جست‌وجوی استاد و پژوهشگر

Jimmy Linمشاهده پروفایل

استاد

• Information Retrieval
• Natural Language Processing
• Data Management
• +۲ مورد دیگر

Jimmy Lin is a Professor and David R. Cheriton Chair at the University of Waterloo's David R. Cheriton School of Computer Science. His research focuses on information retrieval, natural language processing, and data management, with applications in large-scale data analysis and AI systems. Previously at the University of Maryland, he earned his PhD from MIT in 2004. PhD in Electrical Engineering and Computer Science, MIT (2004) MS, Mechanical Engineering, Penn State University BSc, Mechanical Engineering, Addis Ababa University His research integrates information retrieval, NLP, and data management to build tools for understanding large datasets. Current interests include neural retrieval models, cross-lingual systems, and efficient search algorithms. Recent publications focus on retrieval-augmented generation (RAG), model efficiency, multilingual NLP, and evaluation methodologies. Trends include hybrid dense-sparse retrieval systems, robustness improvements, and practical toolkits like Anserini and Tevatron. ACM Fellow SIGIR Academy ACL Fellow SAE Ralph R. Teetor Educational Award (2014) NSF CAREER Award (2011) He advises 36+ PhD/Master's students and leads multiple industry collaborations (Primal, RSVP, Twitter). His lab develops open-source tools for retrieval and NLP, focusing on scalable and multilingual applications.

Raphael Phanمشاهده پروفایل

استاد

• Security
• Cryptography
• Malicious AI
• +۳ مورد دیگر

Raphaël Phan is a Professor at the Malaysia School of Information Technology, Monash University, specializing in security, cryptography, and malicious AI. His research focuses on areas including privacy, emotion recognition, motion analysis, and generative AI, with a particular interest in adversarial behavior. He has published over 200 papers and secured research funding exceeding RM3 million from government and industry sources. Phan led projects such as the privacy-preserving data mining initiative funded by the UK government and Ministry of Defence, and co-designed the hash function BLAKE, a finalist in NIST’s SHA-3 competition. He currently supervises 18 PhD students and has graduated 13, focusing on topics like AI security, generative models, and neurological disease prediction using AI. Recent research contributions include advancements in adversarial AI, brain disorder identification via graph deep learning, and post-quantum cryptography. He actively serves on technical committees for major conferences (e.g., AAAI 2024, Eurocrypt 2024) and has an h-index of 49 with an Erdős number of 2. Key collaborations include projects on Parkinson’s disease tremor analysis, brain network prediction using signal decomposition, and Indo-Pacific post-quantum cryptography initiatives. His work aligns with UN Sustainable Development Goals addressing health and technological innovation.

• Computer Vision
• Object Recognition
• Scene Understanding
• +۷ مورد دیگر

Derek Hoiem is a Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC), affiliated with the Siebel School for Computing and Data Science since 2009. He holds a PhD in Robotics from Carnegie Mellon University (2007) and completed a Beckman Postdoctoral Fellowship (2008). As co-founder and Chief Science Officer of Reconstruct, he develops AI-driven tools for construction progress monitoring. His research focuses on computer vision, object recognition, scene understanding, and graphics. Key honors include IEEE Fellow (2022), University Scholar (2022), and the Sloan Research Fellowship (2013). He has received NSF CAREER and Intel Early Career awards, alongside best paper awards at CVPR (2006) and WACV (2015). His teaching excellence is reflected in numerous 'List of Teachers Ranked as Excellent' accolades across multiple semesters. Research interests span 3D reconstruction, neural networks, and multimodal models. His work addresses challenges in visual program generation, neural radiance fields, and construction monitoring through photogrammetry. He leads projects in continual learning, efficient AI systems, and explainable multimodal interactions. Professional contributions include grants from NSF and industry partnerships. He advises on cutting-edge AI applications in construction, graphics, and vision-language integration. His lab’s innovations bridge theoretical advancements with real-world systems like Reconstruct’s Visual Command Center.

• Digital Communications
• Neural Networks
• Medical Engineering
• +۳ مورد دیگر

Matthieu ARZEL is an Associate Professor in the Department of Mathematical and Electrical Engineering at IMT Atlantique. He holds an HDR (2021), PhD (2006), and Engineer degree (2002) from Telecom Bretagne/ENST. His research focuses on iterative processing for digital communications, low-power integrated circuits, high-speed digital circuits, and FPGA implementations in domains like neural networks, medical engineering, and communication systems. Key research interests include neuromorphic hardware, neural network pruning, federated learning compression, and energy-efficient signal processing. He has supervised 18 PhD students and contributed to projects like Ouessant coprocessor architectures and clique-based neural network circuits. His work bridges algorithm-architecture interactions, emphasizing low-power and embedded system applications. Recent publications highlight innovations in FPGA-based deep learning deployment and efficient neural network compression techniques. Publications span topics from real-time semantic segmentation on FPGA to collusion-resistant watermarking. His contributions address challenges in hardware-software co-design, iterative decoders for MIMO systems, and biomedical signal processing.

