جست‌وجوی استاد و پژوهشگر

Harpreet S. Dhillonمشاهده پروفایل

استاد

• Wireless Communications
• Communication Theory
• Stochastic Geometry
• +۸ مورد دیگر

Harpreet S. Dhillon is the W. Martin Johnson Professor of Engineering and Associate Dean for Research and Innovation at Virginia Tech's College of Engineering. He holds appointments in the Bradley Department of Electrical and Computer Engineering. His research focuses on wireless communications, stochastic geometry, machine learning, and next-generation network systems. Education: Ph.D., University of Texas at Austin (2013); M.S., Virginia Tech (2010); B.Tech., Indian Institute of Technology Guwahati (2008). Research Interests: Communication Theory, Stochastic Geometry, Machine Learning for Communication Systems, Heterogeneous Networks, IoT, and Energy Harvesting. He leads projects on vision-aided localization, LEO satellite systems, and RIS-aided networks. Key Awards: IEEE Fellow (2023), AAIA Fellow (2022), IEEE Heinrich Hertz Award (2016), and numerous early-career recognitions. His work has resulted in over 150 journal/conference publications. Advising: Supervises Ph.D. students in cutting-edge research areas like 6G localization and RIS optimization. His advisees have won awards such as the VT ECE Blackwell Award for Best Dissertation. Labs/Teams: Head of the research group focusing on communication theory and localization. Collaborates on projects funded by agencies like NSF and industry partners.

Jedidiah Crandallمشاهده پروفایل

دانشیار

• Internet Security
• Censorship Resistance
• Network Security
• +۳ مورد دیگر

Jedidiah Crandall is an Associate Professor at Arizona State University's School of Computing and Augmented Intelligence, with an affiliation to the Biodesign Center for Biocomputing, Security and Society. His research focuses on Internet censorship, network security, and privacy-preserving technologies. Crandall collaborates with journalists and activists to expose surveillance mechanisms, particularly in politically sensitive regions like Russia and China. His work includes analyzing VPN vulnerabilities , decentralized censorship systems , and cross-border data flows . He teaches advanced courses in computer network security and advises graduate students on thesis/dissertation research. Research trends in his publications emphasize measuring state-level information control , attack vectors in modern networks , and secure communication technologies . Notable work includes TSPU: Russia's censorship infrastructure and Hidden Links: Analyzing Secret Families of VPN Apps . Crandall's teaching spans courses like Advanced Computer Network Security and Applied Cryptography , reflecting his commitment to preparing the next generation of security professionals. His Censored Planet project tracks global Internet censorship patterns through large-scale measurements.

Martin Nordal Petersenمشاهده پروفایل

دانشیار

• Internet of Things
• Optical Networking
• Wireless Communication
• +۳ مورد دیگر

Martin Nordal Petersen is an Associate Professor at the Department of Electrical and Photonics Engineering , Technical University of Denmark (DTU) . His work spans Internet of Things (IoT) , optical networking , and wireless communication systems, with notable contributions to LoRa , NB-IoT , and LPWAN technologies. He actively supervises PhD projects on topics such as machine learning in IoT edge devices , secure 5G communication , and smart community architectures . Active projects (2024–2027): Machine Learning in IoT Edge Devices , Deterministic and Secure 5G Communication Finished projects (2021–2024; 2018–2021; 2015–2018): Reliable M2M/IoT Communication , Smart Communities , IoT 100% , Network Slicing His research explores: IoT Reliability : Multi-RAT communication, backup systems, and signal propagation Optical Networks : Alien wavelength integration, SDN control, and network emulation platforms Wireless Innovation : GPS-free geolocation, maritime NB-IoT use cases, and multimode fiber distribution Current collaborations emphasize cross-disciplinary applications of IoT in healthcare , industrial ergonomics , and smart environments .

