جستجوی استاد راهنمای دانشگاهی

Qing Yangمشاهده پروفایل

استاد

• Computer Architectures
• Hardware/Software Designs for AI
• Machine Learning
• +۲ مورد دیگر

Qing Yang is a Professor at the University of Rhode Island's College of Engineering , specializing in Electrical, Computer and Biomedical Engineering. His research spans Computer Architectures , Hardware/Software Designs for AI , Machine Learning , Data Storage , and Computer Networks . Ph.D., Computer Engineering, University of Louisiana, Lafayette, 1988 M.A., Electrical Engineering, University of Toronto, 1985 B.S., Computer Science, Huazhong University of Science and Technology, China, 1982 Yang's work focuses on advanced computing systems, including FPGA optimization, in-sensor processing for ADAS, and secure die-to-die communication architectures. His publications highlight innovations in storage systems, graph processing acceleration, and thermal monitoring for data centers. Recent research trends emphasize AI hardware acceleration , runtime security mechanisms , and smart storage solutions , with articles published in venues like FPGA, IEEE NAS, and ACM Transactions on Storage. Yang has secured multiple National Science Foundation grants for projects such as "Introducing a New In-Sensor Computing Architecture for Intelligent 3-D Imaging Systems." His inventions in data recovery and cache coherence have resulted in 14 U.S. patents , several of which were commercialized through a startup.

• Information Systems
• Big Data Analytics
• Online Customer Reviews
• +۷ مورد دیگر

Wenjie Fan is a Visitor (Faculty) at the Department of Information and Service Management, School of Business, Aalto University, Finland. He holds a Doctor of Science in Economics and Business Administration (majoring in Information Systems Science) from Aalto University, awarded on June 27, 2022. His academic journey includes two master's degrees in Business and Economics from the University of Arizona and City University of Hong Kong, and a bachelor's degree in Engineering and Technology from Beijing University of Posts and Telecommunications. Doctor of Science in Economics and Business Administration (Information Systems Science), Aalto University, 2022 Master's degree in Business and Economics, University of Arizona, 2013 Master's degree in Business and Economics, City University of Hong Kong, 2012 Bachelor's degree in Engineering and Technology, Beijing University of Posts and Telecommunications, 2008 His research focuses on leveraging big data to understand consumer behavior, managerial responses, and digital platform dynamics. Key interests include online customer reviews, crowdfunding, tourism industry analytics, and service management. His work integrates theories from information systems, behavioral economics, and data science to explore how digital interactions shape business outcomes. The most recent articles reflect a consistent trend toward empirical analysis of large-scale datasets from online platforms. These studies employ advanced statistical methods to investigate review helpfulness, environmental impacts on tourism, investor behavior in crowdfunding, and engagement patterns on social media. The research bridges academic rigor with practical implications for businesses and policymakers. No scientific awards were explicitly mentioned in the provided text. There is no public information available about Wenjie Fan’s advisees or research grants. However, his doctoral thesis and publications suggest active involvement in interdisciplinary research projects, likely supported by institutional or collaborative funding. His work has been disseminated through high-impact journals and conferences in information systems and management. Wenjie Fan’s research is conducted within the Department of Information and Service Management at Aalto University’s School of Business. While no specific lab or research group is named, his work aligns with digital service innovation, customer analytics, and sustainable business models — themes central to contemporary information systems research.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Övgü Öztürk Ergün is an Assistant Professor in the Department of Software Engineering at Muğla Sıtkı Koçman University's Faculty of Engineering. She holds a Ph.D. from The University of Tokyo's Department of Frontier Informatics. Her research focuses on computer vision, machine learning, and AI applications in areas such as augmented reality, cultural heritage digitization, and human behavior analysis. She has led multiple TÜBİTAK-funded projects including 'Customer Tracking and Interest Measurement Technologies for Retail Sector Sales Performance Improvement' and '3D Modeling of Cultural Heritage Sites.' Her work spans academic publications, industry collaborations, and innovative teaching in software engineering and interactive multimedia systems. Education: Middle East Technical University, B.Sc. Electrical & Computer Engineering (2003) University of Tokyo, Ph.D. Frontier Informatics (2010) Research Interests: Her interdisciplinary research integrates computer vision with real-world applications, including privacy-preserving biometrics, smart environment monitoring, and cultural heritage preservation. She has pioneered ontology-based systems for Turkish cuisine and developed AR tools for virtual try-on. Her work emphasizes ethical AI and context-aware systems, with recent focus on food recognition using CLIP models and churn prediction in telecom sectors. Awards & Grants: TÜBİTAK Undergraduate Software Projects Competition (2013) Robot Days 3rd Place (2013) Completed TÜBİTAK 1001/1505 projects totaling over 500k USD funding Advising & Labs: Supervised BENGÜ-ÖZTÜRK's thesis on food image recognition. Active in the University's Research & Application Centers for Computer Vision and AR/VR development. Collaborates with ISTKA and Ministry of Development on cultural heritage digitization projects.

