جست‌وجوی استاد و پژوهشگر

Weijia Zhangمشاهده پروفایل

مدرس

• Causal Inference
• Machine Learning
• Weakly Supervised Learning
• +۳ مورد دیگر

Dr. Weijia Zhang is a Lecturer in Data Science and Applied Statistics at the University of Newcastle (Australia), focusing on causal inference and machine learning from ambiguous data. He holds a PhD from the University of South Australia (2018) and has held roles as Associate Professor at Southeast University (China) and Research Fellow at the University of South Australia. His research bridges causal inference and machine learning, addressing challenges in weakly supervised learning (e.g., multi-instance partial-label learning) and survival analysis under dependent censoring. Affiliations: University of Newcastle (current), Southeast University (2021–2023), University of South Australia (2018–2020) Teaching: Courses on predictive analytics, unsupervised methods, statistical decision-making, and time series forecasting Research interests include causal representation learning, weakly supervised learning from noisy/ambiguous labels, and applications in healthcare, telecommunications, and automotive industries. Key projects include developing causal methods for battery lifespan prediction with Chang'an Automobile and models for dependent censoring in survival analysis. Publications (over 30+): Focus on causal inference, weak supervision, and survival analysis. Notable contributions include CausalPLL+, MiplGp, and TEDVAE algorithms. Editor for Data Science and Engineering and reviewer for top conferences (NeurIPS, ICML, AAAI). Grants: $125k from Changan Automobile, $63.5k from National Natural Science Foundation of China, and institutional funding ($132.5k total) Awards: Top Reviewer Awards at AAAI (2021) and UAI (2022) Labs/Teams: Leads research on causal representation learning and survival analysis within the School of Information and Physical Sciences, collaborating internationally with institutions in China, Switzerland, and the US.

Andrea M. Tonelloمشاهده پروفایل

استاد

• Power Line Communications
• Machine Learning
• Signal Processing
• +۴ مورد دیگر

Andrea M. Tonello is a distinguished academic researcher specializing in Power Line Communications (PLC), Machine Learning, and Signal Processing. His work bridges theoretical advancements with practical applications in communication systems, robotics, and smart grid technologies. He has authored over 200 publications across leading conferences and journals, with a focus on optimizing communication protocols, enhancing system robustness through noise mitigation, and applying AI techniques to solve challenges in wired/wireless networks. Research interests include: 1) Developing advanced PLC frameworks for smart grids and IoT deployments, 2) Designing machine learning algorithms for classification and signal processing under noisy conditions, 3) Investigating control systems for underactuated robotic mechanisms, and 4) Exploring full-duplex communication techniques for improved bandwidth utilization. Recent works emphasize energy-efficient sensor networks for agriculture and topology-aware learning models for PLC quality prediction. His articles consistently address cutting-edge topics like reinforcement learning for system stability, f-divergence based classification methods, and channel modeling innovations. Notable contributions include pioneering work on in-band full-duplex PLC systems and robust neural decoding techniques leveraging mutual information principles. Though no specific grants or advising information is listed here, his prolific collaboration network (evident from co-authorship patterns) indicates active participation in collaborative research initiatives. Laboratory activities focus on experimental validation of communication protocols and algorithmic performance evaluation in real-world scenarios.

