جستجوی استاد راهنمای دانشگاهی

Noah Snavely is a Professor of Computer Science at Cornell Tech and the Cornell Ann S. Bowers College of Computing and Information Science. His research spans computer vision and graphics with a focus on 3D scene understanding from image collections. He also works at Google DeepMind in NYC and leads the Cornell Graphics and Vision Group. His research interests include computer vision and computer graphics , particularly in recovering 3D structure from large photo collections, neural radiance fields, image-based rendering, and scene understanding. His work enables applications in mapping technologies, immersive VR experiences, and synthesizing 3D worlds from text prompts with implications for game design and filmmaking. Recent publications show a strong trend toward generative 3D modeling and neural rendering , with significant contributions in neural radiance fields, view synthesis, and dynamic scene reconstruction. His research group explores unstructured photo collections to develop new technology for 3D world modeling and image analysis. PECASE Microsoft New Faculty Fellowship Alfred P. Sloan Fellowship SIGGRAPH Significant New Researcher Award ACM Fellow (2023) IEEE Fellow NSF CAREER Award Helmholtz Prize Snavely has mentored numerous PhD students and postdocs including Ruojin Cai, Qianqian Wang, and Zhengqi Li. His teaching includes CS5670: Computer Vision across multiple semesters. He has received the Cornell College of Engineering Mr. and Mrs. Richard F. Tucker Teaching Excellence Award (2012). At Cornell Tech, his research group develops technology for modeling the world in 3D from online photo collections and analyzing images for computer graphics applications. His work has been featured in major news outlets including the New York Times, New Scientist, and MIT Technology Review.

• Computer Vision
• Computer Graphics
• 3D Reconstruction
• +۵ مورد دیگر

Olga Sorkine-Hornung is a Professor of Computer Science at ETH Zurich and head of the Institute of Visual Computing. She leads the Interactive Geometry Lab, focusing on theoretical and practical advancements in digital content creation, geometry processing, and shape modeling. Current position: ETH Zurich, Department of Computer Science Previous roles: Courant Institute (NYU), Technical University of Berlin Education: BSc and PhD from Tel Aviv University, postdoc at TU Berlin Her research spans shape representation, digital fabrication, computer animation, and fundamental geometry processing. Key contributions include Laplacian surface editing, as-rigid-as-possible deformation, and generalized winding numbers. She works on applications in VR, AR, and autonomous systems. Awards include Test of Time Awards (2024), ACM Fellow (2020), ERC Consolidator Grant (2020), and EUROGRAPHICS Young Researcher Award (2008). She has supervised numerous students and co-developed software libraries like libigl and Instant Meshes . Co-chair roles for SIGGRAPH, Eurographics, and Pacific Graphics Editorial board member for ACM Transactions on Graphics and other journals Keynote speaker at VMV, CVPR, and SIAM conferences Her work bridges mathematical rigor with practical implementation, advancing computer graphics and geometry processing through intuitive algorithms that maintain surface detail while enabling efficient computation.

• Digital Content Creation
• Geometry Processing
• Shape Representation and Editing
• +۵ مورد دیگر

Alexander Schwing is an Associate Professor in the Department of Electrical and Computer Engineering and Computer Science at the University of Illinois at Urbana-Champaign, affiliated with the Coordinated Science Laboratory. His research focuses on machine learning and computer vision with applications in 3D scene understanding, generative modeling, and multi-agent systems. Education: Diploma in Electrical Engineering and Information Technology, Technical University of Munich (TUM) PhD in Computer Science, ETH Zurich Postdoctoral Fellow, University of Toronto Research Interests: Structured prediction in deep learning Generative adversarial networks and stability Multi-modal vision-language models 3D scene reconstruction from single images Embodied agent collaboration Semantic segmentation with temporal coherence Recent Publications: Highlight trends in neural rendering, video object segmentation, and reinforcement learning with applications to 3D modeling and multi-agent systems. Notable innovations include SAIL-VOS dataset for amodal segmentation and NeRFDeformer for single-view scene transformation. Scientific Awards: NSF CAREER Award, 3M and Amazon research awards, multiple student recognition awards, ETH Zurich PhD medal, and best paper at Intelligent Tutoring Systems 2014. Teaching: Offers graduate courses in Pattern Recognition (ECE 544) and Machine Learning (CS 446/ECE 449). Previously taught at University of Toronto and ETH Zurich. Labs & Collaborations: Leads research at Coordinated Science Laboratory (UIUC) with collaborations across University of Toronto, ETH Zurich, and industry partners like Samsung SAIT and Amazon.

