جست‌وجوی استاد و پژوهشگر

Sao Mai Nguyenمشاهده پروفایل

مدرس

• Robotics
• Artificial Intelligence
• Cognitive Science
• +۷ مورد دیگر

Sao Mai Nguyen is an Enseignante-Chercheuse (Lecturer-Researcher) at ENSTA Paris, affiliated with the Unité d'Informatique et d'Ingénierie des Systèmes (U2IS). Her research bridges robotics, artificial intelligence, and cognitive science, focusing on cognitive developmental robotics, intrinsic motivation in learning, and human-robot interaction. She explores how robots can adapt to social and physical environments, particularly in physical rehabilitation and smart home applications. Research Interests: Nguyen’s work integrates machine learning, robotic embodiment, child psychology, and neuroscience. Key areas include human-robot interaction, assistive robotics for chronic low back pain rehabilitation, activity recognition in smart homes using IoT sensors, and intrinsic motivation-driven learning frameworks. Recent Contributions: Her 2024 HDR thesis on reinforcement and imitation learning for sequential tasks underscores her expertise in strategic learning systems. Recent publications address bio-inspired robotics models, hierarchical reinforcement learning, and benchmark environments like 'Open the Chests' for activity recognition. Collaborations include projects like the R-COOL randomized trial for robot-coached physical exercises. Labs & Teams: She contributes to the U2IS lab, advancing interdisciplinary research in AI and robotics. Her work spans experimental platforms for sensorimotor learning and healthcare robotics applications.

• Machine Learning
• Social Media Analysis
• Public Health
• +۳ مورد دیگر

Lydia Manikonda is an Assistant Professor in the Department of Information Systems and Web Science at the Lally School of Management, Rensselaer Polytechnic Institute (RPI). She is also affiliated with the AIRC (Artificial Intelligence Research Center) at RPI. Her research focuses on developing intelligent decision-making models using data from social media, online forums, and news articles to address challenges in business, public health, and technology. Her work has been recognized with awards, including the Best Reviewer Award at ICWSM 2016 and the Outstanding Demonstration Award at ICAPS 2014. Dr. Manikonda holds a PhD in Computer Science from Arizona State University (2019). Her research interests span interpretable machine learning, privacy in AI systems, social media analytics, and the societal impacts of technology. She has published extensively on topics like misinformation detection, ethical AI design, and pandemic-related behavioral shifts. Education: PhD in Computer Science, Arizona State University (2019) Her recent work emphasizes interdisciplinary approaches to tackle real-world problems, such as predicting intimate partner violence using machine learning and analyzing anti-Asian hate discourse on Reddit during the pandemic. She collaborates with interdisciplinary teams to bridge gaps between technical innovation and societal needs. Key Awards: Best Reviewer Award at ICWSM 2016 Outstanding Demonstration Award at ICAPS 2014 Dr. Manikonda's research also explores anthropomorphism in voice assistants, temporal privacy concerns, and the role of emotional cues in medical crowdfunding platforms. She actively participates in academic leadership roles, such as organizing the Web Science 2024 conference. Her lab focuses on actionable domain models and data-driven decision-making, with applications in healthcare, technology ethics, and public policy. She mentors students in these areas and is committed to advancing AI for societal good.