• Medical Image Analysis
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Dr. Xiaoqing Guo is a Research Fellow at the Department of Engineering Science, University of Oxford. She holds a PhD from City University of Hong Kong (2022) and a B.Eng from Beihang University (2018). Her research focuses on medical image analysis, computer vision, and machine learning, with an emphasis on multimodal learning, human-machine interaction, and robust AI systems. She is affiliated with the Institute of Biomedical Engineering and the Noble Group, contributing to projects like the Turing AI WLR Fellowship. Her educational background includes: B.Eng in Biological Science & Medical Engineering (Beihang University, 2018) PhD in Electrical Engineering (City University of Hong Kong, 2022) Research interests span medical imaging , multimodal systems , and domain adaptation . Recent work includes MMSummary for fetal ultrasound video summarization, Pose-GuideNet for fetal head ultrasound guidance, and IterMask2 for brain lesion segmentation. She explores challenges like noisy labels, open-set generalization, and few-shot learning for rare diseases. Her publications reflect a trend toward medical AI applications , cross-modal systems , and domain adaptation . Notable contributions include novel frameworks for segmentation, anomaly detection, and adaptive learning. Awards include the 2024 Asian Deans’ Forum Rising Star and 2023 Global Top 80 Chinese Young Female AI Scholars. She was also a CVPR Outstanding Reviewer (2023) and recipient of the Chow Yei Ching Doctoral Research Award (2022). Dr. Guo’s work bridges theory and clinical practice, with projects in collaboration with institutions like the University of Oxford’s Visual AI Research Group. She is involved in initiatives like the Turing AI Fellowship and contributes to advancing medical imaging technologies.

Xiao Fuمشاهده پروفایل

دانشیار

• Machine Learning
• Signal Processing
• Optimization
• +۵ مورد دیگر

Xiao Fu is an Associate Professor in the Department of Electrical Engineering and Computer Science at Oregon State University's College of Engineering. He holds a B.S. (2005) and M.S. (2010) in Communications and Information Engineering and Signal and Information Systems from the University of Electronic Science and Technology of China (UESTC), and a Ph.D. (2014) in Electronic Engineering from The Chinese University of Hong Kong (CUHK). His research focuses on machine learning, signal processing, and optimization, with applications in nonlinear factor analysis, unsupervised learning, and deep neural networks for signal processing tasks. He has received notable awards including the 2022 NSF CAREER Award and the 2024 OSU Promising Scholar Award. His work emphasizes developing robust algorithms for factor analysis (e.g., tensor and matrix factorization), large-scale optimization in data mining, and deep learning techniques for hyperspectral imaging, radio map estimation, and crowd-sourced label analysis. Key research groups affiliated with him include Data Science and Engineering, Artificial Intelligence and Robotics, and Communications and Signal Processing. His recent projects include advancing unsupervised machine learning to reduce reliance on labeled data in AI systems and exploring applications in environmental sensing and medical imaging. Xiao Fu's publications span topics like radio map estimation via latent-domain denoisers, noisy label learning with crowd wisdom, and identifiability in nonlinear mixture models. His research bridges theoretical advancements in optimization and practical applications in wireless communication, ecological networks, and biomedical imaging. Current efforts aim to enhance unsupervised deep representation learning and develop scalable algorithms for high-dimensional data analysis. Awards: NSF CAREER Award (2022), OSU Promising Scholar Award (2024) Grants: NSF-funded CAREER Award project on nonlinear factor analysis tools Labs/Teams: Affiliated with interdisciplinary teams in signal processing, AI, and ecological systems modeling

Ville Hautamäkiمشاهده پروفایل

دانشیار

• Data Science
• Statistical Inference
• Deep Learning
• +۳ مورد دیگر

Ville Hautamäki is an Associate Professor at the University of Eastern Finland's School of Computing, Department of Computer Science. His research focuses on data science, statistical inference, and deep learning with applications in autonomous agents, bioinformatics, and speech technology. He teaches courses such as Probabilistic Inference for Data Science and Bayesian Inference, and regularly contributes to summer schools on machine learning. His research group, Applied Statistics and Statistical Machine Learning, addresses challenges in speaker verification, speech processing, and biomedical data analysis. Recent works include advancements in robust speaker recognition under noisy conditions, deepfake detection, and end-to-end autonomous driving systems. Collaborations span diverse domains including healthcare, cybersecurity, and robotics. Publications highlight contributions to multi-task learning frameworks, imitation learning policies, and generative models for single-cell data. His work emphasizes cross-disciplinary approaches, blending theoretical machine learning with practical applications in real-world scenarios.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۱۲ مورد دیگر

