models for structure-based drug design. He develops tools like AutoPrognosis (automated clinical modeling) and MolSnapper (diffusion-based molecular design). Application areas include early disease detection, risk stratification in oncology, and efficient learning from limited/noisy data in drug discovery. Key Contributions Developed ChemIQ benchmark for evaluating LLM chemical intelligence Pioneered DC-Check framework for data-centric AI Advanced ensemble methods for multimodal healthcare prediction Grants & Collaborations Supported by an iCASE award with Exscientia for his doctoral work. Collaborates with leading institutions in computational biomedicine. Advises students on rotation projects in Oxford's DPhil program. Labs & Teams Participates in cross-disciplinary teams at OPIG and the van der Schaar Lab, focusing on translational AI solutions for healthcare challenges.

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

Associate Professor Weidong Cai is affiliated with the University of Sydney's School of Computer Science, serving as Director of the Multimedia Lab and Associate Director of the Biomedical & Multimedia Information Technology (BMIT) Research Group. He has held visiting roles at Harvard Medical School and holds a PhD in Computer Science from the University of Sydney. His research focuses on medical image computing, computer vision, machine learning, and computational neuroscience, with over 300 peer-reviewed publications. Dr. Cai leads projects on 3D point cloud processing, label-efficient learning, cross-domain medical image analysis, and neuroimaging computing. Dr. Cai's educational background includes a PhD from the University of Sydney (2001) and postdoctoral experience at Harvard Medical School (2014). He supervises students in courses like COMP3419 Graphics and Multimedia and COMP5424 Information Technology in Biomedicine. His current research explores cutting-edge topics like 3D neuron reconstruction, multimodal neuroimaging, and AI-driven medical diagnostics. His research interests span medical imaging, bioinformatics, and computer vision, with a focus on developing algorithms for big data analytics, deep learning, and biomedical applications. Projects include automated neuron tracing, cross-domain adaptation for medical imaging, and geometric deep learning for neuroimaging analysis. Dr. Cai's students have received numerous awards, including the MICCAI IUGC Grand Challenge (2024) and IEEE ISBI Travel Grants. He serves on editorial boards for journals like IEEE Transactions on Image Processing and Brain Informatics, and co-edits Springer's Brain Informatics & Health book series. His lab, the Multimedia Lab, collaborates on projects like 3D point cloud applications, label-efficient feature learning, and cross-domain medical image analysis. Research teams include the BMIT group, focusing on biomedical and multimedia technologies.

• Computer Vision
• Medical Image Analysis
• Machine Learning
• +۲ مورد دیگر

Anna Fabijańska is a Professor at the Institute of Applied Computer Science within the Faculty of Electrical, Electronic, Computer and Control Engineering at Lodz University of Technology, Poland. Her research focuses on computer vision, medical image analysis, and machine learning applications in biomedical engineering and environmental science. Her primary research interests include developing advanced algorithms for image segmentation, 3D reconstruction, and neural network applications in medical diagnostics (especially ophthalmology and pulmonology) and forestry research. She specializes in techniques like graph convolutional networks, hyperspectral imaging, and generative adversarial networks for data augmentation. Through analysis of her recent publications, Fabijańska demonstrates consistent focus on improving computational efficiency and accuracy in image-based diagnostics and monitoring systems. Her work spans industrial tomography, corneal endothelium analysis, timber identification, and glacial sediment studies. Her laboratory develops practical computer vision pipelines for healthcare and environmental applications, demonstrating interdisciplinary collaboration between computer science, medicine, and ecology.

Alexander Bauerمشاهده پروفایل

پژوهشگر

• Deep Learning
• Computer Vision
• Natural Language Processing
• +۲ مورد دیگر

Alexander Bauer is a Postdoctoral Researcher in the Machine Learning Group at Technical University of Berlin, and a member of BASLEARN—a joint lab between BASF and TU Berlin focusing on machine learning applications. His academic background includes a PhD in Computer Science (2017) and a Diploma degree in Computer Science (2012), both from TU Berlin, alongside a B.Sc. in Mathematics (2011) from the same institution. His research interests span Deep Learning , Computer Vision , and Natural Language Processing . In Computer Vision, he specializes in visual anomaly detection , image classification , and semantic segmentation , with industry applications in automotive quality assurance. In NLP, he focuses on transformer architectures and foundation models for tasks like question answering and textual entailment. His earlier work centered on structured prediction , including optimization techniques for MAP inference in graphical models. His publications emphasize algorithmic efficiency and practical applications, with recent work exploring self-supervised learning and model interpretability. Bauer has contributed to both theoretical advances (e.g., exact inference algorithms) and applied solutions (e.g., anomaly detection in manufacturing). He collaborates with industry partners like BASF through BASLEARN, bridging academic research and industrial challenges. His technical expertise spans neural network design, optimization frameworks, and interdisciplinary problem-solving in vision and language domains.

Lifang Heمشاهده پروفایل

دانشیار

• Machine Learning/Deep Learning
• Tensor Analysis
• Data Mining
• +۳ مورد دیگر

Lifang He is an Associate Professor in the Department of Computer Science & Engineering at Lehigh University. She holds a B.S. in Computational Mathematics from Northwest Normal University and a Ph.D. in Computer Science from South China University of Technology. Prior to joining Lehigh, she was a postdoctoral researcher at the University of Pennsylvania’s Perelman School of Medicine and Weill Cornell Medical College. Her research focuses on machine learning/deep learning, data mining, tensor analysis, and biomedical informatics. Notable projects include developing BiomedGPT, an AI model for real-time patient-focused insights, and contributions to structural brain imaging analysis and rare event prediction in materials science. Her work bridges computational methods with healthcare applications, emphasizing multi-modal data fusion and federated learning in medical contexts. Recent publications span topics like self-paced learning for sign language recognition, graph-based brain network analysis, and efficient large language models. She actively participates in symposiums on AI in healthcare and has collaborated on initiatives like the Mountaintop Campus’ interdisciplinary projects.