Vedhus Hoskereمشاهده پروفایل

استادیار

• Image Analysis
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Vedhus Hoskere is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Houston. His research focuses on interdisciplinary areas at the intersection of civil engineering, computer science, and robotics, emphasizing automated infrastructure inspection, machine learning, and AI-driven solutions for structural health monitoring. He holds the Liu Huixian Earthquake Engineering Scholarship (2018) for his work on automated post-earthquake building inspections. Key research areas include image analysis, machine learning, robotics, scientific computing, and visualization. His work integrates computer vision with civil infrastructure challenges, such as flood modeling, structural damage assessment, and autonomous inspection systems using drones and robotics. Notable projects include developing digital twin frameworks for bridges, synthetic environment testing for inspection algorithms, and semi-supervised learning for disaster damage analysis. His recent publications highlight advancements in transformer networks for instance segmentation, physics-informed generative models for damage assessment, and AI-driven hurricane resilience strategies. Hoskere collaborates on platforms like InstaDam for automated damage segmentation and explores Bayesian neural networks for quantifying uncertainties in infrastructure monitoring. Key Awards: Liu Huixian Earthquake Engineering Scholarship (2018) Advising & Labs: While no specific student names or grant details are provided, his research group likely focuses on robotics, computer vision, and AI applications in civil engineering. He contributes to open-source tools like the InstaDam platform and collaborates with institutions like the University of Illinois and USACE.

Yongfeng Zhangمشاهده پروفایل

دانشیار

• Machine Learning
• Data Mining
• Information Retrieval
• +۸ مورد دیگر

Yongfeng Zhang is an Associate Professor in the Department of Computer Science at Rutgers University. He is also the Director of the AIOS Foundation . His research focuses on Machine Learning, Data Mining, Recommender Systems, and Explainable AI , with notable contributions to fair and personalized AI, AI for science, and social good. Education and Experience: PhD in CS (2011–2016) from Tsinghua University Postdoc at UMass Amherst (2016–2017) Joined Rutgers as Assistant Professor (2018), promoted to Tenured Associate Professor (2024) Research Interests: Machine Learning, Data Mining, Information Retrieval, Recommender Systems, Natural Language Processing, ML Systems, AI Agents, Explainable AI, Fairness, and AI for Science. Recent Achievements: Received the 2024 ACM SIGIR Test of Time Award, 2024 Presidential Teaching Excellence Award, and multiple NSF grants. He has advised over 15 PhD students and led projects on trustworthy AI, generative recommendation, and causal inference. Awards: 2024 ACM SIGIR Test of Time Award 2021 NSF CAREER Award 2015 Microsoft PhD Fellowship Grants & Labs: Led NSF grants on explainable AI, conversational recommendation, and neural-symbolic AI. Active in the AIOS platform development and multiple research labs.

• Robotics
• Control Systems
• Human-Robot Interaction
• +۳ مورد دیگر

Cristian Secchi is a Full Professor in the Department of Engineering Sciences and Methods at the University of Modena and Reggio Emilia. His research focuses on robotics, control systems, and human-robot collaboration with an emphasis on safety, automation, and industrial applications. He teaches courses such as Industrial and Collaborative Robotics and Control of Robotic Systems. Research Interests: Design and implementation of control architectures for collaborative robots. Development of safe human-robot interaction frameworks compliant with ISO/TS 15066 standards. Integration of AI (e.g., large language models) into robotic motion planning. Autonomous systems for urban environments and surgical robotics. Publications highlight advancements in: Energy-efficient trajectory planning. Adaptive control strategies for uncertain environments. Multimodal human-robot communication. Diagnosis of mechanical systems via signal analysis. He leads the ARSControl research group and actively contributes to evolving production systems through smart modular technologies. His work bridges theoretical control frameworks with practical industrial and medical applications.

Jin Luمشاهده پروفایل

استادیار

• Machine Learning
• Optimization
• Bio-informatics
• +۶ مورد دیگر

Jin Lu is an Assistant Professor at the University of Georgia's School of Computing, part of the Franklin College of Arts & Sciences. He earned his Ph.D. (2019) and M.S. (2019) in Computer Science and Engineering from the University of Connecticut. Prior to his current role, he served as an Assistant Professor at the University of Michigan–Dearborn (2019–2023). Educational Background: Ph.D. in Computer Science and Engineering, University of Connecticut, 2019 M.S. in Computer Science and Engineering, University of Connecticut, 2019 Research Interests: Dr. Lu focuses on machine learning, optimization, bio-informatics, and smart mobility. His work spans federated learning, healthcare applications (e.g., depression and BMI monitoring), IoT systems, and computer vision. Recent projects explore AGI's potential in medical and educational contexts, leveraging models like CycleGAN and reinforcement learning. Grants & Funding: Develop digital brains to advance portable diagnosis of neurological conditions (Google, 2025) Lab/Teams: While specific lab affiliations are not explicitly stated, his research involves collaborations in interdisciplinary areas such as health informatics and smart mobility.