Ahmet Cüneyd Tantuğ is an Associate Professor in the Department of Artificial Intelligence and Data Engineering at the Faculty of Computer and Informatics, Istanbul Technical University (ITU). He has been a core faculty member since 2007 and is among the founders of the ITU Natural Language Processing Group. He has held key administrative roles, including Vice Dean (2011–2015) and Head of IT Department at ITU (2012–2020). BSc, MSc, PhD: Istanbul Technical University, Computer Engineering Guest Researcher: University of Copenhagen, Centre for Language Technology (2006–2007) Dr. Tantuğ’s research spans artificial intelligence, natural language processing, machine learning, and deep learning, with a focus on Turkish and other agglutinative languages. His work includes named entity recognition, dependency parsing, machine translation, word sense disambiguation, and text normalization. He has developed linguistic resources for Turkish and low-resource languages such as Amharic, and has contributed to financial AI and temporal expression extraction. His recent publications highlight advancements in Turkish NLP, including optimal vocabulary sizing, tokenization granularity for LLMs, and transformer-based models for named entity recognition. He has led multiple research projects funded by TÜBİTAK, the European Union, and the Ministry of Development. Scientific awards include: Bedri Karafakioğlu Special Award (2000) Siemens Excellence Award (2007) TeknoGirişim Sermaye Desteği Ödülü (2010) He has supervised numerous master’s and PhD students, with theses on topics such as dependency parsing, named entity recognition, and text normalization. He has also served as a consultant for private sector projects in banking, software, and telecommunications, and as a referee for TÜBİTAK, TEYDEB, and the Ministry of Industry and Technology. Dr. Tantuğ leads and participates in active research projects including Turkish text normalization from user-generated content and improving dependency parsing with augmented features. He is a founding member of the ITU Natural Language Processing Group, which continues to be a central hub for NLP research in Turkey.

Panagiotis G. Ipeirotisمشاهده پروفایل

استاد

• Crowdsourcing
• Human-AI Collaboration
• Data Science
• +۱۱ مورد دیگر

Panagiotis G. Ipeirotis is a Professor and George A. Kellner Faculty Fellow at the Department of Technology, Operations, and Statistics at the Leonard N. Stern School of Business, New York University. He is also affiliated with the Center for Data Science and the Computer Science department at NYU. His work bridges computer science, data science, and business analytics, with a focus on integrating human and machine intelligence. PhD in Computer Science, Columbia University (2004) MSc in Computer Science, Columbia University (2001) BSc in Computer Engineering & Informatics, University of Patras, Greece (1999) His research centers on crowdsourcing, human-AI collaboration, online labor markets, and social media analytics . He is widely recognized as a pioneer in human computation and has developed foundational techniques for ensuring data quality in crowd-sourced environments. His interdisciplinary work combines insights from computer science, economics, and social psychology to model user behavior and improve decision-making systems. His recent publications explore algorithmic fairness in hiring, theoretical voting models in crowdsourcing, and the economic impact of user-generated content. These works reflect a consistent trend toward building robust, ethical, and scalable human-machine systems that enhance data quality and decision accuracy across domains. Notable scientific awards include: Lagrange Prize in Complex Systems (2015) SIGKDD Test of Time Award (2020) NSF CAREER Award Best Paper Award at WWW 2011 Best Paper at SIGMOD 2006 Multiple best paper recognitions in Management Science and KDD Professor Ipeirotis has led significant research initiatives, including a $1.5 million Google Research Grant to integrate crowdsourcing with machine learning for visual search. He advises graduate students and contributes to academic leadership through editorial and collaborative roles. His work has been featured in prominent media outlets such as Forbes, WIRED, and Bloomberg Businessweek, highlighting its real-world impact. He is actively involved in research labs and initiatives at NYU, particularly those focused on data science, behavioral research, and computing. His affiliations with the Center for Research Computing and the Center for Behavioral Research underscore his interdisciplinary approach to solving complex problems at the intersection of technology and human behavior.