• Machine Learning
• Computer Vision
• Structured Prediction
• +۷ مورد دیگر

David A. Smith is an Associate Professor at the Khoury College of Computer Sciences, Northeastern University. His research focuses on Natural Language Processing (NLP) and computational linguistics, with applications in machine translation, information retrieval, digital humanities, and social sciences. He is a founding member of the NULab for Texts, Maps, and Networks, a research center focused on digital humanities and computational social sciences. Smith's work has been funded by grants from the Mellon Foundation, NEH, and IMLS, supporting projects such as the Viral Texts initiative analyzing 19th-century newspaper networks and the Oceanic Exchanges project tracking transnational information flows. He has contributed to advancements in OCR for historical texts, text reuse detection, and computational analysis of classical languages. He has advised numerous PhD students, including Shijia Liu, Si Wu, and Ryan Muther, and teaches courses like Natural Language Processing and Information Retrieval. His research has been featured in outlets like Wired and the Economist .

• Natural Language Processing
• Computational Linguistics
• Machine Translation
• +۳ مورد دیگر

Prof. Srdjan Capkun is a Full Professor in the Department of Computer Science at ETH Zurich and Director of the Zurich Information Security and Privacy Center (ZISC). He holds a Dipl.Ing. from the University of Split (1998) and a Ph.D. from EPFL (2004). His research focuses on system and network security, including wireless security (e.g., secure positioning), trusted computing, blockchain technologies, and privacy-preserving systems. He co-founded 3db Access (acquired by Infineon) and Sound-Proof, addressing secure distance measurement and authentication. Notable achievements include an ERC Consolidator Grant (2016), ACM Fellowship, and IEEE Fellowship. Education: Dipl.Ing., Electrical Engineering / Computer Science, University of Split, 1998 Ph.D., Communication Systems, EPFL, 2004 Research Interests: Wireless security (e.g., UWB, GNSS, cellular) Trusted execution environments (TEEs) Blockchain and cryptocurrency systems Privacy-preserving technologies Awards & Grants: ERC Consolidator Grant (2016) ACM Fellow and IEEE Fellow Leadership of ZISC Advising & Labs: Leads the System Security Group at ETH Zurich Guided postdoctoral researchers and graduate students in security and privacy Companies: 3db Access (secure distance measurement) Futurae (online authentication)

• Computer Security
• Network Security
• Wireless Security
• +۴ مورد دیگر

Lexing Xie is a Professor in Computer Science at the Australian National University. He leads the ANU Computational Media Lab ( http://cm.cecs.anu.edu.au ) and the ANU Integrated AI Network. His work focuses on the intersection of machine learning, social media analysis, and multimedia understanding. Dr. Xie's research broadly focuses on innovative design and use of machine learning algorithms, especially on large-scale graph data and collective behaviour. His recent work spans several key areas: Popularity in social media -- understanding, predicting, and optimization Multimedia knowledge graphs, vision and language integration Humanising machine intelligence through better understanding of social dynamics His publications reveal a strong trend toward understanding information diffusion patterns in social media, particularly through visual content. He has made significant contributions to the study of visual memes, popularity prediction using point processes, and multimodal learning that connects vision with language. His work often bridges theoretical machine learning with practical applications in social media analysis. Dr. Xie has received recognition for his research, including an Honourable Mention at CSCW 2019 for his work on attention flow in online video networks. His research has been supported by collaborations with major institutions including IBM Research and Columbia University. As an advisor, Dr. Xie has mentored numerous students who have gone on to contribute significantly to publications in top-tier conferences. His lab, the ANU Computational Media Lab, serves as a hub for interdisciplinary research connecting computer science with social sciences.

