جست‌وجوی استاد و پژوهشگر

Courtney N. Reedمشاهده پروفایل

مدرس

• Human-Computer Interaction
• Music Technology
• Digital Instrument Design
• +۵ مورد دیگر

Courtney N. Reed is a Lecturer in Digital Technologies at Loughborough University London, where she joined in November 2023. She maintains a dual role as a visiting research fellow at the Max Planck Institute for Informatics. Her academic journey includes a BMus in Electronic Production and Design from Berklee College of Music (2016), followed by an MSc (2018) and PhD (2023) in Computer Science from Queen Mary University of London. Prior to her current position, she completed postdoctoral research at both the Max Planck Institute for Informatics and King's College London. Bachelor of Music: Electronic Production and Design, Berklee College of Music (2016) Master of Science: Computer Science, Queen Mary University of London (2018) Doctor of Philosophy: Computer Science, Queen Mary University of London (2023) Dr. Reed's research explores the entangled relationships between humans, bodies, instruments, and technology in music interaction, with particular focus on vocal electromyography (VoxEMG) and the vocalist-voice relationship. Her work incorporates feminist and post-human theories to examine sociopolitical contexts within arts technology, aiming to design for creativity while acknowledging individual, messy bodies in artistic practice. She has developed an open-source platform for vocal electromyography to investigate how biosignal feedback changes understanding and perception of the body in vocal performance. Her interdisciplinary approach bridges music technology, human-computer interaction, and embodied interaction studies. Analysis of Dr. Reed's recent publications (2023-2025) reveals a strong thematic focus on embodied interaction in music technology, with particular emphasis on vocal performance, biosignal feedback, and the philosophical underpinnings of digital instrument design. Her work consistently integrates theoretical frameworks like Karen Barad's agential realism with practical applications in digital musical instruments. Key trends include the exploration of ambiguity in data representation, the sociocultural dimensions of timbre in instrument design, and the development of novel methodologies for understanding embodied musical experiences through micro-phenomenology and ethnographic approaches. ACM SIGCHI Outstanding Dissertation Award (2024) for her thesis 'Imagining & Sensing: Understanding and Extending the Vocalist-Voice Relationship Through Biosignal Feedback' Best Newcomer Award at Loughborough University London's Community Awards Celebration (2024) Dr. Reed actively contributes to the academic community through conference organization and leadership roles. She serves as Member-at-Large on the NIME Board, previously chaired papers for NIME 2024, and co-organized the IBM SkillsBuild Sprint at Loughborough London. She has also chaired sessions at the ACM TEI Conference and co-chaired the Student Design Competition. Her collaborative work spans multiple institutions and includes significant contributions to interdisciplinary projects that bridge music, technology, and human experience. She has been instrumental in developing the senSInt research group and the RaveNET wearable network project. Dr. Reed leads the senSInt research group which focuses on sensorimotor interaction in music and performance contexts. The group develops innovative technologies including the VoxEMG platform for vocal electromyography, the Bones anti-corset for vocal performance, and the RaveNET network of wearable biosensing nodes. These projects explore the intersection of biosignals, embodied interaction, and musical expression, creating novel frameworks for understanding how technology mediates human creativity and performance. The group frequently collaborates with musicians, technologists, and theorists to develop and test these systems in real-world performance contexts.

Martin Wellsمشاهده پروفایل

استاد

• Statistical Inference
• Bayesian Methods
• Machine Learning
• +۱ مورد دیگر

Martin Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in Social Statistics, Clinical Epidemiology, and Industrial Labor Relations. Since joining Cornell in 1987, he has developed methodologies spanning Bayesian inference, tensor analysis, and machine learning applications in biomedicine and finance. His research integrates statistical theory with computational innovations, particularly in high-dimensional modeling and quantum-inspired algorithms. Recent work focuses on geometric approaches to tensor decomposition, misclassification correction methods, and phylodynamic models incorporating dormancy effects. Professor Wells teaches statistical methodology across disciplines including law, medicine, and biology, adapting analytical frameworks to diverse research contexts. His interdisciplinary collaborations extend to Weill Medical College and the School of Industrial and Labor Relations.

