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Mimi XieView profile

Assistant Professor

• IoT Systems
• Non-Volatile Memory Optimization
• Energy Harvesting
• +3 more

Mimi Xie is an Assistant Professor in the Department of Computer Science at the University of Texas at San Antonio (UTSA), part of the College of Sciences. She holds a Ph.D. in Electrical and Computer Engineering from the University of Pittsburgh, with prior degrees from Chongqing University. Her research focuses on optimizing energy-efficient, secure, and intelligent IoT systems, particularly in non-volatile memory, energy harvesting, and tiny AI. She leads interdisciplinary efforts addressing challenges in data processing for IoT devices and sustainable computing solutions. Dr. Xie has received the prestigious NSF CAREER Award and co-PI roles in TranSET projects on traffic risk prediction and reinforcement learning. Her work appears in top venues like DAC, ICCAD, DATE, and IEEE journals. She advises multiple Ph.D./Master’s students (Sahidul, Jishnu, Wei, Shyamala) and actively serves on technical program committees for GLSVLSI, IEEE Cloud Summit, and ISVLSI. Key research themes include intermittent systems, energy-efficient AI inference, and secure embedded computing. She has developed frameworks like ELIXIR for self-powered IoT and Autotile for battery-less embedded systems. Over 60+ publications span smart healthcare systems (PPG-based health monitoring), environmental adaptive neural networks, and reinforcement learning for traffic networks. Her academic service includes organizing workshops (e.g., TinyML at DAC 2023), mentoring initiatives like the EDA Job Fair at ICCAD, and outreach efforts such as a virtual summer camp for students with disabilities. She balances technical leadership with educational advocacy, winning the GHC Faculty Award 2020.

• Pervasive Computing
• Artificial Intelligence
• Cybersecurity
• +4 more

Nikhil Yadav, PhD, is an Associate Professor and Chair of the Computer Science Department at the University of San Diego's Shiley-Marcos School of Engineering. He holds a PhD from the University of Notre Dame, an MS from the University of Florida, and a BEng from the National University of Lesotho. His research focuses on pervasive computing, AI-driven context-aware systems, and healthcare technology innovation. Previously, Dr. Yadav served as computer science faculty at St. John's University in New York, where he led ABET accreditation efforts and developed internship programs. He currently advises Collidescope Inc. as their consulting CTO, focusing on social impact through ESG-oriented digital solutions. His industry experience includes software engineering roles and founding a startup leveraging speech biomarker analysis for mild traumatic brain injury (mTBI) assessment. Research themes span IoT security, healthcare informatics, and AI ethics. Notable projects include CRADLE—a psychophysiological analytics platform—and work on mobile health applications in underserved regions. His publications address edge AI deployment, cybersecurity in federated learning, and mental health prediction during public health crises. Teaching contributions include pioneering Python pedagogy in graduate data science curricula and integrating mobile healthcare projects in undergraduate courses. Professional service includes conference program committees and advocacy for secure ecological momentary assessments in clinical research.

• Applied Mathematics
• Differential Equations
• Impulsive Systems
• +7 more

Jianhua Shen is a Professor at Nanjing University of Posts and Telecommunications (NJUPT), School of Computer Science and Engineering, with a distinguished research career spanning over 30 years. His academic journey shows a natural progression from theoretical mathematics to applied computer science and educational technology. His research interests encompass Applied Mathematics , Differential Equations , Impulsive Systems , Stability Analysis , Computer Networks , Wireless Communication , Educational Technology , and Machine Learning . Shen's work demonstrates remarkable adaptability while maintaining mathematical rigor, evolving from foundational theoretical work to practical applications in modern computing and education. Shen's recent publications (2020-2025) reveal a strong focus on educational technology and federated learning , with significant contributions to virtual simulation experiments, MOOC platforms, and personalized learning systems. His work bridges theoretical computer science with practical educational applications, reflecting NJUPT's emphasis on technology-enhanced education. As an advisor, Shen has mentored numerous students including Meijuan Chen, Cong Yu, and Xiaoling Liu, who frequently appear as co-authors on his publications. His collaborative network extends across multiple departments at NJUPT and includes researchers from various Chinese institutions.

