جست‌وجوی استاد و پژوهشگر

Fu Yunمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +۱ مورد دیگر

Yun Fu is a Distinguished Professor at Northeastern University, affiliated with the College of Engineering and Khoury College of Computer Science. He holds tenure in Electrical and Computer Engineering (ECE). His roles include Professor, Senior Vice President at Shiseido Americas, founder of Giaran (acquired by Shiseido), and co-founder of TVision Insights. He earned his Ph.D. from the University of Illinois at Urbana-Champaign. His research focuses on artificial intelligence, computer vision, machine learning, and data mining. Key achievements include over 500 publications, 50+ patents, and prestigious awards like IEEE Fellow, OSA Fellow, and AAIA Fellow. He leads the SMILE Lab, exploring AI applications in vision, robotics, and healthcare. Notable entrepreneurship includes AI-driven ventures in cosmetics and media analytics. Research interests emphasize AI-driven solutions for computer vision challenges, including anomaly detection, trajectory prediction, and multimodal learning. His work bridges academia and industry, with impactful contributions to both fields.

Lesley Shannonمشاهده پروفایل

دانشیار

• Computer Engineering
• Reconfigurable Computing
• FPGA CAD Tools
• +۶ مورد دیگر

Lesley Shannon is an Associate Professor in the School of Engineering Science at Simon Fraser University (SFU), specializing in computing system design and reconfigurable computing. She leads the Reconfigurable Computing Lab (RCL), focusing on FPGA-based architectures, heterogeneous multicore systems, and CAD tools. Her academic journey includes a B.Sc. from the University of New Brunswick (1999), M.A.Sc. and Ph.D. from the University of Toronto (2001 and 2006). Her research spans FPGA CAD algorithms, Networks-on-Chip (NoCs), dynamic partial reconfiguration (DPR), and application-specific architectures. Key projects include FUSE (OS abstraction for hardware accelerators), PolyBlaze (multicore system emulation), and uROAMs (microfluidic reconfigurable devices). She has secured funding from NSERC, Xilinx, and Altera, among others. Education: B.Sc., Electrical Engineering (Computer Option), University of New Brunswick, 1999 M.A.Sc., Applied Science, University of Toronto, 2001 Ph.D., Applied Science, University of Toronto, 2006 Roles: NSERC Chair for Women in Science and Engineering (BC/Yukon) Faculty Advisor, SFU WEST (Women in Engineering, Science, and Technology) Her lab employs 5 current students and has graduated 7, including work on FPGA CAD, microfluidics, and embedded systems. Courses taught include Advanced Digital Systems Design and Real-Time/Embedded Systems. Her research emphasizes reducing design time through on-chip profiling and CAD tools, with applications in biomedical imaging and quantum computing. Awards & Grants: NSERC Discovery Grant NSERC Strategic Grant (Lead PI, 2009; Co-PI, 2010) SFU Startup Grant and President’s Research Grant Lab & Collaborations: The RCL develops silicon and non-silicon architectures, including quantum computing and microfluidic systems. Partnerships include industry collaborations and open-source tools like Odin II synthesis framework.

Julia Schnabelمشاهده پروفایل

استاد

• Medical Image Processing
• Machine Learning
• Image Reconstruction
• +۴ مورد دیگر

Prof. Dr. Julia Schnabel is the TUM Liesel Beckmann Distinguished Professor and Helmholtz Distinguished Professor at TUM's TUM School of Computation, Information and Technology. Her research focuses on computational imaging and AI in medicine, including medical image processing, machine learning, motion modeling, and quantitative imaging. She holds IEEE, Ellis, and MICCAI Society fellowships, and has pioneered work in image reconstruction, artifact correction, and AI-based diagnostics. Educations: Bachelor/Master from TU Berlin (1993) PhD from University College London (1998) Postdocs at UMC Utrecht, King's College London, and UCL Her research interests span medical AI, deep learning for medical imaging, and clinical evaluation methodologies. Key contributions include frameworks for motion artifact correction in MRI, physics-informed neural networks, and benchmark datasets like NOVA for anomaly detection in brain MRI. She has authored over 100 publications, with recent work advancing unsupervised anomaly detection and federated learning in healthcare. Prof. Schnabel leads interdisciplinary projects at TUM and Helmholtz Zentrum München, focusing on AI-driven solutions for diagnostic and therapeutic challenges. Her labs develop tools for real-time cardiac imaging, histopathology segmentation, and trustworthy AI guidelines (FUTURE-AI initiative).

