Find Academic Supervisors

Adnan Siraj RakinView profile

Assistant Professor

• AI Security
• Deep Learning
• Computer Vision
• +3 more

Adnan Siraj Rakin is an Assistant Professor at Binghamton University's School of Computing. He holds a PhD and MS in Computer Engineering from Arizona State University (2022 and 2021) and a BS in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology (2016). His research focuses on AI security, including adversarial attacks on deep learning systems, model stealing, and hardware vulnerabilities. Notable contributions include defenses against bit-flip attacks, weight duplication frameworks, and RowHammer exploits. Research Interests: Adversarial Attacks (Input/Weight/Model Stealing) Deep Learning Security Hardware Vulnerabilities (e.g., FPGA/DRAM) Robust Neural Network Design Publications highlight advancements in detecting and mitigating adversarial perturbations, with work featured in CVPR, ICCV, and IEEE Security & Privacy. His recent efforts address LLM vulnerabilities and edge computing efficiency. He received the 2022-2023 Educator of the Year award from Binghamton's CS department. Current projects include developing full-stack obfuscation frameworks, secure domain adaptation, and exploring adversarial impacts on robotics systems.

Ian CraddockView profile

Professor

• Digital Health
• Internet of Things (IoT)
• Biomedical Engineering
• +3 more

Professor Ian Craddock holds dual roles as a Professor at the School of Engineering Mathematics and Technology and Head of the School of Civil, Aerospace and Design Engineering at the University of Bristol. His research focuses on digital health technologies, IoT systems, and wearable sensors for healthcare applications. He leads the SPHERE initiative, a sensor platform for residential healthcare monitoring, and has pioneered work in activity recognition, indoor localization, and disease management (e.g., Parkinson’s, dementia). Education: B.Eng and Ph.D. in Engineering from the University of Bristol, Chartered Engineer (C.Eng). Key projects include the HEmiSPHERE study assessing post-surgery recovery and the CUBOId study on early dementia detection through behavioral analysis. His work integrates machine learning, computer vision, and wireless communication to address clinical challenges in real-world settings. Research interests span smart home technologies, assistive devices, and ethical AI deployment in healthcare. He collaborates with institutions like the Bristol Medical School and the School of Electrical, Electronic and Mechanical Engineering on interdisciplinary projects. Publications highlight advancements in multimodal data fusion, wearable sensor optimization, and pandemic response systems. His contributions bridge engineering innovation with clinical needs, emphasizing user-centric design and privacy-aware technologies.

• Computer Networking
• Machine-to-Machine Communications
• IoT (Internet of Things)
• +4 more

Gianluca Rizzo is an Adjunct Professor at HES-SO Valais-Wallis, affiliated with the Higher School of Management (Haute Ecole de Gestion) and the Internet of Things (IoT) department linked to EPFL. He holds a Computer Science Bachelor's degree from UC3M University in Madrid. His research focuses on IoT, vehicular communications, AI-driven network optimization, and disaster-resilient systems. Education: Computer Science BSc (UC3M University, Madrid) Affiliations: HES-SO Valais-Wallis, EPFL IoT Group, RECODIS (Post-Disaster Communications Lab) His work spans energy-efficient networking, opportunistic content dissemination (Floating Content), and distributed learning techniques. Key contributions include optimizing multi-agent systems in dynamic environments, developing gossip learning frameworks for urban trajectory prediction, and analyzing SWIPT (Simultaneous Wireless Information and Power Transfer) in vehicular networks. He also explores emergency networks for post-disaster scenarios, leveraging technologies like UAVs and floating content for situational awareness. Recent publications emphasize AI-native vehicular communications, edge computing orchestration, and infrastructure savings via moving base stations. Collaborative projects include V-Edge (virtual edge computing) and the NOSE nomadic sensing ecosystem. His work bridges theoretical models (e.g., stochastic geometry) with practical implementations, addressing challenges in 5G/6G, smart cities, and industrial IoT.

