جست‌وجوی استاد و پژوهشگر

Huaizu Jiangمشاهده پروفایل

استادیار

• Computer Vision
• Computer Graphics
• Natural Language Processing
• +۵ مورد دیگر

Huaizu Jiang is an Assistant Professor at Khoury College of Computer Sciences, Northeastern University. His research bridges computer vision, graphics, and natural language processing to develop AI systems that understand and reconstruct 3D visual environments. Prior to joining Northeastern, he was a Postdoc Researcher at Caltech and Visiting Researcher at NVIDIA. He holds a Ph.D. from UMass Amherst (advised by Prof. Erik Learned-Miller), and M.E./B.E. degrees from Xi'an Jiaotong University. His research focuses on fundamental challenges in 3D scene understanding, including geometry reconstruction, semantic interpretation, novel view synthesis, motion generation, and optical flow estimation. Core interests span video processing, human-object interactions, multimodal reasoning, and efficient edge-device implementations. Recent publications emphasize diffusion models for motion/scene generation, transformer-based 3D perception, and video interpolation. Key trends include multi-view consistency techniques, text-to-3D synthesis, and efficient real-time algorithms for robotics applications. Awards & Honors: Winner of the VQA Challenge 2020 He advises 15+ graduate students on projects spanning 3D reconstruction, motion synthesis, and vision-language models. His group collaborates with institutions like NVIDIA and Caltech, focusing on generative AI for dynamic scene understanding.

Chenyu Youمشاهده پروفایل

استادیار

• Machine Learning
• Medical Image Analysis
• Computer Vision
• +۱۷ مورد دیگر

Chenyu You is an Assistant Professor in the Department of Applied Mathematics & Statistics and Department of Computer Science at Stony Brook University. He is affiliated with CVLab, AI Institute, and Institute for Advanced Computational Science. His research focuses on principles and practice of trustworthy machine intelligence, emphasizing generalization and reliability in machine learning, with applications to healthcare, biomedical imaging, and cognitive neuroscience. Ph.D. in Electrical Engineering from Yale University (2024) M.S. in Electrical Engineering from Stanford University (2019) B.S. in Electrical Engineering from Rensselaer Polytechnic Institute (2017) His research spans three major areas: Efficient World Foundation Models (task-agnostic pretraining, scalable adaptation), Learning with Imperfect Data (label scarcity, class imbalance), and Biomedical Foundation Models (large-scale medical AI agents). Applied work includes healthcare, biomedical imaging, and cognitive neuroscience. Recent publications (2025) include breakthroughs in sparse coding (ICML), cycle-consistent diffusion models (ICCV), optimal transport for survival analysis (MICCAI), and prompt theory (ACL). His team addresses challenges in trustworthy AI, spurious correlation mitigation, and multi-modality robustness. Scientific recognition includes Excellence in Teaching Award (2025) , World's Top 2% Scientists (2024) , and multiple IEEE TMI Platinum Distinguished Reviewer awards. He advises students Qin Ren and Yifan Wang, with alumni pursuing roles at Two Sigma, Amazon Science, and top PhD programs. His lab collaborates with leading institutions and actively seeks motivated students for flexible-start positions. He serves as Associate Editor for IEEE Transactions on Medical Imaging and Area Chair for major conferences like MICCAI and NeurIPS.

• Business Process Management
• Process Mining
• Complex Event Processing
• +۵ مورد دیگر

Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data

Lu Suمشاهده پروفایل

دانشیار

• Internet of Things
• Cyber-physical Systems
• Wireless and Mobile Sensing
• +۷ مورد دیگر

Lu Su is an Associate Professor at the School of Electrical and Computer Engineering , Purdue University , with prior appointments at SUNY Buffalo . His research spans Internet of Things , cyber-physical systems , mmWave sensing , and crowd-sourced data validation , focusing on quality-of-information aware distributed sensing and security in autonomous systems . Ph.D. in Computer Science (2013) and M.S. in Statistics (2012) from University of Illinois at Urbana-Champaign M.E. and B.E. from Harbin Institute of Technology Research Interests: IoT , cyber-physical systems , crowd sensing , security and privacy , and machine learning for sensor networks. His work addresses quality-aware information integration , adversarial attacks in autonomous vehicles , and privacy-preserving crowd-sourced systems . Recent publications focus on mmWave-based sensing (e.g., 3D pose reconstruction), federated learning (driver monitoring), and data poisoning attacks in crowd-sourced systems. His research also extends to traffic optimization and human activity recognition using wireless networks. Professional Roles: Workshop Chair (INFOCOM 2023, 2022) TPC Vice Chair (INFOCOM 2021) Program Committee Member for top conferences Editorial Board, ACM Transactions on Sensor Networks Teaching: Courses on Embedded Systems , Internet of Things , and Network Concepts at both undergraduate and graduate levels.