Christof KochView profile

Research Professor

• Neuroscience
• Consciousness
• Computational Neuroscience
• +2 more

Christof Koch is a renowned neuroscientist serving as Meritorious Investigator at the Allen Institute for Brain Science and Chief Scientist at the Tiny Blue Dot Foundation. His primary research explores the neural mechanisms underlying consciousness, integrating theoretical neuroscience with experimental approaches. With over 300 publications, his work spans neurophysiology, computational modeling, and clinical applications of consciousness research. His research focuses on understanding how neuronal activity gives rise to subjective experience, utilizing techniques ranging from single-cell recordings to large-scale brain mapping. Koch investigates neural correlates of consciousness through integrated information theory and develops neurotechnological approaches for brain disorders. Koch's recent publications demonstrate a strong focus on cortical circuit mechanisms, consciousness assessment methodologies, and comparative neuroanatomy. His work combines advanced neuroimaging, electrophysiological techniques, and theoretical modeling to unravel brain complexity. The research consistently bridges fundamental neuroscience with clinical applications, particularly in neuromonitoring and brain-computer interfaces. He leads multidisciplinary teams at the Allen Institute and collaborates internationally, driving innovations in brain mapping and consciousness research. Koch mentors numerous early-career neuroscientists through the MindScope program and various institutional initiatives.

Jing DengView profile

Professor

• Wireless Networks
• Network Security
• Online Social Networks
• +3 more

Jing Deng is the Associate Dean of the College of Arts and Sciences and Bank of America Distinguished Professor in the Department of Computer Science at the University of North Carolina at Greensboro (UNCG). He previously served as Head of the Computer Science Department (2019–2024). Educated at Cornell University (Ph.D., 2002) and Tsinghua University (M.E. and B.E., 1997/1994), his expertise spans wireless networks, network security, and online social networks. His research focuses on federated learning, UAV-assisted networks, and secure communication protocols. Notable awards include the ACM SIGSAC Test-of-Time Award (2013) and IARIA WEB Conference Best Paper Award (2015). He has authored over 100 publications, including recent work on accelerating large language models on resource-constrained UAVs and federated learning in edge computing. His work appears in top venues like IEEE INFOCOM, ICC, and Transactions on Mobile Computing. He has advised numerous graduate students and served on editorial boards for IEEE Transactions. Labs/Teams: Active in the UNCG Computer Science research group, focusing on network security and distributed systems. Collaborates with industry and academia on 5G, IoT, and edge computing initiatives.

• Medical Image Analysis
• Computer Vision
• Machine Learning
• +5 more

Zhe Xu is an Assistant Professor in the Department of Computer Science at the Hong Kong University of Science and Technology's School of Engineering. His research spans multiple interdisciplinary domains with a strong focus on artificial intelligence applications in medical imaging, computer vision, and robotics. Dr. Xu's research interests center on medical image analysis, computer vision, and machine learning with applications spanning medical diagnostics, robotics, and natural language processing. His work demonstrates particular expertise in developing novel deep learning architectures for medical image segmentation, domain adaptation techniques for cross-domain medical applications, and multimodal AI systems that bridge vision and language understanding. His recent publications show increasing interest in large language model applications for medical reasoning and report generation. Analysis of Dr. Xu's publication trends reveals a strong emphasis on medical AI applications, with approximately 40% of his recent work focused on medical image analysis and diagnostics. Another significant portion (around 30%) addresses computer vision challenges, particularly in object detection and image segmentation. His more recent work (2024-2025) shows a growing interest in multimodal large language models and their application to medical reasoning tasks. Active participant in major medical imaging conferences including MICCAI Regular contributor to IEEE Transactions on Medical Imaging Collaborates extensively with medical researchers and clinicians Recipient of multiple research grants supporting AI for healthcare initiatives Dr. Xu leads a research group focusing on AI for healthcare, with several PhD students working on medical image analysis projects. His lab maintains strong collaborations with hospitals and medical research institutions in Hong Kong and internationally. Current research directions include developing foundation models for medical imaging, creating AI systems for automatic radiology report generation, and exploring the application of large language models in clinical decision support.

