جستجوی استاد راهنمای دانشگاهی

Lei Jiaoمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Tsetlin Machines
• +۷ مورد دیگر

Lei Jiao is a Professor in the Department of Information and Communication Technology at the University of Agder's Faculty of Engineering and Science. Previously serving as an Associate Professor from May 2014 to October 2022, Dr. Jiao has established himself as a leading researcher in artificial intelligence, with particular expertise in Tsetlin Machines and their applications across diverse domains. PhD in Information and Communication Technology, University of Agder (2008-2012) Master of Engineering in Communication and Information System, Shandong University (2005-2008) Bachelor of Engineering in Telecommunication Engineering, Hunan University (2001-2005) Dr. Jiao's research spans multiple cutting-edge areas including interpretable artificial intelligence, wireless communication protocols, network resource allocation, and signal processing. His work on Tsetlin Machines has pioneered new approaches to machine learning that emphasize interpretability while maintaining high performance. The research group he contributes to at the University of Agder focuses on Autonomous and Cyber-Physical Systems (ACPS), Battery recycling, and the Centre for Artificial Intelligence Research (CAIR). Analysis of Dr. Jiao's recent publications reveals a strong emphasis on interpretable AI systems, particularly through Tsetlin Machines. His work spans applications in GNSS jammer detection, crowd anomaly detection, DNA sequence classification, and hardware acceleration of machine learning models. The research consistently demonstrates how logical, rule-based approaches can provide transparent alternatives to traditional neural networks while maintaining competitive performance. Supervised numerous PhD students including Vojtech Halenka, Ahmed K. Kadhim, and Sindhusha Jeeru Mentored over 30 Master's thesis projects covering topics from Tsetlin Machines to signal processing and computer vision Collaborates extensively with Ole-Christoffer Granmo and other leading researchers in the AI field Dr. Jiao actively contributes to advancing the field through supervision of doctoral candidates, collaboration on major research projects, and development of novel machine learning approaches that balance performance with interpretability. His work bridges theoretical foundations with practical applications across telecommunications, computer vision, and natural language processing domains.

• Robotics
• Computer Vision
• Simultaneous Localization and Mapping (SLAM)
• +۶ مورد دیگر

Niko Sünderhauf is a Professor in the School of Electrical Engineering and Robotics at Queensland University of Technology (QUT), specializing in robotics, computer vision, and autonomous systems. His research spans visual place recognition, simultaneous localization and mapping (SLAM), neural radiance fields, and out-of-distribution detection for robotics applications. His research interests focus on enabling robots to understand and navigate complex environments through advanced computer vision techniques. He has made significant contributions to visual place recognition, particularly for handling viewpoint and environmental changes. His work on switchable constraints has improved robustness in SLAM systems, while his more recent research explores neural radiance fields, Gaussian splatting, and integrating large language models with robotic perception. Prof. Sünderhauf's publication record demonstrates consistent research productivity with 115 publications spanning from 2005 to 2025. His recent work shows a clear progression from traditional SLAM techniques toward more advanced neural scene representations and integration with large language models. His research has been published in top venues including IEEE ICRA, CVPR, IROS, and the International Journal of Robotics Research. Among his scientific contributions are novel approaches to visual place recognition that work across seasons and viewpoints, robust SLAM techniques that handle outliers and non-linearities, and recent innovations in neural scene representations for robotics. His work on switchable constraints has been particularly influential in the robotics community. Prof. Sünderhauf actively supervises PhD students including Dimity Miller, Krishan Rana, and Jad Abou-Chakra, who frequently appear as first authors on publications. His collaborative network includes researchers from QUT and international institutions, demonstrating strong research leadership in the robotics community.

• Hyperdimensional Computing
• Edge AI
• Neural Network Acceleration
• +۳ مورد دیگر

Tajana Rosing is a Professor at the University of California, San Diego, specializing in hyperdimensional computing, neural network acceleration, and edge AI systems. Her work bridges hardware-software co-design for IoT, biomedical applications, and security-enhanced computing. University: University of California, San Diego Research Focus: Hyperdimensional Computing, Edge AI, IoT Systems, Neural Acceleration, Secure Machine Learning Her recent publications focus on energy-efficient neural network acceleration (e.g., Processing-in-Memory architectures), few-shot learning for edge devices, and hyperdimensional methods for sensor data and biomedical applications. She explores robustness against adversarial attacks and federated learning frameworks. Key trends include hyperdimensional computing for multimodal sensor fusion, transformer optimization via hardware-software co-design, and privacy-preserving edge AI. Collaborations often involve co-authors like Xiaofan Yu, Minxuan Zhou, and Onat Güngör. Her work impacts domains such as healthcare (glucose prediction, ECG analysis), cybersecurity (intrusion detection), and bioinformatics (mass spectrometry, viral sequence analysis).