Qi Chenمشاهده پروفایل

استاد

• Remote Sensing
• LiDAR Applications
• Geographic Information Systems
• +۵ مورد دیگر

Qi Chen is a Professor in the Department of Geography at the University of Hawaii at Mānoa, specializing in remote sensing and geospatial technologies. His office is located in Saunders Hall, and he teaches undergraduate and graduate courses including GEO 370 (UAV and Aerial Photography), GEO 388 (Introduction to GIS), GEO 470 (Remote Sensing), GEO 489 (Applied GIS), and GEO 762 (Research Seminar: Remote Sensing. His research focuses on transforming earth observation data into actionable knowledge for environmental monitoring. Primary interests include: LiDAR applications for vegetation analysis and biomass estimation Climate change impacts on land cover and coastal systems Machine learning integration with geospatial data High-resolution mapping of agricultural and forest ecosystems Drone and satellite-based environmental assessment Chen's recent publications (2020-2025) demonstrate a strong focus on advancing remote sensing methodologies, particularly through: AI-driven approaches (GANs for vegetation indices, deep learning for marine debris) Multi-sensor fusion (LiDAR with camera systems, hyperspectral-multispectral integration) Novel applications in precision agriculture and infrastructure monitoring Hawaii-specific environmental studies incorporating indigenous knowledge systems He leads the Smart Remote Sensing Lab (smartremotesensing.org) where he mentors graduate students in developing cutting-edge geospatial solutions for ecological and societal challenges.

• Artificial Intelligence
• Robotics
• Data Science
• +۴ مورد دیگر

Cecilio Angulo Bahón is a full Professor at the Polytechnic University of Catalonia (UPC), affiliated with the Barcelona School of Industrial Engineering (ETSEIB) and the Department of Systems, Automatics and Industrial Informatics Engineering . He leads research in Artificial Intelligence and Robotics , with significant contributions to healthcare data analytics, digital twins, and human-robot collaboration. His research spans machine learning for medical data harmonization, generative adversarial networks in health informatics, and evolutionary algorithms for control systems. Recent publications focus on synthetic healthcare data generation, climate-resilient agriculture , and UMAP-based data analysis . His work bridges AI theory with practical applications in industrial and healthcare domains. Scientific awards include the Sant Jordi 2023 Digital Polytechnic Initiative Award . He has supervised doctoral candidates like Carlos Flores-Vázquez and N. Raya, with key collaborations at the IDEAI-UPC Intelligent Data Science and AI Research Group and the Institute of Robotics and Industrial Informatics (CSIC-UPC).

• Scientific Machine Learning
• Climate Science
• Earthquake Seismology
• +۳ مورد دیگر

Karianne Bergen is an Assistant Professor of Data Science and Earth, Environmental & Planetary Sciences at Brown University, with a courtesy appointment in Computer Science. She leads the Scientific Machine Learning (SciML) Research Group, affiliated with the Data Science Institute (DSI), DEEPS, and the SciAI Center. Her research focuses on scientific machine learning (SciML), including surrogate models for climate science, explainable AI (XAI), and foundation models. She holds a Ph.D. and M.Sc. in Computational and Mathematical Engineering from Stanford University and a B.Sc. in Applied Mathematics from Brown University. Her postdoctoral training included a Harvard University HDSI fellowship in Computer Science. Education: B.Sc. Applied Mathematics, Brown University (2009) M.Sc. Computational and Mathematical Engineering, Stanford University (2015) Ph.D. Computational and Mathematical Engineering, Stanford University (2018) Research Interests: Dr. Bergen’s work bridges machine learning and Earth sciences, emphasizing scalable methods for climate modeling and geophysical data analysis. Her group develops emulators for Antarctic ice sheet dynamics, XAI frameworks for climate data interpretation, and scientific foundation models for geoscience applications. Recent projects include GAN-based sea ice resolution enhancement and flow-based neural networks for sea level projections. Awards: Harvard Data Science Initiative Postdoctoral Fellowship (2018–2020) Stanford Graduate Fellowship in Science and Engineering (2011–2015) Outstanding Student Paper Award, American Geophysical Union (2015) Advising & Collaborations: She mentors PhD students in Earth, Environmental, and Planetary Sciences and collaborates with institutions like MIT-Lincoln Laboratory, SciAI Center, and international geoscience groups. Her lab includes postdocs (e.g., Hilarie Sit) and alumni from data science practicum programs. Labs/Teams: The SciML group at Brown University focuses on multidisciplinary projects at the intersection of AI and Earth sciences, with recent presentations at AGU Fall Meetings and the AI for Science Workshop at ICML.

