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Mario KubekView profile

Assistant Professor

• Artificial Intelligence
• Text Mining
• Natural Language Processing
• +10 more

Mario M. Kubek is an Assistant Professor in the Department of Computer Science at Georgia State University. He holds a Dipl.-Inf. (2005) from Ilmenau University of Technology, Germany, a Dr.-Ing. (2012) from the University of Hagen, Germany, and a Habilitation (2018) from the same institution. His research spans artificial intelligence, text mining, natural language processing, and security in mobile communication systems. Education: Dipl.-Inf. (2005), Dr.-Ing. (2012), Habilitation (2018) all in Computer Science His recent work focuses on decentralized web search engines, network-based natural language processing, and contextual search algorithms. He has authored over 40 publications and five books, with expertise in large-scale distributed systems and mobile communication security. The DocAnalyser service he co-developed demonstrates his contributions to automated topic extraction and document analysis.

Hoda EldardiryView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Data Mining
• +6 more

Hoda Eldardiry is an Associate Professor in the Department of Computer Science at Virginia Polytechnic Institute and State University (Virginia Tech), and Director of the Machine Learning Laboratory. Her research focuses on artificial intelligence, machine learning, data mining, and their applications in policy, ethics, and complex systems analysis. Education: Ph.D., Computer Science, Purdue University M.S., Computer Science, Purdue University B.E., Computer and Systems Engineering, Alexandria University, Egypt Research Interests: Eldardiry’s work spans machine learning modeling (e.g., hypergraph neural networks, time-series forecasting), AI ethics and policy education, and interdisciplinary applications in transportation, healthcare, and cybersecurity. She emphasizes socially responsible AI development through curriculum design and policy frameworks. Recent Research Themes: Her 2025 publications highlight advancements in graph-based learning, policy-aware AI education modules, and novel techniques in multi-modal data analysis. Key areas include zero-shot learning, sparse control systems optimization, and collaborative machine learning frameworks. Labs & Teams: Directs the Machine Learning Laboratory at Virginia Tech, fostering innovation in ethical AI systems and data-driven decision-making.

Jie XiongView profile

Associate Professor

• Wireless Sensing
• Mobile Health
• Wireless Networking
• +3 more

Jie Xiong is an Associate Professor at the University of Massachusetts Amherst, affiliated with the Department of Computer Science within the College of Information and Computer Sciences. He holds a PhD from University College London (2015) and has been a faculty member since 2018. His research focuses on wireless sensing, mobile health, and IoT, with notable contributions to sensor-free systems and long-range sensing. He leads the Center for Smart and Connected Society and has been recognized with awards like the SIGMOBILE Test-of-Time Award (2024) and MobiCom Best Paper Award (2024). Education: PhD in Computer Science, University College London (2015) Research Interests: His work spans wireless sensing (e.g., acoustic, RF, LoRa), mobile health monitoring, and IoT applications. He explores sensor-free techniques, long-range through-wall sensing, and leveraging ambient signals for novel applications. Grants & Awards: NSF CAREER Award NIH R01 Grant (Smart and Connected Health) Google European Doctoral Fellowship BCS Distinguished Dissertation Award (Runner-Up) MobiCom '22 Best Paper Award (Runner-Up) Advising & Students: Supervises PhD students including Minhao Cui, Yuda Feng, Binbin Xie, and Dong Li. His students have received accolades like the Google Ph.D. Fellowship (Binbin Xie, 2022). Labs & Teams: Leads research in the Center for Smart and Connected Society, focusing on wireless systems and health applications. Collaborates with industry and academia on projects like EVLeSen (in-vehicle sensing) and SoilCares (agricultural monitoring).

