Find Academic Supervisors

• Pervasive Sensing
• Tiny Machine Learning (TinyML)
• Edge AI
• +7 more

Prof. Eiman Kanjo is a Professor of Pervasive Sensing & TinyML and Head of the Smart Sensing Lab at Nottingham Trent University. She leads the Smart Nottingham and Smart Campus initiatives, focusing on impactful applications of mobile sensing, wearable tech, and edge AI. Her work bridges academia, industry, and community, emphasizing privacy-preserving AI for mental health, environmental monitoring, and social prescribing. Education: PhD in Computer Science (2005), University of Abertay Dundee. Formerly a Research Associate at the University of Cambridge and researcher at the University of Nottingham’s Mixed Reality Lab. Collaborates with organizations like the tinyML Foundation, Health Data Research UK (HDRUK), and the Alan Turing Institute. Research Interests : Pervasive computing, TinyML, Edge AI, wearable devices, environmental sensing, mental health technologies, and community-driven solutions for urban wellbeing. She pioneered early mobile sensing systems like NoiseSPY (2010) and MobSens (2009). Key Projects : 5G Connected Forest (£10m DCMS grant) : Smart environmental monitoring in Sherwood Forest. TagWithMe : AI-driven proximity-based games for community wellbeing. EPSRC Green+ Network : Tech for healthier environments. Awards & Recognition : Top 50 Women in Engineering (2023). Outstanding Educator of the Year Award (EDGE AI Foundation, 2023). 2021 Vice-Chancellor’s Outstanding Research Team Award (Smart Sensing Lab). Grants & Collaborations : Over £10m in funding from EPSRC, InnovateUK, MOD, and DEFRA. Partners include Nottingham City Council, tinyML Foundation, Health Data Research UK, and industry stakeholders. Labs & Initiatives : Leads the Smart Sensing Lab , developing technologies for Green Social Prescribing at Rufford Country Park and Highbury Mental Health Hospital.

Daniel S. BergerView profile

Adjunct Assistant Professor

• Cloud Computing
• Sustainability
• System Reliability
• +7 more

Daniel S. Berger is a Principal Researcher at Microsoft's Azure Systems Research Group in Redmond, focusing on the efficiency, sustainability, and reliability of cloud platforms . He is also an Affiliate Assistant Professor at the Paul G. Allen School of Computer Science at the University of Washington, where he teaches graduate classes. His research spans systems stack innovations for sustainability , including work on memory tiering , repair operations , and cooling systems (Zissou). He leverages system prototyping , simulation , and statistical modeling in his work, often collaborating with PhD students and postdocs from institutions like Columbia, University of Toronto, CMU, and Princeton. Recent publications highlight his leadership in CXL-based memory management , carbon-efficient cloud design , and latency-aware caching . His tools, such as Belatedly and FOO , have demonstrated significant improvements in cache performance and latency optimization. Best Paper Awards: USENIX OSDI 2023, HotCarbon 2023, ACM SOSP 2021. Distinguished Paper: ASPLOS 2023. His work has been integrated into Apache Traffic Server and Microsoft production systems , with open-source tools and datasets released for reproducibility. Collaborations include hardware and OS development teams within Azure and academia.

Keyang YuView profile

Assistant Professor

• Cyber-Physical Systems
• Edge Computing
• Tiny Machine Learning
• +4 more

Dr. Keyang Yu is an Assistant Professor in the Department of Computer Science at Marquette University, affiliated with the College of Engineering. His research focuses on designing data-driven computer systems to enhance cybersecurity and user privacy in Cyber-Physical Systems (CPS) and the Internet of Things (IoT). He teaches COSC 6280: Advanced Computer Security. Research interests include Cyber Physical Systems, Edge Computing, Tiny Machine Learning, and Security & Privacy. He emphasizes privacy-aware automation in CPS at various scales. Recent work explores challenges in IoT privacy preservation despite traffic reshaping and vulnerabilities in smart home router operating systems. Publications highlight innovations such as PACAS (privacy-aware smart cameras), TrafficSpy (disaggregating encrypted IoT traffic), and SmartAttack (open-source security research tools). His work spans both theoretical frameworks and practical implementations in smart home security and renewable energy diagnostics. No scientific awards are explicitly listed, but ongoing contributions to IoT privacy and CPS security reflect impactful research. Advising and grant details are not provided in available texts. His work connects to labs focused on embedded systems security and privacy-preserving technologies.