Lara Andersonمشاهده پروفایل

دانشیار

• String Theory
• Mathematical Physics
• Calabi-Yau Manifolds
• +۴ مورد دیگر

Lara Anderson is an Associate Professor in the Department of Physics at Virginia Tech's College of Science. Her research focuses on the intersection of geometry and string theory, particularly in the context of Calabi-Yau manifolds, F-theory, and heterotic string compactifications. She holds a Ph.D. from the University of Oxford, with a thesis in Mathematical String Theory. Her research interests include string phenomenology, geometric structures in compactifications, and the application of machine learning to approximate Calabi-Yau metrics. Key areas of exploration involve fibrations, Yukawa couplings, and moduli stabilization in heterotic and F-theory frameworks. Anderson has been recognized with a Graduate Research Fellowship in 2004. Her work spans over 50 publications, including contributions to understanding dualities between heterotic and F-theory models, the role of spectral covers in heterotic compactifications, and algorithmic approaches to heterotic phenomenology. She has also organized workshops on F-theory and string geometry, reflecting her leadership in interdisciplinary research. Her research has implications for both pure mathematics and particle physics, bridging abstract geometric concepts with concrete physical predictions. Current projects include exploring the geometric constraints in dual string models and developing numerical tools for studying Calabi-Yau metrics.

Lili Qiuمشاهده پروفایل

استاد

• Internet Networking
• Wireless Networking
• Mobile Systems
• +۶ مورد دیگر

Lili Qiu is a Professor in the Department of Computer Science at The University of Texas at Austin, where she has been a faculty member since January 2005. She is an active member of the Wireless Networking and Communications Group (WNCG) and has made significant contributions to the field of networking research. Dr. Qiu previously spent 2001-2004 as a researcher at Microsoft Research in Redmond, WA, before joining UT Austin. Dr. Qiu's research spans Internet and wireless networking with a current focus on wireless network management and content distribution in mobile networks. Her work extends into diverse applications including acoustic imaging, metasurface applications, healthcare sensing technologies, and AI systems. She has pioneered research in areas such as acoustic motion tracking, passive RFID sensing, and wireless network optimization. Her research demonstrates a consistent pattern of innovation that bridges theoretical networking concepts with practical real-world applications, particularly in mobile and wireless systems. Her extensive publication record shows a clear evolution from fundamental networking research to increasingly interdisciplinary work that combines wireless systems with healthcare applications, AI, and novel sensing technologies. Recent publications demonstrate growing integration of machine learning techniques with traditional networking problems, as well as expansion into healthcare applications like Parkinson's disease modeling and non-invasive glucose monitoring. ACM Fellow IEEE Fellow National Academy of Inventors (NAI) Fellow ACM Distinguished Scientist NSF CAREER award Google Faculty Research Award Best paper award at ACM MobiSys'18 Best paper award at IEEE ICNP'17 Dr. Qiu has supervised numerous students, including a PhD dissertation that won the SIGMOBILE best dissertation award in 2020. She has served in significant leadership roles including chair of ACM SIGMOBILE, General co-chair for ACM MobiCom 2025, and various conference chair positions for IEEE ICNP, ACM CoNEXT, and other major networking conferences. Her research has been supported by substantial grants from NSF, Google, and other organizations, enabling her to lead cross-disciplinary research teams. As a member of the Wireless Networking and Communications Group at UT Austin, Dr. Qiu leads research efforts that combine networking expertise with innovations in sensing technologies, metasurfaces, and AI systems. Her lab has produced numerous influential results in mobile networking, wireless sensing, and network management, with applications spanning healthcare, consumer electronics, and communication infrastructure.

Nicola Dragoniمشاهده پروفایل

استاد

• Cybersecurity
• Internet of Things (IoT)
• Machine Learning
• +۷ مورد دیگر

Nicola Dragoni is a Professor in Cybersecurity Engineering at the Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU). As Deputy Director and Head of Section, he leads research initiatives focused on securing emerging technologies. Key Research Areas : Internet of Things (IoT) security, machine learning for intrusion detection, cyber-deception techniques, fog computing, malware analysis, blockchain applications, and wireless sensor network security. Supervision : Actively supervising multiple PhD students in projects related to cyber-deception, moving target defense, and bio-inspired security mechanisms. Recent Publications : Contributions to IoT honeypots, drone identification via RF signals, passkey adoption challenges, and cyber range taxonomies.