• Biomedical and bioinspired engineering
• Optical/Infrared/mm-Wave/T-ray vision systems
• Game theory and information theory
• +۸ مورد دیگر

Professor Derek Abbott is a faculty member in the School of Electrical and Mechanical Engineering at the University of Adelaide, within the Faculty of Sciences, Engineering and Technology. He holds the academic rank of Professor and is actively involved in supervising Masters and PhD students. His research interests span a wide range of interdisciplinary fields, including biomedical engineering, optical systems, game theory, quantum mechanics, stochastic phenomena, computational neuroscience, and forensic engineering. Professor Abbott has also contributed to energy systems optimization and space-based sensor technology. His recent work includes studies on neural network robustness, gravitational redshift measurement, and cardiovascular disease prediction using machine learning. His research often bridges theoretical concepts with practical applications in engineering and healthcare. Key research areas include the development of terahertz technology, bio-inspired engineering systems, and quantum game theory frameworks. He has published extensively on topics ranging from deep learning security to renewable energy grid optimization. His interdisciplinary approach integrates principles from physics, mathematics, and computer science to address complex real-world problems. Professor Abbott is eligible to supervise postgraduate research students and collaborates internationally on projects related to forensic genealogy, platonism in science, and energy storage solutions. His work often emphasizes the intersection of technology and societal challenges, such as climate change and healthcare innovation.

• Applied Signal Processing
• Inverse Problems
• Greedy Algorithms
• +۵ مورد دیگر

Florian Bossmann is an Associate Professor at the College of Mathematics, Harbin Institute of Technology (HIT), specializing in Applied Signal Processing with a focus on algorithms and applications. His research encompasses inverse problems, greedy algorithms, sparsity, and compressed sensing, with applications in seismic exploration, ptychography, and video processing. He holds a PhD in Mathematics from the University of Göttingen (2013) and a Diplom in Mathematics from the University of Duisburg-Essen (2009). Current projects include NSFC-funded seismic data interpolation and a start-up grant for applied signal processing research. He teaches courses like 'Signal and Image Processing' and 'Calculus for Civil Engineering'. Supervising Master/PhD students in signal processing, he emphasizes collaboration and innovation. His lab, the ASP Group, explores cutting-edge methods in multidimensional data reconstruction and algorithm design. Prominent publications focus on impedance inversion, neural network applications in geoscience, and object reconstruction techniques. He has led collaborative research initiatives with institutions like the Fraunhofer Institute and the Helmholtz Center for Environmental Health.

• Computational Fluid Dynamics
• Computational Electromagnetics
• Mesh Generation and Adaptivity
• +۳ مورد دیگر

Professor Kenneth Morgan is a faculty member in the School of Aerospace, Civil, Electrical and Mechanical Engineering at Swansea University, specializing in Civil Engineering. He holds the rank of Professor and is actively involved in research and postgraduate supervision. His research focuses on computational fluid dynamics, computational electromagnetics, mesh generation, and wave propagation. Education and Affiliations: While specific educational details are not provided, his academic position and research output indicate advanced training in engineering and applied mathematics. He is affiliated with the Morgan Advanced Studies Institute (MASI) as an academic leader and collaborator. Research Interests: Professor Morgan’s work spans advanced numerical methods for engineering simulations, including finite element and volume techniques, mesh optimization, and applications in aerodynamics, electromagnetics, and fluid dynamics. His recent projects include developing neural network-based aerodynamic design tools and high-fidelity simulations for complex geometries. Publications and Awards: He has authored numerous peer-reviewed articles, with recent contributions on NURBS-enhanced FEM, solar absorber design, and aerodynamic analysis of supersonic vehicles. His accolades include fellowships from the Royal Academy of Engineering and the Learned Society of Wales. Grants and Collaborations: He has led or contributed to major grants such as the Platform Grant for mesh generation (EPSRC, 2006–2011) and the FLAVIIR aeronautics initiative (EPSRC/BAE Systems, 2004–2007). His work bridges academia and industry, addressing challenges in aerospace, energy, and computational methods. Labs and Teams: As part of MASI, he collaborates in interdisciplinary teams tackling global challenges like sustainable engineering and advanced material systems.

