جست‌وجوی استاد و پژوهشگر

Nazli Goharianمشاهده پروفایل

استاد بالینی

• Information Retrieval
• Text Mining
• Natural Language Processing
• +۱ مورد دیگر

Nazli Goharian is a Clinical Professor of Computer Science at Georgetown University and Associate Director of the Information Retrieval Lab. She holds a PhD from Florida Institute of Technology and joined Georgetown in 2010 after industry experience and previous academic positions at Illinois Institute of Technology. Education: PhD Computer Science, Florida Institute of Technology (2001) MSc Computer Science, George Mason University (1995) BSc Computer Science, Dortmund University (1992) Her research spans information retrieval, text mining, and natural language processing with applications in health/medical domains. She focuses on developing computational methods for medical search, mental health analysis from social media, clinical text summarization, and adverse drug reaction detection. Her recent publications (2020-2016) predominantly focus on neural ranking models, transformer architectures for document retrieval, and clinical NLP applications. Notable trends include work on BERT-based re-ranking, zero-shot multilingual retrieval, and ontology-aware medical summarization. Awards & Honors: EMNLP 2017 Best Long Paper Award COLING 2018 Honorable Mention & Area Chair Favorite Julia Beveridge Award for Faculty (IIT, 2009) Multiple Teaching Excellence Awards (2002-2007) Research Leadership: She has supervised 6 PhD students to completion with placements at leading institutions. Secured over $500,000 in research funding from NSF, Adobe, and international partners. Founded the Semi-Annual Graduate Research Presentation Days at Georgetown and served as Program Chair for ECIR 2024. She leads the Information Retrieval Lab which focuses on developing novel algorithms for efficient document retrieval, cross-lingual search, and specialized applications in healthcare text analysis.

• Polymeric Biomaterials
• Hydrogels and Microgels
• Infection-Resisting Biomaterials
• +۴ مورد دیگر

Matthew Libera is a Professor of Material Science and Engineering at Stevens Institute of Technology, affiliated with the Charles V. Schaefer, Jr. School of Engineering and Science. He leads the Laboratory for Multiscale Imaging (LMSI), a shared facility for advanced imaging and analysis. His work focuses on biomaterials, hydrogels, infection-resistant surfaces, and electron microscopy techniques. Libera has held roles including Associate Dean of Engineering and Science (2013–2018) and has been a visiting professor at institutions like the University of Rhode Island (2021–2022). He chairs the Stevens Conference on Bacteria-Material Interactions and has authored numerous publications on antimicrobial surfaces and material characterization. His research interests span biomaterials-associated infections, directed self-assembly of polymers, and cryo-electron microscopy applications. He pioneered microgel-based antimicrobial coatings and developed molecular beacon technologies for diagnostics. Libera’s awards include the Morton Professorship for Teaching Excellence (2010–2011) and the Jess N. Davis Award for Research (1998). His work integrates nanotechnology, material science, and biomedicine to address challenges in infection prevention and biomaterial design. Libera’s publications highlight advancements in microgel functionality, surface patterning via electron-beam lithography, and antimicrobial delivery systems. His lab’s capabilities in multiscale imaging enable detailed studies of biomaterial-bacteria interactions. Ongoing efforts aim to optimize self-defensive materials for medical implants and diagnostic tools.