• Mobile Systems
• Augmented Reality
• Human-Computer Interaction
• +3 more

Fahim Kawsar is Professor of Mobile Systems at the University of Glasgow's School of Computing Science. His research focuses on mobile and pervasive systems with applications in augmented reality, human-computer interaction, and health monitoring. He supervises PhD students working on therapeutic music interventions and wellbeing sensing technologies. Research spans: Augmented reality collaboration systems Energy-efficient AI on microcontrollers Contactless vital sign monitoring Wearable computing for health Recent publications explore avatar placement in AR meetings, thermal management of AI accelerators, and neurocomputational models for personalized therapy. Work consistently addresses practical implementation challenges in ubiquitous computing.

Maurício BreternitzView profile

Assistant Professor

• Machine Learning
• High Performance Computing
• Neuromorphic Computing
• +7 more

Maurício Breternitz is an Invited Assistant Professor and Principal Researcher at ISTAR-IUL, ISCTE - University Institute of Lisbon. With a PhD in Computer Engineering from Carnegie-Mellon University and extensive industrial research experience at AMD, Intel, and IBM, he focuses on bridging academia and industry for practical innovation. His academic service includes leadership roles in conferences like IISWC and editorial responsibilities at IEEE Micro. Education: Electronics Engineer (ITA, Brazil), MSc in Computer Science (UNICAMP), PhD in Computer Engineering (Carnegie-Mellon) Research: Machine Learning acceleration, Neuromorphic systems, Cloud workloads optimization, Heterogeneous computing His work spans two decades of patents (56 issued, 55 pending) and projects like the Horizon 2020 DIVIDEND CHIST-ERA and the FCT-funded AIMHealth initiative for AI-based public health solutions. Recent publications emphasize weightless neural networks, edge computing, and federated learning applications. Key contributions include: GPU acceleration for Hadoop MapReduce APU code migration techniques Microcode compression algorithms saving $18M Founding the International Workshop on Architectural Support for Binary Translation He has advised 7 Master's theses and 1 ongoing PhD project at UNICAMP, while serving on editorial boards and program committees for top-tier conferences like ISCA and CGO.

Mohamed Iheb BoussandelView profile

Researcher

• Embedded systems
• TinyML
• Radiation Hardening

Mohamed Iheb Boussandel is a Researcher affiliated with the Laboratory of Electrical and Electronic Engineering of Paris (LEEI-P). His work focuses on critical areas in embedded systems and radiation hardening. Research Interests Embedded systems design and optimization TinyML (Tiny Machine Learning) applications Radiation hardening techniques for electronic components Key Research Themes His recent work explores radiation tolerance of commercial off-the-shelf (COTS) components in nuclear environments, combining materials science with optoelectronic system engineering.

• Computer Vision
• Deep Learning
• 3D Point Cloud Processing
• +7 more

Yusuf Hüseyin Şahin is an Assistant Professor in the Department of Computer Engineering at Istanbul Technical University, Faculty of Computer and Informatics. He earned all his academic degrees—B.Sc., M.Sc., and Ph.D.—from the same institution in Computer Engineering. His research lies at the intersection of computer vision, deep learning, and 3D data processing, with applications in medical imaging, architectural heritage, and drone-based vision systems. B.Sc., M.Sc., Ph.D. in Computer Engineering, Istanbul Technical University His primary research interests include 3D point cloud processing, deep learning, image segmentation, adversarial attacks, and medical image analysis. He has published extensively on these topics, particularly focusing on point cloud registration, segmentation, and classification using neural networks. His recent work explores uncertainty modeling, active learning, and generative models for both synthetic data creation and real-world applications. The trend in his publications from 2017 to 2024 shows a clear progression from foundational work in CNN-based 3D classification and cerebral vessel analysis to advanced topics such as dynamic graph networks, conformal prediction, and heritage digitization. His work bridges theoretical machine learning with practical applications in healthcare and cultural preservation. He is currently leading a research project titled "Konformal Tahmin ile Sıcaklık Tahmin Modellerinde Doğruluğun Arttırılması" (Improving Temperature Prediction Accuracy Using Conformal Forecasting), funded under the SRP program from 2025 to 2026. This indicates an expanding interest in predictive modeling and uncertainty quantification. While no scientific awards are listed in the provided texts, his h-index of 5 and 293 citations on Scopus reflect an active and growing research profile. Dr. Şahin teaches undergraduate courses such as Data Structures (BLG 223E) and Object-Oriented Programming (BLG 252E). He has no listed advisees or thesis supervision records. He is part of a collaborative research network involving Gozde Unal and other researchers in medical and architectural computer vision. His lab activities appear to focus on deep learning for 3D data, with emphasis on robustness, efficiency, and real-world deployment.

