جست‌وجوی استاد و پژوهشگر

Jiajun Wuمشاهده پروفایل

استادیار

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

Jiajun Wu is an Assistant Professor of Computer Science and, by courtesy, of Psychology at Stanford University. He holds multiple affiliations including membership in Bio-X, Faculty Affiliate status at the Institute for Human-Centered Artificial Intelligence (HAI), and membership in both the Wu Tsai Human Performance Alliance and Wu Tsai Neurosciences Institute. Dr. Wu earned his Ph.D. and S.M. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology before joining Stanford. Dr. Wu's research program focuses on creating AI systems that understand and interact with the physical world through the integration of computer vision, machine learning, robotics, and cognitive science. His work emphasizes physics-based modeling combined with deep learning to develop systems capable of perceiving, reasoning about, and predicting physical interactions. Key research areas include 3D scene understanding, neurosymbolic AI approaches, multimodal perception (combining vision, sound, and language), and embodied intelligence for robotics applications. His lab develops novel frameworks that bridge the gap between neural networks and symbolic reasoning to create more interpretable and robust AI systems. Analysis of Dr. Wu's recent publications reveals a strong trajectory toward integrated multimodal understanding for embodied AI. His work increasingly combines vision, sound, and language processing with physical reasoning to create systems that can interact meaningfully with the physical world. There's a clear progression from foundational computer vision research toward practical robotics applications, with significant emphasis on foundation models for robotics, sim2real transfer techniques, and creating comprehensive datasets for embodied AI research. Dr. Wu's exceptional contributions have been recognized with numerous prestigious awards including the NSF CAREER award (2024), Young Investigator Programs from ONR (2024) and AFOSR (2023), the Okawa research grant (2024), and being named to IEEE Intelligent Systems' 'AI's 10 to Watch' (2024). He has received multiple best paper awards at leading conferences including ICRA (2024), SIGGRAPH Asia (2023), and CoRL (2023). Dr. Wu actively mentors a large cohort of students across multiple levels, serving as primary advisor for doctoral candidates, master's students, and numerous independent researchers. His research is supported by substantial funding from major technology companies including Google, Meta, Amazon, Samsung, and J.P. Morgan, as well as government agencies like NSF, ONR, and AFOSR, reflecting the significance and impact of his work in physical AI and multimodal perception systems. Dr. Wu leads a dynamic research group at Stanford that collaborates extensively with the Wu Tsai Neurosciences Institute and Institute for Human-Centered AI. Current projects include developing neurosymbolic models for computer graphics, creating multisensory datasets like OBJECTFOLDER 2.0 for sim2real transfer in robotics, and building foundation models for embodied intelligence that can understand and manipulate objects with human-like physical intuition.

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

Dima Damen is a Professor of Computer Vision at the School of Computer Science, University of Bristol, where she leads the Machine Learning and Computer Vision Group. She also serves as a Senior Research Scientist at Google DeepMind. As an EPSRC Early Career Fellow (2020-2025) and a Fellow of ELLIS for Europe, her research focuses on advancing computer vision, particularly in egocentric (first-person) vision, video understanding, and action recognition. Her educational background and professional journey have positioned her as a leader in the field of computer vision, with a particular emphasis on understanding human activities from wearable cameras. She has received numerous awards including Best Paper at ACCV 2024 and Outstanding Paper at ICASSP 2021. Professor Damen's research interests span multiple areas of computer vision and machine learning. She specializes in egocentric vision, where she has made significant contributions to understanding human activities from first-person perspectives. Her work explores video understanding, action recognition, hand-object interactions, and the development of vision-language models that can interpret and generate instructions from visual data. She has pioneered approaches to unique video captioning, long video understanding through active memory representations, and spatial reasoning from egocentric videos. Her research often bridges the gap between theoretical computer vision and practical applications in human-centered AI. Her recent publications demonstrate a strong focus on egocentric vision, with papers like "AMEGO: Active Memory from long EGOcentric videos" (ECCV 2024) and "HOI-Ref: Hand-Object Interaction Referral in Egocentric Vision" (2024) advancing the state of the art in understanding long-form first-person videos. She has also contributed to vision-language models with works like "ShowHowTo: Generating Scene-Conditioned Step-by-Step Visual Instructions" (CVPR 2025) and "It's Just Another Day: Unique Video Captioning by Discriminitave Prompting" (ACCV 2024, Best Paper). Her research shows a consistent trajectory toward building systems that can understand human activities in natural environments with human-like capabilities. Professor Damen has received significant recognition for her work, including: EPSRC Early Career Fellow (2020-2025) ELLIS Fellow for Europe (Nov 2024) Best Paper at ACCV 2024 Outstanding Paper at ICASSP 2021 (awarded to only 3 out of 1700 papers) Outstanding Reviewer for CVPR 2020 and 2021 Program Chair for ICCV 2021 She has successfully advised numerous PhD students and postdoctoral researchers, many of whom have gone on to prestigious positions in academia and industry. Her group has secured significant research funding including the EPSRC Programme Grant Visual AI and the EPSRC UMPIRE grant. She actively collaborates with industry partners including Google DeepMind, Adobe, and Meta, ensuring her research has practical impact. Professor Damen leads the Machine Learning and Computer Vision Group at the University of Bristol, which focuses on egocentric vision, video understanding, and the development of vision-language models. The group has created influential datasets like EPIC-KITCHENS, which has become a standard benchmark in egocentric vision research. Her team regularly participates in and organizes workshops at major computer vision conferences including CVPR, ICCV, and ECCV.

