Find Academic Supervisors

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.

Visham RamsurrunView profile

Senior Lecturer

• Cybersecurity
• Internet of Things (IoT)
• Network Security
• +7 more

Visham Ramsurrun is a Senior Lecturer and Programme Coordinator of the MSc Cyber Security programme at Middlesex University Mauritius. He holds a Ph.D. and B.Sc. in Computer Science & Engineering from the University of Mauritius, and a Postgraduate Certificate in Higher Education from Middlesex University (UK). His research focuses on cybersecurity, IoT, network design, machine learning, and smart agriculture. Education: Ph.D. in Computer Science & Engineering (2007-2010), University of Mauritius Postgraduate Certificate in Higher Education (2017-2018), Middlesex University (UK) BSc (Hons) Computer Science & Engineering (2000-2003), University of Mauritius Research interests include: Cybersecurity frameworks for IoT and autonomous systems Blockchain applications in healthcare and data management Network defense mechanisms (e.g., SDN-based moving target defense) Energy-efficient sensor networks and transmission optimization Smart agriculture systems and environmental monitoring Recent publications emphasize cybersecurity in autonomous vehicles, IoT-enabled healthcare systems, and sensor-based home security solutions. He has secured grants from the Mauritius Research and Innovation Council (MRIC) and collaborates with industry partners on innovation projects. His work includes developing augmented reality control platforms and low-cost air quality monitoring systems. As a member of the IPv6 Forum Mauritius, he advocates for network architecture advancements. His research spans academic collaborations across disciplines, with a focus on underserved regions' technological challenges.

Ala'a Al-HabashnaView profile

Adjunct Professor

• Wireless Communication Networks
• 5G/6G Technologies
• Machine Learning Applications
• +5 more

Ala'a Al-Habashna is an Adjunct Professor at Carleton University's School of Computer Science within the Faculty of Engineering and Design. He holds a PhD from Carleton University and focuses on advanced wireless communication systems, 5G/6G networks, machine learning applications, and discrete-event simulation. His research integrates AI-driven solutions for network optimization, spectrum management, and smart urban infrastructure analysis. Education: PhD in Computer Science, Carleton University Research Interests: 5G/6G Networks, Machine Learning for Communications, Cognitive Radio Systems, IoT Integration, and Urban Modeling via Computer Vision Focus on resource allocation algorithms, channel modeling, and AI-driven network architectures Key Article Trends: Recent work emphasizes AI integration in next-gen networks (e.g., RIS-assisted MIMO, GenAI-6G), 5G energy efficiency, and urban analytics using street-view imagery. Earlier contributions include DEVS-based simulation frameworks for fire modeling and D2D video streaming optimization. Collaborations & Funding: Active partnerships include Ericsson Canada for projects like Channel Reconstruction for LTE/NR and Spectrum Sharing with Machine Learning . A funded PhD position is available in Wireless Communication and 5G Networks requiring expertise in Mathematical Optimization. Labs & Teams: Engaged in Ericsson-Carleton Partnership initiatives and leads interdisciplinary teams addressing 6G challenges. Research leverages Carleton's advanced wireless and AI facilities.

Qi HanView profile

Professor

• Networking
• Distributed Systems
• Cyber-Physical Systems (CPS)
• +5 more

Qi Han is a Professor in the Department of Computer Science at Colorado School of Mines since 2005. She leads the Pervasive Computing Systems (PeCS) research group, focusing on algorithms and systems for pervasive/mobile computing applications. Her work bridges networking, distributed systems, and robotics with applications in cyber-physical systems (CPS), IoT, environmental monitoring, energy-efficient infrastructure, and mine safety. She holds a Ph.D. in Computer Science from the University of California-Irvine (2005). Education : - Ph.D. Computer Science, University of California-Irvine (2005) Research Interests : Dr. Han's research spans robotics, mobile sensing, and CPS, emphasizing interdisciplinary projects like underground mine safety, smart buildings, and oil refinery inspection. Recent efforts include drone-UGV collaboration, AR systems, and edge computing optimizations. She collaborates globally with institutions like CSIRO (Australia) and Chinese Academy of Sciences. Grants & Collaborations : - Multiple NSF grants secured - Active international partnerships in CPS/IoT research Labs & Projects : - PeCS Group: Develops systems for mobile/robotic sensing applications - Current projects include energy-aware path planning, AR pose estimation, and multi-drone coordination

Li ChouView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Data Science
• +3 more

Dr. Li Chou is an Assistant Professor of Computer Science in the College of Engineering at West Texas A&M University. He holds a B.S. in Computer Science and an M.B.A. from Southern Methodist University, and an M.S. and Ph.D. in Computer Science from The University of Texas at Dallas. Prior to joining WTAMU, he was a Postdoctoral Research Associate at Rice University's Department of Computer Science. His research focuses on artificial intelligence , machine learning , data science , and scientific computing , with applications in recommendation systems, genomic data analysis, and federated learning. His work emphasizes efficient algorithms, model compression, and optimization for large-scale data processing. Notable contributions include the Random Offset Block Embedding (ROBE) technique for recommendation systems and diversified sampling methods for genomic streams. Dr. Chou teaches courses such as Programming Principles II, Data Structures and Algorithms, Computer Organization, and Artificial Intelligence. He is affiliated with the university's ECORE Lab and Human-Machine Teaming Laboratory , contributing to interdisciplinary research initiatives in engineering and computer science. His publications span topics like neural network optimization, federated learning frameworks, and bioinformatics applications, demonstrating a commitment to advancing both theoretical and applied computing.

Yasaman AmannejadView profile

Associate Professor

• Machine Learning
• Data Analytics
• Cloud Computing
• +7 more

Yasaman Amannejad is an Associate Professor in the Department of Mathematics and Computing at Mount Royal University (MRU), Faculty of Science. She holds a PhD in Software Engineering from the University of Calgary (2017) and an MSc/BSc in Computer Information Technology from Amirkabir University of Technology (2011/2008). Her research focuses on applying machine learning to healthcare diagnostics, performance analysis of cloud and edge computing systems, and addressing social challenges like domestic violence and homelessness. Her work has been funded by NSERC, Petro-Canada, and the New Frontiers in Research Fund. Education: PhD in Software Engineering, University of Calgary (2017) MSc in Computer Information Technology, Amirkabir University of Technology (2011) BSc in Computer Information Technology, Amirkabir University of Technology (2008) Research Interests: Machine Learning for Healthcare (e.g., tropical disease diagnosis, Multiple Myeloma cancer) Performance Optimization in Cloud/Edge Systems Resource-Constrained Device Learning (wearables, IoT) Social Impact Technologies (domestic violence detection, homelessness solutions) Awards & Grants: NSERC Discovery Grant (2020) New Frontiers in Research Fund-Exploration (2020) Petro-Canada Young Innovator Award (2019) Multiple Teaching Awards (2015-2016) Teaching & Industry: Incorporates industry experience into courses, emphasizing real-world applications Outstanding Teaching Performance Award (2016) Over 5 years of industry experience in software engineering Labs/Teams: Part of interdisciplinary teams at MRU's Faculty of Science Collaborates on NSERC-funded projects