جستجوی استاد راهنمای دانشگاهی

Guoqiang Liمشاهده پروفایل

دانشیار

• formal verification
• programming language theory
• zero-knowledge proofs
• +۳ مورد دیگر

Dr. Guoqiang Li is an Associate Professor in the School of Computer Science at Shanghai Jiao Tong University, where he conducts research at the intersection of formal methods, programming languages, and security. His academic journey includes prior positions as Assistant Professor (2009-2013) and subsequent promotion to Associate Professor (2014-present) at the same institution. He has held international appointments including a postdoctoral fellowship at Nagoya University (2008-2009), an academic visit at the University of Oxford (2015-2016), and a Guest Associate Professorship at Kyushu University's Research and Development Center for Smart Mobility (2016-2020). Dr. Li earned his B.S. from Taiyuan University of Technology (2001), M.S. from Shanghai Jiao Tong University (2005), and Ph.D. from Japan Advanced Institute of Science and Technology (2008). His educational background reflects a strong foundation in both Chinese and Japanese academic traditions. His research focuses on formal verification, programming language theory, zero-knowledge proofs, knowledge reasoning, and intelligent system verification and security. Over the past decade, his work has evolved from traditional formal methods applied to timed automata and process calculi to contemporary topics including neural network verification, zero-knowledge proof systems, and the application of large language models to program analysis. His publication record demonstrates a clear progression toward addressing verification challenges in increasingly complex modern systems. Dr. Li's recent publications reveal a strategic research trajectory: early work centered on timed automata and formal verification of concurrent systems, while current research addresses the verification of AI systems, zero-knowledge cryptographic protocols, and LLM-enhanced program analysis. This evolution reflects his commitment to applying rigorous formal methods to emerging computational challenges. Distinguished Paper Award at ICSE 2020 for 'Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning' Consistent publication in top-tier venues including ASE, ICSE, FSE, and OOPSLA Recognition through multiple National Natural Science Foundation of China (NSFC) grants as Principal Investigator Dr. Li actively contributes to the academic community as a Senior Member of the China Computer Federation, Deputy Director of Theoretical Computer Science for the Shanghai Computer Society, and committee member for multiple technical organizations. He serves as Organization Chair for SEKM'20-22 and FMAC'17, Publicity Chair for TASE'21-23, and has participated in program committees for numerous international conferences. His teaching portfolio includes undergraduate and graduate courses in algorithm design, mathematical foundations, and scientific writing, demonstrating his commitment to both theoretical computer science education and practical application.

Shweta Bhardwajمشاهده پروفایل

پژوهشگر

• Computer Vision
• Explainable Artificial Intelligence
• Model Compression

Shweta Bhardwaj is a Researcher at Google Research (via Optimum InfoSys Ltd), working in the Mixed-Mode User Understanding team. Previously, she served as a Data Scientist at Flipkart (2019-2021) and held a research internship at NVIDIA Bangalore (2018). She holds an M.S. (by Research) from IIT Madras (2016-2019), advised by Dr. Mitesh M. Khapra, and a B.Tech. in Computer Science from Guru Nanak Dev University. Education: M.S. (by Research), IIT Madras, 2016-2019 B.Tech. in Computer Science, Guru Nanak Dev University Her research focuses on Computer Vision , Explainable AI , and Model Compression . She emphasizes ethical AI applications for societal challenges like criminal justice and healthcare. Recent work includes developing input-adaptive neural networks for fine-grained visual recognition and benchmarking AI systems for sustainability goals. Her publications span topics like video classification optimization, neural network plasticity, and interpretable subspaces in image representations. She collaborates on benchmarks like Colorbench (2025) and Editval (2023), advancing AI robustness and multimodal understanding. Advising & Grants: Advised by Dr. Gaurav Aggarwal (Google Research), Dr. Shourya Roy (Flipkart), and Dr. Mitesh M. Khapra (IIT Madras). No explicit grants mentioned in the text. Labs & Teams: Associated with RBC-DSAI Lab at IIT Madras (led by Dr. Balaraman Ravindran) and currently part of Google's Mixed-Mode User Understanding team.