David J. Crandallمشاهده پروفایل

استاد

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

David J. Crandall is the Luddy Professor of Computer Science at Indiana University's Luddy School of Informatics, Computing, and Engineering. He serves as Director of the Luddy Artificial Intelligence Center and leads the IU Computer Vision Lab. With joint appointments in Informatics, Cognitive Science, Data Science, and Statistics, his work spans computer vision, machine learning, and AI. He holds a Ph.D. from Cornell University and previously worked at Eastman Kodak Research Labs. His research focuses on developing statistical and machine learning methods to analyze visual information, including object recognition, human activity analysis in video, 3D reconstruction, social media mining, and computational studies of visual attention. Key applications include egocentric vision systems, social robotics for healthcare, and cross-disciplinary collaborations with developmental psychology. Recent publications demonstrate strong emphasis on egocentric video analysis (Ego4D), human-robot interaction (CHI/HRI), and explainable AI (IJCAI). Medical imaging, nanoscale security systems, and computational social science represent emerging interdisciplinary directions. His work consistently integrates deep learning with real-world applications in health, environmental monitoring, and cultural analytics. Tracy M. Sonneborn Award (2024) Distinguished Member of the ACM (2023) Luddy Professorship (2021) NSF CAREER Grant (2013) Trustees Teaching Award (2017) He has advised over 20 Ph.D. graduates, with current students working on computer vision, robotics, and AI ethics. Major grants include $20M for the NSF AI Institute on Engaged Learning, $4.4M for trusted AI research, and funding from NIH, Google, ONR, and NASA. He directs the Computer Vision Lab and collaborates with Selma Sabanović's robotics group on social agents for older adults.

Shinji Watanabeمشاهده پروفایل

دانشیار

• Automatic Speech Recognition
• Speech Enhancement
• Spoken Language Understanding
• +۳ مورد دیگر

Shinji Watanabe is an Associate Professor at Carnegie Mellon University's Language Technologies Institute and a Courtesy Professor in the Electrical and Computer Engineering department. He holds a Ph.D. (Dr. Eng.) from Waseda University, Japan, and has held research roles at NTT Communication Science Laboratories, Mitsubishi Electric Research Laboratories (MERL), and Johns Hopkins University. His research focuses on automatic speech recognition, speech enhancement, and machine learning for speech processing. Watanabe has published over 300 peer-reviewed papers and received the Best Paper Award at IEEE ASRU 2019. His work emphasizes robust speech processing in challenging environments, multilingual models, and neural audio codecs. He leads the ESPnet toolkit development for end-to-end speech processing systems and contributes to technical committees like IEEE SLTC and APSIPA SLA. Recent research trends include streaming speech systems, universal speech enhancement (URGENT challenges), and fusion of discrete speech units with self-supervised representations. He explores scalable speech foundation models through benchmarks like ML-SUPERB 2.0 and investigates cross-modal audio-visual processing in challenges like MISP 2025. Education : B.S., M.S., Ph.D. (Waseda University) Affiliations : CMU Language Technologies Institute, CMU ECE, Former roles at MERL and Johns Hopkins Key Projects : ESPnet, OpenWhisper-Style Models, URGENT Challenge Frameworks