Jason Hartfordمشاهده پروفایل

استاد پژوهش

• Machine Learning
• Causal Inference
• Deep Learning
• +۳ مورد دیگر

Jason Hartford is a Research Professor and Dame Kathleen Ollerenshaw Fellow specializing in Machine Learning and Robotics. He leads the Research Unit at Valence Labs since April 2023 and holds a Doctor of Science from the University of British Columbia (2021). His research focuses on causal representation learning, causal inference from unstructured data, and deep learning methodologies. Education: Doctor of Science (2021), University of British Columbia, specializing in Architectures and learning algorithms for data-driven decision making. Research Interests: Hartford's work bridges causal inference and machine learning, addressing challenges in representation learning, active learning, and multimodal data integration. He explores how AI can enhance scientific discovery, particularly in biological and medical domains through projects like Virtual Cells and Mendelian randomization applications. Awards: Recipient of the prestigious Dame Kathleen Ollerenshaw Fellowship, highlighting his contributions to causal machine learning and interdisciplinary research. Grants & Projects: Leads the MCAIF: Centre for AI Fundamentals (2021–2026), a collaborative initiative involving over 20 researchers and students, focusing on foundational AI advancements in reinforcement learning, neural networks, and approximation algorithms. Students: Supervises multiple PhD candidates including Hosseinzadeh, Mousa, and Das, fostering innovation in AI-driven scientific exploration.

Bernardo Pereira Nunesمشاهده پروفایل

مدرس ارشد

• Web Science
• Technology-Enhanced Learning
• Semantic Web
• +۷ مورد دیگر

Bernardo Pereira Nunes is a Senior Lecturer and Sub Dean of Educational Programs at the Australian National University (ANU) within the College of Engineering, Computing and Cybernetics. He holds a PhD in Informatics from the Pontifical Catholic University of Rio de Janeiro (PUC-Rio) and has held adjunct roles at PUC-Rio and visiting professorships at institutions such as the University of Pennsylvania. His professional affiliations include the IEEE Technical Committee on Learning Technology and the International Journal of Educational Technology in Higher Education. Education: Bachelor's in Computer Engineering (PUC-Rio, 2006) Master's in Informatics (PUC-Rio, 2009) PhD in Informatics (PUC-Rio, 2014) with a research period at Leibniz Universität Hannover Research focuses on Web Science , Technology-Enhanced Learning , and Semantic Technologies , with emphasis on educational search behavior, bias in information systems, and AI-driven learning tools. Notable projects include the FireMe app (widely reported globally) and the BiasChecker . He has published over 100 papers and serves as an editor/reviewer for top journals like IEEE Transactions on Learning Technologies and conferences such as ACM Hypertext. Key awards include the Senior Fellow of the Higher Education Academy (SFHEA). His work spans educational game design (e.g., Treasure Explorers), social media analysis (e.g., mourning behavior studies), and collaborative tools like TagTheWeb and VocRec. He has collaborated internationally with institutions like L3S Research Center (Germany) and Consiglio Nazionale delle Ricerche (Italy). Grants and labs: Active in securing funding for projects on learning analytics and digital humanities. Leads research teams focused on educational technology innovation and open data applications.