Matthew Wickerمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Formal Verification
• Adversarial Robustness
• +۳ مورد دیگر

Matthew Wicker is an Assistant Professor (Lecturer) at Imperial College London and a Research Associate at The Alan Turing Institute. His research focuses on developing formally verified, trustworthy machine learning systems with provable guarantees for safety, robustness, and fairness. He collaborates extensively with academic and industry partners, including Accenture. Research Interests: Formal verification of ML systems (e.g., neural network robustness/fairness) Bayesian neural networks and uncertainty quantification Adversarial attack/defense strategies Causal structure learning with probabilistic methods Certifiable guarantees for model explanations His recent publications (ICLR, ICML, NeurIPS) converge on theoretical and practical frameworks for trustworthy AI, emphasizing adversarial robustness in Bayesian models, global fairness certification, and scalable uncertainty methods. A consistent theme is bridging formal methods with real-world ML deployment challenges. Awards: Best Paper at TPM 2022 for causal structure learning research He maintains the deepbayes repository, enabling reproducibility of his PhD work on Bayesian neural network robustness.

Hannah Kernerمشاهده پروفایل

استادیار

• Machine Learning
• Remote Sensing
• Geospatial Data Analysis
• +۱۲ مورد دیگر

Hannah Kerner is an Assistant Professor in the School of Computing and Augmented Intelligence at Arizona State University, where she leads cutting-edge research at the intersection of machine learning and remote sensing. Her work applies AI to global challenges such as food security, climate change, and planetary exploration, with strong collaborations through NASA Harvest and NASA ACRES consortia. Research Interests: Her research focuses on algorithmic advances for sparse and heterogeneous geospatial data, including agricultural land cover classification, novelty/anomaly detection, change detection, geographic fairness in AI, and AI-assisted planetary exploration. She develops methods to improve model generalization, mitigate geographic bias, and enable scalable ground-truth labeling via projects like Street2Sat. Publication Trends: Her recent publications demonstrate a strong trend toward foundational models for Earth monitoring (e.g., Galileo), fairness in AI (geographic and directional bias), automated detection of natural phenomena (volcanic features, seagrass), and the development of benchmark datasets (Fields of the World). Her work spans computer vision, NLP, and environmental science, emphasizing real-world impact. Scientific Awards: Forbes 30 Under 30 Best Paper Award at CVPR 2024 Oral Presentation at WACV 2023 (2.5% acceptance rate) Spotlight Poster at ICML 2024 (3.5% acceptance rate) Top 10 project by IRCAI for SDG impact Advising and Grants: Dr. Kerner actively mentors a large research group, advising numerous PhD, MS, and undergraduate students across ASU and partner institutions. Her research is supported by major funders including NASA, NSF, Google, and the Taylor Geospatial Engine, reflecting its high impact and interdisciplinary nature. Labs and Teams: She leads a dynamic research group at ASU focused on geospatial machine learning and is a key contributor to the NASA Harvest and ACRES consortia, which apply satellite observations to global agricultural monitoring and food security.

Martin J. Hayesمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Wireless Communications
• +۵ مورد دیگر

Professor Martin J. Hayes serves in the Department of Electronic and Computer Engineering at the University of Limerick's Faculty of Science and Engineering. His institutional affiliation includes campus location ERB2-021 and contact details: email Martin.J.Hayes@ul.ie and phone +353 (0)61 202577. His research concentrates on Artificial Intelligence and Computer Vision applications within Smart Manufacturing and Autonomous Vehicles . Key specialties include deep learning for defect detection, visual question answering systems, and real-time failure monitoring in industrial environments. He also investigates interoperable IoT security frameworks and private 5G network implementations for Industry 4.0. Analysis of recent publications (2023-2025) reveals dominant themes in AI-driven manufacturing solutions and autonomous driving technologies. His work consistently addresses data-constrained training environments and real-time performance requirements, with growing emphasis on human-machine interaction patterns and subjective evaluation frameworks for vision systems. Scientific Awards: No scientific awards were documented in available sources Advising and Grants: While specific student advisement records and grant details were not provided, Professor Hayes' extensive publication output across manufacturing automation, autonomous systems, and wireless communications indicates active leadership in multiple research initiatives. His involvement in the REEdI (Rethinking Engineering Education in Ireland) project demonstrates commitment to educational innovation alongside technical research.