• Audio Signal Processing
• Spatial Audio Technology
• Medical Engineering
• +۳ مورد دیگر

Craig Jin is an Associate Professor at the University of Sydney, leading the CARlab (Computing and Audio Research Laboratory) and Spatial Audio Research initiatives within the School of Electrical and Computer Engineering. He holds a BS from Stanford University, an MS from Caltech, and a PhD from the University of Sydney. His work focuses on immersive audio technologies, biomedical signal processing, and assistive technologies for sensory augmentation. Research interests include spatial audio reproduction, binaural processing, acoustic sensing for accessibility, and machine learning applications in signal processing. Key contributions span HRTF interpolation, noise reduction algorithms, and acoustic touch systems for the visually impaired. Recent projects include real-time MRI analysis of vocal tract dynamics and sparse recovery techniques for sound field reconstruction. His publications span over 150 peer-reviewed articles in journals like IEEE Transactions on Audio, Speech, and Language Processing, and conferences such as ICASSP. He advises four current PhD/Master’s students on projects like predictive gesture tracking, voice disorder classification, and magnetic resonance imaging techniques.

Berrak Sismanمشاهده پروفایل

استادیار

• Artificial Intelligence
• Speech Processing
• Machine Learning
• +۳ مورد دیگر

Berrak Sisman is an Assistant Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, affiliated with the Data Science and AI Institute and the Center for Language and Speech Processing (CLSP). She leads the Speech & Machine Learning Lab (SmILe Lab), focusing on AI-driven speech technologies. She received her PhD from the National University of Singapore in 2020 and was previously a tenure-track faculty member at the University of Texas at Dallas (2022–2024). Research Interests: Her work spans artificial intelligence, speech synthesis, voice conversion, emotion analysis in speech, medical speech applications, and secure speech technology. She develops neural models for expressive and adaptive speech processing. Publications: Her recent articles (2024–2025) emphasize speech emotion recognition, zero-shot prosody control, accent conversion, and disentangled representations in TTS, reflecting a focus on cross-modal learning, robustness, and real-world applications. Awards & Grants: NSF CAREER Award (2024) Amazon Faculty Research Award (2022) Singapore Ministry of Education Award (2021) A*STAR Singapore International Graduate Award (2016–2020) Leadership: She directs the SmILe Lab, recruiting PhD/Master’s students for projects in neural speech modeling. Her grants include NSF and Amazon funding for voice conversion and emotion synthesis research.

Adam Finkelsteinمشاهده پروفایل

استاد

• Computer Science
• Audio Processing
• Image Processing
• +۳ مورد دیگر

Adam Finkelstein is a Professor in the Department of Computer Science at Princeton University, where he has been a faculty member since 1997. He holds a PhD and Master's in Computer Science from the University of Washington and a dual degree in Physics and Computer Science from Swarthmore College. Finkelstein is renowned for his interdisciplinary work at the intersection of computer graphics, audio processing, and machine learning, and he co-organized the Art of Science exhibition at Princeton. Education: PhD, Computer Science, University of Washington MS, Computer Science, University of Washington BA, Physics and Computer Science, Swarthmore College His research spans audio processing (e.g., speech enhancement, voice conversion, audio metrics), computer graphics (e.g., line drawing algorithms, stylized rendering, image manipulation), and machine learning (e.g., self-supervised learning, differentiable programming). His work often bridges technical and creative domains, exemplified by collaborations at Pixar and Adobe Creative Technologies Lab. Recent publications highlight advancements in audio super-resolution and voice conversion using deep learning frameworks, as well as stylized line rendering for animated 3D models. His contributions to perceptual audio metrics and shader optimization further underscore his impact on human-centric computational systems. Scientific Awards: NSF CAREER Award Alfred P. Sloan Fellowship Fellow of the Association for Computing Machinery (ACM) Finkelstein has secured foundational grants for his research and actively mentors students, though no specific advisees are listed. He also explores collaborative tools for internet music performance, reflecting his broader interest in distributed systems and user interfaces.