• Computational Biology
• Structural Biology
• Bayesian Inference
• +۱۰ مورد دیگر

Dr. Roy Lederman is an Assistant Professor at the Department of Statistics and Data Science , Yale University. He is affiliated with the Quantitative Biology Institute (QBio) , the Applied Math Program , the Institute for Foundations of Data Science (FDS) , and the Wu Tsai Institute (WTI) . He was awarded the Sloan Research Fellowship (2023) . He previously held a Gibbs Assistant Professorship at Yale (2014-2015) and a postdoc at Princeton University (2015-2018) . Education: PhD in Applied Mathematics, Yale University (2014); dual BSc in Physics and Electrical Engineering, Tel-Aviv University. Teaching: Courses include Computational Tools for Data Science, Signal Processing, and Mathematical Machine Learning. Research Areas: Dr. Lederman works at the intersection of computational biology , structural biology , Bayesian inference , numerical analysis , and machine learning . His recent work focuses on cryo-EM and hyper-molecules for studying molecular heterogeneity, alternating diffusion for common variable recovery, and Zernike polynomials for 3D imaging. He also develops Hamiltonian Monte Carlo methods and randomized DNA sequencing algorithms . Publications Trends: His publications (15 most recent) emphasize structural biology and cryo-EM applications, machine learning (Bayesian deep learning, diffusion maps), numerical analysis (Fourier/Laplace transforms), and computational biology (DNA sequencing algorithms). Key sub-fields include heterogeneity analysis , manifold learning , Hamiltonian Monte Carlo , and Zernike polynomials . Scientific Awards: Sloan Research Fellow (2023) Dr. Lederman actively mentors graduate students and postdocs at Yale, and co-organizes the One World Cryo-EM seminar series . His lab develops open-source software (e.g., prolate function implementation ) and explores theoretical bounds on transforms and common variable recovery in multi-sensor experiments.

Joris M. Mooijمشاهده پروفایل

استاد

• Causality
• Causal Modeling
• Causal Discovery
• +۵ مورد دیگر

Joris M. Mooij is a Professor of Mathematical Statistics at the Korteweg-De Vries Institute of the University of Amsterdam, Netherlands. His research focuses on causality, spanning causal modeling, discovery, and inference with applications in biology, medicine, fairness, and business analytics. He combines mathematical modeling with statistical and algorithmic approaches in his work. Dr. Mooij received his PhD with honors from Radboud University Nijmegen in 2007, focusing on approximate inference in graphical models. After postdoctoral work at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany, he obtained an NWO VENI grant in 2011 for further postdoctoral research at Radboud University. He became Assistant Professor at the University of Amsterdam's Informatics Institute in 2013, was promoted to Associate Professor in 2017, and became a full Professor of Mathematical Statistics in 2020. Dr. Mooij's research centers on causal inference, with particular expertise in structural causal models, cyclic causal systems, and causal discovery algorithms. His work addresses fundamental questions about when causal relationships can be identified from observational data and how to develop robust causal discovery methods that work in complex real-world settings with latent variables, cycles, and selection bias. He has made significant contributions to understanding the limitations of existing causal discovery approaches and developing new methods that overcome these limitations. His research group organizes the Amsterdam Causality Meeting series and develops theoretical frameworks for causal modeling that encompass both acyclic and cyclic systems. Dr. Mooij has collaborated extensively on applications of causal methods in biological systems, including protein signaling networks and gene expression data. The group's recent work explores performative predictions, causal domain adaptation, and robust causal discovery methods that account for selection bias and missing data. Dr. Mooij has received numerous awards for his research, including: Best paper award at UAI for "Establishing Markov equivalence in cyclic directed graphs" IEEE Geoscience and Remote Sensing Society 2011 Letters Prize Paper Award ICML Test of Time Honorable Mention Best student paper award at UAI 2010 He has secured competitive research funding through an NWO VENI grant, NWO VIDI grant, and an ERC Starting Grant, which supported the establishment of his research group consisting of 3 PhD students and 3 postdocs focused entirely on causality. Dr. Mooij has supervised several PhD students, including Tineke Blom, whose work on "Causality and Independence in Perfectly Adapted Dynamical Systems" significantly influenced his thinking about causality in complex systems. He has co-taught the MasterMath course on Causality and published lecture notes titled "A Mathematical Introduction to Causality." His research continues to push the boundaries of causal inference methodology and its applications across diverse scientific domains.