• Machine Learning
• Social Media Analysis
• Computer Vision
• +۷ مورد دیگر

Sebastian Scherer is an Associate Research Professor at the Robotics Institute (RI), Carnegie Mellon University (CMU), where he leads cutting-edge research in autonomous aerial systems and robotics. His work focuses on enabling unmanned rotorcraft to operate safely and efficiently in cluttered, low-altitude, and extreme environments. Education: Ph.D. in Robotics, Carnegie Mellon University (2010) MS in Robotics, Carnegie Mellon University (2007) BS in Computer Science (Minor in Robotics), Carnegie Mellon University (2004) His research interests span robotics, artificial intelligence, autonomous navigation, obstacle avoidance, SLAM, visual-inertial odometry, energy infrastructure, and public policy . He has made seminal contributions to UAV autonomy, including the first obstacle avoidance for micro aerial vehicles in natural environments (2008) and the first automatic landing zone detection and landing on a full-size helicopter (2010). His recent publications (2023–2025) demonstrate a strong focus on resilient autonomy, multi-robot exploration, foundation models for robotics, and large-scale dataset development. His team has released key datasets like TartanGround , BETTY , and SubT-MRS , and simulation tools like Pegasus Simulator , indicating a systems-level approach to advancing real-world autonomy. The research trends emphasize self-supervised learning, robust perception, risk-aware planning, and multi-modal fusion for off-road and urban environments. Scientific Awards: Popular Science Best of What's New 2010 Award AIAA@Infotech Best Paper Runner-up Award (2010) Siebel Scholar Dr. Scherer has advised numerous students and leads a vibrant research group focused on high-impact robotics applications. He has secured significant grants related to UAV autonomy, energy infrastructure, and urban air mobility. His lab develops experimental infrastructure such as AIrTonomy for testing next-generation autonomous aerial vehicles. He is actively involved in advancing SLAM and localization in extreme environments, notably through participation in the DARPA Subterranean Challenge. His team develops large-scale datasets and benchmarking frameworks to push the boundaries of robustness and generalization in mobile robotics.

• Robotics
• Unmanned Aerial Vehicles
• Autonomous Navigation
• +۱۳ مورد دیگر

Dr. Olle Järv is an Academy Research Fellow at the University of Helsinki's Department of Geosciences and Geography. As Docent in Human Geography and holder of a joint PhD from University of Tartu and Ghent University, his research focuses on leveraging big data sources like mobile phone records and social media for analyzing human mobility patterns across European cross-border regions. Key research areas: Spatial segregation, urban diversity, transnationalism, and pandemic-induced mobility changes Current projects: BORDERSPACE (2020-2025) – Mapping transnational spaces through mobility data MOBI-TWIN (2023-2026) – Regional mobility patterns in twin transitions WinWin4WorkLife (2024-2027) – Sustainable cross-border remote work His methodological innovations include developing tools for analyzing social media data and mobile phone records to study urban dynamics. Recent recognitions include the 2024 Research Council of Finland Award and 2021 Best Paper Award from Journal of Location Based Services.

• Human mobility
• Big data analysis
• Spatial segregation
• +۷ مورد دیگر

Haibin Ling is the SUNY Empire Innovation Professor in the Department of Computer Science at Stony Brook University, part of the College of Engineering and Applied Sciences. His research focuses on computer vision, medical image analysis, augmented reality, and AI applications in science. He holds a Ph.D. from the University of Maryland (2006) and prior degrees from Peking University. Previously, he worked at Temple University (2008–2019) and held roles at Siemens Corporate Research, UCLA, and Microsoft Research Asia. Professor Ling's work spans biomedical imaging, AI for science, and human-computer interaction. He leads the CV Lab and collaborates with the AI Institute at Stony Brook. Awards include the NSF CAREER Award (2014), Best Student Paper (ACM UIST 2003), and IEEE Fellow (2020). He serves on editorial boards for IEEE Trans. PAMI, Pattern Recognition, and CVIU, and chairs major conferences like CVPR. His research group includes over 50 students and alumni, with active projects in tracking benchmarks (LaSOT), Leafsnap, and medical imaging tools. Notable publications address OCTA flow estimation, backdoor attacks on vision models, and topology-guided medical learning. Collaborations involve institutions like Temple University and Stony Brook's Department of Applied Mathematics and Statistics.