Muhammad Hamad AlizaiView profile

Associate Professor

• Internet of Things
• Cyber-Physical Systems
• Embedded AI
• +7 more

Muhammad Hamad Alizai is an Associate Professor in the Department of Computer Science at the School of Science and Engineering (SBASSE), Lahore University of Management Sciences (LUMS), Pakistan. His research spans the Internet of Things (IoT), Cyber-Physical Systems, Embedded AI, and intermittent computing, with a focus on sustainable and accessible technologies for developing regions. His research interests include energy harvesting, batteryless computing, GenAI for IoT, wireless sensor networks, and distributed systems . He explores how generative AI can democratize IoT access in low- and middle-income countries and addresses societal challenges through computational solutions. His work is supported by grants from HEC, LUMS, DAAD, and NCBC. Recent publications reveal a strong trend in intermittent computing, energy-efficient IoT, and AI-driven system optimization , with high-impact papers in ACM SenSys, IPSN, BuildSys, and CACM. His team develops innovative systems like CheckMate, Glitch in Time, and Guardian Angel, tackling reliability, security, and accessibility in transiently powered devices. Scientific recognitions include: Best Paper Candidate at ACM SenSys 2019 Audience Choice Award at ACM BuildSys 2017 Best Abstract Award at ACM SenSys 2010 He has advised numerous graduate students, including PhDs like Saad Ahmed and Samar Abbas, and leads pedagogical innovation as head of the LUMS Learning Institute , promoting teaching excellence and AI integration in education. He has served on technical committees for ACM CoNEXT, IPSN, MobiSys, and BuildSys, and organized key workshops in transient computing. His lab focuses on building privacy-preserving, energy-neutral, and AI-augmented embedded systems , particularly for applications in water, energy, and transportation in developing regions.

Antoine GrenierView profile

Researcher

• Global Navigation Satellite Systems (GNSS)
• Approximate Computing
• Edge AI
• +5 more

Antoine Grenier is a researcher in the Department of Electrical Engineering at Tampere University, focusing on Global Navigation Satellite Systems (GNSS), Approximate Computing, and Edge AI. His work spans signal processing, sensor technology, and low-power system design for positioning applications. Developing energy-efficient GNSS receivers using approximate computing Creating benchmarking environments for GNSS algorithms Analyzing ionospheric errors for LEO-PNT solutions Exploring consumer-grade sensors in Android wearables for robust positioning His recent publications (2022-2025) emphasize interdisciplinary applications of GNSS in IoT, edge computing, and satellite-based navigation systems. Collaborations with researchers like Elena-Simona Lohan and Aleksei Ometov highlight his contributions to open-access datasets and algorithm optimization frameworks.

David BrooksView profile

Professor

• Computer Architecture
• Energy-Efficient Computing
• Sustainable Computing
• +3 more

David Brooks is the Haley Family Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences. His research focuses on energy-efficient computing architectures, sustainable systems, and hardware/software co-design for AI. He holds a Ph.D. from Princeton University (2001) and a B.S. from the University of Southern California (1997). Research interests include energy-efficient computer architectures for high-performance and embedded systems, sustainable computing ecosystems, and AI hardware resilience. His innovations like dynamic thermal management and AI co-design have been adopted in industry. Recent publications emphasize reducing computing's environmental impact, with work on carbon-aware systems, PFAS mitigation in semiconductors, and quantum computing performance modeling. Awards include the ACM Maurice Wilkes Award and IEEE Fellow status. Brooks leads initiatives such as the NSF-funded Carbon Connect and collaborates with industry partners like Meta. His work bridges academic research with practical sustainability solutions for computing infrastructure.

Ang LiView profile

Assistant Professor

• Machine Learning
• Edge Computing
• Federated Learning
• +4 more

Dr. Ang Li is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Maryland, College Park (UMD), affiliated with the A. James Clark School of Engineering. He joined UMD in 2023 as a tenure-track faculty member, following a research associate position at Qualcomm AI Research. His research focuses on the intersection of machine learning and edge computing, emphasizing scalable, secure, and trustworthy intelligent systems. Dr. Li holds dual Ph.D.s in Electrical and Computer Engineering from Duke University (2022) and Computer Science from the University of Arkansas (2018), alongside an M.E. in Management of Innovation from Peking University and a B.S. in Computer Science from Henan University. His work addresses challenges in federated learning, edge AI deployment, and ethical AI systems. Notable achievements include the IEEE TCCPS Outstanding Ph.D. Dissertation Award, ACM KDD Best Student Paper Award (2020), and multiple recent recognitions such as the Qualcomm Innovation Fellowship (2025) and Cisco Research Award (2025). Dr. Li currently leads a research group exploring topics like counterfactual fairness, efficient LLM serving on edge devices, and medical image domain adaptation. Recent publications highlight contributions to federated learning frameworks (e.g., Hermes, FedHyper), edge computing optimizations (EdgeLoRA), and AI safety (Moderator). His group's work on MAPSeg achieved CVPR 2024 acceptance for medical image segmentation advancements. Grants include funding from Cisco for edge intelligence research. Dr. Li also serves on editorial and program committees, including IEEE TNNLS Associate Editor and ICLR 2025 Area Chair.