Wei Dingمشاهده پروفایل

استاد

• Knowledge Discovery
• Data Mining
• Machine Learning
• +۷ مورد دیگر

Wei Ding is a Professor in the Department of Computer Science at the University of Massachusetts Boston (UMass Boston). She earned her Ph.D. in Computer Science from the University of Houston in 2008. From 2019 to 2023, she served as a Program Director at the National Science Foundation's Division of Information and Intelligent Systems (IIS), overseeing programs in Information Integration, Smart Health, Deep Learning Foundations, and Scalable Systems. Her research integrates knowledge discovery, data mining, and machine learning with applications spanning health sciences, astronomy, geosciences, and environmental sciences. She employs advanced techniques like spatio-temporal modeling, deep neural networks, and semantic analysis to address complex real-world problems such as disease subtyping, physical activity prediction, and environmental forecasting. Her work emphasizes interdisciplinary collaboration and societal impact. Analysis of her recent publications reveals a focus on AI-driven healthcare solutions (e.g., neuroimaging biomarkers, disorder diagnosis), fundamental ML advancements (e.g., generalization, GAN stability), and cross-domain applications (e.g., climate forecasting, animal behavior analysis). Recurring themes include low-data learning, interpretability, and scalable algorithms. Awards & Honors: IEEE Fellow (2023) NSF Director's Award (2022) WISAY Distinguished Woman in Science Award, Yale University (2019) AI for Earth Award (2018) Best Paper Awards (ICTAI 2011, ICCI 2010) Advising & Grants: She mentors PhD and Master’s students in the Knowledge Discovery Lab (KDLab), with alumni at institutions like Facebook, Google, and McKinsey. Her research is funded by NSF, NIH, NASA, and DOE, including: NIH R01: Predicting youth physical activity (2016) NSF EAGER: Machine learning for cancer subtyping (2017) NIH R01: Accelerometer/gyroscope data for activity estimation (2022) Leadership: She directs the KDLab and co-founded the Women in Sciences Club (WINS). She serves as Associate Editor for ACM TKDD, TIST, and KAIS journals.

Jiaojiao Jiangمشاهده پروفایل

مدرس ارشد

• AI for Cybersecurity
• Misinformation Detection
• Network Analysis
• +۳ مورد دیگر

Dr. Jiaojiao Jiang is a Senior Lecturer in the School of Computer Science and Engineering at the University of New South Wales (UNSW). She holds a Ph.D. from Deakin University (Melbourne, Australia) and has published over 45 articles with 1,100+ citations. Her research focuses on AI-driven cybersecurity solutions, particularly misinformation detection and modeling information propagation dynamics. She is affiliated with UNSW's Sydney campus and can be contacted at jiaojiao.jiang@unsw.edu.au . Education: Ph.D., Deakin University, 2010s Research Interests: Artificial Intelligence applications in cybersecurity Misinformation detection and network analysis Machine learning for network security Data privacy in IoT systems Publications span topics like fake news detection via graph neural networks, multiplex network robustness, and cyber threat intelligence frameworks. Her work bridges theoretical network science with practical cybersecurity challenges.

Taein Kwonمشاهده پروفایل

پژوهشگر ارشد

• Egocentric Vision
• Action Recognition
• Contextual AI
• +۶ مورد دیگر

Taein Kwon is a postdoctoral research fellow at the Visual Geometry Group (VGG) within the Department of Engineering Science at the University of Oxford, working under Prof. Andrew Zisserman. Previously, he completed his PhD at ETH Zurich under Prof. Marc Pollefeys and earned master's and bachelor's degrees from UCLA and Yonsei University, respectively. His educational background includes: Bachelor's in Electrical Engineering from Yonsei University, Seoul, Korea Master's degree from UCLA PhD from ETH Zurich (defended July 2024) His research spans Egocentric Vision, Action Recognition, Hand-object Interaction, Video Understanding, AR/VR, and Multi-modal Learning, with emphasis on first-person perspective analysis for AI assistants and human-computer interaction. His work integrates 3D reconstruction, pose estimation, and multimodal signals to model complex human activities and physical interactions. Analysis of his 2021-2025 publications reveals a consistent focus on egocentric vision datasets (H2O, HoloAssist, EgoPressure) and novel frameworks for hand-object interaction, action recognition, and gesture understanding. His research demonstrates strong interdisciplinary connections between computer vision, robotics, and human-centered AI, with increasing emphasis on pressure sensing, co-speech gestures, and cross-modal alignment. His scientific recognition includes: CVPR Egovis 2022/2023 Distinguished Paper Award for HoloAssist (July 2024) SNSF Postdoc.Mobility fellowship (May 2024) He actively mentors students on egocentric vision projects, supervising master's theses, semester projects, and collaboration initiatives leading to publications at top conferences. His research is supported by the SNSF fellowship and industry collaborations with Meta Reality Labs and Microsoft Research. As part of Oxford's Visual Geometry Group, he contributes to cutting-edge computer vision research while maintaining strong ties with ETH Zurich's computer vision community through ongoing collaborations and dataset development efforts.