Gaurav ChoudharyView profile

Associate Professor

• Cybersecurity
• Machine Learning
• Internet of Things
• +8 more

Gaurav Choudhary is an Associate Professor at the Technical University of Denmark (DTU), affiliated with the College of Engineering and the Cybersecurity Engineering department. His research focuses on emerging threats in digital systems, with a particular emphasis on Malware Detection, Internet of Things (IoT) security, and Machine Learning applications in cybersecurity. His recent work explores intrusion detection in IoT ecosystems, drone identification using RF signals, and the analysis of virtual private networks and proxies. These studies span subfields like Tiny Machine Learning, Scalable Deployment, and Tor Browser forensics, reflecting his interest in both theoretical and applied security challenges. The majority of his publications from 2025 revolve around enhancing cybersecurity frameworks through innovative machine learning techniques, user-centric authentication methods, and forensic investigation tools. Despite his active research contributions, no specific scientific awards or students he has advised are mentioned in the provided data.

Hui HanView profile

Senior Lecturer

• Edge AI
• TinyML
• Sustainable Supply Chain Management
• +7 more

Dr. Hui Han is a Senior Lecturer at Luleå University of Technology (LTU) in Sweden, working within the Machine Learning Group with a Wallenberg AI, Autonomous Systems and Software Program (WASP) professorship. She is affiliated with the Department of Computer Science, Electrical and Space Engineering, specifically within the Embedded Intelligent Systems LAB. Her office is located in Luleå at room A3424. Dr. Han earned her Ph.D. in Industrial Engineering from the Technical University of Madrid (UPM), Spain. Following her doctoral studies, she completed postdoctoral research at the Data Science department of Fraunhofer IESE in Germany and later served as a postdoctoral fellow in the Department of Computer Science at the Norwegian University of Science and Technology (NTNU), Norway. Dr. Han's research focuses on data science with specific expertise in Edge AI and TinyML (Tiny Machine Learning). She leads research efforts in developing sustainable modeling for Sustainable Supply Chain Management, particularly in reverse logistics and circular economy. Her work also encompasses social commerce and Industry 4.0 applications. As the first author, she has published 17 papers including 10 journal papers, 4 conference papers, and 3 magazine papers, accumulating over 1,000 citations according to Google Scholar. Her publications appear in high-impact journals such as Technological Forecasting & Social Change (IF 13.3), Expert Systems with Applications (IF 8.5), and Education and Information Technologies (IF 5.5). Dr. Han's teaching portfolio includes Management Information Systems, Project in Industrial AI, and TinyML. She has created an introductory TinyML course available on her YouTube channel. Her research spans multiple interdisciplinary areas including Industry 4.0, IoT, Cloud Computing, Big Data Analytics, Cyber-Physical Systems, Multicriteria decision support systems (particularly Fuzzy TOPSIS), Complex Relationships Analysis (PLS-SEM), Data Science, TinyML, Edge AI, Random Forest techniques, Information Systems, Social commerce, E-commerce, Sustainable Supply Chain Management, Reverse logistics, Closed-loop Supply Chains, Circle Economy, and Healthcare applications. Her Embedded ML for Health Project demonstrates practical applications of TinyML in healthcare, using Arduino Nano 33 BLE Sense and OV7675 Camera for melanoma detection through image processing and machine learning algorithms. This project showcases her ability to apply theoretical research to real-world healthcare challenges.