Adrien Bousseauمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Image Processing
• Non-Photorealistic Rendering
• +۵ مورد دیگر

Adrien Bousseau is a Senior Researcher at Inria within the GraphDeco group at Université Côte d'Azur. He earned his PhD from Inria Rhône-Alpes under Joëlle Thollot and François X. Sillion, with internships at Adobe and MIT. His postdoctoral research at UC Berkeley focused on computational design and graphics. Notable roles include leading the ANR DRAO project (2012–2015) and coordinating the CRISP associate team with UC Berkeley. He has received prestigious awards such as the Eurographics 2011 PhD Award and ERC grants for projects on 3D design and circular design. Research interests span image creation, stylization, vector graphics, and sketch-based modeling. Recent work explores computational methods for circular design and AI-driven design tools. He has supervised over 13 PhD students, including Nicolas Rosset (aerodynamics) and Emilie Yu (VR sketching). Key projects include ERC-funded initiatives on drawing interpretation and material acquisition. Service contributions include co-chairing Eurographics 2025 and EGSR 2021, and editorial roles at Computer Graphics Forum. Publicly accessible tools include Photoshop watercolor filters and academic resources like BendFields tutorials.

Mengjie Xuمشاهده پروفایل

استادیار

• Financial Accounting
• Corporate Governance
• Information Economics
• +۴ مورد دیگر

Mengjie Xu is an Assistant Professor of Accounting at Duke University's Fuqua School of Business. Her research focuses on financial accounting, corporate governance, and information economics, particularly analyzing how information creation and dissemination influence market participant strategies and outcomes. She holds a background from Frankfurt School of Finance & Management. Key research areas include high-frequency data analysis, social media-driven insights (e.g., Reddit, Twitter), EDGAR database tracing, and Glassdoor-based workplace studies. She has developed methodologies for parsing satellite imagery, short-sale data, and SEC filings. Technical expertise spans Python, Stata, SAS, and API integrations for data collection. Her work bridges accounting theory with practical data science, emphasizing replicable workflows for complex datasets. No scientific awards are explicitly mentioned in the sources provided.

Kristof Coussementمشاهده پروفایل

استاد مهمان

• Business Analytics
• Data Science
• Text Analytics
• +۳ مورد دیگر

Kristof Coussement serves as a Visiting Professor at the University of Western Australia's Business School within the Marketing department, while holding a primary position as Professor of Business Analytics at France's IÉSEG School of Management. His expertise centers on developing business-value creating data science frameworks through advanced analytics methodologies. His research specializes in integrating textual data into predictive modeling using text analytics and deep learning, with applications spanning banking, retail, social media, and telecommunications. Key contributions include innovative decision support systems that transform unstructured data into actionable business insights, particularly in customer relationship management and political content analysis. Recent publications demonstrate a concentrated focus on social media dynamics, digital retail engagement, and advertising effectiveness through graphic design elements. These works consistently employ machine learning techniques to decode complex human behaviors in online environments, revealing patterns in political polarization, consumer decision-making, and emotional responses to visual stimuli. Professor Coussement chairs the IÉSEG Center for Marketing Analytics (ICMA) and leads multiple industry research chairs with Crédit Agricole, Enfocus, and Oney Bank. His executive education initiatives have solved data science challenges for global companies including Amazon, AXA, and Orange, translating academic research into practical business solutions across eight industry sectors.

Francis Ogokeمشاهده پروفایل

استادیار

• Artificial Intelligence
• Deep Learning
• Physics-Informed Machine Learning
• +۷ مورد دیگر

Francis Ogoke is an incoming Assistant Professor in the Department of Mechanical Engineering at Carnegie Mellon University, set to begin in Fall 2025. He is currently a postdoctoral associate at the Massachusetts Institute of Technology. His academic journey includes a Ph.D. in Mechanical Engineering from Carnegie Mellon University (2024) and a B.S.E. in Chemical and Biological Engineering from Princeton University (2019). His research lies at the intersection of artificial intelligence and engineering systems, with a focus on developing foundational AI methods for complex engineering problems. Key areas include: Physics-informed deep learning Uncertainty quantification and probabilistic modeling Representation learning for generalization Applications in additive manufacturing, digital twins, and cyber-physical systems The recent articles reflect a strong trend in leveraging deep learning—especially vision transformers, generative models, and reinforcement learning—for accelerating simulations, enhancing in-situ monitoring, and improving control in additive manufacturing. His work consistently bridges AI innovation with real-world engineering challenges, particularly in metal 3D printing and multiphysics modeling. Notable scientific awards include: Presidential Fellowship in the College of Engineering, Carnegie Mellon University G.E.M. Fellowship Francis Ogoke advises emerging researchers and is expected to lead a research group focused on AI-driven engineering systems. His lab will likely focus on developing intelligent frameworks for digital twins and autonomous manufacturing. He has not yet advised any students as per current records. He is actively involved in pioneering research that integrates AI into core engineering workflows, supported by advanced computational and experimental infrastructure. He is affiliated with the College of Engineering at Carnegie Mellon University and conducts research relevant to advanced manufacturing, sensing technologies, and intelligent systems.