• AI for Software Engineering (AI4SE)
• Machine Learning
• Data Mining
• +۵ مورد دیگر

David Lo is the OUB Chair Professor of Computer Science at Singapore Management University's School of Computing and Information Systems, where he directs the Information Systems and Technology Cluster and the Center for Research on Intelligent Software Engineering. An ACM Fellow, IEEE Fellow, and ASE Fellow, his research focuses on AI for Software Engineering (AI4SE), leveraging machine learning, data mining, and NLP to enhance software analytics and automation. Research Highlights: AI4SE, code LLMs, human-AI synergy in software engineering, software reliability, and empirical studies of practitioner pain points Awards: IEEE TCSE Distinguished Service Award, university-wide Teaching Excellence Award, Outstanding Graduate Supervisor Award, 2 Test-of-Time Awards, and 11 ACM SIGSOFT/IEEE TCSE Distinguished Paper Awards Leadership: General Chair of ASE'16 and MSR'22, PC Co-Chair for ASE'20, FSE'24, and ICSE'25, ACM SIGSOFT Executive Committee member His work has received over 20 awards, 37,000 citations, and an H-index of 100. As an educator, he has mentored trainees who became faculty and R&D experts globally.

Naren Ramakrishnanمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Urban Analytics
• +۲ مورد دیگر

Naren Ramakrishnan is the Thomas L. Phillips Professor of Engineering in the Department of Computer Science at Virginia Tech, where he directs the Sanghani Center for AI and Data Analytics. He also serves as AI and Machine Learning Lead for the Virginia Tech Innovation Campus. His research spans data science, machine learning, urban analytics, forecasting, and computational epidemiology. Recent publications (2024-2025) focus on language model optimization, AI applications in government and environmental conservation, and spatiotemporal data analysis. Work demonstrates strong emphasis on real-world AI deployments in regulatory compliance, supply chain verification, and network optimization. Methodological innovations include prompt engineering techniques, world models for reinforcement learning, and specialized embedding methods. Dr. Ramakrishnan has received prestigious fellowships from ACM, AAAS, and IEEE. His research has been supported by numerous agencies including DARPA, NSF, NIH, and industry partners like Amazon and Boeing, with 36 PhD students mentored to completion.

• Cybersecurity
• Blockchain
• Machine Learning
• +۱۲ مورد دیگر

Edgar Weippl is a Professor at the Faculty of Computer Science, University of Vienna, where he serves as Vice Dean and Head of the Research Group Security and Privacy. His work spans cybersecurity, blockchain, and machine learning, with teaching roles in information security and software security courses. Current Positions: Vice Dean (Faculty of Computer Science), Head (Security and Privacy Research Group), Deputy Head (Neuroinformatics & Knowledge Engineering Groups) Research Interests: Cybersecurity, blockchain, IoT security, code obfuscation, privacy technologies, reinforcement learning, and socio-technical systems security Selected Publications: Focus on blockchain privacy, VoWiFi security, code obfuscation, and reinforcement learning applications

Ramsey Faragherمشاهده پروفایل

پژوهشگر

• Infrastructure-free Indoor Positioning
• Simultaneous Localisation and Mapping (SLAM)
• Smartphone Sensing
• +۹ مورد دیگر

Dr. Ramsey Faragher is a Senior Research Associate at the Computer Laboratory , University of Cambridge, and a Bye-Fellow at Queens' College. His work focuses on infrastructure-free indoor positioning systems, sensor fusion, and improvements to smartphone sensing capabilities. Academic Affiliation : University of Cambridge (Computer Laboratory) Professional Roles : Bye-Fellow at Queens' College, Senior Research Associate His research spans multiple disciplines within computer science and engineering, emphasizing innovative navigation solutions and signal processing techniques. Key areas include GNSS robustness, wireless security, and machine learning applications for positioning systems. Recent publications highlight advancements in supercorrelation for automotive GNSS, sensor data calibration, and motion-compensated signal processing. Articles frequently address challenges such as spoofing mitigation, urban navigation, and infrastructure-free localization. Scientific Recognition Fellow of the Royal Institute of Navigation Chartered Physicist (CPhys)