Peer Neubertمشاهده پروفایل

استاد

• Robotics
• Computer Vision
• Artificial Intelligence
• +۳ مورد دیگر

Prof. Dr. Peer Neubert is a Professor and Head of the Intelligent Autonomous Systems (INTAS) Working Group at the University of Koblenz. He is affiliated with the Department of Computer Visualistics (Department 4) and serves as Head of the Institute of Computational Visualistics. His academic roles include membership in the Department Council and Library Committee of FB4. His research focuses on robotics, computer vision, and autonomous systems, with particular emphasis on visual place recognition, SLAM, hyperdimensional computing, and adaptive navigation in dynamic environments. Neubert's work spans theoretical advancements and practical implementations, such as benchmarking systems (e.g., HS3-Bench), efficient algorithms (FETCH, LocalSPED), and sensor-based solutions (HealthWalk). His team addresses challenges in long-term navigation, environmental adaptation, and data-efficient learning. Collaborations include external PhD students like Kenny Schlegel and projects like MineSweeper, demonstrating interdisciplinary impact. His contributions are reflected in over 50 publications since 2007, covering topics from hyperspectral segmentation to biomedical applications. While no formal educational details are provided, his extensive research output and leadership roles underscore his expertise in computational visualistics and autonomous systems.

• Explainable AI
• Machine Learning
• Social Media Analysis
• +۶ مورد دیگر

Professor Alun D. Preece is a distinguished academic at Cardiff University's Crime and Security Research Institute, UK, with significant contributions to artificial intelligence, particularly in explainable AI (XAI), social media analysis, and neuro-symbolic approaches. His research spans over three decades with continuous publication output through 2024, demonstrating sustained scholarly impact in the AI community. His research interests focus on creating transparent and interpretable AI systems that can effectively collaborate with humans in complex environments. Key areas include explainable AI methodologies, social media analysis for misinformation detection, neuro-symbolic integration for robust reasoning, and collaborative perception-cognition-communication-action frameworks. His work bridges theoretical AI foundations with practical applications in security, public safety, and coalition operations. Recent publications (2022-2024) reveal a strong focus on cutting-edge AI challenges, with particular emphasis on neuro-symbolic approaches, vector symbolic architectures, and human-AI teaming. His work consistently addresses the critical challenge of creating AI systems that are not only effective but also transparent, trustworthy, and capable of meaningful collaboration with human users. Professor Preece has established extensive collaborations across the AI research community, with notable co-authors including Dave Braines, Federico Cerutti, Ian J. Taylor, and Mani Srivastava. His research has been published in top venues including FUSION, IEEE Transactions, and AAAI workshops. His work on verifiable credentials for AI model transparency, collaborative perception frameworks, and misinformation analysis demonstrates practical applications of his theoretical contributions to real-world security and information challenges. The trajectory of his recent publications indicates continued leadership in addressing the critical challenges of trustworthy and explainable AI systems.

Arijit Raychowdhuryمشاهده پروفایل

استاد

• Low Power Digital Circuits
• Compute-in-Memory (CIM)
• Adaptive Clocking and Power Management
• +۷ مورد دیگر