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

Elias Abad Rocamora is a Researcher and Doctoral Assistant at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Laboratory for Information and Inference Systems (LIONS) under the Institute of Electrical Engineering (IEM) within the School of Engineering (STI) . He is concurrently pursuing a Doctoral Program in Electrical Engineering through EPFL's Doctoral School of Engineering (EDoc-EDEE). His research focuses on adversarial machine learning, robustness certification, and efficient neural network architectures. Key areas include adversarial attacks against vision-language models, certified defenses under bounded perturbations, and regularization techniques to prevent catastrophic overfitting. His work also extends to 3D pointcloud processing using graph-based methods and formal verification of polynomial networks. Rocamora has contributed to cutting-edge research in AI safety, language model security, and geometric deep learning. His recent work emphasizes dual-domain robustness in multimodal models and efficient single-pass detection mechanisms for LLM jailbreaking inputs. Articles span cybersecurity applications, optimization strategies, and foundational aspects of model robustness. No scientific awards are currently listed. He collaborates actively within the LIONS lab, focusing on interdisciplinary projects at the intersection of electrical engineering and computer science.

Sergio Rodriguez Mendezمشاهده پروفایل

پژوهشگر ارشد

• Knowledge Graphs
• Ontology Engineering
• Linked Data & Semantic Web
• +۷ مورد دیگر

Sergio Rodriguez Mendez is a Research Fellow in Knowledge Graph Engineering at the School of Computing, Australian National University. His work focuses on advancing ontology engineering, linked data, semantic web technologies, and their applications in domains like astronomy, brain-computer interfaces, and IoT. He is a member of the Australian Government Linked Data Working Group (AGLDWG) and the W3C Knowledge Graph Construction Community Group, and holds a senior role at the Software Innovation Institute (SII). His research interests include Knowledge Graphs, Ontology Engineering, Linked Data, Data Science, Machine Learning, Natural Language Processing, Brain-Computer Interfaces, IoT, Cloud/Fog/Edge Computing, and Software Engineering. He has contributed to frameworks like pathfinder for astronomical literature review and Doc-KG for document-to-KG conversion. Recent work emphasizes integrating large language models (LLMs) with knowledge graphs, such as AstroLLaVA for astronomical data unification and hybrid frameworks for entity linking. His publications span conferences like WWW, JCDL, and the ACM/IEEE Joint Conferences. He actively supervises research students and is involved in initiatives like the ASKG project for enriching scholarly knowledge graphs. His work addresses challenges in semantic data representation, automated query processing, and syntactic complexity reduction, with a focus on domain-specific applications in science and cultural heritage.

• Interdisciplinary Engineering
• Mathematical Sciences
• Renewable Power and Energy Systems Engineering
• +۱۱ مورد دیگر

Professor Matthew Piggott is a distinguished academic at Imperial College London , holding the Chair in Computational Geoscience and Engineering within the Faculty of Engineering . His research bridges interdisciplinary engineering, mathematical sciences, and environmental data science, with a focus on renewable energy systems, computational fluid dynamics, and geophysical modeling. He leads the MSc Environmental Data Science & Machine Learning program and contributes to initiatives like the Energy Futures Lab and Grantham Institute . Education : PhD in Mathematics (University of Bath, 2002), MMath in Mathematics (University of Bath, 1998) Academic Positions : Professor (2016–present), Reader (2008–2016), Research Fellow (2005–2008) Piggott's research integrates mathematical modeling, machine learning, and high-performance computing to address environmental challenges. Key areas include flood forecasting , tidal and wind energy optimization , coastal change analysis , and geophysical fluid dynamics . His work leverages advanced numerical methods, data assimilation, and mesh adaptation techniques for applications in climate science and marine engineering. Recent publications highlight his expertise in data assimilation (e.g., latent 3D-Var, 4D-Var), machine learning for environmental flows , and renewable energy system design . His team employs convolutional autoencoders, graph transformers, and deep learning to model unstructured grid data, optimize wind farms, and assess climate impacts on small island nations. Articles also emphasize mesh adaptation for ice flow and tidal turbine arrays, remote sensing for marine hydrodynamics, and multi-fidelity transfer learning for wake modeling. As a member of the Applied Modelling and Computation Group , Ocean Plastic Solutions Network , and Data Science Institute , Piggott collaborates across disciplines. His work often involves Firedrake and PETSc frameworks, with applications in tsunami risk assessment , microbial patchiness , and rock coast evolution . Current projects address offshore wind wake losses, tidal turbine economics, and climate change mitigation through computational methods.