Paul Van Oorschotمشاهده پروفایل

استاد

• Computer Security
• Authentication
• Internet Security
• +۷ مورد دیگر

Paul Van Oorschot is a Professor at the School of Computer Science, Carleton University. He has held the Canada Research Chair in Authentication and Computer Security from 2002 to 2023. His expertise spans authentication, applied cryptography, and network security. He co-authored the seminal Handbook of Applied Cryptography and led the NSERC Internetworked Systems Security Network (2008–2013). His research focuses on enhancing security in systems, software, and web authentication, including methods to augment passwords with geolocation and device recognition. He holds a Ph.D. from the University of Waterloo and was awarded the J.W. Graham Medal (2000) and Fellowship in the Royal Society of Canada (2011). Education: Ph.D. in Computer Science from the University of Waterloo (1988) His research interests include authentication systems, public-key infrastructure, smartphone security, and usability challenges in security design. He has contributed to frameworks like OWL for password-based key exchange and SLV for server location verification. His work addresses both technical and human factors in securing modern computing environments. Key contributions include analysis of TLS interception, memory safety in programming languages, and evaluating IoT security best practices. He has published extensively on topics ranging from side-channel attacks to cryptographic protocol vulnerabilities. Awards: J.W. Graham Medal in Computing and Innovation (2000), Fellow of the Royal Society of Canada (2011) His academic leadership includes roles in shaping cybersecurity education and policy, emphasizing the need for rigorous scientific approaches in security research. His research group, the Carleton Computer Security Lab (CCSL), drives interdisciplinary projects in software security and system administration tools.

Jasmin Grosingerمشاهده پروفایل

دانشیار

• Radio Frequency Identification
• Antenna Design
• Internet of Things
• +۲ مورد دیگر

Jasmin Grosinger is an Associate Professor at Graz University of Technology's Institute of Microwave and Photonic Engineering, specializing in wireless systems and RF engineering. Her research advances sustainable wireless technologies through innovations in energy harvesting and communication systems. Primary research areas include: Radio Frequency Identification (RFID), antenna design for IoT applications, wireless power transfer efficiency, and development of batteryless sensor systems. Recent work emphasizes miniaturization challenges in metal environments and radiation-hardened space applications. Publication analysis reveals strong focus on: impedance measurement methodologies, NFC/WPT interoperability solutions, ultra-low power circuit design, and robust wireless systems. Research consistently addresses energy efficiency in passive electronics. Awards recognize contributions to microwave theory and practical implementations: Administrative Committee Member of IEEE MTT-S, Distinguished Microwave Lecturer award, and multiple best paper contest awards. Current projects investigate electromagnetic compatibility in challenging environments and next-generation wireless standards.

• Antenna Design
• Microwave Engineering
• Dielectric Materials
• +۲ مورد دیگر

Wolfgang Bösch is a Professor at Graz University of Technology's Institute of Microwave and Photonic Engineering, specializing in advanced RF components and measurement techniques. His research advances high-frequency systems through innovations in antenna technology and electromagnetic theory. Research domains include: metamaterial-based antennas, precision measurement calibration, microwave filter optimization, and 3D-printed RF components. Recent work demonstrates strong focus on millimeter-wave systems and reconfigurable antenna arrays. Publications highlight expertise in: machine learning for filter design, metasurface applications, PCB transitions for high-frequency systems, and uncertainty quantification in RF engineering. Research consistently addresses miniaturization and performance optimization challenges. Awards recognize contributions to measurement science and antenna design: Fellow of IET, Houska Prize, and best paper awards. Current laboratories investigate liquid crystal antenna systems and error calibration methodologies for next-generation wireless systems.

Amir Rahmatiمشاهده پروفایل

استادیار

• System Security
• Privacy
• Internet of Things
• +۴ مورد دیگر

Overview Amir Rahmati is an Assistant Professor in the Department of Computer Science at Stony Brook University , where he directs the Ethos Security & Privacy Lab and is a member of the National Security Institute. His work focuses on securing emerging technologies like IoT, AR, and ML systems. Education PhD in Computer Science & Engineering, University of Michigan (2017) Research Focus His research addresses security threats in emerging systems, including: Cyber-Physical System vulnerabilities Adversarial ML attacks Hardware security Privacy-preserving frameworks Notable contributions include work on cryptocurrency scam detection, neural network robustness, and AR system security. Grants & Awards Supported by the Air Force Office of Scientific Research (AFOSR), Office of Naval Research (ONR), Meta, NVIDIA, and IBM. His research has been featured in MIT Technology Review , Washington Post , and Bloomberg . Advising Seeking students with expertise in hardware, software, ML, UX, or network protocols passionate about security/privacy. Apply via the graduate program and fill out his interest form. Labs & Collaborations Leads the Ethos Lab focusing on securing IoT, AR, and ML systems. Collaborates on projects like Erebus (AR access control) and Valve (serverless computing security).