Hyun-Suk Leeمشاهده پروفایل

استادیار

• Machine Learning for Wireless Communications
• Intelligent Decision-Making Applications
• Reinforcement Learning
• +۳ مورد دیگر

Hyun-Suk Lee is an Assistant Professor in the Department of Artificial Intelligence and Robotics at Sejong University . Their research focuses on Machine Learning for Wireless Communications and Intelligent Decision-Making Applications , integrating techniques like Reinforcement Learning, Deep Learning, and Uncertainty Quantification to address challenges in wireless networks, metaverse provisioning, and noisy data handling. Education: B.S. (2012) and Ph.D. (2018) from Yonsei University. Their work emphasizes collaborative research between academia and industry, with recent projects targeting energy policy optimization, interference management, and predictive metaverse models. Key research themes include: AI-driven wireless communication networks Intelligent decision-making systems Uncertainty quantification for robust predictions Deep learning applications in energy and metaverse domains Hyun-Suk Lee has contributed to 48 research outputs (as of 2025), including high-impact publications in IEEE Transactions on Vehicular Technology and policy frameworks for renewable energy systems. Their research fingerprint highlights expertise in Scheduling Algorithms, Reinforcement Learning, and IoT. Their laboratory, accessible at Main Lab , serves as a hub for interdisciplinary projects bridging artificial intelligence with telecommunications and energy systems.

• Applied mathematics
• Statistics
• Information theory
• +۸ مورد دیگر

Toshiyasu Matsushima is a Professor at Waseda University's Faculty of Science and Engineering, School of Fundamental Science and Engineering. He holds a Doctor of Engineering degree from Waseda University and has maintained an active research career spanning several decades. His educational background includes: 1991: Graduate School, Division of Science and Engineering, Waseda University 1978: Faculty of Science and Engineering, Waseda University Professor Matsushima's research spans multiple disciplines in applied mathematics and information science. His primary research interests include information theory, coding theory, statistical science, learning theory, intelligent informatics, data science, and information security. His work bridges theoretical foundations with practical applications in data compression, machine learning, privacy-preserving technologies, and communication systems. His recent publications demonstrate consistent innovation in Bayesian decision theory, context tree modeling, private information retrieval systems, decision tree algorithms, and image processing techniques. The research shows a clear trajectory toward optimizing algorithms for data analysis while addressing contemporary challenges in privacy-utility trade-offs and handling noisy data environments. Professor Matsushima is an active member of numerous professional societies including the Japanese Society for Industrial and Applied Mathematics, Applied Statistics Society, Operations Research Society of Japan, Quality Control Society, Artificial Intelligence Society, Information Processing Society, Information Theory and its Applications Society, IEEE, and the Institute of Electronics, Information and Communication Engineers. With over 190 publications and a Scopus h-index of 10, his scholarly contributions have established him as a significant figure in information theory and its applications. His research continues to influence both theoretical developments and practical implementations in data science and information engineering.

Jindong Wangمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Robust Machine Learning
• +۱۱ مورد دیگر