Georgi Ganevمشاهده پروفایل

پژوهشگر

• Machine Learning
• Privacy-Preserving Technologies
• Data Synthesis
• +۳ مورد دیگر

Georgi Ganev is a PhD Researcher at University College London (UCL) and Principal Research Scientist at SAS, following the acquisition of Hazy, a synthetic data company. He is part of UCL's Information Security Research Group under Professors Emiliano De Cristofaro and David Barber. His research focuses on advancing privacy-preserving synthetic data techniques, particularly in machine learning and differential privacy. Key areas include developing DP generative models, auditing privacy mechanisms, and addressing legal implications of synthetic data. Education: MSc in Computational Statistics and Machine Learning (UCL, supervised by Prof. Sebastian Riedel) and BSc in Business Mathematics and Statistics (LSE, supervised by Prof. Wicher Bergsma). Research Interests: Privacy-preserving synthetic data, differential privacy, privacy auditing, generative models, and regulatory compliance. His work has led to 25+ discovered privacy bugs in open-source libraries and vulnerabilities in deployed systems, with publications in top-tier conferences like IEEE S&P, USENIX Security, and ICML. Notable Achievements: Distinguished Paper Award at IEEE S&P 2025. His research has been integrated into SAS's synthetic data products, impacting industry adoption. Open-source contributions include dpmm and dpart libraries for DP synthetic data generation. Publications: Over 15 peer-reviewed articles, including works on privacy metrics inadequacy, PATE-GAN benchmarking, and synthetic data regulatory challenges. Active in workshops on generative AI and law, and deployment challenges in enterprise settings.

Youssef Marzoukمشاهده پروفایل

استاد

• Uncertainty Quantification
• Bayesian Computation
• Inverse Problems
• +۴ مورد دیگر

Youssef Marzouk is a Professor of Aeronautics and Astronautics at MIT, serving as co-director of the MIT Center for Computational Engineering and director of the Aerospace Computational Design Laboratory. His research focuses on integrating physical modeling with statistical inference, emphasizing Bayesian computation, uncertainty quantification, and optimal experimental design. He holds a SB, SM, and PhD from MIT and has been recognized with prestigious awards including the DOE Early Career Award and the Junior Bose Teaching Prize. Education: PhD in Aeronautics and Astronautics, MIT SM in Aeronautics and Astronautics, MIT SB in Aeronautics and Astronautics, MIT Research Interests: Uncertainty Quantification techniques for complex systems Bayesian computational methods and inverse problem solutions Optimal experimental design strategies Interdisciplinary applications in geophysics, environmental science, and engineering Awards: 2022: Report to the President, Center for Computational Science and Engineering 2021: Bayesian Inference Software Framework (hIPPYlib-MUQ) 2012: MIT School of Engineering Junior Bose Award 2010: DOE Early Career Research Award Labs & Leadership: Aerospace Computational Design Laboratory (Director) MIT Center for Computational Engineering (Co-Director) Editorial Board roles: SIAM Journal on Scientific Computing, Advances in Computational Mathematics

• Bioinformatics and Computational Biology
• Machine Learning
• Data Science
• +۳ مورد دیگر

Fardina Alam is a Lecturer in the Department of Computer Science at the University of Maryland, College Park. She holds a Ph.D. from George Mason University (2023), where she specialized in Structural Bioinformatics and Machine Learning. Her academic rank reflects UMD's Professional Track Faculty, equivalent to an Assistant Professor of Teaching. Dr. Alam's research focuses on deep learning applications in protein structure prediction, generative AI, and ethical data practices. She has contributed to projects funded by an NSF FET Grant (#1900061) and published in journals like Biomolecules. Awards include the 2023 Outstanding Dissertation Award and 2022 Editor's Choice Article recognition. Education: Ph.D. (Computer Science, George Mason, 2023); M.S. (Computer Science, George Mason, 2019); B.S. (Computer Science and Engineering, Military Institute of Science and Technology, Bangladesh, 2013). Her teaching emphasizes data science ethics and interdisciplinary applications, including a new course for UMD's Data Science minor. Professional roles include Associate Guest Editor at Bioinformatics Advances (2024), Faculty Advisor to the Bangladeshi Graduate Student Association, and Program Co-Chair for the ACM-BCB Computational Structural Bioinformatics Workshop (2023). Research Interests: Structural Bioinformatics, Generative AI, Responsible AI Ethics, Deep Learning, Machine Learning, and Data Science. Her work bridges computational biology and AI, with a focus on equitable data practices and protein structure analysis. Recent projects address challenges in generating physically-realistic protein structures and improving question-answering systems through equitable data strategies. Awards: Recipient of the 2023 Outstanding Dissertation Award (George Mason University), 2023 Best Paper Award, and 2022 Biomolecules Editor's Choice Article. These accolades highlight contributions to protein structure prediction and bioinformatics methodology. Advising & Grants: NSF FET Grant #1900061 supported her computational biology research. She advises the Nobanno graduate student group and chairs workshops in her field. Her teaching and research aim to promote inclusivity, particularly for underrepresented groups in STEM. Labs/Teams: Active contributor to the Computational Biology Lab at George Mason University and collaborates with UMD's interdisciplinary teams on data ethics and AI applications. Her work aligns with UMD's vision for data-driven education and innovation.