Xuming Heمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۹ مورد دیگر

Xuming He is an Associate Professor at the School of Information Science and Technology (SIST), ShanghaiTech University, where he leads the PLUS Lab. His research spans computer vision and machine learning with a focus on developing algorithms that operate effectively under limited supervision and evolving data conditions. His core research interests include weakly-supervised and few-shot learning for scenarios with sparse annotations, continual learning frameworks for knowledge retention during sequential task acquisition, semantic segmentation techniques for scene understanding, and multimodal vision-language representations. He emphasizes interpretable machine learning to build transparent AI systems capable of human-understandable reasoning, addressing critical challenges in model trustworthiness and deployment reliability. Recent publications reveal strong trends toward novel class discovery in long-tailed recognition scenarios, physics-informed generative modeling for scientific applications, and robust segmentation under distribution shifts. His work increasingly integrates large language models for multimodal reasoning while maintaining focus on efficiency in resource-constrained environments like robotic grasping and medical imaging analysis. He actively mentors students, having supervised Qian He to PhD completion and Chuanyang Hu to Master's degree in 2023. He welcomes prospective graduate students through ShanghaiTech's Computer Science & Technology program and offers undergraduate research projects requiring minimum six-month commitments. The PLUS Lab under his direction drives innovation in learning under supervision constraints, with recent work spanning medical tumor analysis, cross-view geolocation, photonic computing, and semiconductor design verification. The lab's research bridges theoretical advances with practical applications across healthcare, robotics, and scientific discovery domains.

Chadi Barakatمشاهده پروفایل

پژوهشگر

• Internet Measurement
• Network Traffic Analysis
• Quality of Experience (QoE)
• +۳ مورد دیگر

Chadi Barakat is a Senior Researcher (Directeur de Recherche) at Université Côte d'Azur's Inria research center, leading the DIANA project-team. He holds a PhD in Computer Science from University of Nice Sophia Antipolis (2001) and Habilitation (HDR) in 2009, with academic credentials from Lebanese University (1997) and French institutions. PhD: Computer Science (2001), University of Nice Sophia Antipolis HDR: Computer Science (2009), University of Nice Sophia Antipolis Master's: Computer Science (1998), University of Nice Sophia Antipolis BSc: Electrical & Electronics Engineering (1997), Lebanese University His research focuses on Internet measurement and traffic analysis , with significant contributions to Quality of Experience (QoE) modeling, 5G/ICN/SDN network architectures , and network performance evaluation . Recent articles highlight browser-based network monitoring, fidelity-aware network emulation, and ray tracing optimization for radio frequency mapping. He has supervised 12 PhD students to completion and currently directs the Academy of Excellence 'Networks, Information, and Digital Society' at Université Côte d'Azur. His work has received multiple best paper awards at CNSM, CloudNet, and SECON conferences, while serving as associate editor for Elsevier Computer Networks journal and active in ACM/IEEE conference committees. Director, Academy of Excellence 'Networks, Information, and Digital Society' (2025-present) Senior IEEE Member (2010) & ACM Senior Member (2018) General Co-Chair: ACM IMC 2022, ACM CoNEXT 2012 Guest Editor: IEEE JSAC special issue on Internet Sampling

• Computational Intelligence
• Cloud Computing
• Big Data Applications
• +۶ مورد دیگر

Jun Shen is a Professor at the School of Computing and Information Technology, University of Wollongong. He specializes in computational intelligence, cloud computing, and big data applications, with a focus on AI-driven solutions for real-world challenges in transport systems, healthcare, education, and environmental management. He has secured over 40 research grants totaling AU$4.5 million and supervised 26 completed PhD projects. His work spans interdisciplinary areas including bioinformatics, smart manufacturing, and digital health. Research interests include bio-inspired algorithmic optimization, AI in arts/media, and edge computing for IoT systems. He has pioneered research centers in applied computing since 2014 and holds editorial roles in top journals like IEEE Transactions. As an IEEE Distinguished Lecturer, he actively promotes AI ethics and interdisciplinary collaboration. Recent publications emphasize adversarial machine learning defenses, UAV systems, and multimodal data fusion. His supervision includes projects in intelligent transport systems, cloud computing, and e-learning. Grants include projects on resilient energy systems and UAV geolocation verification. Leadership roles include leading over 20 researchers and chairing conferences. He advocates for digital transformation in public services and has conducted fieldwork at MIT, UCI, and Georgia Tech.

