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Sayed Ahmad SalehiView profile

Assistant Professor

• VLSI Circuits
• Stochastic Computing
• DNA Computing
• +7 more

Sayed Ahmad Salehi is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Kentucky's College of Engineering, where he directs the Computing with Unconventional Technologies (CUT) Lab. His research spans energy-efficient VLSI circuits for deep learning, stochastic computing, and DNA-based molecular programming. His research focuses on unconventional computing paradigms to address limitations in traditional electronics. Key areas include stochastic and approximate computing for energy-efficient deep learning hardware targeting IoT and edge devices, and biomolecular computing using DNA for environments incompatible with silicon. His lab develops tools like FUNSC (stochastic computing) and FUNDNA (DNA computing) that enable mathematical function computation through novel encoding schemes. Analysis of his recent publications reveals strong emphasis on in-memory computing architectures (particularly for DRAM and phase-change memory), stochastic-to-binary conversion techniques , and DNA circuit synthesis for mathematical operations. His work bridges theoretical computer science with practical hardware implementations for resource-constrained environments. Award highlights include: BIOMOD competition silver award (2023) Best paper nomination at Asilomar Conference (2022) ESWEEK Student Travel Grant (2019) He actively mentors PhD and undergraduate researchers, with recent graduates joining companies like Applied Materials. His lab secures significant NSF funding for interdisciplinary projects in stochastic computing and DNA-based systems. Current initiatives include CUT Lab's Kentucky iGEM team and collaborations on photonics-integrated computing. The CUT Lab operates at the intersection of computer architecture, molecular biology, and signal processing, with research impacting edge AI hardware, biocompatible computing, and next-generation memory systems.

Amine MezghaniView profile

Assistant Professor

• Wireless Communication
• Digital Signal Processing
• Communication Systems
• +12 more

Dr. Amine Mezghani is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Manitoba's Price Faculty of Engineering. His research focuses on wireless communication systems, signal processing, and antenna engineering, particularly under hardware constraints like 1-bit quantization and phase-only DACs. Education: PhD (2015) in Electrical Engineering, Technical University of Munich; Dipl.-Ing. (Electrical Engineering), Technical University of Munich; Diplôme d'Ingénieur, École Centrale Paris His work explores sustainable wireless infrastructure, with emphasis on reconfigurable intelligent surfaces (RIS), millimeter-wave Massive MIMO, V2X communications, and physically consistent modeling combining Shannon and Maxwell theories. Publications include over 100 papers on hardware-constrained systems. Scientific contributions have earned him the 2021 IEEE Signal Processing Society Best Paper Award and the 2016 Rohde & Schwarz Outstanding Dissertation Award . He serves as an Associate Editor for IEEE Transactions on Wireless Communications . Current research includes electromagnetic consistency in multi-band MIMO, unlabeled compressed sensing, and VAMP-based algorithms. He advises graduate students in wireless communications and signal processing at the Intelligent Communication and Sensing Lab .

Jianyi YangView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Computing Systems
• +7 more

Jianyi Yang is an Assistant Professor in the Department of Computer Science at the University of Houston. He holds a PhD from UC Riverside (2023), advised by Prof. Shaolei Ren, and has held postdoctoral positions at Caltech and UC Riverside. His research focuses on advancing trustworthy and efficient AI through methodologies like reinforcement learning, online optimization, and learning-augmented algorithms. Education: PhD in Computer Science (UC Riverside, 2023); Postdoctoral work at Caltech and UC Riverside. Research interests include AI/ML applications in computing systems, safety guarantees for AI systems, and sustainable computing. Recent work addresses energy-efficient water infrastructure control and equitable public AI models. Key publications include papers on online budgeted matching, learning-augmented control for water systems, and socially equitable AI. He advises PhD/Master's students and collaborates widely, with a focus on fostering interdisciplinary research. Labs/Teams: Member of the UH Computational Intelligence Group (CIG), mentoring students in AI/ML and systems research.