Muhammad Aslam Jarwarمشاهده پروفایل

مدرس ارشد

• Internet of Things (IoT)
• Cyber Physical Systems
• Digital Twin
• +۹ مورد دیگر

Dr. Muhammad Aslam Jarwar is a Senior Lecturer in Smart Computing at the Department of Computing, Sheffield Hallam University. He holds senior roles in academia and industry, including Editorial Board membership at Intelligent Automation & Soft Computing and Managing Guest Editor at Sustainable Energy Technologies and Assessments . His research focuses on IoT security, cybersecurity modeling, AI-driven IoT systems, and digital twin applications. He leads the Secure Ontologies for IoT Systems (SOfIoTS) project, aimed at enhancing IIoT security through ontology integration and edge computing. Education includes a Ph.D. from Hankuk University of Foreign Studies (South Korea), MS from Quaid-i-Azam University (Pakistan), and BCS (Hons) from University of Sindh. He has extensive industry experience, including a Postdoctoral Researcher role at University of Manchester and UCL. Research interests span IoT-enabled healthcare systems, trustworthy IoT, industrial IoT, and federated learning. Key projects include RiverSense (river pollution monitoring) and collaboration with AquaSensor. His work emphasizes practical applications of AI and IoT in healthcare, energy, and smart cities. He has supervised multiple Ph.D. and Master’s students in IoT and cybersecurity. Awards include travel grants and research networking awards. His recent publications address IoT middleware, blockchain integration, and AI-empowered healthcare solutions.

Liu Hanمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Systems
• Machine Learning
• +۷ مورد دیگر

Liu Han is the Orrington Lunt Professor of Computer Science at Northwestern University, with a courtesy appointment in Industrial Engineering & Management Sciences. He directs the MAGICS (Modern Artificial General Intelligible and Computer Systems) Lab and the Center for Foundation Models and Generative AI (CFMG). Previously, he served as a professor at Princeton University and Johns Hopkins University, and as Director of the Deep Reinforcement Learning Center at Tencent AI Lab. His research focuses on integrating AI with computer systems, leveraging large foundation models and probabilistic graphical models to advance science, engineering, and business. He holds a joint PhD in Machine Learning and Statistics from Carnegie Mellon University, advised by John Lafferty and Larry Wasserman. Key research interests include transformer architectures, edge AI deployment, reinforcement learning, and genomic modeling . He actively contributes to academic governance as an associate editor for journals like the Journal of American Statistical Association and Technometrics , and serves as area chair for NeurIPS, ICML, and ICLR. His work emphasizes scalable machine learning solutions for heterogeneous data, with applications in genomics, traffic prediction, and proton beam control systems. Awards: Presidential Early Career Award (2025?), Alfred P Sloan Fellowship (Mathematics), IMS Tweedie New Researcher Award, ASA Noether Young Scholar Award, NSF CAREER Award, Howard B Wentz Award, and Umesh Gavaskar Memorial Dissertation Award. Advising & Grants: Advised students at top-tier institutions (specific names omitted due to lack of explicit listing). His grants focus on edge AI, foundation models, and ethical AI frameworks. Collaborates with Fermilab and CERN on real-time control systems using ML. Labs/Teams: MAGICS Lab (Northwestern) and CFMG Center, previously led Tencent's Deep Reinforcement Learning Center. Involved in interdisciplinary teams like Hetegraph-Mamba and the KGML-xDTD project.

Zijie Zhangمشاهده پروفایل

استادیار

• Federated learning
• Graphs
• Trustworthy machine learning
• +۳ مورد دیگر

Zijie Zhang is an Assistant Professor in the Department of Computer Science at the University of Texas at San Antonio (UTSA), affiliated with the College of Sciences. His research focuses on federated learning, graph-based machine learning, and trustworthy AI systems. He holds a Ph.D. in Computer Science from Auburn University and an M.S. from Stevens Institute of Technology. Education: Ph.D. in Computer Science, Auburn University M.S. in Computer Science, Stevens Institute of Technology His work emphasizes secure and robust machine learning techniques, including adversarial defense mechanisms, federated learning optimizations, and graph alignment algorithms. Recent publications explore topics like machine unlearning, resilient graph matching, and cross-lingual knowledge graph alignment. His research bridges theoretical advancements with practical applications in distributed systems and cybersecurity. Dr. Zhang’s contributions address challenges in privacy, model efficiency, and adversarial robustness, reflecting a commitment to advancing trustworthy AI methodologies.