• Signal Processing
• Machine Learning
• Computer Engineering
• +۳ مورد دیگر

Ehsan Namjoo serves as a Research Fellow in the Department of Electronics & Computer Engineering at the University of Limerick, with a primary affiliation at Lero – the Irish Software Research Centre. His multidisciplinary work bridges theoretical signal processing, machine learning applications, and hardware implementation for real-world systems. His research spans signal processing (DOA estimation under non-ideal noise conditions, EEG source imaging), machine learning (explainable AI for medical diagnostics, feature selection in cancer detection), and computer engineering (polar code decoders, visible light communication systems). Notable biomedical applications include breast cancer diagnosis and epileptic source analysis, while cybersecurity work focuses on intrusion detection in cloud environments. Recent publications (2021-2025) reveal a consistent trajectory toward developing lightweight, interpretable models for tabular medical data alongside robust signal processing frameworks for communication systems. His work increasingly integrates hardware-aware algorithms, particularly in polar coding implementations, and emphasizes practical validation through experimental demonstrations in visible light networks. As an active Lero researcher, Namjoo participates in Ireland's national software research ecosystem, contributing to interdisciplinary projects that connect theoretical innovations with healthcare, communications, and security applications through collaborative frameworks.

Anca Draganمشاهده پروفایل

دانشیار

• AI Safety and Alignment
• Robotics
• Machine Learning
• +۵ مورد دیگر

Anca Dragan is an Associate Professor in the EECS Department at UC Berkeley and currently leads AI Safety and Alignment at Google DeepMind. She specializes in human-AI interaction, focusing on aligning AI systems with human goals through algorithmic methods. Her work spans robotics, machine learning, and cognitive science, emphasizing safety, collaboration, and understanding human preferences. Affiliations: UC Berkeley EECS, Center for Human-Compatible AI, Google DeepMind Education: B.Sc. (Jacobs University Bremen), Ph.D. (Carnegie Mellon University) Research: Her lab (InterACT) develops algorithms for robots and AI to collaborate safely with humans. Key areas include reward learning from diverse feedback (e.g., demonstrations, corrections), game-theoretic coordination, and mitigating risks in advanced AI systems. Notable contributions include Inverse Reward Design and cooperative inverse reinforcement learning. Publications: Over 100+ papers in top venues (ICRA, CoRL, NeurIPS) on topics like human-robot collaboration, safety-critical systems, and alignment challenges. Recent work addresses risks in large models and scalable alignment techniques. Awards: Sloan Fellowship, MIT TR35, Okawa Award, NSF CAREER, PECASE. Recognized for teaching excellence (McEntyre Award) and contributions to AI ethics. Grants & Leadership: NSF and ONR grants, co-PI of Human-Compatible AI. Advised Waymo on safety for autonomous systems. Active in professional roles (e.g., conference chairs, steering committees). Labs/Teams: InterACT Lab (UC Berkeley), leading safety teams at DeepMind. Collaborations with BAIR Lab and academic-industry partnerships.