• Computer Vision
• Medical Image Analysis
• Augmented Reality
• +۳ مورد دیگر

Erik B. Sudderth is a Professor of Computer Science and Statistics and Chancellor's Fellow at the University of California, Irvine (UCI). He leads the Learning, Inference, & Vision Group and directs multiple research centers, including the UCI Center for Machine Learning and Intelligent Systems and the HPI Research Center in Machine Learning and Data Science. He previously served as an Associate Professor at Brown University. Education: B.S. (summa cum laude) in Electrical Engineering from UC San Diego (1999), M.S. and Ph.D. in EECS from MIT (2002, 2006). His research focuses on statistical methods for scalable machine learning, Bayesian nonparametrics, probabilistic graphical models, and applications in computer vision, AI, and environmental science. Key areas include nonparametric clustering, deep generative models, and particle-based inference algorithms. Research interests span diverse topics: advancing Bayesian nonparametric models for medical time series, scalable variational inference, and AI ethics. Notable contributions include the NET-VISA seismic monitoring system (ISBA Mitchell Prize, 2014), the BNPy toolbox (NSF CAREER Award), and work on diverse particle max-product algorithms for continuous inference. Scientific awards include the NSF CAREER Award, ISBA Mitchell Prize, and recognition as one of "AI's 10 to Watch" (IEEE). He has served as editor for top journals (JMLR, IEEE PAMI) and conference chairs (NeurIPS, CVPR). His work bridges theory and practice, with applications in robotics, climate science, and healthcare. Labs/Teams: UCI Learning, Inference, & Vision Group; UCI Center for Machine Learning; CREATE Technology Center. Grants include NSF funding for visually impaired collaboration tools and soil biogeochemical modeling.

• Machine Learning
• Computer Vision
• Statistical Inference
• +۳ مورد دیگر

Rui Ni is an associate professor in the Department of Mechanical Engineering at Johns Hopkins University, directing the Fluid Transport Lab. His research focuses on experimental fluid mechanics, turbulence, multiphase flows, and their applications in energy systems, environmental engineering, and physiological processes. He holds a PhD in Physics from the Chinese University of Hong Kong (2011), followed by postdoctoral work at Yale and Wesleyan Universities. Before joining JHU, he held the Kenneth Kuan-Yun Kuo Early Career Professorship at Penn State University. His research interests include dusty flows, Lagrangian particle tracking, and animal collective behaviors. Notable projects include collaborations with NASA on plume-surface interaction and the development of advanced diagnostic tools like physics-informed machine learning and 3D particle tracking. He has received prestigious awards, including the NSF CAREER Award and ACS-PRF New Investigator Award, and leads studies on turbulence modulation by deformable bubbles, fish schooling efficiency in turbulent environments, and interfacial mass transfer dynamics. Key Projects: Plume-Surface Interaction (NASA collaboration), Fish Aquarium with Turbulent Environment (FATE) facility, V-ONSET multiphase flow facility. Grants: Gordon and Betty Moore Foundation’s Experimental Physics Investigators Initiative Grant. Lab Focus: Experimental and computational studies of multiphase flows, physiological flows, and complex systems. Ni’s work bridges fundamental fluid dynamics with practical applications, such as improving energy efficiency and understanding biological systems like fish schooling and nasal drug delivery mechanisms.

• Experimental Fluid Mechanics
• Turbulence and Turbulent Multiphase Flow
• Dusty Flow and Particle-Laden Flow
• +۵ مورد دیگر

Patrick McDaniel is a Professor in the School of Computer, Data & Information Sciences at the University of Wisconsin-Madison. He is a Fellow of IEEE, ACM, and AAAS, and leads the MadS&P (UW-Madison Security and Privacy) group. His research spans computer and network security, adversarial machine learning, and technical public policy. Academic Affiliation : Tsun-Ming Shih Professor of Computer Sciences Key Research Areas : Mobile/IoT security, election systems security, AI policy Research Trends : His recent publications and funded projects focus on adversarial machine learning, SDN security, and robustness of AI systems. Keywords include cybersecurity, machine learning, network security, and privacy. Grants and Leadership : He has secured over $5M in NSF funding for end-to-end trustworthiness of ML systems and leads collaborative projects with Army Research and UW-Madison. Awards : Multiple best/most influential paper awards (ICSE 2015, PLDI 2014, ACSAC 2024, EthiCS 2023) Advising : Mentored 13+ PhD students and postdocs now at top institutions like Google, Purdue, and University of Toronto

• Security of Machine Learning/AI Systems
• Adversarial Machine Learning
• Systems Security
• +۴ مورد دیگر