Christian Poellabauerمشاهده پروفایل

استاد

• Mobile Sensing
• Data Analytics
• Healthcare Technology
• +۷ مورد دیگر

Christian Poellabauer is a Professor at Florida International University (FIU) in the Knight Foundation School of Computing and Information Sciences, serving as Interim Associate Dean for Research and Graduate Studies in the College of Engineering & Computing. He holds a Ph.D. from Georgia Institute of Technology (2004) and a Diplom-Ingenieur from TU Vienna (1998). His research focuses on mobile sensing, data analytics, and healthcare technologies, leading the MOSAIC Lab which develops solutions for healthcare, IoT, and smart cities. He previously led the Mobile Computing Lab at the University of Notre Dame and held leadership roles in data science institutes. Research Interests: His work spans digital biomarkers for neurodegenerative diseases, speech analysis for mental health, wearable device authentication, and wireless sensor networks. The MOSAIC Lab addresses challenges like real-time sensor data analysis on constrained devices and translating insights into clinical applications. Teaching: He has taught courses on Operating Systems, Mobile Computing, and Smart Health at both FIU and Notre Dame. Recent courses include COP4610 (Operating Systems Principles) and COP5614 (Graduate Operating Systems) at FIU. Service: He serves as Associate Editor for IEEE Transactions on Network Science and Engineering, and has organized conferences like ICNC 2023 and IEEE MASS 2021. His academic service includes roles on editorial boards and technical program committees for major conferences in distributed computing and networking. Advising & Labs: Advises current Ph.D. students in areas like multi-modal sensing for affective computing and mental health crowdsensing. Past students have pursued roles in academia and industry (e.g., Rose-Hulman Institute of Technology, Facebook, Microsoft). The MOSAIC Lab collaborates on projects like digital clinical outcome assessments and motor impairment detection.

• Machine Learning
• Signal Processing
• Speech Processing
• +۳ مورد دیگر

Dr. Andrew Hines is a Researcher at the School of Computer Science, University College Dublin, specializing in machine learning applications for signal processing in speech, audio, and video domains. His work focuses on Quality of Experience (QoE) modeling, speech quality assessment, and immersive media analysis. He has held leadership roles in European COST Actions like Qualinet and CryptoAction, and previously worked in industry as a Director of Engineering. University: University College Dublin Role: Director of Research, Innovation and Impact Key Collaborations: IEEE (Senior Member), Audio Engineering Society (Ireland) Research interests center on machine learning for QoE optimization, audio-visual integration, and healthcare applications like heart sound classification and stroke rehabilitation. His recent publications explore self-supervised learning, neural speech codecs, and contextual factors in speech/audio quality assessment. Scientific contributions include awards like IEEE Senior Membership, and his work spans both academic research and industrial engineering in finance and aviation sectors. He leads the QxLab research team at UCD and develops open-source platforms such as WARP-Q and AQP for quality metrics.

• Artificial Intelligence
• Machine Learning
• AI Alignment
• +۲ مورد دیگر

Michael Riegler is a Researcher at the AI Department, Simula Research Laboratory , focusing on interdisciplinary applications of Artificial Intelligence in healthcare, sports analytics, and multimedia systems. His work bridges Machine Learning , AI Alignment , and Applied AI across clinical and real-world domains. Key Affiliations: Simula Research Laboratory (AI Department Head) Research Themes: Explainable AI in medicine, multimodal data analysis, and AI-driven health monitoring Research Interests include: Developing AI/ML algorithms for medical imaging (e.g., polyp detection, embryo analysis) Addressing missing data challenges in healthcare through novel imputation techniques Creating multimodal virtual avatars for investigative interview training Designing edge AI systems for sports analytics and sustainable fishing Recent Publications highlight collaborations with institutions in Norway and globally, with a focus on: Medical Applications: Polyp segmentation, ECG analysis, and explainable models for disease detection Sports Analytics: Athlete performance prediction and soccer video processing Data Infrastructure: Lifelogging datasets (ScopeSense), semantic representation frameworks Labs & Teams include leadership in Simula’s AI Department and participation in projects like Medico Multimedia Task , ImageCLEF , and MediaEval workshops. His work emphasizes responsible AI innovation in public sectors and privacy-preserving systems for edge environments.