Wei-Chiu Maمشاهده پروفایل

استادیار

• 3D Computer Vision
• 4D Computer Vision
• Robotics
• +۵ مورد دیگر

Wei-Chiu Ma is an Assistant Professor of Computer Science at Cornell University, where he leads research at the intersection of 3D/4D computer vision and robotics. His work focuses on building AI systems that can understand, reconstruct, and re-simulate our dynamic world to enable more robust autonomous systems and advance entertainment applications. Prior to joining Cornell, Dr. Ma was a Young Investigator/Postdoc at AI2 / University of Washington. He received his Ph.D. from MIT, working with Antonio Torralba and Raquel Urtasun. Before his Ph.D., he was a Senior Research Scientist at Uber ATG R&D and Waabi working on self-driving vehicles, and completed his M.S. in Robotics at Carnegie Mellon University under the advisement of Kris M. Kitani. Dr. Ma's research interests span several interconnected areas in computer vision and robotics: 3D/4D Computer Vision: Focused on scene reconstruction, modeling, and understanding from visual inputs Neural Radiance Fields (NeRF): Developing techniques for novel view synthesis and scene representation Robotics and Simulation: Creating realistic sensor simulation for autonomous systems Digital Twins: Building interactive, game-engine compatible virtual environments Self-Driving Technology: Working on scene flow estimation, sensor simulation, and vehicle localization His recent publications demonstrate a strong focus on pushing the boundaries of 3D scene understanding, with particular emphasis on extreme-view geometry, neural rendering techniques, and creating realistic simulations for autonomous systems. His work often bridges theoretical computer vision with practical applications in robotics and autonomous vehicles. Dr. Ma has received several notable recognitions including being selected as a Cyber-Physical Systems (CPS) rising star and a Siebel Scholar. He has also earned the Best Application Award for his work on TDTOS: T-Shirt Design and Try On System. As an educator and mentor, Dr. Ma is actively involved in guiding the next generation of researchers. He hosts pro bono office hours for students from underrepresented groups and is committed to fostering diversity in the field. He is currently building his research group at Cornell and plans to hire 1-2 graduate students during the 2024-25 cycle. Dr. Ma has organized several influential workshops including the "Synthetic Data for Computer Vision" workshop at CVPR 2025, the "Agent in Interaction, from Humans to Robots" workshop at CVPR 2025, and the "3D Modeling, Reconstruction, and Generation in the Wild" workshop at ECCV 2024, demonstrating his leadership in the computer vision community.

Daoyuan Wuمشاهده پروفایل

استادیار

• AI Security
• LLM Security
• Blockchain Security
• +۳ مورد دیگر

Daoyuan Wu is an Assistant Professor at the School of Data Science, Lingnan University, Hong Kong, one of eight UGC-funded universities in the region. Previously, he held positions as a Research Assistant Professor at HKUST CSE, Senior Research Fellow at Nanyang Technological University, Senior Researcher at Huawei HKRC, and Research Assistant Professor in the Department of Information Engineering at The Chinese University of Hong Kong (CUHK), where he also served as an Adjunct Assistant Professor from 2022-2023. His research focuses on the intersection of Large Language Models and security, with specialization in LLM for Security and Security of AI/Blockchain/Code/Mobile . His work spans multiple domains including AI/LLM4Sec (using LLMs for vulnerability detection), AI/LLM-Sec (securing LLMs themselves), Blockchain and Web3 Security, and Mobile and Software Security. He leads the AIS2Lab which is actively researching LLM applications in cybersecurity contexts. His recent publications demonstrate a strong trend toward applying LLMs to security problems across multiple domains, with significant contributions to smart contract security through tools like PropertyGPT (which received a Distinguished Paper Award at NDSS 2025), GPTScan, and ACFix. His work combines program analysis with LLM capabilities to address complex security challenges that traditional methods struggle with. Distinguished Paper Award at NDSS 2025 for PropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented Property Generation Dr. Wu actively advises PhD and research students, with several former students now working at top institutions and companies including Huawei, OKX, and academia. He's currently hiring PhD students for Fall 2026 with scholarship support of approximately HK$19,000 per month. His lab receives funding from multiple internal and external grants supporting PhD students, RAs, and PostDocs. He leads the AIS2Lab which focuses on AI/LLM applications in security contexts across multiple domains including blockchain, mobile security, and software security. The lab maintains active collaborations with researchers at top institutions globally and has developed multiple influential tools and frameworks for security analysis.