Maire O'NeillView profile

Professor

• Hardware Security
• Cryptography
• Approximate Computing
• +7 more

Maire O'Neill is a Professor at the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast, affiliated with the Secure Digital Systems (SDS) group and the Institute of Electronics, Communications & Information Technology (ECIT). Her research focuses on hardware security, cryptography, and secure embedded systems. She has held significant leadership roles and pioneered work in FPGA security, approximate computing, and post-quantum cryptography. Dr. O'Neill's academic journey includes over 20 years of contributions to secure digital systems, with a particular emphasis on cryptographic hardware, side-channel analysis, and IoT security. She has led major research projects such as the EU-funded 'TruDetect' initiative for hardware Trojan detection and the 'Secure IoT Processor Platform' project. Her work bridges theoretical research with practical applications, emphasizing real-world security challenges in electronics and computing. Education: Background in electrical engineering and computer science (details not explicitly stated in the text) Research Interests: Her work spans hardware security primitives, FPGA-based cryptographic solutions, and energy-efficient computing. She explores vulnerabilities like Rowhammer attacks and side-channel leaks while developing defenses through approximate computing and machine learning techniques. Her research addresses emerging threats in IoT, 5G networks, and post-quantum cryptography. Publications Trends: Recent work emphasizes machine learning applications in security (e.g., ML-KEM accelerator designs), hardware Trojan detection, and energy-efficient approximate computing. She frequently publishes in top-tier venues like IEEE Transactions and ACM conferences, with a focus on practical implementations and FPGA demonstrators. Awards: 2007: BFIIN ITEC Platinum Award, British Female Inventor of the Year, European Union Women Innovators 2015: Fellow of the Irish Academy of Engineering 2015: INVENT Award for collaborative innovation Advising & Grants: Supervised 8 PhD students (explicitly stated). Active in securing research grants (18 projects listed), including EU and industry collaborations. Engages in academic service roles like IEEE Distinguished Lecturer and international conference organization. Labs/Teams: Leads the Secure Digital Systems (SDS) group, collaborating with global partners on hardware security and IoT initiatives. Maintains strong ties with industry through projects like NIO New Deal Cyber Bid and TruDetect.

• Machine Learning
• Healthcare Analytics
• Telecommunication Industry Analysis
• +3 more

Karl Hall is a Lecturer in Computing & Games at the Department of Computing & Games, Teesside University. His research focuses on machine learning applications across diverse domains including healthcare analytics, telecommunication, traffic modeling, and social network analysis. He specializes in developing predictive models for credit risk assessment, disease diagnosis, customer behavior analysis, and infrastructure optimization. Key research interests include machine learning algorithms (e.g., Random Forest, CNNs), explainable AI systems, and their practical implementations in real-world scenarios. He has conducted studies on diabetes prediction, pandemic forecasting, and metaverse risk assessment. His work often bridges theoretical models with practical industry challenges, particularly in finance, healthcare, and transportation sectors. Recent research trends show a focus on interdisciplinary applications of AI, such as biomedical information retrieval frameworks and transformer-based models. He has collaborated on projects analyzing UK traffic datasets and Bitcoin trader networks, highlighting his versatility in data-driven research. No awards or grants are explicitly mentioned in the provided texts. His advising and professional activities are not detailed in the current data, but his publications reflect active engagement with both academic and applied research challenges.