• Distributed Machine Intelligence
• Low-Power AI for IoT
• Indoor Localization
• +3 more

Anuradha Ravi is a Research Assistant Professor in the Department of Information Systems at the University of Maryland, Baltimore County (UMBC). Previously, she served as a Research Scientist at the Living Analytics Research Center, Singapore Management University (2018-2023), and as an Assistant Professor at Shiv Nadar University (2016-2018). She holds a Ph.D. in Computer Science from the Indian Institute of Technology Roorkee, where her doctoral research focused on optimizing energy efficiency and latency reduction for mobile devices through intelligent offloading strategies in heterogeneous networks. Her research interests span multiple cutting-edge domains in computing and networking: Distributed machine intelligence at the edge Low-power AI systems for IoT devices Indoor localization and occupancy sensing Wireless networking protocols Mobile and ubiquitous computing Smart building management systems Analysis of her recent publications (2015-2025) reveals a consistent focus on efficiency optimization in constrained environments, with key themes including autonomous robotics, AI-driven compression techniques, multi-sensor localization, and energy-aware mobile systems. Her work increasingly emphasizes real-world applications in contested or resource-limited settings, leveraging machine learning for adaptive solutions. She actively mentors graduate students at UMBC in projects related to perception systems for unmanned vehicles, compression-aware federated learning, and network middleware development. During her tenure at Shiv Nadar University, she supervised undergraduate research on cooperative IoT strategies and 5G edge computing. She previously contributed to the Living Analytics Research Center's initiatives in smart building occupancy detection and collaborative machine intelligence frameworks for IoT devices.

Alireza EsfahaniView profile

Senior Lecturer

• Cybersecurity
• Network Security
• IoT Security
• +5 more

Dr. Alireza Esfahani is a Senior Lecturer in Cyber Security at the School of Computing and Engineering (SCE) at the University of West London, where he also serves as the Course Leader for the BSc (Hons) Cyber Security and BSc (Hons) Cyber Security with Foundation Year programs. Previously, he held a lecturing position at the University of Greenwich from 2021 to 2023. He earned his PhD in Telecommunications from the University of Porto, Portugal, through the MAP-TELE doctoral program in 2017, followed by postdoctoral research at the Instituto de Telecomunicações in Portugal (2017–2019). Education: PhD in Telecommunications (University of Porto, 2017) Experience: Research Associate at SnT-CritiX (University of Luxembourg), contributing to EU cybersecurity projects SPARTA and CyberSec4Eu Affiliations: European Cyber Security Organisation (ECSO) and IEEE member Research Interests: Dr. Esfahani focuses on cybersecurity in IoT, connected vehicles, intrusion detection, machine learning applications, and semiconductor supply chain security. His work emphasizes secure authentication mechanisms, network coding security, and resilient systems design. Teaching: He teaches courses in BSc and MSc programs, including Cyber Security, Computer Science, and specialized modules in network security and secure communication protocols. Grants & Projects: Instrumental in EU-funded initiatives like SPARTA and CyberSec4Eu, focusing on cybersecurity frameworks and distributed systems. Labs/Teams: Collaborated with SnT-CritiX at the University of Luxembourg, developing cybersecurity solutions for critical infrastructure.

• Computer Vision
• Machine Learning
• Deep Learning
• +7 more

Dr. Samar Ansari is a Senior Lecturer at the University of Chester's Faculty of Science, Business and Enterprise, within the School of Computer and Engineering Sciences. With a 20-year academic career spanning India, Ireland, and the UK, he has guided over 40 Master's dissertations and 30 undergraduate projects, and mentored two successful PhD students. His research focuses on advancing AI-driven solutions in computer vision, healthcare informatics, and IoT applications. Dr. Ansari holds a Senior Membership in IEEE and an Associate Fellowship from the Higher Education Academy (HEA). Education: B.Tech (Electronics Engineering, Aligarh Muslim University, 2001), M.Tech (2007), PhD (2012), and a PG Certificate in Learning & Teaching (2022-2024, ongoing). Research Interests: His work bridges theoretical machine learning with practical applications, including activity recognition, medical image analysis (Alzheimer's, brain tumors), lightweight fire detection models, and privacy-preserving surveillance. He has pioneered techniques using GANs for dataset augmentation and developed energy-efficient neural networks for edge devices. Publications: Over 140 high-impact papers in IEEE Access, Elsevier, Springer, and others, with a focus on deep learning, IoT, and medical AI. Awards: Recognized for contributions to teaching and research through IEEE Senior Membership (2019) and HEA Associate Fellowship (2023). Teaching & Supervision: Leads courses in AI, risk management, and business intelligence at the University of Chester. Supervised research projects on FinFET-based circuits and analog signal processing.