Saikat Duttaمشاهده پروفایل

استادیار

• Software Engineering
• Machine Learning
• Program Analysis
• +۷ مورد دیگر

Saikat Dutta is an Assistant Professor in the Department of Computer Science at Cornell University, where he joined in August 2024. His research sits at the intersection of Software Engineering and Machine Learning, with a focus on improving the reliability of machine learning systems and leveraging machine learning techniques to solve challenging software engineering problems. His research interests span several key areas including automated test generation and debugging of ML/DL libraries , using AI/ML for automated software engineering , improving performance and effectiveness of regression tests in ML libraries , and static and dynamic analyses for probabilistic programming . His work bridges theoretical foundations with practical applications in real-world systems. Dutta has developed multiple influential frameworks and tools including BugsInDLLs (a database of reproducible bugs in deep learning libraries), FLEX (for fixing flaky tests in ML projects), and TERA (for optimizing stochastic regression tests). His research has been published in top-tier venues including ICSE, FSE, ISSTA, PLDI, and ICLR. Amazon Research Award 2025 Meta AI LLM Evaluation Research Grant 2025 Mavis Future Faculty Fellowship 2022-23 Facebook PhD Fellowship 2020-22 3M Foundation Fellowship 2019-2020 Dutta actively mentors PhD students including Yingao (Elaine) Yao, Shinhae (Joseph) Kim, and Junkai Huang. He has served on program committees for major conferences including ASE, ISSTA, and ICSE. His teaching includes courses on Software Engineering in the Era of ML/AI.

Jingyi Wangمشاهده پروفایل

استادیار

• Software Engineering for AI
• Formal Methods
• Security
• +۴ مورد دیگر

Jingyi Wang is an Assistant Professor at Zhejiang University (ZJU), China, leading the IS2 (Intelligent System Security) Lab. He holds a US-equivalent tenure-track position and has established himself as a prominent researcher in software engineering for AI and formal methods applied to security. His educational background includes: Bachelor's degree from Xi'an Jiaotong University (2013) PhD from Singapore University of Technology and Design (2018) Research Fellow at National University of Singapore (2019-2020) Wang's research focuses on developing principled methodologies for building trustworthy AI models and secure systems. His work spans testing, verification and repair of AI models/systems, AI safety and security, and formal reasoning of security. He has developed innovative approaches for neural network testing, verification, and repair, with particular emphasis on explainability, fairness, and robustness. His publication record shows a clear progression from foundational work on concolic testing (2018) to increasingly sophisticated methods for evaluating and improving AI systems, with recent focus on large language models and decentralized identity systems. His research bridges formal methods with practical software engineering challenges in AI security. His notable achievements include: Two ACM SIGSOFT Distinguished Paper Awards (ICSE 2018 and 2020) ACM SIGSOFT Research Highlights Best Paper Award Runner-up at IEEE TDSC 2024 Wang actively contributes to the research community as Program Committee member for top conferences including CCS, ICSE, ISSTA, ASE, WWW, and AAAI. He serves as PC Co-chair for ICFEM 2025, Large Model Safety Workshop 2025, and SAC-SVT 2026. His leadership in the IS2 Lab demonstrates his commitment to advancing intelligent system security through rigorous research.

Ye Hongمشاهده پروفایل

پژوهشگر

• Geoinformatics
• Urban Planning
• Transportation Engineering
• +۳ مورد دیگر

Dr. Ye Hong is a Researcher affiliated with the Institute of Cartography and Geoinformatics at ETH Zürich, specifically within the Department of Geoinformation Engineering. Their work focuses on geospatial data analysis, urban mobility modeling, machine learning applications in transportation systems, and sustainable urban planning. They contribute to open-source tools like Trackintel for mobility analysis and have published extensively on topics such as mobility data synthesis, traffic prediction, and privacy-preserving techniques. Research interests emphasize integrating multi-source geospatial data with deep learning to address challenges in urban sustainability, poverty reduction, and transportation efficiency. Their articles highlight innovations in trajectory generation, uncertainty quantification, and contextual-aware neural networks for spatial-temporal prediction. Dr. Hong’s publications explore the interplay between urban infrastructure, human behavior, and environmental impact. Key themes include accessibility measurement in cities, carbon footprint analysis, and the ethical implications of tracking mobility data. Their work bridges theoretical advancements in geoinformatics with practical applications in urban policy-making and infrastructure design. Notable contributions include frameworks for causal intervention in mobility data, methods to estimate poverty reduction efficiency using remote sensing, and analyses of tracking duration effects on location privacy. These efforts demonstrate a commitment to leveraging geospatial technologies for socially impactful research.