Chew Lock Yueمشاهده پروفایل

استاد

• Complex Systems
• Nonlinear Dynamics
• Quantum Physics
• +۵ مورد دیگر

Professor Chew Lock Yue is an Associate Dean (Students) in the College of Science and a Full Professor in the School of Physical & Mathematical Sciences at Nanyang Technological University (NTU). He holds a B.Eng (Hons) in Electrical Engineering from the National University of Singapore (1991), an M.Sc in Electrical Engineering from the University of Southern California (1997), and a Ph.D. in Theoretical Physics from NUS (2004). His research focuses on complex systems, nonlinear dynamics, quantum thermodynamics, and urban systems modeling. Current projects include thermodynamics of information processing, machine learning integration with complex systems, and statistical physics of sea-level rise. Professional roles span technical leadership at DSO National Laboratories (1992-2005), academic appointments since 2005 (Assistant Professor to Full Professor), and administrative roles including Cluster Deputy Director at NTU’s Data Science & Artificial Intelligence Research Centre (2018-2021). He has received multiple teaching awards, including the Nanyang Award for Excellence in Teaching (2007) and the Best Faculty Mentor Award (2013). Research interests also encompass social-ecological systems, quantum heat engines, and spatial agglomeration patterns in urban contexts. His work bridges physics with interdisciplinary challenges like climate modeling and machine learning, with over 150 publications in peer-reviewed journals. Active in education, he teaches courses on quantum mechanics, nonlinear dynamics, and statistical physics.

• Smart Cities
• Cyber-Physical Systems (CPS)
• Internet of Things (IoT)
• +۱۰ مورد دیگر

Dr. Hung Cao is an Assistant Professor of Computer Science at the University of New Brunswick, where he directs the Analytics Everywhere Lab. His work focuses on interdisciplinary research in Cyber-Physical Systems (CPS), IoT, Edge/Fog/Cloud Computing, and Explainable AI, addressing societal challenges through data-driven solutions. Prior roles include PostDoc Fellow and Data Scientist at the People in Motion Lab, UNB, and Lecturer/Researcher at Vietnam National University. He holds a Ph.D. in Geomatics Engineering (specializing in Data Science) from UNB (2020), an M.Sc. in Computer Science from University College Dublin (2015), and a B.Eng. from Vietnam National University (2011). Research interests span Smart Cities, Embedded AI, TinyML, Federated Learning, and Real-time Systems. He has led projects with Cisco, NB Power, and other industry partners to develop scalable analytics frameworks for IoT applications. Dr. Cao actively contributes to technical communities (IEEE Smart City, Edge Computing, etc.), serving as a reviewer for journals and conferences, and a Topic Editor for Electronics Journal . His innovations include the Analytics Everywhere framework for spatio-temporal data analysis, MACeIP platform for smart cities, and energy-efficient IoT systems for environmental monitoring. Current work emphasizes human-centered AI for healthcare diagnostics and industrial inspection systems.