Hao ZhangView profile

Associate Professor

• Robotics
• Artificial Intelligence
• Computer Vision
• +6 more

Hao Zhang is an Associate Professor in the Department of Computer Science at the Manning College of Information and Computer Sciences (CICS), University of Massachusetts Amherst. He directs the Human-Centered Robotics Laboratory (HCRLab), focusing on lifelong collaborative autonomy, robot adaptation, and human-robot teaming. His research integrates robotics, AI, and machine learning to develop algorithms for real-world applications like manufacturing, autonomous driving, and environmental monitoring. He holds an NSF CAREER Award and DARPA Young Faculty Award, among other recognitions. Dr. Zhang earned a PhD from the University of Tennessee, Knoxville (2014) and an MS from the Chinese Academy of Sciences (2009). His work addresses challenges in unstructured environments through innovations like self-reflective terrain adaptation and graph-based perception systems. He actively promotes equity in robotics through his PROGRESS outreach program. His research sponsors include NSF, DARPA, and industry partners such as Toyota. Publications span conferences like RSS, ICRA, and IROS, with best paper awards. He serves on editorial and program committees for top-tier journals/conferences including RA-L, NeurIPS, and AAAI.

Hyojoon KimView profile

Assistant Professor

• Computer Networks
• Programmable Networks
• Software-Defined Networking
• +3 more

Hyojoon Kim is an Assistant Professor in the Department of Computer Science at the University of Virginia. His research focuses on computer networks and distributed systems, emphasizing in-network computing, programmable networks, software-defined networking (SDN), network measurement, and security. He received his Ph.D. from Georgia Tech (2015) and B.S. from the University of Wisconsin-Madison (2005). Prior to UVA, he worked as an Associate Research Scholar at Princeton University. Education: Ph.D. in Computer Science, Georgia Institute of Technology, 2015 B.S. in Computer Science, University of Wisconsin-Madison, 2005 Research Interests: Programmable Networks & SDN Network Measurement & Performance Analysis Network Security & Privacy In-Network Computing & Real-Time Monitoring Teaching: CS 7457: Advanced Computer Networks (Graduate) CS/ECE 4457: Computer Networks (Undergraduate) CS 6501: Software-Defined Networking & Programmable Networks (Graduate) Lab & Group: The Network Mechanics Group at UVA focuses on improving network monitoring, troubleshooting, and configuration through SDN, P4, and programmable data planes. Advising: Current advisees include Di Zhu (PhD) and Carson Kuzniar. The group actively seeks PhD students interested in systems and networking research.

Marynel VázquezView profile

Assistant Professor

• Human-Robot Interaction (HRI)
• Artificial Intelligence
• Robotics
• +3 more

Marynel Vázquez is an Assistant Professor in Yale University's Computer Science Department, leading the Yale Interactive Machines Group (IMG). Her research focuses on Human-Robot Interaction (HRI), particularly in multi-party settings, advancing perception and decision-making algorithms for socially aware robots. She holds a PhD from Carnegie Mellon University and previously worked at Stanford and Disney Research. Her research combines computer science, behavioral science, and design, emphasizing interdisciplinary approaches. Key projects include the social robots Chester and Shutter, and frameworks like SEAN-VR for evaluating robot navigation in virtual reality. Vázquez has received prestigious awards including the NSF CAREER Award (2022) and AFOSR YIP Award (2024). Teaching includes courses on interactive machines, robotics, and human-computer interaction. She actively mentors students and emphasizes ethical and socially responsible robotics development.

Vivienne SzeView profile

Professor

• Energy-Efficient Machine Learning
• Computer Vision
• Video Compression/Processing
• +7 more