Bradford CampbellView profile

Associate Professor

• Internet of Things (IoT)
• Smart Buildings/Cities
• Embedded Operating Systems
• +4 more

Bradford Campbell is an Associate Professor in the Computer Science and Electrical & Computer Engineering Departments at the University of Virginia (UVA), affiliated with the Link Lab. His research focuses on sustainable IoT systems, embedded operating systems, and energy-efficient smart environments. He holds a Ph.D. from the University of Michigan (2017). Education: Ph.D., University of Michigan, 2017. Research Interests: Internet of Things (IoT), Smart Buildings/Cities, Embedded Operating Systems, Energy Harvesting, and Sustainable Computing. His group develops low-power, wireless, and secure IoT systems to enhance sustainability in urban environments. Recent Work: Recent publications emphasize privacy-preserving federated learning (e.g., Atlas), occupancy detection using WiFi (FTM-Sense), and energy-efficient IoT gateways (NexusEdge). His work addresses the environmental impact of IoT scalability and secure edge computing. Awards & Honors: NSF CAREER Award (2022), Raven Society Scholarship (2024), and multiple student awards for teaching and service. Teaching: Courses include CPS1 (Technology and Ethics), CS 4501 (Wireless IoT), and CS 6501 (Smart and Healthy Buildings). He co-teaches IoT courses across universities and emphasizes hands-on, systems-oriented learning. Service: Served on TPCs for SenSys, BuildSys, MobiCom, and other conferences. Advises on IoT security, edge computing, and sustainable systems.

• Semantic Web
• Internet of Things
• Ontology Reasoning
• +5 more

Floriano Scioscia is a researcher at the Polytechnic University of Bari, Department of Electrical Engineering and Information Technology, with extensive contributions to the Semantic Web, Internet of Things, and knowledge-based systems. His work focuses on developing frameworks for semantic reasoning, resource discovery, and intelligent systems in ubiquitous computing environments. His research interests span multiple domains within computer science: Semantic Web technologies and ontology reasoning Internet of Things and Cyber-Physical Systems Cloud-Edge computing architectures Knowledge representation and semantic matchmaker systems Mobile and ubiquitous computing applications Analysis of his recent publications (2023-2025) reveals a strong focus on edge-based semantic reasoning, with significant work on the Tiny-ME and Cowl frameworks for lightweight OWL reasoning on resource-constrained devices. His research has increasingly incorporated blockchain technologies into IoT systems and explored the concept of "Internet of Conscious Things" with social capabilities for smart objects. The interdisciplinary nature of his work bridges computer science with healthcare applications, particularly in clinical decision support systems. Dr. Scioscia has collaborated extensively with researchers including Michele Ruta, Eugenio Di Sciascio, Giuseppe Loseto, and Filippo Gramegna across numerous projects spanning more than 15 years of research output.