• Channel decoders
• Circuits for communications
• Cryptography
• +۸ مورد دیگر

Guido Masera is a Full Professor at the Department of Electronics and Telecommunications (DET) at the Polytechnic University of Turin, where he has been actively involved in teaching and research for over two decades. He serves as a Member of the Board of Directors, Member of the GEDI Observatory for Gender Equality, Diversity and Inclusion, and Member of the Permanent University Observatory for monitoring the academic supply chain. His research interests span across channel decoders, circuits for communications, cryptography, deep learning, digital integrated circuits, field programmable gate arrays (FPGA), and hardware design. His work focuses on VLSI architectures for image and video coding, digital architectures for error correcting codes, application specific approximate computing, VLSI architectures for machine learning, digital architectures for bio-inspired processing, digital architectures for post-quantum cryptography, bio-inspired electronics for robotics and biomedical applications, RISC-V extensions and hardware accelerators, and circuit architectures for efficient machine learning and artificial intelligence. His recent publications (2025) demonstrate a strong focus on RISC-V architecture, particularly in the context of cryptographic implementations, hardware security, and post-quantum cryptography. His research group VLSILAB is actively engaged in cutting-edge research in hardware security, efficient processor design, and specialized computing architectures. Among his notable recognitions are the Premio Francesco Carassa awarded by the Telecommunications and Information Technologies Group Association (gtti) in 2010, and his recognition as a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) since 2007. He also serves as an Associate Editor for several prestigious journals including ELECTRONICS (2019-present), IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS (2015-2019), and IET CIRCUITS, DEVICES & SYSTEMS (2013-2016). Professor Masera has advised numerous PhD students working on advanced topics in VLSI design, post-quantum cryptography, hardware accelerators, and machine learning implementations. His current research projects include ISOLDE (2023-2026) and TRISTAN (2022-2025), both EU-funded projects focused on RISC-V technology and domain-specific ecosystems. He leads the VLSILAB research group at the Department of Electronics and Telecommunications, which focuses on cutting-edge research in VLSI architectures, hardware security, and specialized computing systems. The group collaborates with industry partners and participates in major European research initiatives.

Sadegh Talebiمشاهده پروفایل

استادیار

• Reinforcement Learning
• Markov Decision Processes
• Online Learning
• +۲ مورد دیگر

Sadegh Talebi is a Tenure Track Assistant Professor in the Machine Learning Section at the Department of Computer Science, University of Copenhagen . His research focuses on theoretical aspects of reinforcement learning, Markov decision processes, online learning, stochastic multi-armed bandit problems, and resource allocation in networks. Education BSc in Electrical Engineering (minor: Electronics) from Iran University of Science and Technology (IUST) (2004) MSc in Electrical Engineering (minor: Communication Systems) from Sharif University of Technology (2006) PhD in Electrical Engineering from the Department of Automatic Control at KTH Royal Institute of Technology (supervised by Alexandre Proutiere and Mikael Johansson) Research Specializes in theoretical foundations of reinforcement learning and online learning Key contributions in stochastic optimization, MDPs, and bandit algorithms Collaborates on applications in resource allocation and quantum computing Publications include high-impact work on offline RL, differentially private exploration, and scalable MDP solutions in journals like Neural Processing Letters and conferences such as NeurIPS and UAI.

Tuukka Ruotsaloمشاهده پروفایل

دانشیار

• Brain-Computer Interfaces
• Human-Information Interaction
• Recommender Systems
• +۴ مورد دیگر

Tuukka Ruotsalo serves as Associate Professor in the Machine Learning Section at the Department of Computer Science, University of Copenhagen. His research bridges human cognition with computational systems through brain-computer interfaces and physiological computing. As Academy Research Fellow at University of Helsinki (2019-2024), he maintained dual institutional affiliations while leading cutting-edge work in neuro-linguistic modeling and affective relevance. His research focuses on brain-computer interfaces for information retrieval , where he pioneers methods to decode cognitive states from neural signals to improve search systems. Key areas include affective relevance modeling that integrates emotional states into search algorithms, and neuro-linguistic reconstruction that translates brain activity into language. His work on fairness-relevance tradeoffs in recommender systems established Pareto frontier evaluation frameworks now widely adopted in ethical AI research. Recent publications demonstrate how physiological signals like EEG and galvanic skin response can create more adaptive human-information interaction systems. Ruotsalo's scientific recognition includes the prestigious Academy Research Fellow position. His publications in IEEE Transactions on Human-Machine Systems , Journal of the Association for Information Science and Technology , and Communications Biology reveal growing interdisciplinary impact. His advising spans cognitive neuroscience and machine learning students, with notable collaborations across the SCIENCE AI Centre. Current projects include the TreeSense initiative for remote sensing of global tree resources and development of quantum-inspired neural architectures. His lab leverages the department's powerful compute cluster for large-scale physiological data analysis.