Vivienne Sze is a Professor at MIT's Department of Electrical Engineering and Computer Science (EECS), part of the School of Engineering. Her research focuses on energy-efficient computing systems for machine learning, computer vision, and video compression, with applications in autonomous systems, healthcare, and IoT. She leads projects integrating algorithmic innovations with hardware design to achieve low-power solutions for embedded and mobile devices. Her work has been recognized through prestigious awards, including the Primetime Engineering Emmy Award for co-developing the HEVC video compression standard and multiple faculty awards from tech giants like Google and Qualcomm. She co-authored the book *Efficient Processing of Deep Neural Networks*, emphasizing practical hardware-software co-design strategies. Research Interests: Energy-Efficient Machine Learning Accelerators Video Coding and Compression Standards Embedded Systems and Mobile Computing Processing-in-Memory (PIM) Architectures AI for Health Monitoring and Digital Health Sustainability in AI Infrastructure Publications highlight trends in: Optimizing DNNs for edge devices Innovations in entropy coding and CABAC Memory-efficient Gaussian-based algorithms Energy-aware design for photonic computing Awards include IEEE conference best paper awards and industry recognitions for her contributions to video coding and hardware acceleration. Her lab's collaborative efforts span academia and industry, aiming to bridge theoretical research with real-world deployable systems.

• Internet of Things (IoT)
• Cybersecurity
• Sensor Networks
• +3 more

Shiwei Fang is an Assistant Professor in the Department of Computer & Cyber Sciences within the School of Computer and Cyber Sciences at the University of North Georgia. He holds a Ph.D. in Computer Science from the University of North Carolina (2021) and a B.E. in Computer Science from the State University of New York (2015). His research focuses on IoT systems, cybersecurity, sensor networks, and edge computing, with notable contributions to multimodal analytics, privacy visualization tools, and geospatial tracking datasets. He advises the Graduate Student Organization and serves on the SCCS Academic Web Oversight Committee. Education: Ph.D., Computer Science, University of North Carolina, 2021 B.E., Computer Science, State University of New York, 2015 Research interests include IoT security, context-aware systems, and sensor fusion. His work on IoBT-MAX and GDTM datasets highlights expertise in experimentation frameworks and geospatial tracking. Recent publications explore privacy risks in IoT, AR-based privacy visualization, and efficient inference models for edge computing. He has contributed to over 25 peer-reviewed articles since 2015, with a focus on real-world IoT applications and hardware-software co-design. Service roles include faculty advising and committee participation. He teaches courses like CSCI 3170/5170 on Computer Organization, bridging theoretical computer science with practical hardware concepts.

• Human-Computer Interaction
• Virtual Reality
• Augmented Reality
• +4 more

Jörg Müller is a Professor in the Department of Computer Science at the University of Bayreuth, Germany. His research focuses on Human-Computer Interaction (HCI), Virtual Reality (VR), and Augmented Reality (AR), with a strong emphasis on interaction techniques, public displays, and multimodal interfaces. He has held academic positions at multiple institutions including Aarhus University, HIIG, and RWTH Aachen University. His work spans technical contributions in haptic interfaces, biomechanical modeling, and user behavior analysis in VR environments. Notable projects include OptiTrap for acoustic levitation displays, SIM2VR for biomechanical testing in VR, and the boomRoom system for mid-air sound interaction. Müller collaborates extensively with industry partners like Telekom Laboratories and Siemens. Research interests include: interaction design for immersive environments, embodied interaction, and the social impact of extended reality technologies. His publications demonstrate leadership in CHI, UIST, and IEEE Visualization venues, with over 120 peer-reviewed articles since 2006. Current research priorities include optimizing VR workspace ergonomics, developing tactile feedback systems for mid-air interaction, and exploring public display networks for urban environments. Müller leads the Interactive Systems group at Bayreuth, supervising PhD students in VR engineering and spatial computing.