Yuan-Fang Liمشاهده پروفایل

دانشیار

• Knowledge Graphs
• Natural Language Processing
• Multimodality
• +۵ مورد دیگر

Yuan-Fang Li is an Associate Professor in the Department of Data Science & AI at Monash University's Faculty of Information Technology. He also serves as Associate Dean International. His research focuses on knowledge graphs, natural language processing, multimodality, and graph representation learning. He holds a PhD from National University of Singapore (2006) and a Bachelor of Computing (Honours) from the same institution (2002). Affiliations: Monash University (since 201?), National University of Singapore (PhD 2002-2006) Key Projects: Leading research on neuro-symbolic systems (HARNESS project), large-scale multimodal knowledge management, and maritime knowledge graphs Teaching: Taught courses including FIT4002, FIT4004, and supervised over 20 PhD students Research interests include complex question answering over knowledge graphs, knowledge extraction from text/images, and structural/temporal graph learning. He has published 152+ works with notable contributions to scene graph generation, event extraction, and LLM-based reasoning. Key awards include the 2020 Best Student Paper Award and 2017 Kurzweil Prize. Grants: ARC Discovery Projects, industry collaborations (e.g., Outotec Oy) Labs/Teams: Active in Monash's Data Science & AI research groups, leading neuro-symbolic AI initiatives

Vivy Suhendraمشاهده پروفایل

دانشیار

• Software Assurance
• Security and Privacy
• Cybersecurity
• +۱ مورد دیگر

Vivy Suhendra serves as Associate Professor of Practice and Programme Director for Master Programmes at the National University of Singapore's School of Computing, while also holding the position of Assistant Dean for Graduate Studies. Previously, she led the Singapore Cybersecurity Consortium (SGCSC) as Executive Director from 2016 to 2022, driving collaborative cybersecurity research between academia, industry, and government agencies. Her career at NUS spans over two decades, beginning as a Research Assistant before advancing to her current leadership roles. Her academic credentials include: Ph.D. in Computer Science, National University of Singapore (2009). Thesis: "Memory Optimizations for Time-predictable Embedded Software". Advisors: Abhik Roychoudhury and Tulika Mitra. B.Comp. (Honors) in Computer Science, National University of Singapore (2004). Dr. Suhendra's research integrates Software Assurance, Cybersecurity, Security and Privacy, and Embedded Systems domains. Her work bridges theoretical foundations with practical applications, particularly in national cybersecurity ecosystem development, smart grid security protocols, denial-of-service mitigation techniques, and real-time embedded system optimization. This interdisciplinary approach enables innovative solutions for critical infrastructure protection and time-predictable software execution in multi-core environments. Her 14 selected publications (2004-2020) demonstrate an evolving research trajectory from foundational embedded systems timing analysis to applied cybersecurity solutions. Early work focused on memory optimization for predictable execution in multi-core embedded systems, which naturally transitioned into cybersecurity applications for smart grids, cloud environments, and national infrastructure. This progression highlights her ability to translate low-level system expertise into high-impact security frameworks for complex real-world systems. Scientific recognition includes: Microsoft Research Asia Fellowship (2006) Valedictorian at NUS School of Computing Ph.D. Commencement (2010) While specific graduate student advising details aren't provided, her leadership as SGCSC Executive Director involved extensive mentorship across academic-industry partnerships. She has also contributed significantly to the research community through roles including Conference Chair for ESEC/FSE 2022 and Workshops Committee Member for ICSE 2024. Dr. Suhendra established the Singapore Cybersecurity Consortium as a national platform for collaborative R&D during her directorship (2016-2022). Though no personal laboratory is specified, her research leadership manifests through cross-institutional teams focused on cybersecurity innovation, particularly in critical infrastructure protection and embedded systems security where she maintains active publication records.