Verena Zimmermann serves as Assistant Professor (Tenure Track) for Security, Privacy and Society at ETH Zurich within the Department of Humanities, Social and Political Sciences. She leads the Security, Privacy and Society research group, focusing on human-centered approaches to safety, IT security, and privacy challenges in emerging technologies. Her academic foundation includes a PhD in Usable Security from TU Darmstadt, where her dissertation From the Quest to Replace Passwords towards Supporting Secure and Usable Password Creation earned dual awards from the German Association for Data Protection and Data Security (GDD) and the Ernst-Ludwigs-Hochschulgesellschaft. Zimmermann's research pioneers the shift from viewing humans as security vulnerabilities to recognizing them as integral solutions. Her work spans password security, phishing interventions, smart home privacy, and cybersecurity education, consistently applying human factors principles to design systems that align with natural user behavior. Current projects explore emotional dimensions of security engagement, quantum governance frameworks, and LLM-related privacy concerns through interdisciplinary lenses combining computer science, psychology, and safety science. Recent publications reveal strong thematic trends toward understanding user motivations in security behavior, developing personalized training through augmented reality and adaptive systems, and translating safety science principles to cybersecurity incident management. Her work increasingly addresses societal implications of emerging technologies like quantum computing and generative AI. Her distinguished recognitions include: GDD Dissertation Award for Usable Security research Ernst-Ludwigs-Hochschulgesellschaft Dissertation Award IANUS Award (2nd place) for Moving from a 'Human-as-Problem' to a 'Human-as-Solution' Cybersecurity Mindset Zimmermann actively mentors through ETH's Human-Centered Security and Privacy Lab while collaborating internationally with institutions including ATHENE (German National Center for Applied Cybersecurity), Abertay University's Cybersecurity Division, and the Safety Science Innovation Lab. Her research receives support from Swiss and European funding bodies focused on privacy-preserving technologies and organizational security behavior. At ETH Zurich, she directs the Security, Privacy and Society group which employs design thinking, LEGO Serious Play, and mixed-methods research to tackle real-world security challenges. The team partners with industry stakeholders to develop practical solutions for phishing resistance, smart home privacy, and cybersecurity education while maintaining strong ties to the Research Training Group 2050 Privacy and Trust for Mobile Users.

• Usable Security
• Privacy
• Human-Computer Interaction
• +۳ مورد دیگر

Michael Baldea is an Associate Professor in the Department of Chemical Engineering at the University of Texas at Austin . He holds a Ph.D. in Chemical Engineering from the University of Minnesota (2006), with prior degrees from 'Babeş-Bolyai' University in Romania (M.Sc. 2001, Diploma 2000). His research group develops theoretical and computational methods for Process and Energy Systems Engineering , focusing on integrated decision-making, performance optimization, and process intensification with industrial validation. Education: Ph.D., Chemical Engineering, University of Minnesota (2006) M.Sc., Interface Process Engineering, 'Babeş-Bolyai' University (2001) Diploma, Chemical Engineering, 'Babeş-Bolyai' University (2000) Research Thrusts: Integrated decision-making in chemical/energy supply chains Process performance monitoring and optimization Process integration and intensification Key applications include grid-responsive chemical plants, intensified distillation/column designs, and renewable energy integration for building systems. Scientific Awards: Frank A. Liddell, Jr. Fellowship NSF CAREER Award (2015-2020) Moncrief Grand Challenges Faculty Award (2014) AIChE Outstanding Young Researcher Award (2017) Implementation : His group has translated research into commercial tools through partnerships with industrial test beds and is working to integrate methods into commercial simulators. They explore predictive approaches for building energy management and strategic capital investment analysis in next-generation energy systems.

• Process Systems Engineering
• Energy Systems
• Nonlinear Optimization
• +۳ مورد دیگر

Kaveh Razavi is an Assistant Professor at ETH Zurich in the Department of Information Technology and Electrical Engineering, supervising the Computer Security Group. He is also affiliated with the Department of Computer Science. Research focuses on commodity hardware and operating systems security Teaching: Computer Engineering (BSc 2022–2025), Hardware Security (MSc 2016–2025), Advanced Operating Systems (MSc 2016–2019) Research Interests: Razavi's work bridges computer systems and security, emphasizing microarchitectural vulnerabilities, hardware exploitation, and security verification. His group develops tools for analyzing hardware-software interactions and mitigating memory attacks like Rowhammer. Scientific Awards: Distinguished/Best Paper Awards at IEEE S&P, USENIX Security, MICRO Five Pwnies Awards for innovative research Dutch Veni Grant, ERC Starting Grant Jochen Liedtke Young Researcher Award Golden Owl Teaching Award (2022) ETH Medal, Intel Bounty Reward, BlackHat USA Recognition

• Computer Systems
• Computer Security
• Hardware Security
• +۵ مورد دیگر