Wenwen Wangمشاهده پروفایل

دانشیار

• Computer Systems
• Compiler Design
• Computer Architecture
• +۴ مورد دیگر

Wenwen Wang is an Associate Professor in the School of Computing at the University of Georgia's Franklin College of Arts & Sciences. His research focuses on computer systems, compiler design, and embedded systems security. He holds a Ph.D. in Computer Science from the University of Chinese Academy of Sciences (2014). Education: Ph.D., Computer Science, University of Chinese Academy of Sciences, 2014 His research emphasizes dynamic binary translation, compiler optimization, and secure embedded systems. Notable contributions include frameworks like JavART (JIT compiler optimization) and BSan (memory error detection). He received the 2021 M. G. Michael Award for Sciences from the Franklin College. Wang has secured two NSF grants totaling $1.2 million, including CSR: Small grants for FALCON (2023–2027) and Modernizing Dynamic Binary Translation Systems (2023–2027). He advises three graduate students: Ruili Fang, Yage Hu, and Boyang Yi. His work addresses challenges in cross-architecture virtualization, GPU-based graph computing, and hardware-triggered security mechanisms. Recent projects include Liberator (GPU graph processing) and InvisiGuard (embedded device integrity).

• Cybersecurity
• Explainable AI (xAI)
• Machine Learning
• +۵ مورد دیگر

Aleksandra Pawlicka is a prominent researcher specializing in cybersecurity , explainable AI , and disinformation detection . Her work bridges ethical considerations with technical innovations in network security and AI applications , particularly focusing on the intersection of human-robot collaboration , fake news analysis , and IoT vulnerabilities . Key research areas: Cybersecurity Ethics, AI Explainability, Disinformation Mitigation Notable collaborations: Marek Pawlicki, Rafal Kozik, Michal Choras Her publications analyze xAI challenges in intrusion detection, machine learning for network security, and ethical dilemmas in cybersecurity. She contributes to projects like ULTIMATE (robotic AI) and SWAROG (fake news detection), emphasizing transparency and practical implementation. Recent work explores adversarial attacks against AI systems , few-shot learning for cybersecurity, and neuro-symbolic reasoning for trustworthy AI. Her 2023 study on ChatGPT's impact on scientific communication highlights evolving technological and ethical landscapes in academic practices.

• Computer vision
• Machine learning
• Statistical data analysis
• +۳ مورد دیگر

Ceyhun Burak Akgül is a Part-Time Lecturer specializing in Computer Vision, Machine Learning, and Statistical Data Analysis. His research focuses on interdisciplinary applications of visual data processing, including medical imaging and 3D object recognition. He maintains a personal website at cba-research.com and can be contacted at cb.akgul@gmail.com . His work spans topics such as image captioning, visual dictionaries, and symbolic feature detection. Key contributions include developing algorithms for action recognition using depth cameras and frameworks for leaf and object recognition. His research also integrates medical applications, such as analyzing Alzheimer’s patient movements and automated diagnosis using imaging data. Akgül’s publications frequently address challenges in 3D shape descriptors, feature selection, and interdisciplinary methodologies. His recent work includes exploring visual dictionaries and improving image processing techniques through model-driven approaches. His academic contributions are evident in journals like the Journal of Visual Communication and Image Representation, and he has participated in competitions like SHREC. Despite his extensive publication record, no formal awards or grants are explicitly mentioned in the provided data.