Christos Nicolaidesمشاهده پروفایل

استادیار

• Data Science
• Machine Learning
• Social Networks
• +۵ مورد دیگر

Christos Nicolaides is an Assistant Professor at the Department of Business and Public Administration within the School of Economics and Management at the University of Cyprus (UCY), holding a secondary appointment as a Digital Fellow at MIT's Initiative on the Digital Economy. Previously, he spent three years as a James McDonnell Foundation-funded Postdoctoral Fellow at MIT Sloan School of Management. His educational background includes a PhD in Engineering from Massachusetts Institute of Technology (2014), SM from MIT (2011), MSc in Applied Mathematics from Imperial College London (2009), and BSc in Physics from University of Thessaloniki (2008). Nicolaides' research applies mathematical, statistical, and computational tools to large-scale empirical questions in social influence mediated by digital technologies. His work spans Data Science , Machine Learning , Social Networks , and Computational Social Science , with significant contributions to understanding human mobility patterns, disease transmission dynamics, and social contagion effects. His research has established novel methodologies for analyzing complex network structures in mobility data and social interactions. Analysis of his 15 most recent publications reveals a consistent focus on applying network science to real-world problems, particularly in pandemic response (12 publications), human mobility analytics (9 publications), and social contagion dynamics (7 publications). His work demonstrates increasing interdisciplinary integration, combining computer science, epidemiology, and organizational behavior since 2020. Marie S. Curie Fellow Two Highly Cited Papers by Web of Science (2017, 2020) Best Paper Award by Risk Analysis Society (2019) Professor of The Week by Poets & Quants (2020) As principal institutional investigator, Nicolaides has secured over €1 million in research funding from the European Commission, industry partners, Cyprus Innovation and Research Foundation, and Cyprus Ministry of Health. His current teaching includes Social Networks and Entrepreneurship, Introduction to Operations Management, and Quantitative Methods in Management. Media coverage of his work spans major outlets including The New York Times, CNN, Nature, and Science, with significant impact on public health policy discussions during the COVID-19 pandemic.

• Big data analytics
• Data analysis
• Data management
• +۸ مورد دیگر

Daniele Apiletti is an Associate Professor at the Polytechnic University of Turin , affiliated with the Department of Control and Computer Engineering (DAUIN). He serves as a member of the Interdepartmental Center SmartData@PoliTO and acts as Academic Advisor for the Master's degree program in Data Science and Engineering. Research Groups: DBDM - Database and Data Mining Group (DAUIN) ERC Sectors: Algorithms, Artificial Intelligence, Machine Learning, Web and Information Systems Research Interests span Big Data Analytics, Data Science, Machine Learning, Computer Vision, and Quantum Computing. His work focuses on integrating data-driven and theory-guided approaches for heterogeneous data querying, cloud continuum machine learning, and spatio-temporal models for crisis management. Recent Publications highlight trends in medical image segmentation, predictive industrial modeling, and fault-tolerant data systems. Key subfields include AI in healthcare, scalable manufacturing analytics, and vision-language models for game tutorials. Teaching roles include course ownership of Big Data: Architectures and Data Analytics and Internships across multiple academic years. He has collaborated on courses in Data Science, Database Technologies, and Data Management. PhD Students Supervised: Etibar Vazirov (Cloud Continuum Machine Learning) Gabriele Scaffidi Militone (Cloud Storage Microservices) Daniele Rege Cambrin (Spatio-Temporal Ecology Models) Simone Monaco (Theory-Guided Data Science) Research Projects include commercial contracts on: - Natural language querying of corporate research archives - National tourism ecosystem platforms - AI for thermotechnical system design - Machine Learning in clinical trials and supply chains