• Operational Research
• Optimization Methods
• Logistics and Supply Chain Management
• +7 more

Manuel Iori is a Full Professor at the Department of Engineering Sciences and Methods, University of Modena and Reggio Emilia (UNIMORE), Italy. His primary research focuses on operational research, optimization methods, and logistics systems. He specializes in vehicle routing problems, scheduling algorithms, and decision support systems with applications in industrial automation, healthcare, and service industries. Iori is actively involved in teaching advanced optimization courses for engineering students, emphasizing practical applications in data-driven decision-making and simulation. Research Interests: His work addresses complex optimization challenges such as multi-trip vehicle routing with time windows, scheduling under resource constraints, and tool switching in manufacturing systems. He integrates machine learning and metaheuristics to develop innovative solutions for logistics, production planning, and healthcare operations. Collaborations with industry partners (e.g., pharmaceutical distributors, printing companies) ensure practical relevance of his research. Teaching: Iori teaches courses like Optimization Methods for Data-Driven Engineering Processes , Models for Logistics and Production Optimization , and Methods and Algorithms for Optimization in Digital Industries . These courses combine theoretical foundations with hands-on labs using tools like Python, Xpress, and Anylogic. Key Contributions: He developed decision support systems for multi-trip routing in pharmaceutical distribution and supplier selection in facility management. His research on satellite scheduling and attended home delivery systems advances both theoretical and applied domains. As a member of CIRRELT (Canada), he collaborates on logistics optimization projects. Professional Activities: Iori’s work is reflected in over 80 peer-reviewed publications and contributions to conferences. He advises graduate students on optimization challenges and serves as a reviewer for top journals in operations research.

Carl E. MotsenbockerView profile

Professor

• Sustainable Agriculture
• Vegetable Crops Physiology
• Local Food Systems
• +4 more

Carl E. Motsenbocker is a Professor in the School of Plant, Environmental and Soil Sciences at Louisiana State University (LSU), affiliated with the LSU AgCenter and the College of Agriculture. He holds roles as Executive Director of the Louisiana Farm to School Program and Co-State Coordinator for Louisiana Sustainable Agriculture Research and Education (SARE) Professional Development Program. His expertise spans sustainable agriculture, vegetable crops physiology, and international agricultural development. Education: Ph.D., Horticultural Science, North Carolina State University, 1990 M.S., Horticultural Science, North Carolina State University, 1987 B.S., General Plant Science, Cornell University, 1980 Research Interests: Focuses on local food systems, farm to school initiatives, sustainable farming practices, and tropical horticulture. He leads programs like Louisiana Farm to School, which promotes education, gardening, and institutional procurement of local produce. His work emphasizes community engagement and extension services to bridge farmers and institutions. Recent Articles: Recent publications include guides on organic gardening, Harvest of the Month educational materials, and studies on urban agriculture and farmer training programs. His research often integrates practical solutions for small-scale and limited-resource farmers. Awards: 2021 Farm Credit National MarketMaker Innovation Award (Third Place) 2019 Denver T. and Ferne Loupe Extension Team Award Multiple recognitions for service-learning and teaching excellence Grants & Advising: Managed over $5 million in grants, including the Louisiana Farm to School Program and Grow Louisiana farmer training. Advised numerous undergraduate research projects and developed courses like Sustainable Agriculture (HORT 4545) and LSU’s Thailand study abroad program. Labs/Teams: Oversees the Louisiana Farm to School program team and collaborates with the LSU AgCenter International Programs, supporting initiatives in Liberia and Thailand. Active in curriculum development for sustainable agriculture education.