• Cybersecurity
• Formal Verification
• Cyber-Physical Systems
• +7 more

Marjan Sirjani is a Professor at Mälardalen University, affiliated with the School of Innovation, Design and Engineering, and the Division of Computer Science and Software Engineering. Her research focuses on cybersecurity, formal verification, and cyber-physical systems, with notable contributions to actor-based modeling (e.g., Timed Rebeca) and tools like AFRA for model analysis. She specializes in integrating formal methods into safety-critical systems, including automotive cybersecurity, ROS2 robotics, and blockchain-based IoT systems. Her work emphasizes rigorous analysis of protocols, concurrency, and real-time constraints. Recent projects include the CRYSTAL framework for CPS assurance, Tiny Twins for runtime attack detection, and applying LLMs for automated test generation. She also explores semantic segmentation in construction and compositional analysis of distributed systems. Publications highlight advancements in protocol learning, controller synthesis for safety, and model-driven development. Her research bridges theoretical foundations (e.g., automata theory, temporal logics) with practical applications in autonomous systems, medical device interoperability, and smart mobility. Labs/Teams: Involved in the Rebeca tool development (AFRA) and collaborative projects on CPS security. Grants: Not explicitly listed but implied through project involvement.

• IoT Security
• Digital Forensics
• Web Application Security
• +3 more

George Li is an Associate Professor in the Department of Computer Science at Southeast Missouri State University, based in Dempster Hall 214. He can be contacted at (573) 651-2653 or zli2@semo.edu, with mailing address One University Plaza, MS 5950, Cape Girardeau, MO 63701. His research spans critical cybersecurity domains including: IoT Security and device forensics Digital evidence network construction Web application security pedagogy Reverse engineering for vulnerability analysis Tiny Machine Learning (TinyML) education platforms Face-recognition IoT security applications Dr. Li's 2021-2023 publications reveal a consistent focus on practical security solutions, with 50% dedicated to IoT forensics methodologies and 30% to educational platforms. His work bridges academic research and real-world security challenges, particularly in evidence retrieval from constrained IoT devices and developing hands-on cybersecurity curricula. He actively presents findings at major conferences including IEEE GLOBECOM, EDSIG, and ISCAP, with recent talks covering forensic evidence networks, IoT device analysis, and security lab exercises.

Asra AslamView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +11 more

Dr. Asra Aslam is an incoming Assistant Professor of Data Science and AI at the University of Sheffield, UK, and currently serves as a Research Fellow at the University of Leeds, a Principal Investigator at the Alan Turing Institute, and a Visiting Researcher at Newcastle University. Her interdisciplinary work bridges artificial intelligence, computer vision, and healthcare data science. Ph.D. in Computer Vision and Machine Learning, University of Galway, Ireland (2021) M.Tech and B.Tech in Computer Science and Engineering, Aligarh Muslim University, India Former Assistant Professor at AMU, India Machine Learning Research Scientist at mindtrace.ai, Manchester, UK Her research focuses on Computer Vision , Deep Learning , Graph Neural Networks , and Health Data Sciences , with applications in multimorbidity, medical imaging, and smart cities. She develops AI tools for clinical decision support, including the DynAIRx project for medicines optimization in patients with multiple long-term conditions. Dr. Aslam's recent publications highlight trends in automated clinical codelist development , temporal graph neural networks for patient clustering , GAN-based medical data augmentation , and efficient fire detection models . Her work is published in top venues including CVPR, IEEE Access, Springer, and Elsevier journals. Women of the Future Award (Highly Commended), 2023 Education/Academic Leader of the Year, 2024 Rising Star of the Year, Women in Tech Excellence Awards, 2024 Hildegard Franke Early Career Award, 2024 Global Talent Award (Exceptional) by UKRI, 2023 IEEE/CVF, ICML, AISTATS Travel Awards She leads and co-leads major research grants including the NIHR Team Science Grant (£2.8M) , Alan Turing DSG (£80K) , and Horizon One: Global Academy Crucible . She supervises PhD and Master's students and mentors early-career researchers. Dr. Aslam is a strong advocate for diversity, serving as General Chair of Women in Computer Vision (WiCV) , AI Lead for Women in AI UK , and Early Career Researcher Representative across multiple institutions. She is actively involved in organizing workshops at CVPR, NeurIPS, and AIM-RSF, and serves on editorial boards of Discover Data (Springer Nature) and MDPI Journal of Imaging .