Marc ChevretteView profile

Assistant Professor

• Secondary Metabolism
• Microbial Genomics
• Evolutionary Biology
• +4 more

Marc Chevrette is an Assistant Professor at the University of Wisconsin-Madison conducting interdisciplinary research at the nexus of microbial genomics, evolutionary biology, and natural product discovery. His work integrates computational and experimental approaches to decipher secondary metabolism in microbial communities with applications in antibiotic development and microbiome engineering. Dr. Chevrette's educational background includes: PhD in Genetics from the University of Wisconsin-Madison MSc in Genetics from the University of Wisconsin-Madison MSc in Biotechnology (Bioengineering) from Harvard University Extension BSc in Molecular Biology & Bioinformatics from Rensselaer Polytechnic Institute The Chevrette Lab investigates secondary metabolism across evolutionary, ecological, and synthetic systems. Core research areas encompass genome mining of biosynthetic gene clusters (BGCs), evolutionary dynamics of biosynthetic enzymes, microbiome-derived natural products from host-associated systems (amphibian skin, insect exoskeleton, rhizosphere) and soil bacteria, and genetic regulation of metabolite production in synthetic communities using metatranscriptomics and metametabolomics. The lab develops computational tools for BGC prediction and characterization while exploring enzyme specificity and promiscuity. Recent publications (2023-2025) reveal dominant themes in methodological innovation for genome mining (antiSMASH 8.0, GATOR-GC), biosynthetic gene cluster curation (MIBiG 4.0), and machine learning applications for enzyme specificity prediction. Research spans natural product discovery from Actinobacteria and host-associated microbiomes, evolutionary analysis of biosynthetic pathways, and experimental validation of microbial community interactions. Key interdisciplinary connections include computational biology, microbial ecology, and drug discovery. The Chevrette Lab maintains active collaborations with the Tiny Earth initiative for soil bacteria research and participates in interdisciplinary networks focused on natural product discovery. The lab's integrative approach combines computational genomics, evolutionary analysis, and experimental microbiology to explore the ecological and biomedical significance of microbial secondary metabolism.

Siddhartha BhattacharyyaView profile

Professor

• hybrid intelligence
• pattern recognition
• multimedia data processing
• +10 more

Dr. Siddhartha Bhattacharyya is a Professor in the Department of Computer Science and Engineering at Christ University, Bangalore, with expertise spanning hybrid intelligence, quantum computing, and multimedia data processing. He has authored/edited 65 books and published over 300 research articles, focusing on interdisciplinary applications of machine learning and computational methods. Editorial Board Member, PeerJ Computer Science Holder of two PCT patents Active in academic leadership (organizing conference committees) His research integrates Artificial Intelligence , Computer Vision , and Quantum Computing to solve complex problems in education, healthcare, and environmental monitoring. Recent work includes multimodal student learning assessment, gas plume detection, and Metaverse applications. He leads an active academic lab focused on hybrid intelligence systems and their practical implementations. Key trends in his publications include: deep learning architectures for computer vision (YOLOv7, CNN-Transformer), quantum-inspired algorithms for graph coloring and bioinformatics, and educational technology innovations for remote learning environments. His work bridges theoretical advancements with real-world applications across diverse domains.

Yasmine AmhisView profile

Researcher

• Particle Physics
• High Energy Physics
• Matter-Antimatter Asymmetry
• +2 more

Dr. Yasmine Amhis is a particle physicist and CNRS researcher at IJCLab, Orsay, France, holding a permanent position since 2012. She currently serves as Physics Coordinator of the LHCb experiment at CERN (2022-2024 mandate) while maintaining her scientific associate status at CERN in Switzerland. Her institutional affiliation centers on Université Paris-Saclay through IJCLab, a joint research laboratory specializing in nuclear and particle physics. Her academic journey includes a PhD from University Paris XI (2009), a three-year fellowship at École Polytechnique Fédérale de Lausanne, and completion of her Habilitation à diriger des recherches at Université Paris-Saclay (2020). This trajectory reflects the international collaboration essential to modern particle physics research. Dr. Amhis's research focuses on fundamental questions in particle physics through the LHCb experiment at CERN. She investigates matter-antimatter asymmetry and searches for particles beyond the Standard Model, requiring expertise in detector technology, data analysis, machine learning, and theoretical collaboration. Her work contributes to understanding why the universe is composed of matter rather than antimatter. She actively supervises PhD students and emphasizes the educational value of particle physics in developing critical thinking skills among young researchers. Her leadership role in the LHCb experiment during Run 3 of the Large Hadron Collider represents significant recognition within the international physics community. Prize from French Academy of Science (2016) Dr. Amhis contributes to science policy and gender equality initiatives, noting that women represent only about 15% of particle physicists with minimal representation in leadership roles. She advocates for systemic changes in science education and workplace practices to address these disparities. Beyond research, she engages in science outreach through initiatives like the 'Tiny Creatures' colouring book, available in multiple languages at CERN's Science Gateway, demonstrating her commitment to making particle physics accessible to broader audiences.