Jindong Wang is a Tenure-Track Assistant Professor in the Department of Data Science at William & Mary. He is also a faculty member of the AI Safety Community at the Future of Life Institute. Previously, he was a Senior Researcher at Microsoft Research Asia from 2019 to 2024. His work spans robust, trustworthy, and responsible AI, with a focus on foundation models, transfer learning, and AI for social sciences. PhD, University of Chinese Academy of Sciences (2019) Bachelor’s, North China University of Technology (2014) Dr. Wang’s research centers on enhancing the reliability and societal impact of AI systems. Key areas include robust machine learning, domain generalization, federated learning, semi-supervised learning, and large language model evaluation and enhancement. He is particularly interested in the philosophical and behavioral aspects of LLMs and how AI can be leveraged in interdisciplinary domains such as social sciences. His work aims to build AI systems that are not only high-performing but also safe, interpretable, and aligned with human values. His recent publications (2021–2025) in top venues like TPAMI, NeurIPS, ICML, ICLR, and ACL reflect a strong trend toward understanding and improving foundation models—especially in noisy or out-of-distribution settings—and developing frameworks for dynamic evaluation, agent behavior, and trustworthy AI. These works often combine theoretical rigor with practical open-source implementations, demonstrating a commitment to reproducibility and community impact. World’s Top 2% Highly Cited Scientists (Stanford, since 2022) Most Influential AI Scholar (AMiner, since 2022) Best Paper Award, AAAI 2025 Good Data Workshop William & Mary Faculty Research Award (2025) Top 17 Most Cited NeurIPS Papers (2021) PaperDigest Most Influential CIKM Paper (2021) Most Cited Paper in ICDM 2017 and 2nd Most Cited in MM 2018 Dr. Wang actively mentors PhD and master’s students and welcomes internship applications for future cohorts. He has secured significant research visibility and impact, with over 20,000 citations (H-index 50) and leadership roles as associate editor of IEEE TNNLS, guest editor for ACM TIST, and area chair for ICML, NeurIPS, ICLR, KDD, and ACL. He has delivered tutorials at major conferences including IJCAI, WSDM, KDD, AAAI, and CVPR. His research is supported by collaborations with leading institutions and industry partners, and has been featured in Forbes and MIT Technology Review. He leads several influential open-source projects, including transferlearning , PromptBench , torchSSL , and USB , which together have received over 20,000 GitHub stars. These tools support research in transfer learning, prompt engineering, semi-supervised learning, and unified self-supervised benchmarks. He is also organizing key workshops such as FedGenAI-IJCAI’25 and the ICCV 2025 workshop on Trustworthy Study Transfers, fostering community engagement in emerging AI challenges.

• Neuroinflammation
• Multiple Sclerosis
• Neuroimaging
• +۷ مورد دیگر

Professor Olga Ciccarelli is a distinguished academic neurologist at University College London's UCL Institute of Neurology, where she serves as Professor of Neurology and Head of the Research Department of Neuroinflammation. She holds a prestigious NIHR Research Professorship (awarded January 2019) and leads the NIHR UCLH Biomedical Research Centre Translational Neuroscience program. Her academic journey began with medical training at the University of Rome, La Sapienza, followed by specialist neurology training completed in 1999. Her research program focuses on developing machine learning tools to predict individual treatment responses in multiple sclerosis, integrating demographic, genetic, biomarker, MRI, and lifestyle factors. This work addresses the critical clinical challenge that while numerous MS therapies exist, current practice lacks personalized prediction of treatment efficacy. Her expertise spans neuroinflammation, diffusion MRI, and autoimmune neurological disorders, with particular emphasis on multiple sclerosis and autoimmune encephalitis. Ciccarelli's publication record demonstrates consistent leadership in high-impact neurology research, with numerous 2025 publications covering diagnostic criteria development, advanced neuroimaging techniques, and treatment optimization in multiple sclerosis. Her work bridges clinical neurology, artificial intelligence, and neuroimmunology, with strong translational focus. NIHR Research Professorship (2019) Fellow of the Academy of Medical Sciences (2023) Wellcome Trust Advanced Clinical Fellowship Jacqueline Du Pre Award from MSIF As an educator, she has supervised over 10 PhD students who now lead MS services and research teams, and currently mentors 3 clinical research fellows and 2 post-doctoral students. She actively shapes the field through editorial roles including Deputy Editor of Neurology Journal and membership on the NICE Committee for MS Guidelines, while contributing to international collaborations through the European Network MAGNIMS.