Haibo LiView profile

Professor

• Media Technology
• Digital Signal Processing
• Image and Video Compression
• +5 more

Haibo Li is a Full Professor of Media Technology at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and Digital Futures Faculty. His roles include leading the Media Lab in MID, directing Master’s programs in Media Technology, and teaching courses like Generative AI for Media Technology and Video Technology. He holds a Technical Doctorate from Linköping University (1993) and received the Nordic Best PhD Thesis Award (1994) and Docent title in Image Coding (1997). Research focuses on digital media technologies, including image/video compression, human-computer interaction, and AI-driven media systems. He pioneered the Digital Media Lab at Umeå University and co-founded UCIT. His work spans blind image deblurring, thermal comfort sensing, and multimodal emotion recognition. Over 250 publications and six patents highlight contributions to multimedia, AI, and signal processing. Key projects include developing contactless thermal measurement systems for smart buildings, real-time gesture recognition for wearable devices, and advanced neural networks for image restoration. Awards include the Nordic Best Thesis and leadership in EU projects. He chairs conferences and actively participates in MPEG standardization efforts.

Gavin BrownView profile

Professor

• Machine Learning
• Statistics
• Information Theory
• +3 more

Professor Gavin Brown is a faculty member in the Department of Computer Science, focusing on theoretical and methodological foundations of Machine Learning. His work bridges statistics, information theory, and information geometry to develop robust ML methods for reproducibility and hypothesis testing in non-standard scenarios. His research has been applied to predictive policing, clinical trials, and energy-efficient ML algorithms for plastic electronics. Key publications include A Unified Theory of Diversity in Ensemble Learning (JMLR, 2023) and On the Stability of Feature Selection in the Presence of Feature Correlations (ECML, 2019), supported by EPSRC funding. Recent work (2024) investigates the distinction between bias-variance and approximation-estimation error, alongside hardware-efficient ML solutions for FPGAs and flexible electronics. His book How to Get Your PhD (OUP, 2021) offers practical and emotional guidance for doctoral students, co-authored with 12 experts on career planning, diversity in science, and risk management in research. Developed theories for ensemble diversity (2020-2023) Created stability metrics for feature selection (2016-2022) Explored ML applications in healthcare and public safety Current activities include a research sabbatical (2021-2022) working on novel ML frameworks. He leads a research group at the Kilburn Building (Office G11) and contributes to pedagogy discussions, particularly around PhD training and conceptual understanding in deep learning.

Qasim AhmedView profile

Professor

• Wireless Communications
• Millimeter Wave Communications
• Ultra-Wideband Systems
• +7 more

Qasim Ahmed is a Professor in the Department of Engineering at the University of Huddersfield, UK. He holds affiliations with the School of Computing and Engineering and the Centre for Efficiency and Performance Engineering. His academic journey includes a BEng from NUST (Pakistan), MSc from USC (USA), and PhD from the University of Southampton (UK). He has held roles at NUCES (Pakistan), KAUST (Saudi Arabia), and the University of Kent before joining Huddersfield in 2017. His research focuses on wireless communications, millimeter wave systems, ultra-wideband (UWB) technologies, and 5G/6G networks. Key interests include antenna design, cooperative communication, and blockchain applications in telecommunications. He leads EU-funded projects like MOTOR5G (€3.9M) and RECOMBINE (€0.5M), and has secured grants for innovations like the 'Smart Mattress' (£175k) and EVOLVE (€0.7M). His work contributes to UN Sustainable Development Goals related to affordable and clean energy (SDG7) and industry innovation (SDG9). Recent publications emphasize indoor positioning systems, deep learning for spectrum sensing, and hybrid blockchain solutions for network resource sharing. Research grants and projects include EU H2020 funding for beyond 5G ecosystems and smart healthcare systems. He actively collaborates on antenna measurements, vehicular communications, and tiny machine learning (TinyML) applications. His team’s work spans theoretical advancements and practical implementations in next-generation wireless infrastructure.

• Machine Learning
• Control Systems
• Signal Processing
• +3 more

Shuo Huang is an academic researcher with a focus on interdisciplinary fields spanning Machine Learning, Control Systems, and Signal Processing. Their work integrates theoretical advancements with practical applications in areas like neural networks, robotics, and sensor technology. They have contributed significantly to methodologies in predictive modeling, privacy-preserving machine learning, and optimization algorithms. Key research areas include the development of distributed Kalman filters for robotics, semi-supervised learning frameworks, and deep learning applications in computer vision and autonomous systems. They also engage in statistical process control and quality assurance, reflecting a blend of theoretical and applied engineering expertise. Notable contributions include advancements in privacy-preserving neural networks, adaptive control systems for electromechanical devices, and sensor design innovations. Their interdisciplinary approach bridges computer science, electrical engineering, and industrial applications.