Xuming Heمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۹ مورد دیگر

Xuming He is an Associate Professor at the School of Information Science and Technology (SIST), ShanghaiTech University, where he leads the PLUS Lab. His research spans computer vision and machine learning with a focus on developing algorithms that operate effectively under limited supervision and evolving data conditions. His core research interests include weakly-supervised and few-shot learning for scenarios with sparse annotations, continual learning frameworks for knowledge retention during sequential task acquisition, semantic segmentation techniques for scene understanding, and multimodal vision-language representations. He emphasizes interpretable machine learning to build transparent AI systems capable of human-understandable reasoning, addressing critical challenges in model trustworthiness and deployment reliability. Recent publications reveal strong trends toward novel class discovery in long-tailed recognition scenarios, physics-informed generative modeling for scientific applications, and robust segmentation under distribution shifts. His work increasingly integrates large language models for multimodal reasoning while maintaining focus on efficiency in resource-constrained environments like robotic grasping and medical imaging analysis. He actively mentors students, having supervised Qian He to PhD completion and Chuanyang Hu to Master's degree in 2023. He welcomes prospective graduate students through ShanghaiTech's Computer Science & Technology program and offers undergraduate research projects requiring minimum six-month commitments. The PLUS Lab under his direction drives innovation in learning under supervision constraints, with recent work spanning medical tumor analysis, cross-view geolocation, photonic computing, and semiconductor design verification. The lab's research bridges theoretical advances with practical applications across healthcare, robotics, and scientific discovery domains.

Sara Magliacaneمشاهده پروفایل

استادیار

• Causality
• Causal Representation Learning
• Causal Discovery
• +۴ مورد دیگر

Sara Magliacane is an Assistant Professor at the University of Amsterdam and a Research Scientist at the MIT-IBM Watson AI Lab . She leads research at the intersection of causality and machine learning , focusing on improving AI robustness, generalization, and safety through causal reasoning. Her work spans causal representation learning , causal discovery , and causality-inspired ML in domains like reinforcement learning and dynamical systems. PhD in Artificial Intelligence (2017), VU Amsterdam MSc in Computer Engineering (2011), Politecnico di Milano/Torino BSc in Computer Engineering (2008), Università degli Studi di Trieste Her research explores causal variable identification from high-dimensional data (e.g., images, sequences) and causal graph discovery for domain adaptation. Methods include CITRIS , FANS-RL , and SNAP , with applications in embodied AI and biomedical data. The group emphasizes theoretical guarantees and scalable algorithms for real-world systems. Recent work trends include temporal causal modeling , intervention-efficient learning , and nonstationary reinforcement learning . Publications cover topics like causal discovery in partially observed settings , causal graph pruning , and sample-efficient concept learning , often combining neurosymbolic approaches with deep learning. Scientific Awards : ELLIS Scholar Sara supervises PhD students across universities (UvA, University of Pisa) and collaborates with institutions like TU Delft , Harvard , and IBM Research . She co-organizes workshops at premier conferences (NeurIPS, ICML, AISTATS) and teaches causality courses at the University of Amsterdam and Harvard Data Science Initiative. Her lab, Amsterdam Machine Learning Lab (AMLab) , investigates causal structure in embodied agents , safe reinforcement learning , and hybrid dynamical system modeling . The group maintains active partnerships with institutions such as MIT-IBM Watson AI Lab , Qualcomm , and Adyen .