Enzo MastinuView profile

Associate Professor

• Biomedical Engineering
• Prosthetics and Neuroprosthetics
• Embedded Systems
• +4 more

Enzo Mastinu is an electronic engineer specialized in embedded systems for biomedical applications, holding an Associate Professor qualification in Bioengineering. He earned his bachelor's and master's degrees in electronic engineering from the University of Cagliari and a PhD in biomedical signals and systems from Chalmers University of Technology, Sweden. His research focuses on advanced prosthetics and neuroprostheses for upper limb amputations, incorporating embedded systems design, control algorithms, sensory feedback, signal processing, AI, and osseointegration. Key projects include the HAND and HAND2 initiatives, funded by the EU and the Italian Ministry of Research, aiming to develop semi-autonomous prosthetic hands. He has published 25 journal articles (75% in Q1) and 21 conference papers, contributing to a PCT patent. Mastinu is a Senior Member of IEEE EMBS and RAS, reviews for ~90 journals/conferences, and edits Transactions on Medical Robotics and Bionics (IEEE) and Scientific Data (Nature). He has supervised ~40 students across PhD, master's, internships, and postdocs, and teaches courses in biomedical engineering and STEM education. His scientific awards include the Marie Skłodowska-Curie Fellowship (2021), National Qualification as Associate Professor (2024), and a Young Researcher Grant (2025). Research emphasizes clinical implementation of prosthetics with neural feedback and intuitive control, as highlighted in high-impact journals like the New England Journal of Medicine.

Nora McDonaldView profile

Assistant Professor

• Digital Privacy
• Artificial Intelligence
• Machine Learning
• +7 more

Nora McDonald is an Assistant Professor in the Department of Information Sciences and Technology at George Mason University. She holds a PhD in Information Science from Drexel University and a BS in English from Cornell University. Her research focuses on digital privacy, ethical technology design, and the societal impacts of emerging technologies like AI. She investigates privacy practices in dynamic surveillance environments, particularly for marginalized populations, and examines how data relations shaped by social media algorithms influence identity and societal norms. Her work has been published in top venues including ACM CHI and CSCW, earning multiple Best Paper awards. She is actively involved in designing curricula addressing AI ethics and generative AI policy in higher education. Currently, she leads a National Science Foundation-funded study on privacy ecosystems and collaborates on projects like equitable community internet access during crises. Research interests span artificial intelligence, machine learning, cybersecurity, and human-centered computing. Notable projects include studies on post-Roe reproductive privacy challenges, neurodivergent identity verification systems, and Gen Z's interaction with AI technologies. She teaches courses on human-computer interaction (HCI) and technology ethics, mentoring students pursuing PhDs in privacy and health informatics. Awards: Best Paper Awards at ACM CHI and CSCW Grants: NSF SaTC grant for privacy ecosystems research Labs/Teams: Leads George Mason's Privacy & Health Informatics Initiative

• Computer Science
• Machine Learning
• Data Science
• +4 more

Yong Zhang is affiliated with Tsinghua University's Research Institute of Information Technology in Beijing, China. His research focuses on machine learning, optimization algorithms, edge computing, and their applications in areas like time series analysis, federated learning, and sensor networks. He has collaborated on projects involving neural networks, scheduling problems, and privacy-preserving techniques. Education: Yong Zhang earned a PhD in Computer Science and Engineering from Fudan University in 2007. His academic career includes roles at institutions like the Chinese Academy of Sciences and the University of Hong Kong, reflecting a strong interdisciplinary background. Research Contributions: His work spans theoretical computer science, algorithm design, and applied machine learning. Notable areas include developing efficient scheduling algorithms for energy systems, creating robust federated learning frameworks for industrial demand forecasting, and advancing methods for sentiment analysis using multimodal data. He has also contributed to biomedical engineering through smartphone-based health monitoring systems. Collaborations: He frequently collaborates with researchers at institutions like the University of Electronic Science and Technology of China, Nanyang Technological University, and The Hong Kong Polytechnic University. Key projects involve data caching optimization in edge computing, distributed algorithms for dynamic networks, and combinatorial optimization problems. Labs & Future Work: His team explores cutting-edge topics in AI-driven systems, including trust-aware machine learning, distributed resource allocation, and real-time data processing for IoT applications. Current research emphasizes scalable solutions for complex optimization challenges in both academic and industrial settings.