• Machine Learning
• Data Systems
• Resource-Aware Computing
• +5 more

Pinar Tözün is an Associate Professor and Head of the Data, Systems, and Robotics department at the IT University of Copenhagen. She leads multiple research groups, including the Resource-Aware Data Systems and Data-intensive Systems and Applications. Her roles also include academic responsibility for DASYA and involvement in the Center for Climate IT. Her research focuses on resource-aware computing, machine learning systems, heterogeneous hardware optimization, and sustainable data management. Key areas include workload characterization, GPU utilization, and benchmarking frameworks for edge devices and cloud systems. Dr. Tözün has led significant projects such as MOTH (Machine Learning on Tiny Hardware), RAD+ (Resource-Aware Data Science), and DAPHNE (Integrated Data Analysis Pipelines), funded by institutions like the Novo Nordisk Foundation and the European Commission. She has published extensively in top venues, including Proc. ACM Manag. Data and Dagstuhl Reports, with a focus on efficient machine learning pipelines and hardware-aware systems. Her work has been highlighted in media discussions on sustainable hardware and software practices. Tözün also actively participates in academic governance, serving on hiring committees and shaping future faculty recruitment in data-intensive systems.

• Chaos theory
• Compressed sensing
• Electromagnetic interference
• +14 more

Fabio Pareschi is an Associate Professor at the Department of Electronics and Telecommunications (DET), Politecnico di Torino, where he conducts research in circuit architectures, embedded systems, and signal processing with applications in security, AI, and power electronics. He is affiliated with the VLSILAB research group and leads multiple high-impact research projects. Research Interests: Chaos theory and true random number generation for cryptographic applications Compressed sensing for secure and efficient signal acquisition EMI reduction techniques in DC-DC power converters Tiny machine learning and low-power embedded systems Circuit design for IoT and biomedical applications The recent articles highlight a strong trend in integrating compressed sensing with encryption, leveraging chaos-based randomness for security, and optimizing power electronics for EMI reduction. His work bridges theoretical foundations with practical hardware implementations in microelectronics and embedded systems. Scientific Awards: Best Student Paper Award (IEEE, 2005) Best Paper Award (IEEE, 2005) IEEE PRIME Gold Leaf Certificate (2019) BioCAS Transactions Best Paper Award (2019) Best Student Paper Award at EMCCompo (IEEE, 2019) Advising and Grants: He supervises multiple PhD students in the Electrical, Electronics, and Communications Engineering program. He is the Scientific Director of the CESOIA project (Non-EU International Research, 2025–2028) on low-complexity AI models, and leads the ECS4DRES project (EU-funded, 2024–2027) on resilient energy systems. He also heads a commercial research project on high-performance DC-DC converters (2022–2025). His editorial roles include Associate Editor for IEEE Transactions on Circuits and Systems and guest editorships in multiple IEEE journals. Labs and Teams: He is a key member of the VLSILAB Group (DET), which focuses on VLSI systems, embedded signal processing, and secure hardware design.

Morten Opprud JakobsenView profile

Associate Professor

• Embedded Systems
• Tiny Machine Learning
• Condition Monitoring
• +2 more

Morten Opprud Jakobsen is an Associate Professor at the Department of Electrical and Computer Engineering , Aarhus University. His research focuses on condition monitoring of rotating machinery , with special emphasis on lubrication condition detection using acoustic signals and implementation of Embedded Machine Learning for predictive maintenance. Primary Research Areas: Embedded Systems, Tiny Machine Learning, Machine Diagnostics Key Techniques: MEMS sensors, Acoustic Emission Analysis, LoRa/Bluetooth Low Energy communication Recent work demonstrates technical expertise in developing low-cost condition monitoring systems that combine vibration and acoustic analysis for early failure detection in mechanical components. Publications in Mechanical Systems and Signal Processing and Tribology International highlight interdisciplinary applications of machine learning in mechanical engineering. Contact: morten@ece.au.dk | +45 21 36 70 53