• Ancient Greek Philosophy
• History of Technology
• Philosophy of Technology
• +۵ مورد دیگر

Leopoldo Iribarren is a Lecturer at the School for Advanced Studies in the Social Sciences (EHESS), Paris, and serves as Vice-President in charge of International Relations. He holds a PhD in philology and history of Greek philosophy from EHESS (2009) and has held postdoctoral fellowships at Harvard University (2011-12) and the Sorbonne (2013). His research focuses on ancient Greek conceptions of technology and their intersection with modern philosophical debates. He previously taught at Leiden University (2014-18). His research explores how ancient Greek thought addresses technology’s role in shaping human-nature relationships, labor systems, and instrumental rationality. He bridges learned discourses (poetry, philosophy) with artisan practices, analyzing their theoretical and practical gaps. He also examines modern thinkers like Hegel, Heidegger, and Stiegler to understand technology’s symbolic and instrumental dimensions. Key publications include *Fabriquer le monde* (2018), analyzing archaic Greek poetry’s cosmogonic visions, and articles on Hesiod, Parmenides, and Prometheus mythology. His work spans philosophy, anthropology, and literary analysis of ancient texts. Awards: None explicitly listed. Fellowships include Marie Curie (Sorbonne) and Harvard’s Center for Hellenic Studies. Teaching and advising: Leads ANHIMA (Anthropology and History of the Ancient Worlds). His courses integrate historical and modern perspectives on technology’s societal impacts.

Antoine Miechمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• Natural Language Processing
• +۷ مورد دیگر

Antoine Miech is a Researcher at DeepMind's Vision Group , with prior affiliations at Inria and Ecole Normale Supérieure where he completed his computer vision Ph.D. under Ivan Laptev and Josef Sivic . He has collaborated with researchers from Facebook AI and Google during his academic career. Research Interests span video understanding, weakly-supervised machine learning, and multimodal analysis. His work focuses on: Text-video embedding Self-supervised video representation Action localization Anticipatory video modeling Scalable multimodal learning Scientific Contributions include: HowTo100M - A massive dataset of narrated instructional videos MIL-NCE - A novel loss function for video-text alignment MEE - A model for handling heterogeneous data Context Gating - Learnable pooling architecture Awards & Recognition : Google Ph.D. Fellowship (2018) Technical Leadership : Created the LOUPE TensorFlow toolbox for feature pooling and maintained annotated video dataset catalogs. Organized the Data Science Game competition (2016-2017).

• Human-Computer Interaction
• Virtual Reality
• Augmented Reality
• +۵ مورد دیگر

Christian Eichhorn is an Assistant Professor in the Department of Computer Science at Technical University of Munich, specializing in Human-Computer Interaction, Virtual Reality, and Augmented Reality applications. His research primarily focuses on developing serious games and immersive technologies for healthcare, education, and older adults. He has established a strong collaborative network, particularly with David A. Plecher and Gudrun Klinker, resulting in numerous publications in top HCI and VR venues. Dr. Eichhorn's research interests center around creating accessible and engaging technology applications. His work demonstrates particular expertise in developing serious games for various educational purposes (language learning, history education), healthcare applications (rehabilitation, dementia care), and technologies specifically designed for older adults. His research combines technical innovation in AR/VR systems with practical applications addressing real-world challenges in healthcare and education. Analysis of his recent publications (2022-2025) reveals a consistent focus on practical applications of immersive technologies. His work spans educational tools for programming and language learning, healthcare applications for rehabilitation and clinical training, and specialized technologies for older adults. The research demonstrates a strong interdisciplinary approach, bridging computer science with healthcare, education, and gerontology. His technical contributions include innovations in AR/VR system design, collaborative environments, and novel interaction techniques. Dr. Eichhorn has been actively involved in organizing workshops and contributing to major conferences in the VR/AR field, including IEEE VR and ISMAR. His collaborative approach is evident in his extensive co-authorship network spanning multiple institutions. His laboratory work focuses on developing practical AR/VR applications, with particular emphasis on creating systems that bridge virtual and physical experiences. Current projects include serious games for health behavior change, rehabilitation assessment tools using wearable sensors, and social VR environments for older adults.