• Surveying
• Spatial Science
• Built Environment
• +7 more

Dr. Armin Agha Karimi is a Lecturer in the School of Surveying and Built Environment at the University of Southern Queensland. He holds a BSc in Civil Engineering from Tabriz University, an MSc from Middle East Technical University (METU), and a PhD from the University of Newcastle. His research focuses on spatial data integration, cadastral systems modernization, environmental monitoring using remote sensing, and sea level variability analysis. Key research interests include 3D cadastral boundaries in BIM environments, digital twin applications in built environments, and the impact of hydrological loading on land motion. He has contributed to studies on erosion hotspot mapping in Queensland and the implications of coal seam gas activities on land subsidence. His work on Baltic Sea sea level dynamics and Australian coastal projections has advanced understanding of climate-driven environmental changes. Dr. Karimi is affiliated with the Centre for Sustainable Agricultural Systems and actively publishes on geomatics, climate science, and legal aspects of digital surveying. His recent articles highlight innovations in VR-ready survey data transformation and the legal challenges of electronic cadastral plans.

Tsung-Yi HoView profile

Professor

• Design Automation for Microfluidic Biochips
• Nanometer Integrated Circuits
• Embedded Systems
• +3 more

Tsung-Yi Ho is a Professor in the Department of Computer Science at National Tsing Hua University, Taiwan. He holds the Hans Fischer Fellowship at the Technical University of Munich's Institute for Advanced Study (TUM-IAS). His primary research focuses on design automation for microfluidic biochips and nanometer integrated circuits, emphasizing reliability, optimization, and interdisciplinary applications in bioengineering. Ho received his Ph.D. in Electrical Engineering from National Taiwan University in 2005. He has held positions at National Cheng Kung University and National Chiao Tung University before joining National Tsing Hua University. His work bridges algorithmic design with practical biochip fabrication, addressing challenges like contamination control, routing optimization, and fault tolerance in microfluidic systems. His research interests span design automation for emerging technologies, including paper-based biochips and 3D microfluidic architectures. He has pioneered methods for integrating hardware-software co-design principles into biochip development, enhancing both functionality and reliability. His contributions include novel routing algorithms, contamination mitigation techniques, and reliability-aware synthesis frameworks. Ho has authored over 100 publications, including influential papers in IEEE Transactions on CAD and ACM journals. He serves on the editorial boards of multiple top-tier journals and chairs professional chapters for ACM and IEEE. His awards include the Humboldt Research Fellowship, Dr. Wu Ta-You Memorial Award, and Best Paper Awards at VLSI Test Symposium and IEEE Transactions on CAD. His current projects involve optimizing control-fluidic co-design for paper-based biochips and developing AI-driven frameworks for microfluidic functionality prediction. He collaborates widely, leading cross-disciplinary initiatives at TUM-IAS and Taiwan's academic institutions.

Urs HengartnerView profile

Associate Professor

• Information Privacy
• Computer Security
• Network Security
• +3 more

Urs Hengartner is an Associate Professor at the Department of Computer Science, University of Waterloo. His research focuses on information privacy, computer and network security with emphasis on smartphones, IoT, and machine learning-based authentication systems. He holds a Ph.D. (2005) and M.Sc. (2003) from Carnegie Mellon University, and a Diploma from ETH Zürich (1997). His work spans Adaptive security attacks on ML systems Implicit user authentication frameworks Privacy-preserving technologies for location and genomic data Secure authentication systems resilient to voice/spoofing attacks Recent publication trends show a strong focus on adversarial attack detection (e.g., watermarking evasion, diffusion model attacks) and context-aware authentication systems . His frameworks like MRAAC and SHRIMPS address multi-stage authentication challenges in mobile ecosystems. Key contributions include frameworks for evaluating multi-user authentication systems (SHRIMPS), risk-aware access control (MRAAC), and novel defense strategies against collaborative robot traffic fingerprinting. His work bridges security mechanisms with user-centric design principles.