Miguel Mascaró Portellsمشاهده پروفایل

مدرس ارشد

• Computer Science
• Web Development
• Cloud Computing
• +۷ مورد دیگر

Dr. Miguel Mascaró Portells is a Senior Lecturer at the University of the Balearic Islands in the Department of Mathematics and Computer Science. He holds a PhD in Computer Science and actively contributes to research groups focused on computer graphics, AI, and multimedia technologies. Research Focus: His work encompasses web development, cloud computing, Big Data applications, neural vision systems, multimedia content management, and geolocation technologies. Specific interests include: Object-Oriented Programming (OOP) and SOA services Mobile device programming and TDT visualization Cloud-based multiprocessing systems Home automation and control systems Teaching: Current courses include: Advanced Algorithms Programming - Computer Science I Final Degree Project supervision SOA solutions for tourism Affiliations: Active member of: Computer Graphics, Vision and AI Unit (UGIVIA) Multimedia Information Technology (TIM) Research Group

Brian Nussbaumمشاهده پروفایل

استادیار

• State and Local Government Cybersecurity
• Cybersecurity in Smart Cities and Critical Infrastructure
• Intelligence Analysis and Communication
• +۳ مورد دیگر

Brian Nussbaum is an Assistant Professor in the College of Emergency Preparedness, Homeland Security and Cybersecurity (CEHC) at the University at Albany. He is concurrently a Fellow of the Cybersecurity Initiative at New America and an Affiliate Scholar at Stanford Law School's Center for Internet and Society (CIS). Previously, he served as a Senior Intelligence Analyst at the New York State Office of Counter Terrorism (OCT), where he established the Cyber Analysis Unit (CAU) at the New York State Intelligence Center (NYSIC). Education: Ph.D. in Political Science, University at Albany, 2009 M.A. in Political Science, University at Albany, 2007 B.A. in Political Science, Binghamton University, 2002 Nussbaum's research examines cybersecurity governance with emphasis on state/local capabilities, critical infrastructure protection, and intelligence communication. His core interests include: Policy frameworks for municipal cybersecurity resilience Threat assessment methodologies for emerging technologies Cross-jurisdictional information sharing architectures Operational challenges in smart city security His recent publications (2020-2024) demonstrate multidisciplinary analysis of cyber-physical threats, with recurring themes in: Critical infrastructure vulnerability and crisis management Evolution of financial cybercrime and regulatory responses Attribution challenges in cyber incidents Intersections of disinformation, surveillance, and democratic processes Nussbaum contributes to field-building through affiliations with New America and Stanford CIS, extending his impact beyond academia into national policy discourse. His prior operational experience at NYSIC informs applied research on public-sector cybersecurity capacity.

Kelvin Kam-fai TSOI蔡錦輝مشاهده پروفایل

دانشیار

• Hypertension and Cardiovascular Diseases
• Cognitive Impairment and Dementia
• Digital Health
• +۴ مورد دیگر

Prof. Kelvin Kam-fai TSOI is an Associate Professor at the JC School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong (CUHK). He holds courtesy appointments at the Stanley Ho Big Data Decision Analytics Research Centre and The Jockey Club Institute of Ageing. A Visiting Associate Professor at Nanyang Technological University’s Lee Kong Chian School of Medicine and Visiting Professor at Shenzhen Institutes of Advanced Technology, he specializes in Digital Health and Epidemiology. Education: BSc in Statistics and PhD in Public Health from CUHK. Postdoctoral training in Gastroenterology and Hepatology at CUHK’s Department of Medicine and Therapeutics. Served as Director of the CUHK JC Bowel Cancer Education Centre and worked in the Hospital Authority on chronic disease management projects. Research interests focus on digital innovations for chronic disease management, including AI in hypertension, cognitive screening technologies, and wearable health devices. His work combines traditional epidemiology with big data analytics, emphasizing interdisciplinary collaboration between medicine and engineering. Key projects include developing a mobile blood pressure management platform via DeepHealth Limited, a social enterprise he founded in 2019. Current research explores seasonal effects on blood pressure variability and AI applications in healthcare. He co-founded the International Society for Digital Health and organizes global symposiums on digital health innovation. Grants & Awards: Supported by CUHK’s Sustainable Knowledge Transfer Fund (SKPF). Awards include IBM Honorarium Award (2020) and President’s Prize for Best Paper Presentation (2017).