Nick Heardمشاهده پروفایل

استاد

• Computational Bayesian Inference
• Clustering
• Changepoint Analysis
• +۶ مورد دیگر

Nick Heard is a Professor and Chair in Statistics at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. His research focuses on computational Bayesian inference, clustering, and changepoint analysis applied to dynamic networks (e.g., computer networks, social networks) and bioinformatics. He leads the EPSRC-funded NeST project on Network Stochastic Processes and Time Series, collaborating with universities including Bristol, Oxford, and LSE. His work bridges statistical theory with applied problems in cyber-security and neuroscience. Research Interests - Modelling large dynamic networks - Changepoint analysis and anomaly detection - Statistical methods for cyber-security - Bayesian computation and inference - Spectral clustering and graph embeddings Grants & Collaborations - Co-leads the NeST project on Dynamic graph embeddings: procedures and inference - EPSRC Programme Grant (EP/T004870/1) supporting Network Stochastic Processes and Time Series research Software & Tools - Developed open-source packages for Bayesian changepoint analysis (e.g., changepoints ) - Code for p-value combination methods ( standardised_partial_product )

Simon Mooreمشاهده پروفایل

استاد

• Computer Architecture
• Computer Security
• Hardware Security
• +۳ مورد دیگر

Simon Moore is a Professor of Computer Engineering at the University of Cambridge's Department of Computer Science and Technology. He leads the Computer Architecture research group, focusing on secure processors and subsystems, particularly the CHERI project. His work emphasizes formal verification, hardware-software co-design, and scalable security solutions. He is a Fellow and Director of Studies at Trinity Hall, overseeing undergraduate admissions and mentoring in Computer Science. Research Interests: Moore's primary focus is the CHERI secure processor architecture, integrating RISC-V cores with formal verification. His work spans secure hardware design, memory safety, and embedded systems. Notable contributions include the CHERI-RISC-V microarchitecture, CheriABI, and formal verification frameworks. Key Projects: CHERI, CheriBSD, Morello (ARM collaboration) Recent Achievements: Test of Time Award (IEEE Security & Privacy 2025), finalist for Bhattacharyya Award (2022) Grants: Innovate UK Digital Security by Design, DARPA Mission Oriented Resilient Clouds Publications: Over 200 papers on secure architectures, including influential work on CHERI's capability model, formal verification, and hardware security. Recent focus areas include temporal memory safety, embedded system security, and GPU-based capability systems. Labs/Teams: Directs the Computer Architecture Group and collaborates with industry partners like ARM and Microsoft on CHERI implementations.

• Machine Learning
• Data Mining
• Computer Vision
• +۳ مورد دیگر

Professor Xue Li is a faculty member in the School of Electrical Engineering and Computer Science at the University of Queensland. His research focuses on machine learning, data mining, and their applications in healthcare, materials science, and computer vision. He has authored over 300 publications, including seminal works on knowledge graph completion, video quality enhancement, and alloy design using machine learning. His work bridges theoretical advancements with real-world applications, such as clinical diagnosis andTinyML systems. Key research interests include graph representation learning, medical informatics, and efficient algorithms for multimedia data. Notable contributions include developing commonsense-enhanced relation extraction models and frameworks for compressed video reconstruction. His research also addresses challenges in federated learning and privacy-preserving genomics. Prof. Li has collaborated extensively with industry and academia, contributing to projects in RFID systems, electronic nose pattern recognition, and cybersecurity. His work is published in top-tier venues like IEEE Transactions and ACM conferences. Despite no listed awards, his prolific output underscores academic impact.

Marius Portmannمشاهده پروفایل

استاد

• Cybersecurity
• Computer Networks
• Internet of Things (IoT)
• +۳ مورد دیگر

Professor Marius Portmann is the UQ-Cisco Chair of Network Security at the School of Electrical Engineering and Computer Science (EECS), University of Queensland. His expertise spans Cybersecurity, IoT, and Applied AI. He holds a PhD from ETH Zurich (2003) and has led research in Software Defined Networking (SDN), blockchain, and energy-harvesting IoT systems. Education: PhD in Electrical Engineering from Swiss Federal Institute of Technology (ETH Zurich), 2003. Research focuses on securing IoT networks, AI-driven intrusion detection, and sustainable sensor systems. He has pioneered self-powered IoT systems using energy harvesters and developed frameworks like FlowTransformer for network analysis. His work bridges theoretical advancements with practical applications in smart tourism, energy efficiency, and edge computing. Recent publications highlight innovations in DDoS detection (P4-Secure), sensor-based environmental monitoring (EcoShower), and graph-based anomaly detection (XG-BoT). His datasets (e.g., NF-ToN-IoT-v3) are widely used in ML-based cybersecurity research. Collaborations include industry partners like Cisco and institutions like RMIT. Grants and leadership roles in interdisciplinary projects underscore his impact. He advises on IoT security standards and contributes to open-source tools for network research. Current projects explore edge-AI integration and sustainable sensor networks.