Li Angمشاهده پروفایل

استادیار

• Machine Learning
• Edge Computing
• Federated Learning
• +۳ مورد دیگر

Dr. Li Ang is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Maryland, College Park. He joined UMD in August 2023 as a tenure-track faculty member. Prior to this, he was a Research Associate at Qualcomm AI Research and completed his Ph.D. in Electrical and Computer Engineering at Duke University in 2022, alongside another Ph.D. in Computer Science from the University of Arkansas. His research focuses on the intersection of machine learning and edge computing, emphasizing large-scale networked and trustworthy intelligent systems. Dr. Li has received notable awards, including the IEEE TCCPS Outstanding Ph.D. Dissertation Award, ACM KDD Best Student Paper Award, and the Cisco Research Award. Ph.D. in Electrical and Computer Engineering, Duke University (2022) Ph.D. in Computer Science, University of Arkansas MS in Management of Innovation and Venture Capital, Peking University Bachelor's in Computer Science, Henan University, China His research interests are centered at the intersection of machine learning and edge computing. He focuses on developing large-scale networked and trustworthy intelligent systems that address practical challenges in a collaborative, scalable, secure, and ubiquitous manner. Key areas include federated learning, distributed machine learning, edge computing, AI for IoT, and ensuring the trustworthiness of machine learning systems through fairness and robustness. Dr. Li has been recognized with several prestigious awards and honors: IEEE TCCPS Outstanding Ph.D. Dissertation Award ACM KDD Best Student Paper Award (2020) Duke ECE Department Outstanding Dissertation Award (2022-2023) Cisco Research Award (2025) Invited as an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems (TNNLS) Distinguished Paper Award at ACM CCS’24 (2024) Dr. Li advises several doctoral and master’s students, including notable advisees Guoheng Sun and Shwai He, who have received the Qualcomm Innovation Fellowship. He actively seeks motivated students to join his research group. His work has been supported by grants such as the Cisco Research Award and he has been involved in various collaborations, including with Qualcomm AI Research and academic institutions. Dr. Li leads a research group focused on edge intelligence and federated learning. His work often involves interdisciplinary collaborations, leveraging expertise in machine learning, systems, and distributed computing to advance practical AI solutions. Key projects include developing efficient federated learning frameworks and exploring trustworthy AI systems for edge devices.

Edith Cheuk Han NGAIمشاهده پروفایل

دانشیار

• Internet-of-Things
• Edge Intelligence
• Data Analytics
• +۵ مورد دیگر

Dr. Edith Cheuk Han Ngai is an Associate Professor in the Department of Electrical and Electronic Engineering at the University of Hong Kong (HKU), where she has been since 2020. Previously, she held an Associate Professor position at Uppsala University, Sweden. Her research focuses on Internet-of-Things (IoT), edge intelligence, data analytics, machine learning, network security, smart cities, and health informatics. She is an ACM Senior Member, IEEE Senior Member, and IEEE ComSoc Distinguished Lecturer (2023-2024). Her research contributions include the Green IoT project in Sweden (2014–2017), recognized in IVA’s 100-list (2020). She has led interdisciplinary projects like the Smart Water Auditing initiative, combining IoT and machine learning to address water conservation. Ngai has received awards such as the VINNMER Fellowship (2009) and a Meta Policy Research Award (2022). Her work on energy-aware edge computing and federated learning has been widely cited, ranking her in the top 1% globally by Clarivate Analytics (2021–2023). Ngai serves as an Associate Editor for multiple prestigious journals, including IEEE Transactions on Mobile Computing and IEEE Transactions on Industrial Informatics. She has organized conferences like IEEE GreenCom 2022 and IEEE IWQoS 2024. Her lab, the HKU IoT Lab, explores AI-driven smart systems, edge general intelligence, and healthcare applications. Recent work includes Radio2Text (mmWave-based speech recognition) and HealthPrism (multimodal health analytics). Ngai advises a dynamic research group with postdocs and PhD students focused on federated learning, edge computing, and wearable health sensing. Notable students include Zhihan Jiang (HKU Foundation PhD Award 2024) and Sukanya Jewsakul (BuildSys 2024 Best Paper Candidate). She collaborates with institutions like the HKU Center for Water Technology and Policy on projects like smart water auditing and health informatics.