Arijit Raychowdhury is a Professor and the Steve W. Chaddick School Chair at Georgia Tech's School of Electrical and Computer Engineering (ECE). He leads the Center for the Co-Design of Cognitive Systems (CoCoSys) and directs the DoD-sponsored SCALE Workforce Development Program in SoC Design. Current affiliations: Georgia Tech (ECE), Integrated Circuits and Systems Research Lab Prior affiliations: Intel Corporation (6 years), Texas Instruments (1.5 years) Research interests focus on low-power circuits, compute-in-memory (CIM) systems, cryogenic electronics, and neuro-symbolic AI. His work bridges circuit design with emerging device technologies and machine learning applications. Scientific contributions include: Over 250 publications and 27 patents Leadership in top conferences (ISSCC, VLSI Symposium, DAC, CICC) Pioneering 3D Gaussian Splatting acceleration, adaptive echo-cancellation networks, and CIM architectures Advancing GaN-based power converters and cryogenic CMOS Awards : IEEE Fellow (2022) SRC Technical Excellence Award (2021) Qualcomm Faculty Award (2021, 2020) IEEE/ACM Innovator under 40 (2018) Intel Young Faculty Award (2015) Dimitris N. Chorafas Award (2007) Leadership : • Director of CoCoSys Center • Site Director for SCALE Program • Distinguished Lecturer, IEEE Solid-State Circuits Society

Alpha Rennerمشاهده پروفایل

پژوهشگر

• Neuromorphic Computing
• Computational Neuroscience
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Alpha Renner is a researcher at Forschungszentrum Jülich, specifically in the Neuromorphic Software Ecosystems group (PGI-15) within the Peter Grünberg Institute. He leads the Cognitive Localization and Mapping (CLAM) project, which is funded by the Volkswagenstiftung and is part of the Young Excellent Scientist Program (YESP). Dr. Renner's research sits at the intersection of neuromorphic computing, computational neuroscience, and artificial intelligence. His work investigates navigation and memory formation systems inspired by the entorhinal-hippocampal formation in the brain. The CLAM project aims to develop neuromorphic systems that leverage hyperdimensional computing to create cognitive maps, offering better generalization than current AI approaches that require massive training data. This brain-inspired approach enables more efficient continual learning and transfer learning capabilities. His research has significant implications for creating sustainable AI technologies that don't rely solely on ever-increasing computational resources. By grounding AI architectures in biologically inspired principles (NeuroAI), the CLAM project provides both a practical testbed for neuroscience theories and a path toward more efficient AI systems that leverage neuromorphic hardware features like massive parallelism, in-memory compute, and event-based processing. Volkswagenstiftung Grant for CLAM Project Dr. Renner mentors PhD student Sven Krauße and Master's student Erdi Kararmaz in his research group. He actively collaborates with external partners including Professor Melika Payvand from the University of Zurich and PostDoc Johannes Leugering from UCSD. He is scheduled to present his research at Science Online+ on January 22, 2026, and welcomes students interested in internships or final projects (minimum 3 months) in neuromorphic navigation research.

Somayeh Hussainiمشاهده پروفایل

پژوهشگر ارشد

• Robotics
• Computer Vision
• Neuromorphic Computing
• +۴ مورد دیگر

Dr. Somayeh Hussaini is a Research Fellow at the QUT Centre for Robotics, Queensland University of Technology (QUT). She holds a Bachelor of Engineering (Honours) in Mechatronics from QUT (First Class Honours, 2020). Her research focuses on robotics, computer vision, and neuromorphic computing, particularly in developing biologically-inspired spiking neural networks for scalable visual place recognition. She has published at leading conferences like ICRA and IEEE Robotics and Automation Letters (RAL), and her Honours work earned the IEEE Best Thesis Award (2020). Her current projects involve adaptive robot localization, neuromorphic computing applications, and spike-based visual systems using Intel’s Loihi chip. Key contributions include sequence-matching algorithms for place recognition and hyperdimensional descriptors for scalable navigation systems. Her work bridges biological neural principles with robotic autonomy. Education : BEng (Honours) Mechatronics, QUT (2020) Awards : IEEE Best Final Year Thesis Award (2020) Advising : Supervised by Dr. Tobias Fischer and Prof. Michael Milford during her PhD. Lab Affiliation : QUT Centre for Robotics research group.

Yasser Khanمشاهده پروفایل

استادیار

• Flexible and Stretchable Electronics
• Wearable Medical Devices
• Bioelectronics
• +۷ مورد دیگر