Marc Toussaintمشاهده پروفایل

استاد

• Robotics
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Marc Toussaint is Full Professor leading the Learning & Intelligent Systems Lab at TU Berlin's EECS Faculty. His research integrates machine learning, optimization, and AI reasoning to solve fundamental robotics problems like physical reasoning and human-robot interaction. He holds a physics diploma from University of Cologne and PhD from Ruhr-Universität Bochum. Key research themes include: Task-motion planning integration Reinforcement learning for robotics Physical simulation and control Probabilistic inference methods Recent publications focus on efficient kinodynamic planning, belief space planning under uncertainty, and neural policy learning. He develops open-source robotic tools like the 'robotic python package' used in academic courses worldwide. Toussaint collaborates with Amazon Robotics and MIT CSAIL, and has held positions at Max Planck Institute and University of Stuttgart.

Flora Salimمشاهده پروفایل

استاد

• Machine Learning
• Time-series Analysis
• Spatio-temporal Data Mining
• +۵ مورد دیگر

Flora Salim is a Professor in the School of Computing Technologies at RMIT University. She serves as co-Deputy Director of the RMIT Centre for Information Discovery and Data Analytics (CIDDA) and an Associate Investigator of the ARC Centre of Excellence in Automated Decision Making and Society. Her research focuses on human behavior modeling, machine learning with time-series and spatio-temporal data, and edge AI applications in IoT and wearables. Flora has secured over $10M in research funding from ARC, industry partners, and government bodies. Notable awards include the 2021 PACM IMWUT Distinguished Paper Award, 2019 Humboldt-Bayer Fellowship, and RMIT's 2018 Research Impact Award. She leads the CRUISE research group and has held visiting professorships at the University of Kassel and University of Cambridge. Editorial roles: Associate Editor of PACM on IMWUT, Area Editor of Pervasive and Mobile Computing Steering Committee member of ACM UbiComp Her work bridges ubiquitous computing and machine learning, with applications in urban analytics, mobility, and health monitoring. Recent projects include self-supervised learning for multimodal data and forecasting with heterogeneous time-series. Supervision areas: Deep learning for sensor data, explainable AI, and wearable-based emotion sensing Teaching programs: Master of Artificial Intelligence and Master of Data Science

Sonit Singhمشاهده پروفایل

مدرس

• Artificial Intelligence
• Computer Vision
• Natural Language Processing
• +۷ مورد دیگر

Dr. Sonit Singh is a Lecturer at the School of Computer Science and Engineering at the University of New South Wales (UNSW), Sydney, Australia. Prior to this role, he served as a Postdoctoral Research Fellow at the same institution. He holds a PhD from Macquarie University, completed in collaboration with Macquarie. His research focuses on artificial intelligence, computer vision, natural language processing, and their applications in healthcare, particularly in medical imaging and diagnostic systems. Dr. Singh's academic background includes a strong emphasis on biomedical imaging, intelligent robotics, and computing education. His work bridges machine learning advancements with real-world clinical challenges, such as tumor segmentation, radiology report generation, and disease detection through imaging modalities like CT scans and ultrasound. His recent publications highlight innovations in 3D medical image localization, robust tumor segmentation networks, and clinical context-aware radiology report systems. These contributions underscore his expertise in integrating deep learning techniques with healthcare workflows. Dr. Singh collaborates with multidisciplinary teams, including medical professionals, to develop solutions for challenges like placenta segmentation and dietary intake monitoring using AI. While no specific awards or grants are explicitly listed, his extensive publication record reflects sustained academic engagement in high-impact areas. His research often addresses practical clinical needs, such as improving diagnostic accuracy and patient care through advanced computational methods.