Alexander Bastounisمشاهده پروفایل

مدرس

• Artificial Intelligence
• Computational Mathematics
• Optimization
• +۵ مورد دیگر

Alexander Bastounis is a Lecturer in Applied Mathematics at King's College London, affiliated with the Department of Mathematics and the King’s Institute for Artificial Intelligence. His research focuses on computational mathematics, optimization, and the trustworthiness of AI systems. He holds a PhD from the University of Cambridge and has held academic roles at institutions including Leicester University, City University of Hong Kong, and TU Berlin. Education: PhD in Applied Mathematics from DAMTP, University of Cambridge (2018). Earlier academic qualifications not specified. Research interests include foundational aspects of computational mathematics, AI limits and robustness, adversarial attacks, and inverse problems. His work explores computational barriers in estimation and learning, with recent attention on stealth attacks in AI models and feature selection reliability. Received the Leslie Fox Prize (2019) for work on inverse problems Contributed to SIAM News articles on compressed sensing and AI challenges Advising and grants: Currently supervises the EPSRC-funded project '50:50 Haleon/EPSRC DLA Studentship' (2025–2029). No listed students. Labs/teams: Active in King’s Institute for Artificial Intelligence and collaborates on interdisciplinary projects across computational mathematics and AI security.

Alex Gittensمشاهده پروفایل

استادیار

• Machine Learning
• Computational Linear Algebra
• Randomized Numerical Linear Algebra
• +۳ مورد دیگر

Alex Gittens is an Assistant Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI), joined in 2017. His research focuses on algorithmic trade-offs between computational efficiency and accuracy in large-scale linear algebra and machine learning contexts. He has expertise in kernel methods, randomized numerical linear algebra, and low-rank approximation techniques. Education: PhD in Applied and Computational Mathematics, Caltech (2013) Industry Postdoc at eBay Research Labs (2013-2015) Postdoctoral Scholar at International Institute of Computer Science (2015-2016) His research explores scalable machine learning algorithms, nonlinear and multilinear sketching applications, and sampling for low-rank tensor/matrix approximation. Current technical interests include attention mechanisms for knowledge graph completion, federated learning trade-offs, and causal inference in adversarial settings. Recent publication trends show active contributions in federated learning (privacy-fairness optimization), causal information extraction (financial text analysis), and adversarial machine learning (robustness-security trade-offs). His work emphasizes trustworthy ML systems and computational efficiency in high-dimensional data processing. Teaching includes foundational discrete mathematics (CSCI 2200) and advanced machine learning courses (CSCI 6968/4968). He offers advising through Slack channels and via email, focusing on course selection, research opportunities, and graduate school preparation.

Miao Panمشاهده پروفایل

دانشیار

• Privacy Preserving Deep Learning
• Wireless Networking
• Internet of Things
• +۳ مورد دیگر

Dr. Miao Pan is an Associate Professor in the Department of Electrical and Computer Engineering at the Cullen College of Engineering, University of Houston. He directs the PAN Lab (panlab.ece.uh.edu) focusing on wireless networking, security, and IoT applications. His educational background includes a B.S. in Electrical Engineering from Dalian University of Technology (2004), M.S. from Beijing University of Posts and Telecommunications (2007), and Ph.D. from the University of Florida (2012). Dr. Pan's research spans privacy-preserving deep learning, wireless networking, machine learning applications in communications, underwater systems, and cognitive radio networks. His interdisciplinary approach combines theoretical foundations with practical implementations in emerging technologies. Recent publications demonstrate strong focus on federated learning optimizations, wireless sensing innovations, and security mechanisms for next-generation systems. Key trends include energy-efficient mobile AI, robust authentication methods, and adaptive underwater networking solutions. Honors include: NSF CAREER Award (2014) 5 IEEE Best Paper Awards (2015-2019) University of Florida Graduate Fellowship (2007) He leads multiple federally funded projects and advises graduate researchers in wireless systems and security. The PAN Lab collaborates with industry partners to translate research into practical solutions for IoT and 5G/6G networks.