Carl Vondrickمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Robotics
• +۵ مورد دیگر

Carl Vondrick is an Associate Professor of Computer Science at Columbia University. He leads the Perception and Robotics research group focused on creating intelligent systems that understand and interact with the physical world through vision and robotics. His work emphasizes interpretable models, multimodal learning, and robustness. Education: PhD in Computer Science from MIT (2017), advised by Antonio Torralba BS in Computer Science from UC Irvine (2011), advised by Deva Ramanan Research Interests: Generative models for physical intelligence Interpretable machine learning Robotics and embodied AI Large-scale video understanding AI for scientific discovery Article Trends: Recent work focuses on neuro-symbolic methods , generative AI safety , and robot learning . Notable contributions include DIFFusion for subtle visual learning, DiSciPLE for scientific discovery, and differentiable robot rendering frameworks. Awards: 2024 PAMI Young Researcher Award 2021 NSF CAREER Award Advising & Grants: Current PhD students (9) and former advisees (4). Active grants from NSF, DARPA, Toyota Research Institute, and Amazon. Program chairs for ICLR 2025/2026. Labs/Teams: Leads Columbia's Perception Lab and collaborates with industry partners like Google, Cruise, and TRI.

Hamdi Tchelepiمشاهده پروفایل

استاد

• Reservoir Engineering
• Energy Resources
• Subsurface Fluid Flow
• +۵ مورد دیگر

Hamdi Tchelepi is the Max Steineke Professor at Stanford University and a Senior Fellow at the Precourt Institute for Energy. His academic work focuses on reservoir simulation, subsurface energy systems, and computational geosciences. He holds a Ph.D. in Petroleum Engineering from Stanford (1994), an M.S. from King Fahd University (1988), and a B.S. from the University of Petroleum and Minerals (1985). Research interests span reservoir simulation methodologies, CO2 sequestration, battery technology, and multiphase flow dynamics in porous media. His work integrates numerical methods, machine learning, and experimental validation to address challenges in energy systems and environmental engineering. Publications emphasize computational frameworks for flow-geomechanics coupling, pore-scale modeling, and data-driven approaches for subsurface processes. His articles frequently explore stability criteria, algorithmic efficiency, and scalability in reservoir simulation. Notably, Tchelepi has developed open-source tools like GEOS for multiphysics subsurface simulation and contributed to the FluidFlower benchmark study for CO2 storage validation. His research bridges microscale phenomena with macroscale reservoir engineering applications.

Lu Heمشاهده پروفایل

استادیار

• Health Informatics
• Human-Computer Interaction
• Natural Language Processing
• +۳ مورد دیگر

Lu He is an Assistant Professor in the Department of Health Care Informatics at the University of Wisconsin-Milwaukee (UWM). She holds a PhD in Informatics from the University of California, Irvine (2023) and a BS in Computer Science from the University of Minnesota (2017). Her research focuses on leveraging computational methods like Natural Language Processing (NLP) to analyze health-related data from social media, Electronic Health Records (EHR), and clinical notes. Dr. He’s work examines temporal trends in public health attitudes, patient concerns about healthcare quality, and social determinants of health in rare cancer cases. She has developed NLP pipelines for extracting clinical information and evaluating sentiment analysis tools. Recent Research Trends Large Language Models (LLMs) for healthcare worker burnout analysis Social media image analysis during pandemics AI explainability in health news evaluation Telehealth implementation in developing countries Clinical NLP applications for veterans' health Scientific Awards Editor’s Choice, JAMIA (2021) Graduate Dean’s Dissertation Fellowship, UCI (2022) Best Poster Award, SoCal NLP Symposium (2019) Multiple student paper nominations at AMIA and MedInfo conferences Dr. He serves on the Student Editorial Board for JAMIA and reviews for health informatics journals. She is committed to reducing biases in computational health methods and improving patient outcomes through interdisciplinary collaboration.