• Ultra-low power systems
• Microelectronics
• Integrated Circuits
• +۱۱ مورد دیگر

Professor Aniruddha Desai is a Research Professor and Director of the Centre for Technology Infusion (CTI) at La Trobe University. He holds a Bachelor’s in Industrial Electronics, a Master’s in Micro-electronics, and a PhD in Computer Science. His expertise spans microelectronics, AI, IoT, and sensor networks, with a focus on socially impactful applications like transportation, healthcare, and precision agriculture. Research Interests: Ultra-low power systems Micro-nano electronics AI/ML and edge computing IoT and sensor networks Transportation and logistics Major Projects: Led multi-million-dollar R&D programs in areas such as smart cities, energy management, and smart farming. Notable collaborations include the IIT Kanpur - La Trobe University Research Academy and the Asian Smart Cities Research and Innovation Network. Awards: Recipient of the 2016 Vice-Chancellor’s Award for Research Excellence and the 2020 Victorian Tall Poppy Award for Science. Served on advisory panels for the Australian Research Council and provided expert testimony in parliamentary inquiries. Labs/Teams: Directs the CTI, which delivers technology-based innovations to industry and government. Co-founded the Asian Smart Cities network to advance urban technology solutions.

• Artificial Intelligence
• Neurocomputing
• Quantum AI
• +۵ مورد دیگر

Dr. Richard Jiang is a Senior Lecturer (Associate Professor) at Lancaster University's School of Computing and Communications. His research focuses on Artificial Intelligence, Neurocomputing, Quantum AI, Privacy Computing, and Medical Computing. He has pioneered secure pattern recognition in encrypted domains and quantum neuromorphic computing. With over £1M in research grants from EPSRC and others, he has authored 100+ publications and supervised over 20 PhD students. Dr. Jiang's work includes the Face2Brain method for neurodegenerative assessment and explainable models for brain aging analysis. He contributes actively to academic committees, editorial boards, and conferences like the World Conference on eXplainable AI. His research spans ethical AI frameworks, quantum algorithms for medical imaging, and privacy-preserving biometric systems.

Ediz Cetinمشاهده پروفایل

دانشیار

• Radio Frequency Interference Detection
• Fault-Tolerant Reconfigurable Circuits
• Machine Learning Applications
• +۴ مورد دیگر

Ediz Cetin is an Associate Professor in Digital Electronics Engineering at Macquarie University's School of Engineering and a member of the Astrophysics and Space Technologies Research Centre. He serves as Course Director for the MEng Electronics Engineering program and Chair of the School's Postgraduate Coursework Committee. His research focuses on radio frequency interference mitigation, fault-tolerant reconfigurable circuits for space applications, machine learning in RF signal analysis, and low-power digital circuit design. Education: PhD in Signal Processing (Unsupervised Adaptive Signal Processing Techniques for Wireless Receivers) B.Eng. (Hons.) in Control and Computer Engineering Research Interests: RF interference detection and localization GNSS anti-jamming and spoofing detection FPGA-based reconfigurable systems Space instrumentation and CubeSat technologies Machine learning for signal processing Awards: Excellence in Learning Innovation (FSE Teaching Award, 2022) Highly Commended Finalist – Vice-Chancellor’s Award for Learning Innovation (2022) Innovative Approaches – Highly Commended (FSE Teaching Award, 2020) Key Projects: SmartSat CRC (2020–2026): Smart Satellite Technologies and Analytics Spacecraft Innovation Lab (2021–2022) CubeSat Biological Payload (2019–2022) Teaching Contributions: Led the 'Improving Student Engagement with Anywhere and Any-time Laboratory Access' initiative (2019–2020), enhancing remote lab accessibility for students.