• Computer Vision
• Machine Learning
• Medical Imaging
• +۳ مورد دیگر

Stella Yu is a Professor specializing in computer vision, machine learning, and AI applications across medical imaging, robotics, and wildlife recognition. She emphasizes adaptive advising tailored to individual student strengths, with regular one-on-one and group meetings to discuss research progress and paper presentations. Yu's research group focuses on unsupervised learning, deep learning workflows, and interdisciplinary applications such as MRI reconstruction, meibography analysis, and aerial wildlife monitoring. Her work bridges theoretical advancements with practical tools like DeepInPy for inverse problems. She strongly advocates for teaching experience through GSI roles and ensures students have conference funding to present findings at top venues. Education Expectations: Regular literature review, lab presence for junior students, and clear authorship protocols. Key Research Themes: Feature learning, medical AI, computational optics, and wildlife population surveys. Her lab promotes a collaborative environment with structured feedback loops, emphasizing both technical rigor and creative problem-solving. Current projects include BatVision for 3D spatial navigation using audio signals, and AI-driven solutions for dry eye diagnosis and building information modeling.

Paul Taeleمشاهده پروفایل

عضو هیئت علمی

• Sketch Recognition
• Human-Computer Interaction (HCI)
• Artificial Intelligence
• +۵ مورد دیگر

Paul Taele is an Instructional Assistant Professor and Deputy Lab Director in the Sketch Recognition Lab at Texas A&M University's Department of Computer Science & Engineering. He holds a Ph.D. (2019), M.S. (2010), and dual B.S. degrees in Computer Science and Mathematics from the University of Texas at Austin (2006). His research focuses on sketch recognition, haptics, and intelligent interfaces for education and accessibility, with notable work in mid-air gesture recognition, educational sketching tools, and assistive technologies for disabilities. He has contributed to projects like Kanji Workbook , Hashigo , and HaptiMoto , and has published over 50 peer-reviewed articles across venues like CHI, IUI, and AAAI. Taele has received awards including the NSF Student Travel Grant (2014) and Ford Foundation Honorable Mention (2015). He teaches courses in capstone design, programming, and sketch recognition, and mentors students across all academic levels through strict eligibility criteria for research collaborations. Education : Ph.D. Computer Science, Texas A&M University (2019) M.S. Computer Science, Texas A&M University (2010) B.S. Computer Science & Mathematics, University of Texas at Austin (2006) Concentration in Mandarin Chinese, National Chengchi University (2007) Research Interests : Taele's work bridges HCI and AI to create accessible educational interfaces. His projects emphasize: Sketch Recognition : Developing algorithms for mid-air gestures, children's developmental assessments, and language learning Accessibility : Haptic systems for visually impaired learners and algebra education Educational Tech : Intelligent tutoring systems for music, math, and East Asian languages Awards & Grants : EAAI-20 Travel Grant (2020) Ford Foundation Dissertation Honorable Mention (2015) NSF East Asia-Pacific Summer Institutes (2013, 2012) Royce E. Wisenbaker Fellowship (2009) Lab & Teams : Director of the Sketch Recognition Lab (SRL) and collaborator with global institutions like Singapore Management University and National Taiwan University. Active in organizing workshops like SketchRec at IUI conferences.

Muchao Yeمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• AI Safety
• +۷ مورد دیگر

Muchao Ye is an Assistant Professor in the Department of Computer Science at the University of Iowa. He earned his Ph.D. from Pennsylvania State University's College of Information Sciences and Technology in 2024 and a Bachelor of Engineering in Information Engineering from South China University of Technology. Ph.D., Information Sciences and Technology, Pennsylvania State University (2024) B.Eng., Information Engineering, South China University of Technology His research focuses on the intersection of Artificial Intelligence, Machine Learning, and AI Safety, particularly adversarial robustness in language models and vision-language models. He designs methods to enhance the security and reliability of deep learning systems for safety-critical applications like video surveillance and healthcare. Recent publications highlight adversarial robustness frameworks (e.g., UniT , PAT ), vision-language models for explainable video anomaly detection ( VERA ), and healthcare risk prediction techniques ( MedPath , MedRetriever ). His work appears in top venues such as NeurIPS, KDD, AAAI, ACL, and CVPR. Professional experience includes Applied Scientist internships at Amazon (2022–2023) and teaching roles at the University of Iowa and Pennsylvania State University. He serves as a reviewer for conferences like NeurIPS, ICML, and journals including IEEE TPAMI.