Chanchal K. Royمشاهده پروفایل

استاد

• Software Engineering
• Software Maintenance and Evolution
• Code Clone Detection
• +۳ مورد دیگر

Dr. Chanchal K. Roy is a Professor of Software Engineering/Computer Science at the University of Saskatchewan (USask), Canada, and Director of the NSERC CREATE SOAR program. He leads the Software Research Lab (SRLab) and is renowned for his work on code clone detection (NiCad tool) and software maintenance. His research spans software evolution, big data analytics, and quantum computing applications in software engineering. Dr. Roy holds a Ph.D. from Queen’s University, an M.Sc. from RWTH Aachen University, and a B.Sc. from Khulna University. Research interests include software clone detection, maintenance, and evolution, with emphasis on semantic analysis and cross-language clones. He has published over 240 papers (h-index 52) and attracted $6M+ in funding, including NSERC grants and CFI-JELF support. Awards include the GSA Advising Excellence Award, Outstanding Young Computer Science Researcher Award, and multiple Most Influential Paper awards. Key contributions include developing NiCad, advancing Stack Overflow search techniques, and leading collaborative projects in software analytics. His work has been featured in ACM Tech News, TechRepublic, and Stack Overflow blogs. Dr. Roy actively engages in keynotes at conferences like WCRE, IWSC, and BIM.

Qian Liuمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Applied Statistics
• +۵ مورد دیگر

Dr. Qian Liu is an Assistant Professor in the Department of Applied Computer Science at the University of Winnipeg . She holds a PhD in Individual Interdisciplinary Studies, integrating Computer Science, Statistics, and Medical Genetics. Her research focuses on advanced data science techniques for healthcare and material science, including multi-modal data integration and interpretable AI. Education : 2023: Postdoc, Western University, Canada 2023: PhD, University of Manitoba, Canada 2019: MSc, University of Manitoba, Canada Research Interests : Machine learning, deep learning, bioinformatics, computational biology, medical imaging, and their applications in healthcare and material informatics. Her lab develops tools for genomic integration, radiogenomics, and AI-driven biomarker discovery. Recent Contributions : Her work spans AI in nanomaterial classification, breast cancer biomarker discovery, and emotion recognition. Articles highlight innovations in graph neural networks, diffusion models, and interpretable deep learning. Awards : 2024 University of Manitoba Distinguished Dissertation Award 2019 Best Oral Paper at IEEE Bioinformatics Conference Grants : NSERC 2024 Discovery Grant (Principal Investigator) CIHR 2023 Project Grant (Co-Applicant) Labs/Teams : Active in interdisciplinary collaborations across computer science, statistics, and biomedical domains. Lab members include postdocs, PhD/MS students, and undergraduate researchers.

• Stochastic Analysis
• Mathematical Finance
• Option Pricing
• +۵ مورد دیگر

Blanka Horvath is a Lecturer at King's College London and Honorary Lecturer at Imperial College London's Department of Mathematics (Faculty of Natural Sciences). Her research focuses on stochastic analysis and mathematical finance, particularly in numerical methods, machine learning applications, and volatility modeling (e.g., SABR and rough volatility models). She holds a PhD from ETH Zurich (2015), a Diplom in Mathematics from the University of Bonn, and an MSc in Economics from the University of Hong Kong. She has organized major conferences such as the SIAM MMF 2017 mini-symposium and co-organized the Rough Volatility Meeting at Imperial College. Her honors include the 2019 Risk Rising Star Award and the 2024-25 LMS Emmy Noether Fellowship. She collaborates with institutions like UBS, The Alan Turing Institute, and Quantennium LTD. Her teaching includes courses on numerical methods in finance and Python/R programming. She supervises PhD and MSc students in areas like rough volatility, machine learning, and quantitative finance. Recent work explores quantum GANs for option pricing and regime detection using Wasserstein distances.