• Computer Networks
• Edge Computing
• Quantum Computing
• +5 more

Xiaojun Shang is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington. His research focuses on distributed computing environments, including edge computing, quantum computing, and network optimization for AI applications. PhD in Computer Engineering from Stony Brook University (2023) MS in Electrical Engineering from Columbia University (2016) BS in Information and Communication Engineering from Zhejiang University (2014) Research interests span computer networks , edge computing , edge AI , and quantum computing , with emphasis on algorithm development for distributed environments. Recent work includes quantum interconnection networks and edge AI co-design , with applications in connected vehicles and 6G systems. Publications demonstrate trends in service function chain deployment , quantum entanglement routing , and mobility-aware edge computing . His team's work on blockchain-based O-RAN optimization and distributed quantum computing topology has won Best Paper Awards at ICDCS 2024 and INFOCOM 2020. Best Paper Award, IEEE ICDCS 2024 UT System STARs Award 2023 Best Paper Award, IEEE INFOCOM 2020 As a Faculty Mentor for OurCS Workshop and committee member for CSE PhD Admissions, he advises PhD students including Wei Lin and Wentao Dexter Gao . Reviewer roles include IEEE TPDS, ACM Computing Surveys, and IEEE IoT Journal.

Sanja AleksicView profile

Associate Professor

• Microelectronics
• Semiconductor Physics
• Applied Physics
• +3 more

Sanja Aleksic is an Associate Professor at the Faculty of Electronic Engineering, University of Nis, specializing in Microelectronics and Microsystems. Her academic career began with a Physics degree from the Faculty of Philosophy in Nis (1995), followed by a Master's (2009) and PhD (2015) in Applied Physics from her current faculty. Her research focuses on semiconductor materials, photoacoustic spectroscopy, and electronic device modeling. She has published extensively in peer-reviewed journals, with 9 papers indexed with impact factors. Her work bridges applied physics and engineering, particularly in analyzing thermal and elastic properties of materials using advanced techniques like photoacoustic analysis and neural network modeling. Education: Bachelor's in Physics, Faculty of Philosophy, University of Nis (1995) Master's in Applied Physics, Faculty of Electronic Engineering, University of Nis (2009) PhD in Applied Physics, Faculty of Electronic Engineering, University of Nis (2015) Research Interests: Microelectronics, semiconductor characterization, thermoelastic effects, photoacoustic signal analysis, and material property modeling. Notably, her work employs electro-acoustic analogies to study silicon and TiO₂-coated materials, as well as neural network approaches for high electric field stress modeling in VDMOSFETs. Her recent studies (2020–2025) emphasize cutting-edge topics like solar parking canopy optimization and biomarker-based clinical outcomes in pandemic scenarios. Publications: Over 30 peer-reviewed articles, with a focus on photoacoustic techniques, semiconductor device simulation, and energy systems. Her work spans journals like Journal of Applied Physics , International Journal of Thermophysics , and Journal of Computational Electronics . Key themes include material property extraction, thermal-mechanical coupling, and sustainable energy solutions. Awards: None explicitly listed in provided texts. Labs/Teams: Engaged in interdisciplinary research at the Faculty of Electronic Engineering, collaborating on projects involving material characterization, renewable energy systems, and bio-medical applications. Active in both national and international research networks, though specific lab affiliations are not detailed here.

Mieszko Jan Ferens MichalekView profile

Researcher

• Computer Science
• Artificial Intelligence
• Internet of Things
• +5 more

Mieszko Jan Ferens Michalek is a Researcher at Aalborg University's Department of Electronic Systems within The Technical Faculty of IT and Design. His work focuses on cybersecurity, machine learning, and edge computing with applications in IoT and cloud-edge networks. Education: Master in Telecommunication Engineering (2022), University of Valladolid Bachelor in Telecommunication Engineering Technologies (2020), University of Valladolid Research Interests: Explores machine learning for secure IoT systems, PUF-based hardware authentication, adversarial attack modeling, and simulation environments for cloud-edge networks. Specializes in integrating quantum-resistant cryptography into IoT infrastructure through projects like QUCISAFE. Projects: QUCISAFE (2025-2027) : Develops post-quantum cryptography and QKD for critical infrastructure in cloud-edge-IoT systems. IoTalentum (2020-2025) : Advances IoT training and machine learning for secure IoT ecosystems. Key Contributions: Developed predictive adversarial systems to model attackers targeting PUFs, created the STEROCEN simulation tool for cloud-edge resource management, and proposed MetaLung architecture for secure metaverse healthcare systems.