Giuseppina Andresiniمشاهده پروفایل

استادیار

• Data Mining
• Cybersecurity
• Intrusion Detection
• +۲ مورد دیگر

Giuseppina Andresini serves as an Assistant Professor in the Department of Computer Science at the University of Bari Aldo Moro, specializing in deep learning, data mining, cybersecurity, and big data analytics with applications in malware analysis, intrusion detection, and environmental monitoring. Her research bridges critical security challenges and ecological observation through advanced AI techniques. She earned her Laurea degree in Computer Science from the University of Bari Aldo Moro in April 2018, graduating with highest honors (110/110 cum laude) for her thesis on data mining. Her academic foundation directly informs her dual research trajectory in cybersecurity and remote sensing. Andresini's primary research domains include Data Mining, Cybersecurity, Intrusion Detection, Big Data Analytics, and Deep Learning. She pioneers explainable AI (XAI) integration in security systems to enhance transparency while developing novel deep learning architectures for satellite-based environmental monitoring. Her work consistently addresses real-world challenges in network security and forest ecosystem analysis. Analysis of her 15 most recent publications reveals a distinct dual research focus: cybersecurity (65% of output) emphasizing malware detection, intrusion systems, and adversarial robustness; and environmental remote sensing (35%) applying deep learning to satellite imagery for forest health monitoring. She frequently employs ensemble methods, graph neural networks, and semantic segmentation techniques across both domains, with increasing emphasis on explainability and data-centric approaches since 2022.

Manfred Jaegerمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Bayesian Networks
• +۵ مورد دیگر

Manfred Jaeger is an Associate Professor at the Department of Computer Science, Technical Faculty of IT and Design, Aalborg University. His research focuses on Artificial Intelligence , Bayesian Networks , and Graph Neural Networks , with significant contributions to probabilistic reasoning and relational learning. University: Aalborg University School: Technical Faculty of IT and Design Department: Department of Computer Science Jaeger's research explores inductive and probabilistic reasoning , statistical relational learning , and model checking . His recent work integrates heterogeneous graph neural networks with relational Bayesian network encodings to enhance reasoning capabilities in complex systems. Key trends in his publications include relational deep learning , probabilistic inference , and graph-based modeling . He has contributed to applications in social network community detection , reinforcement learning for MDPs , and latent variable models for graph learning . Jaeger collaborates on projects involving incomplete data analysis , modularization of complex tasks , and probabilistic logic . His datasets on multi-multi-instance learning networks are publicly available for research use.

Chris Develderمشاهده پروفایل

دانشیار

• Natural language processing
• Information extraction
• Smart grid
• +۲ مورد دیگر

Chris Develder is an Associate Professor with the research group IDLab in the Department of Information Technology (INTEC) at Ghent University - imec, Ghent, Belgium. He received his MSc degree in computer science engineering and PhD in electrical engineering from Ghent University in Jul. 1999 and Dec. 2003 respectively (as a fellow of FWO). He has held research visitor positions at UC Davis, CA, USA (Jul.-Oct. 2007) and at Columbia University, NY, USA (Jan. 2013 - Jun. 2015). Prof. Develder leads two research teams within the Internet Technology and Data Science Lab (IDLab): one focused on converting text to knowledge (NLP, primarily information extraction using machine learning), and another on data analytics and machine learning for smart grids. His Text-to-Knowledge (T2K) research group has a strong track record in information extraction across various domains including news, human resources, and biomedical applications, as well as text classification tasks such as sentiment analysis. More recently, the group has expanded into conversational agents and generative models for educational applications. With his team, Prof. Develder has published over 200 papers in international journals and conferences including EMNLP, CoNLL, EACL, ACL, ECIR, CIKM, WSDM, WWW, and NIPS. His recent publications demonstrate a growing focus on practical applications of NLP in healthcare (personality style recognition from speech, biomedical adverse drug event extraction), education (question generation, gap-filling exercises), and labor market analysis (career path prediction, skill extraction). His work bridges theoretical advances in machine learning with real-world applications across multiple domains. Prof. Develder actively supervises PhD students and has co-supervised numerous successful doctorates, including Maarten De Raedt (2024), Semere Kiros Bitew (2024), Yiwei Jiang (2024), Amir Hadifar (2023), and Klim Zaporojets (2022). His former students have gone on to work at organizations including EarlyTracks, Zoom, Nokia Bell Labs, Clarivate, and various academic institutions.