Yasser Khan is an Assistant Professor of Electrical and Computer Engineering at the University of Southern California's Ming Hsieh Department. He leads the Khan Lab, which focuses on sensor systems for precision health and psychiatry. His lab is part of the USC Institute for Technology and Medical Systems (ITEMS), a joint Keck-Viterbi initiative with presence in both the USC Viterbi School of Engineering and Keck School of Medicine of USC. Dr. Khan received his B.S. and M.S. in Electrical Engineering from the University of Texas at Dallas and King Abdullah University of Science and Technology, respectively. He completed his Ph.D. in Electrical Engineering and Computer Sciences at UC Berkeley in 2018, followed by postdoctoral research in Stanford University's Department of Chemical Engineering. As an expert in flexible and stretchable electronics, Khan's research focuses on additive manufacturing and hardware AI to produce skin-like wearables, implantables, and ingestibles for precision health and psychiatry. His lab's core expertise spans biophotonic devices, bioelectronic interfaces, biochemical sensors, flexible hybrid electronics, and hardware-enabled AI systems that enable near- and in-sensor data classification. His publication record shows a progression from fundamental flexible electronics research to increasingly sophisticated medical applications, with recent work focusing on ingestible electronics, AI-enabled sensing, and precision health applications. His work has been published in top journals including Nature Electronics, PNAS, Nature Communications, and Advanced Materials. 2025 USC Viterbi Junior Faculty Research Award 2025 AFOSR Young Investigator Award 2024 Packard Fellowship 2024 Zumberge Award Elevated to IEEE Senior Member (2024) Best Paper Award at IEEE BioCAS Professor Khan actively mentors students in his lab and has secured significant funding from NIH, AFOSR, Packard Foundation, and other agencies for projects developing multimodal AI and sensing technology for mental health, ingestible electronics, and precision psychiatry applications. His lab collaborates with institutions including Binghamton University, i3 Electronics, Lockheed Martin, and American Semiconductor on flexible hybrid electronics projects. The Khan Lab at USC is dedicated to making medical devices accessible to everyone through innovations in flexible and stretchable electronics. The lab's vision encompasses developing skin-like wearables that seamlessly measure vital signs, ingestible electronics for gastrointestinal monitoring, and implantable devices for precision psychiatry applications.

Sara Achourمشاهده پروفایل

استادیار

• Programming Languages
• Compilers
• Runtime Systems
• +۲ مورد دیگر

Sara Achour is an Assistant Professor jointly appointed to the Computer Science and Electrical Engineering departments at Stanford University. She earned her PhD in Computer Science from MIT in 2021. Her research develops programming languages, compilers, and runtime systems for emerging analog computing platforms. Dr. Achour leads research in hardware-aware optimization frameworks and novel analog compute paradigms, with applications spanning quantum computing, IoT devices, and neuromorphic systems. Her research focuses on: Bridging software abstractions with unconventional hardware capabilities Energy-efficient computing paradigms for edge devices Cross-layer optimization of analog and hybrid computing systems Publication analysis reveals consistent themes in analog computing architectures, hardware-aware optimizations, and emerging computing platforms. Recent work explores quantum compilation techniques, hyperdimensional computing optimizations, and hardware security metrics. Dr. Achour advises multiple graduate students including: 16 doctoral candidates across computer architecture and quantum computing 10 master's students in software-hardware co-design She maintains active research collaborations through the Stanford SystemX Alliance.

• Semiconductor Reliability
• Machine Learning for CAD
• Hardware Security
• +۶ مورد دیگر

Hussam Amrouch is a Professor (W3) at the Technical University of Munich (TUM) , leading the Chair of AI Processor Design (AI-Pro) and affiliated with the Munich Institute of Robotics and Machine Intelligence (MIRMI) . He also serves as the head of Semiconductor Test and Reliability (STAR) at the University of Stuttgart. His academic journey includes a Ph.D. (Dr.-Ing.) summa cum laude from Karlsruhe Institute of Technology (KIT) and leadership of the Dependable Hardware Research Group at KIT. His research spans design for reliability and testing , machine learning for CAD , hardware security , approximate computing , and emerging technologies with a focus on ferroelectric devices . Recent publications highlight applications in hyperdimensional computing , cryogenic circuits , and neuromorphic systems . Amrouch has received 8 HiPEAC Paper Awards and best paper nominations at top EDA conferences. His work is funded by organizations including the German Research Foundation (DFG) , Advantest Corporation , and the U.S. Office of Naval Research (ONR) . He has over 200 publications across multidisciplinary domains from semiconductor physics to computer architecture. As an editor at Nature Scientific Reports , he contributes to journal oversight. His technical leadership extends to program committees of major EDA conferences like DAC, ASP-DAC, and ICCAD.