Mark S. ShephardView profile

Professor

• Scientific Computing
• High-Performance Computing
• Numerical Simulation
• +5 more

Mark S. Shephard is the Samuel A. Johnson '37 and Elizabeth C. Johnson Professor of Engineering and Director of the Scientific Computation Research Center (SCOREC) at Rensselaer Polytechnic Institute, with joint appointments in Mechanical, Aerospace and Nuclear Engineering and Computer Science departments. His research pioneers Scientific Computing and High-Performance Simulation , driving innovations in automatic mesh generation , adaptive analysis methods , and parallel adaptive simulation technologies . SCOREC's work spans five core areas: High-Performance Simulation Methods - advanced mathematical models and discretization Simulation Reliability – uncertainty quantification and adaptive techniques Massively Parallel Computations – scalable solutions for real-world engineering problems Multiscale Computations - cross-scale modeling frameworks Construction of Simulation Systems – collaborative workflow development Applications include fusion plasma, soft tissue modeling, additive manufacturing, and microelectronics. Recent publications (2022-2025) reveal intense focus on GPU-accelerated unstructured mesh methods for fusion energy research and multiscale material science, with growing emphasis on exascale computing and cyberinfrastructure for plasma physics. His work increasingly bridges computational theory with industrial applications in CAE and medical device evaluation. Professor Shephard has graduated 24 Ph.D. students and secured over 65 research grants from 13 government agencies including DOE (SciDAC Institutes, Exascale Computing Program), NSF, NIH, DoD, and NASA, plus funding from 44 industry partners. His leadership extends to SCOREC's collaborations with ten+ universities and commercial impact through medical simulation software used in arterial stent evaluation. As SCOREC's founder and director for 32 years, he integrates faculty from seven departments across Rensselaer to advance simulation technologies. His Simmetrix Inc. co-founding role demonstrates commitment to translating research into engineering solutions, with current work targeting fusion plasma systems and heterogeneous supercomputing environments.

Milton HalemView profile

Research Professor

• Climate Science
• Machine Learning
• Quantum Computing
• +5 more

Milton Halem is a Research Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC), affiliated with the College of Engineering and Information Technology. He also holds an Emeritus position as Chief Information Research Scientist at NASA Goddard Space Flight Center's Earth Sciences Directorate. His expertise spans machine learning, quantum computing, atmospheric science, and climate modeling. Halem's research focuses on integrating AI with environmental systems, including wildfire digital twins, planetary boundary layer estimation, and climate forecasting. Key research interests include applying deep learning to wildfire prediction, developing quantum algorithms for optimization, and advancing climate observation systems. His work bridges disciplines like remote sensing, cybersecurity, and geophysical data analytics. Awards include NASA's Exceptional Scientific Achievement Medal (2022), Outstanding Leadership Medal (2018), and the Distinguished Service Medal (1996). Affiliations: UMBC CSEE, NASA Goddard (Emeritus) Notable Projects: Wildfire Digital Twin Initiative, SOAR atmospheric radiances system, AI-enhanced air quality forecasting Publications emphasize machine learning applications in climate science, quantum computing, and environmental monitoring. Collaborations include NASA, NOAA, and academic partners worldwide. Current projects explore AI-driven climate models and real-time wildfire impact assessment systems.

Nur Imtiazul HaqueView profile

Visiting Assistant Professor

• Cybersecurity
• Machine Learning
• Formal Methods
• +3 more

Nur Imtiazul Haque is a Visiting Assistant Professor at the University of Cincinnati's School of Information Technology within the College of Education, Criminal Justice, and Human Services (CECH). He holds a Ph.D. and M.Sc. in Electrical and Computer Engineering and Computer Engineering from Florida International University, USA, and a B.Sc. in Computer Science and Engineering from Khulna University of Engineering and Technology (Bangladesh). Previously, he served as a lecturer at Daffodil International University and a graduate research assistant in the Analytics for Cyber Defense lab, where he contributed to NSF and DOE projects. His research focuses on securing cyber-physical systems through formal methods, machine learning, and mathematical optimization, particularly in smart healthcare, smart homes, and industrial IoT. He has published 12 peer-reviewed papers, one book chapter, and patented a tool. Notable achievements include the Best Paper Award at IEEE/IFIP DSN 2023 and the Dissertation Year Fellowship Award. Dr. Haque has organized the International Workshop on AIOTS as publication chair and is an active IEEE member. His work addresses critical infrastructure vulnerabilities, with contributions to frameworks like SHATTER, PHASE, and iDDAF for detecting and mitigating advanced cyber threats.