• Dependable computing
• Networking algorithms
• Cloud computing
• +۳ مورد دیگر

Alex X. Liu is a Professor in the Department of Computer Science & Engineering at Michigan State University (2016-2022), currently serving as Chief Information Security Officer and President of Midea Software Engineering Institute. His academic career includes roles as Associate Professor (2012-2016) and Assistant Professor (2006-2012) at the same institution. He holds a Ph.D. and M.S. in Computer Science from The University of Texas at Austin, and a B.S. in Computer Science from Jilin University, China. Education Ph.D. in Computer Science (UT Austin, 2006) M.S. in Computer Science (UT Austin, 2002) B.S. in Computer Science (Jilin University, 1996) Liu's research focuses on Dependable computing , Networking algorithms , Cloud computing , Mobile computing , Privacy computing , and Computer/network security . His work spans secure systems, network protocols, and resource optimization in distributed environments. Recent publications address quantum neural networks , microservices autoscaling , RFID tag recognition , network traffic classification , and hybrid physical-layer authentication , demonstrating expertise at the intersection of AI and network security. Key trends include deep learning applications for cloud systems and robust security protocols. Scientific Awards IET Fellow (2021) IEEE Fellow (2019) ACM Distinguished Scientist (2019) Withrow Distinguished Scholar Awards (Senior 2019, Junior 2011) NSF CAREER Award (2009) IEEE & IFIP William C. Carter Award (2004)

• Cybersecurity
• Data Security and Privacy
• Context-Aware Access Control
• +۵ مورد دیگر

Dr ASM Kayes serves as Senior Lecturer in Cybersecurity and Cyber Curriculum Lead at La Trobe University's Department of Computer Science and Information Technology, where he shapes cybersecurity education programs including Master's, Bachelor's, and Double Degrees. His academic journey began with a PhD from Swinburne University of Technology in 2015, followed by postdoctoral research at La Trobe before joining as Lecturer in 2019 and promotion to Senior Lecturer in 2022. His research spans critical cybersecurity domains including data security, privacy preservation, context-aware access control, malware/ransomware defense, and IoT/fog/cloud security leveraging AI/ML techniques. Dr Kayes has established himself as a leading voice in blockchain security frameworks, privacy policy analysis, and cyber incident response through publications in top-tier venues like ACM Computing Surveys, IEEE Internet of Things Journal, and Computers & Security. His recent publications reveal a strong trajectory toward integrating AI with traditional security frameworks, particularly in blockchain risk assessment (2025), cross-domain access control (2025), and IoT behavior prediction (2024). The research demonstrates consistent focus on practical security solutions addressing ransomware mitigation, privacy breaches, and emerging threats in decentralized systems. Over $880,000 secured as Chief Investigator for cybersecurity projects Australian Government Department of Social Services grant (2023-2026) for cyberbullying prevention AustCyber research funds with industry partners (2020-2023) SmartSat CRC and ASCRIN PhD scholarship grants (2021) Dr Kayes has successfully supervised 5 PhD candidates to completion and currently mentors 5 doctoral students across diverse topics including AI-driven threat hunting, satellite network security, and blockchain risk frameworks. His collaborative network spans UK, USA, Europe, and Asia, with active industry partnerships through Westpac, BHP, and Quantum Victoria. He serves on editorial boards for leading cybersecurity journals and has examined HDR dissertations globally, reflecting his significant standing in the academic community.