Ridha Khedriمشاهده پروفایل

استاد

• Cybersecurity
• Formal Methods in Software Engineering
• Information Security Ontology
• +۶ مورد دیگر

Ridha Khedri is a Professor in the Department of Computing and Software at McMaster University . His research spans formal methods in software engineering, cybersecurity, information security ontology, network segmentation, and covert channels analysis. Full Professor since 2000 Contact: khedri@mcmaster.ca Research Interests : Prof. Khedri develops algebraic frameworks for software security, with recent work on network segmentation , ontology engineering , and covert channel detection . His interdisciplinary efforts include hybrid machine learning-ontology models for environmental predictions (e.g., river ice breakup) and digital twin healthcare systems . Article Trends : His 15 most recent works (2016-2025) focus on network security , knowledge representation , and formal verification . Notable trends include automated security testing , ontology modularization , and multi-context reasoning systems . Teaching : He has taught courses like Software Design (CAS 703), Discrete Mathematics (SFWRENG 2DM3), and Algebraic Methods in Software Engineering (CAS 738) since 2017.

• Machine Learning
• Domain Adaptation
• Transfer Learning
• +۳ مورد دیگر

Dr. Sinno Jialin Pan is a leading researcher in machine learning and artificial intelligence at Nanyang Technological University, Singapore. His work focuses on domain adaptation, sentiment analysis, and efficient neural network optimization. Key research areas: Machine Learning, Domain Adaptation, Reinforcement Learning, Sentiment Analysis Recent publications demonstrate expertise in time-series classification (2025) using hierarchical domain adaptation, LLM efficiency (2025) through expert pruning, and graph generation (2024) via spectral diffusion. His work spans both theoretical advancements and practical applications in neural architecture optimization and adversarial learning. Scientific contributions include: 2025: Virtual-label hierarchical domain adaptation 2024: Spectral diffusion for graph generation 2024: Multilingual jailbreak analysis in LLMs Current trends show increasing focus on large language model optimization and robust neural architectures , with applications in fault diagnosis, recommender systems, and misinformation detection.

Oscar Mendez Maldonadoمشاهده پروفایل

مدرس

• Robotics
• Artificial Intelligence
• Computer Vision
• +۳ مورد دیگر

Oscar Mendez Maldonado is a Lecturer in Robotics and Artificial Intelligence at the University of Surrey's School of Computer Science and Electronic Engineering, affiliated with the Robotics Department and CVSSP Centre. He holds a PhD (2018) and BEng (2013) from the University of Surrey. His research focuses on Machine Learning, Computer Vision, and Robotics, with emphasis on autonomous systems, localisation, and SLAM applications. Key projects include the Autonomous Valet Parking (AVP) system for indoor navigation and the SMILE project for sign language assessment using AI. He has supervised students like James Ross (Autonomous Vehicles), Xihan Bian (Reinforcement Learning), and Nimet Kaygusuz (Visual Odometry). Notable achievements include the Sullivan Thesis Prize (2018) and impactful publications in IEEE conferences (e.g., ICRA, CVPR, IROS). Research spans topics like 3D hand pose estimation via diffusion models, graph-based visual odometry fusion, and Raman spectroscopy for localisation. He contributes to open-source tools (e.g., RaSpectLoc GitHub) and collaborates with industry partners like Parkopedia. His work bridges theoretical advances with real-world applications in autonomous systems and healthcare.

Adín Ramírez Riveraمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Representation Learning
• +۶ مورد دیگر

Adín Ramírez Rivera is a Professor in the Digital Signal Processing and Image Analysis (DSB) group at the Department of Informatics, University of Oslo. His research focuses on representation learning and computer vision, particularly exploring machine learning methods to describe and understand visual data. He is a Senior Member of the IEEE and a member of the ELLIS Society. Education : PhD from Kyung Hee University's Image Processing Lab, South Korea; Bachelor's degree in Engineering from Universidad de San Carlos de Guatemala, majoring in Computer Science and Systems Engineering. Ramírez Rivera's research spans diverse computer vision tasks including facial analysis, object detection, image enhancement, and vision transformers. His work emphasizes self-supervised learning, fair representation learning, and novel neural network architectures for image segmentation and classification. Recent publications highlight trends in vision transformers, crowd counting, facial expression recognition, and fair representation learning. His articles frequently address statistical modeling, feature extraction, and deep learning techniques for visual tasks. Scientific Awards : Senior Member of the IEEE, Member of the ELLIS Society. He collaborates with researchers across institutions, contributing to projects involving anomaly detection, multilingual translation, and astrophysical modeling. His lab affiliations include the Digital Signal Processing and Image Analysis group and the Section for Machine Learning at the University of Oslo.