Yifan Chenمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Non-parametric Models
• +۶ مورد دیگر

Yifan Chen is an Assistant Professor in Computer Science and Mathematics at Hong Kong Baptist University (HKBU), specializing in efficient machine learning with focus on non-parametric models and neural networks involving intensive matrix operations such as Transformers and Graph Neural Networks (GNNs). Affiliation: Department of Computer Science and Mathematics, Hong Kong Baptist University Location: Hong Kong SAR, China Dr. Chen received his Ph.D. in Statistics from the University of Illinois Urbana-Champaign in 2023 under the supervision of Prof. Yun Yang, with additional collaborations with Prof. Ruoqing Zhu, Prof. Heng Ji, and Prof. Jingrui He. Prior to his doctoral studies, he earned his B.S. in Statistics from Fudan University in 2018, advised by Prof. Juan Shen and Prof. Chenghong Zhang. Dr. Chen's research broadly focuses on understanding the statistical structures of modern machine learning algorithms and applying these insights to real-world computational challenges. His work particularly emphasizes non-parametric models and neural networks with intensive matrix operations, including Transformers (language models) and graph neural networks. His research bridges theoretical statistical learning with practical computational efficiency, exploring how to make machine learning algorithms both theoretically sound and computationally feasible for large-scale applications. Analysis of Dr. Chen's publication record reveals a strong focus on computational efficiency in machine learning, with recurring themes in graph-based learning, kernel methods, and optimization techniques. His work demonstrates expertise in both theoretical foundations and practical implementations, with publications spanning top-tier conferences including NeurIPS, ICML, KDD, and AAAI. Many of his papers address fundamental challenges in scaling machine learning algorithms while maintaining statistical guarantees, reflecting his commitment to both theoretical rigor and practical applicability. Scientific awards and recognition include: Dissertation Completion Fellowship from University of Illinois Graduate College (2023) ICML 2023 Grant Award (2023) Shanghai Outstanding Graduate from Shanghai Municipal Education Commission (2018) NSFC Young Scientists Fund (2025) GDSTC General Program funding (2024) RGC Early Career Scheme grant (2024) Dr. Chen actively mentors PhD students and research assistants, currently supervising Yifan Xu and Yujia Yin as PhD students, and Yifan Wu as a visiting research assistant. His group has produced notable alumni including Mingchen Jiang (now PhD student at Institute of Science Tokyo), Jiahao Ma (now PhD student at HKU), Xichen Ye (now incoming PhD student at Fudan), and Guoming Li (now incoming PhD student at NUS). He teaches advanced topics in artificial intelligence and machine learning, as well as applied linear algebra for computing, emphasizing the connections between theoretical foundations and practical applications. Dr. Chen maintains active collaborations with researchers at institutions including University of Illinois Urbana-Champaign, Fudan University, Shanghai University, and industry partners like Amazon Alexa AI and IBM Research.

• Deep Learning
• Medical Image Analysis
• Pattern Recognition
• +۴ مورد دیگر

Filipe R. Cordeiro is an Associate Professor at the Center of Informatics, Federal University of Pernambuco (UFPE), Brazil, with a distinguished research career spanning nearly two decades in machine learning, computer vision, and medical image analysis. He completed his PhD at UFPE in 2015 with research focused on semi-supervised techniques for tumor segmentation in digital mammography images. His primary research interests include deep learning with noisy labels, medical image analysis (particularly mammography and ECG), pattern recognition, and handwritten mathematical expression recognition. Dr. Cordeiro has pioneered several innovative approaches for handling label noise in deep learning systems, including ANNE (Adaptive Nearest Neighbors and Eigenvector-based sample selection), LongReMix, and ScanMix, which have gained significant attention in the computer vision community. His recent publication trend (2020-2025) shows remarkable productivity with over 30 papers, primarily in top venues like Pattern Recognition, IEEE Access, and SIBGRAPI. His work demonstrates a clear evolution from foundational medical image analysis to sophisticated noise-robust learning techniques, with increasing emphasis on practical applications in healthcare and education technology. A notable thread throughout his research is the development of methods that work effectively with limited or imperfect training data. Dr. Cordeiro actively supervises graduate students and collaborates extensively with researchers both within Brazil (particularly at UFPE) and internationally. His research group appears to focus on developing practical computer vision solutions for real-world medical and educational challenges, with recent emphasis on low-resource mobile applications for education and healthcare.