• Cyber Security
• Cyber-Physical Systems
• Formal Methods
• +۳ مورد دیگر

Samir Ouchani is a Research Director at the CESI LINEACT laboratory (Aix-en-Provence, France), affiliated with the CESI Engineering School. He holds a PhD in Computer Science from Concordia University (2013) and an HDR (Accreditation to Supervise Research) from CNAM Paris (2022). His research focuses on securing cyber-physical systems (CPS) through formal methods, blockchain, and AI-driven approaches. Key roles include leading projects on resilient CPS architectures, IoT security, and federated learning in industrial contexts. Education: 2022: HDR in Security and Reliability of Smart CPS (CNAM Paris) 2013: PhD in Computer Science (Concordia University, Montreal) 2006: Master in Computer Science (Lorraine University, France) 1997: Engineering Degree in Computer Science (Djillali Liabess University, Algeria) Research Interests: His work emphasizes secure CPS design, including cryptographic protocols for IoT, formal verification frameworks, and AI applications for intrusion detection. He explores blockchain for smart cities, federated learning in distributed systems, and resilience engineering for autonomous vehicles. Recent projects include developing PUF-based authentication protocols and digital twin architectures for resource-constrained systems. Advising & Collaborations: Supervised PhD theses on IoT security (Fahem Zerrouki), smart city formal verification (Walid Miloud Dahmane), and federated learning in industrial CPS (Souhila Bedra Guendouzi). Collaborates with institutions like Blida University (Algeria) and HESAM University. Active in conferences such as CRISIS, ICFNDS, and IEEE WETICE. Labs & Teams: Leads the Engineering and Numerical Tools research team at CESI LINEACT, focusing on model-based design, CPS simulation, and cybersecurity tool development. Engaged in EU-funded projects on Industry 4.0 and smart infrastructure security.

Aida X El-Khadraمشاهده پروفایل

استاد

• Theoretical Particle Physics
• Lattice Quantum Chromodynamics (QCD)
• Muon Anomalous Magnetic Moment (g-2)
• +۳ مورد دیگر

Professor Aida X El-Khadra is a leading theoretical physicist at the University of Illinois Urbana-Champaign, affiliated with the Department of Physics within the Grainger College of Engineering. She holds the rank of Professor since 2008, following roles as Associate and Assistant Professor. Her research focuses on precision calculations in lattice QCD, particularly in the context of the muon's anomalous magnetic moment (g-2) and hadronic vacuum polarization. She chairs the Muon g-2 Theory Initiative and is a key contributor to the Particle Data Group and Snowmass process. Education: PhD from UCLA (1989), Diplom in Physics from Freie Universität Berlin (1984). Research Highlights: Lattice QCD applications to Standard Model precision tests, CKM matrix determinations, and quantum simulations for high-energy physics. Her work addresses discrepancies between experimental muon g-2 results and theoretical predictions, with contributions to resolving these via lattice computations and data-driven analyses. Awards include the Simons Fellowship, AAAS Fellowship, and Fermilab Distinguished Scholar appointment.

• Urban Computing
• Spatiotemporal Data Management
• Artificial Intelligence
• +۳ مورد دیگر

Associate Professor Ke Deng is affiliated with the School of Computing Technologies at RMIT University. His research focuses on urban computing, spatiotemporal data analysis, and social networks, with expertise in data mining and artificial intelligence. He holds a PhD in Computer Science from The University of Queensland (2007), a Master's in Information and Communication Technology (2001), and a Bachelor's in Electrical Engineering (1994). Previously, he was a postdoctoral researcher at CSIRO ICT Centre and a researcher at Huawei Noah's Ark Lab. His work emphasizes practical applications of AI, such as fairness-aware recommendation systems, traffic scenario modeling, and energy-efficient protocols for IoT. Dr. Deng's career includes roles as an acting lecturer at The University of Queensland and co-supervisor of a PhD student. He has supervised numerous research projects, including advancements in quantum annealing for recommenders, fake news mitigation via reinforcement learning, and smart human sensing using millimeter-wave radar. His research outputs span journals like ACM Transactions on Information Systems and IEEE Transactions on Knowledge and Data Engineering. Education: PhD in Computer Science, The University of Queensland (2007) MSc in Information and Communication Technology (2001) BEng in Electrical Engineering (1994) Key Research Themes: Urban computing and smart cities Mechanisms for fair AI systems Energy-efficient IoT protocols Spatiotemporal data mining Grants and Collaborations: ARC Australian Postdoctoral Fellowship (2010–2012) Research at Huawei Noah's Ark Lab (2013) His recent projects highlight interdisciplinary innovation, such as quantum computing for feature selection and self-supervised networks for traffic scenario clustering. Dr. Deng is actively involved in mentoring PhD/Masters students and contributes to RMIT’s research initiatives in AI and urban informatics.