• System Security
• Cloud Computing Security
• Network Security
• +۷ مورد دیگر

Ki-Woong Park is a tenure-track full Professor in the Department of Computer and Information Security at Sejong University. He leads the System Security and Computer Engineering Research (SysCore) Lab, which focuses on system security research with numerous ongoing projects funded by major Korean research institutions including IITP, NRF, and KRIT. Sejong University, Department of Computer and Information Security System Security and Computer Engineering Research (SysCore) Lab Leader Member of IEEE, IEEE Computer Society, and ACM Education: Ph.D. in Electrical Engineering & Computer Science, KAIST (Advisor: Prof. Kyu-Ho Park) M.S. in Electrical Engineering & Computer Science, KAIST (Advisor: Prof. Kyu-Ho Park) B.S. in Computer Science, Yonsei University (Summa Cum Laude) Exchange Student at University of California, Los Angeles (UCLA) Professor Park's research focuses on designing, building, and analyzing secure systems, particularly for cloud computing, networked systems, and embedded systems. His work often involves reevaluating existing security mechanisms and actual system implementations with subsequent evaluation in real computing environments. He has made significant contributions to areas including cloud security, IoT security, ransomware detection, moving target defense, and metaverse security. His research approach emphasizes both theoretical foundations and practical implementation, with numerous publications in top-tier security and systems venues. His recent publications (2023-2024) demonstrate a strong focus on emerging security challenges in modern computing environments, particularly in metaverse platforms, UAV systems, and edge computing. These works span both theoretical security frameworks and practical implementations, with an emphasis on visualization techniques, hardware-based security mechanisms, and AI-enhanced security analysis. His research shows a clear progression from traditional cloud and network security toward next-generation security challenges in immersive virtual environments and cyber-physical systems. Scientific Awards: Microsoft Research Fellowship (2009-2010) Best Poster Gold Award at WISA 2020 Best Paper Award at MobiSec'18 Professor Park actively mentors numerous graduate and undergraduate students through the SysCore Lab, with current members including Ph.D. students, MS students, and undergraduate researchers. His research is supported by multiple significant grants, including the NRF Outstanding Researcher-Mid-career Researcher project, IITP Information Security Core Source Technology Development, and Defense Technology Advancement Research Institute projects. These grants total tens of billions of Korean won and address critical national security challenges in cyber defense, cloud security, and metaverse technologies. The SysCore Lab, under Professor Park's leadership, maintains a strong industry and government collaboration network, with part-time researchers from organizations including Hyundai Duty Free, Astron Security, Korea University, and various military cyber commands. This unique structure enables the lab to address both theoretical security challenges and practical implementation issues in real-world systems.

Jaime S. Cardosoمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Decision Support Systems
• +۳ مورد دیگر

Jaime S. Cardoso is an Associate Professor with Habilitation at the Faculty of Engineering of the University of Porto (FEUP) and a Senior Researcher in the 'Information Processing and Pattern Recognition' Area at INESC TEC's Telecommunications and Multimedia Unit. He has been serving as Research Coordinator since September 15, 1998, and is a Senior Member of IEEE as well as co-founder of ClusterMedia Labs. His educational background includes a Licenciatura in Electrical and Computer Engineering (1999), an MSc in Mathematical Engineering (2005), and a Ph.D. in Computer Vision (2006), all from the University of Porto. Cardoso's research focuses on three major areas: computer vision, machine learning, and decision support systems. His work spans medical image analysis, explainable AI, semantic audio-visual analysis, and pattern recognition. He has co-authored over 150 papers, with more than 50 published in international journals, and has accumulated over 6,500 citations. His recent publications demonstrate expertise in cell nuclei segmentation, ordinal regression for CNNs, semantic segmentation with ordinal relationships, face recognition using synthetic data, and explainable vision language models for medical applications. Honorable Mention in the Exame Informática Award 2011 for 'Semantic PACS' First Place in the ICDAR 2013 Music Scores Competition Cardoso has supervised numerous graduate students at UP-FEUP, with recent theses focusing on multimodal explanations, autonomous driving, medical diagnosis, and explainable AI. His research group works at the intersection of computer vision, machine learning, and practical applications in healthcare and autonomous systems.