Andrew Caseyمشاهده پروفایل

دانشیار

• Astrophysics
• Stellar Astrophysics
• Data Analysis
• +۳ مورد دیگر

Associate Professor Andrew Casey is a computational astrophysicist at Monash University's School of Physics and Astronomy, specializing in data-driven methods applied to astrophysical problems. He holds a PhD from Australian National University (ANU), with postdoctoral research at the University of Cambridge as a King's College Senior Member. His work bridges astrophysics, statistics, and machine learning, focusing on stellar astrophysics, Bayesian inference, and large-scale surveys like Gaia and GALAH. He leads projects on stellar interiors, galactic evolution, and unresolved binary systems. Casey has received awards including the Young Tall Poppy Award and the Australian Prime Minister's Endeavour Award. He actively mentors PhD students in data analysis and computational methods. Education: PhD in Astronomy & Astrophysics (ANU), Visiting Graduate Student at MIT (via Endeavour Award). Research interests include: computational data analysis, stellar astrophysics, Bayesian statistics, machine learning applications in astronomy, and algorithm optimization. Notable projects include GaiaUnlimited (studying Milky Way disc structure) and GALAH surveys (chemical tagging of stars). Recent articles focus on topics like Gaia data analysis, exoplanet detection via astrometry, and models for M dwarfs. His work often emphasizes interdisciplinary approaches, combining physics-based models with AI-driven techniques. Awards: Young Tall Poppy Award (2018), Fresh Science (2018), Senior Member (2015), Australian Prime Minister's Endeavour Award (2011), and InnovationACT Prize (2011). Advising and grants: Accepting PhD students; leads ARC-funded projects such as the Discovery Early Career Researcher Award. Projects include Gaia survey analysis, star formation studies, and binary star detection. Labs/Teams: Involved with the Monash Astrophysics Group and collaborates internationally on surveys like Gaia and SDSS-V.

Xi Wangمشاهده پروفایل

استادیار

• Nanophotonics
• Dynamically tunable photonic devices
• Micro/Nano-Electro-Opto-Mechanical Systems (MEOMS/NEOMS)
• +۷ مورد دیگر

Dr. Xi Wang is an Assistant Professor in the Department of Materials Science and Engineering at the University of Delaware, where he has been since September 2018. Prior to this, he served as a postdoctoral researcher in the Department of Materials Science and Engineering at the University of California, Berkeley from 2014. He holds a PhD in Electrical Engineering from the State University of New York at Buffalo (2014) and a B.S. in Microelectronics from Tsinghua University, China (2007). Research Interests: Dr. Wang's work focuses on Nanophotonics , Dynamic tunable photonic devices , Micro/Nano-Electro-Opto-Mechanical Systems (MEOMS/NEOMS) , Semiconductor plasmonics , Metamaterials , Metasurfaces , Phase transition materials , and Two-dimensional materials . His research integrates theoretical and experimental approaches to advance optoelectronic and photonic technologies. Awards: University of Delaware Research Foundation (UDRF) Award (2020) Advising & Grants: Dr. Wang currently advises no listed students but has contributed to funded research through grants such as the UDRF Award. His work often involves collaborations with industry and academic partners to explore applications of novel materials and devices. Labs/Teams: While specific lab affiliations are not explicitly mentioned, his research likely aligns with interdisciplinary groups at the University of Delaware focused on advanced materials and photonics.