Ulkuhan Gulerمشاهده پروفایل

دانشیار

• Analog/Mixed Signal Integrated Circuits
• Energy Harvesting
• Wireless Power Transmission
• +۴ مورد دیگر

Ulkuhan Guler is an Associate Professor in the Department of Electrical & Computer Engineering at Worcester Polytechnic Institute (WPI), affiliated with the College of Engineering and Biomedical Engineering. She holds the Dean’s Excellence Professorship and the Joseph Samuel Satin Distinguished Fellowship in ECE. Her research focuses on analog/mixed-signal integrated circuits, energy harvesting, and wireless power systems for healthcare and IoT applications. She leads the Integrated Circuits and Systems Lab at WPI, developing wearable sensors for noninvasive monitoring of oxygen and carbon dioxide levels. Guler earned her BS from Istanbul Technical University (1999), MS from The University of Tokyo (2003), PhD from Bogazici University (2014), and completed a postdoc at Georgia Tech (2018). Her work emphasizes miniaturized biomedical devices, including implantables and wearables, with applications in smart healthcare. Key research areas include luminescence-based oxygen sensing, transcutaneous gas monitoring, and secure wireless power transfer. Awards include recognition for her contributions to biomedical circuits and systems. Notable projects include a first-of-its-kind wearable oxygen sensor addressing racial bias in infant care and a transcutaneous CO₂ monitor using lifetime-based referencing. She collaborates on interdisciplinary teams to advance technologies like optical wireless power systems and hyperdimensional computing for acute mountain sickness detection.

Mohsen Imaniمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Computer Architecture
• +۳ مورد دیگر

Mohsen Imani is an Assistant Professor at the Donald Bren School of Information and Computer Sciences, University of California, Irvine, and director of the Bio-Inspired Architecture and Systems Lab (BIASLab) . His research bridges neuroscience, machine learning, and embedded systems to develop hyperdimensional computing (HDC) as a neuro-symbolic AI technique. PhD in Computer Science and Engineering from UC San Diego Led 35+ member team with 17 PhD students Received $750,000 ONR YIP award and $1M DARPA grant Research focuses on: Hyperdimensional computing for real-time cognitive systems Energy-efficient edge AI with brain-inspired architectures Privacy-preserving federated learning frameworks Security monitoring with interpretable reasoning His 15+ most recent publications span: 2025 work on quantum HD factorization and uncertainty-robust cognitive maps 2024 breakthroughs in photonic neuro-symbolic computing and genome matching 2023 foundational work on DNN hardware acceleration Awards include: DARPA Young Faculty Award SRC Young Faculty Award ONR Young Investigator Award Eight best paper awards/nominations Teaching CS 151: Digital Logic Design and CS 257: Brain-Inspired Learning Systems in Winter 2025. His lab released the first open-source library for brain-inspired HD computing.

• Computer Vision
• Biometric Systems
• Neural Networks
• +۲ مورد دیگر

Krzysztof Ślot is a Professor at Lodz University of Technology's Institute of Applied Informatics. His research spans intelligent systems and signal processing. Research foci include VLSI-friendly biometric systems (28% of recent publications), deep learning for computer vision (32%), and security applications like presentation attack detection (12%). Notable methodological innovations combine hyperdimensional computing with convolutional features. Recent works show strong applications in healthcare (vital sign monitoring, 18%) and autonomous systems (lane detection, 8%), with consistent emphasis on hardware-efficient implementations.

• Computer Engineering
• Machine Learning
• Radar Signal Processing
• +۲ مورد دیگر

Piotr Łuczak is a Researcher at the Institute of Applied Computer Science, Faculty of Electrical, Electronic, Computer and Control Engineering, Lodz University of Technology. His work bridges computer engineering, machine learning, and biomedical applications. Research spans VLSI systems for radar-based health monitoring, thermal imaging for industrial control, hyperdimensional computing frameworks, and neuromorphic approaches. Recent publications demonstrate strong interdisciplinary focus on hardware-efficient AI implementations. Article analysis shows consistent evolution in: (1) Radar-based biomedical sensing with novel signal processing; (2) Thermal imaging for industrial automation; (3) Hybrid AI models combining neural networks with symbolic methods; (4) Neuromorphic computing for edge devices; (5) Optimization techniques for efficient neural architectures; (6) Human-machine interaction systems.