Raj Rao Nadakuditiمشاهده پروفایل

دانشیار

• Statistical Signal Processing
• Random Matrix Theory
• Machine Learning
• +۷ مورد دیگر

Raj Rao Nadakuditi is an Associate Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan, College of Engineering. His research bridges statistical signal processing, random matrix theory, and machine learning with applications in sensing, imaging, and communications. Research Interests: Statistical Signal Processing and Learning Random Matrix Theory and Applications Signal Processing for Sensor Networks Light Transport Through Opaque Media Eigen-wavefront Imaging for Brain Circuit Mapping High-Dimensional Data Analysis His recent work focuses on overcoming fundamental limits in imaging through highly scattering media, such as brain tissue, by leveraging eigen-wavefronts and algorithmic innovations. This has direct implications for the BRAIN Initiative and optical neuroimaging. Publications Trends: His most recent articles reflect a strong emphasis on the application of random matrix theory to signal detection, covariance estimation, and imaging through random media. Key themes include eigenvalue analysis, high-dimensional statistics, spectral methods, and wavefront shaping for deep-tissue imaging. Scientific Awards: DARPA Young Faculty Award (2014) ONR Young Investigator Program Award AFOSR Young Investigator Program Award Advising and Grants: Prof. Nadakuditi has advised students in advanced signal processing and machine learning. His research is generously supported by the National Science Foundation, DARPA, Office of Naval Research, Air Force Office of Scientific Research, and Army Research Office’s MURI program. He has led projects on fundamental limits of imaging and robust signal processing in low-SNR environments. Labs and Teams: He collaborates with Prof. Eric Michielssen on computational electromagnetics and is part of the broader EECS research community at the University of Michigan focused on autonomy, AI, and precision health. His group works on theoretical and applied aspects of sensing and imaging systems.

Hui Liمشاهده پروفایل

دانشیار

• Information Retrieval
• Data Mining
• Data Management
• +۸ مورد دیگر

Hui Li is an Associate Professor in the Department of Computer Science and Technology at the School of Informatics, Xiamen University. He is affiliated with the Media Analytics & Computing Laboratory (MAC Lab) and holds a Ph.D. in Computer Science from the University of Hong Kong. Ph.D., Computer Science, University of Hong Kong His research centers on Information Retrieval, Data Mining, and Data Management, with a strong emphasis on Recommender Systems. Key areas include shilling attacks and defenses, bias analysis and debiasing, large language model-based recommendation, generative information retrieval, code retrieval, and multi-modal large language models. His work bridges theoretical innovation with practical applications in real-world platforms. His recent publications span top-tier journals and conferences such as IEEE TKDE, TNNLS, TOIS, KDD, SIGIR, AAAI, ACL, and ICLR. The publications demonstrate a consistent focus on robustness, fairness, and efficiency in recommender systems, increasingly integrating large language models and graph-based methods. Themes include adversarial attacks, self-supervised learning, and data augmentation using LLMs. Hui Li actively supervises graduate students, many of whom have published in CCF-A and CCF-B ranked venues. Notable students include Zhang Sheng, Liu Hao, and Li Lianyun. He has contributed to open science by releasing datasets like the Dianping datasets for sequential and social recommendation research. His work has practical implications in e-commerce, social platforms, and software engineering tools. He collaborates with industry partners such as Alibaba, Tencent, and Pinduoduo, where several of his students have been placed. His research is supported by publications in high-impact journals and participation in leading AI conferences.

Sine Reker Hadrupمشاهده پروفایل

استاد

• T Cell Immunology
• Cytotoxic T-Cell
• Epitope
• +۴ مورد دیگر

Professor Sine Reker Hadrup is a leading academic at the Technical University of Denmark , where she serves as Head of Section in the Department of Health Technology, Experimental & Translational Immunology . Her research focuses on T-cell immunology , cancer immunotherapy , and neoantigen discovery , with a particular emphasis on leveraging immunogenicity prediction and peptide-MHC multimer technology for advanced cancer treatments. Department: Experimental & Translational Immunology School: School of Health Technology Academic Rank: Professor Her work bridges computational biology with clinical immunology , exploring cross-reactive CD8+ T-cell responses to tumor and microbiota-derived antigens, as well as allogeneic stem cell transplantation monitoring. Recent publications highlight her contributions to T-cell receptor modeling , iPSC-T cell differentiation , and GEAR-T cell therapy development. The latest studies by Prof. Hadrup's team demonstrate innovations in neoantigen vaccine design and computational prediction of immune regulators using single-cell transcriptomics . Her group's modular protein language models and minibinder engineering are pushing the frontiers of precision immunotherapy for cancer patients.