Caroline ChanelView profile

Professor

• Autonomous Systems
• Decision-Making Algorithms
• Robotics
• +3 more

Caroline Chanel is a Professor at ISAE-SUPAERO (DCAS), holding the CASAC Chair since 2022. She has extensive experience in research and teaching, with roles including Associate Professor (2019-2024), Research Engineer (2013-2019), and HDR accreditation (2023). Her research focuses on embedded decision-making for autonomous systems, including proactive architectures, multi-robot cooperation, and human-machine interaction. She leads and participates in high-profile projects like SAMURAI (AID), CHIP-GT (ANR-DFG), and HAICO (CASAC Chair). She has supervised numerous PhD students and post-doctoral researchers, collaborating with institutions like ONERA, INRAe, and international partners. Education: PhD in Control and Embedded Systems (University of Toulouse 2013), Master's in Automatic Systems (Université Paul Sabatier 2008-2009), and Engineer Diploma from PUCRS (2002-2008). Awards include the Amelia Earhart Fellowship (2011) and ISAE-SUPAERO Foundation PhD Fellowship (2013). Research interests span decision-making algorithms, multi-agent systems, and neuroergonomics. Key projects address UAV navigation, blockchain integration for autonomous systems, and physiological computing for human-robot interaction. Teaching responsibilities include modules on autonomous systems, control systems, and neuroergonomics at ISAE-SUPAERO's engineering and master's programs. She co-leads the DECISIO doctoral team (ISAE-ONERA) and is involved in executive roles for conferences like EDSYS'.

• Requirements Engineering
• Software Engineering
• Cybersecurity
• +3 more

Prof. Dr. Samuel Fricker is a Lecturer in Requirements Engineering at the FHNW Institute of Interactive Technologies, part of the School of Computer Science at FHNW University of Applied Sciences and Arts Northwestern Switzerland. He leads the Horizon Europe project IMPORTANT and provides advisory services for doctoral studies. His work bridges academia and industry, focusing on software engineering, cybersecurity, and agile methodologies. Samuel’s research interests span Requirements Engineering, Cybersecurity for SMEs, Agile Practices, and Software Product Management. He actively contributes to workshops like REFrame and publishes in leading conferences such as IEEE Requirements Engineering. His recent publications emphasize healthcare systems integration, cybersecurity solutions for SMEs, and educational frameworks in Requirements Engineering. He has collaborated on projects addressing clinical trial recruitment and digital health innovation. Samuel’s professional activities include leading the FHNW Institute’s advisory roles and contributing to international standards through organizations like the International Software Product Management Association.

Frits de NijsView profile

Research Fellow

• Artificial Intelligence
• Renewable Energy Systems
• Reinforcement Learning
• +3 more

Frits de Nijs is a Research Fellow in the Department of Data Science & AI at Monash University. His work focuses on advancing artificial intelligence techniques for energy systems optimization, multi-agent decision-making, and sustainable infrastructure. He has contributed to projects such as the Human-in-the-loop Microgrid Project and RACE for 2030 CRC, addressing challenges in renewable energy integration and smart grid management. Key research areas include reinforcement learning applications for energy curtailment, HVAC control systems, and resource-constrained multi-agent planning. His projects often bridge AI methodologies with real-world energy challenges, aiming to enhance grid reliability, carbon efficiency, and fair resource allocation. Notable collaborations involve researchers in machine learning, sustainable energy, and building automation. He has actively participated in 6 major research projects since 2020, including initiatives on AI-ready buildings and probabilistic multi-agent learning environments. His publications span conferences like AAAI, NeurIPS, and IEEE Power & Energy Society events, emphasizing peer-reviewed contributions to energy-AI intersections.

• Edge Computing
• Internet of Things
• Vehicular Networks
• +5 more

Muhammad Bilal is an Associate Professor at Zhejiang University's School of Computer Science, Department of Computer Science and Technology. His research spans multiple cutting-edge domains in computer science and engineering, with a particular focus on edge computing, Internet of Things, and vehicular networks. His research interests include Edge Computing , Internet of Things , Vehicular Networks , Federated Learning , Digital Twins , and Cloud Computing . His work addresses critical challenges in resource allocation, latency optimization, privacy preservation, and intelligent decision-making in distributed computing environments. He has made significant contributions to developing novel frameworks for service caching, computation offloading, and secure data processing in next-generation networks. His recent publications reveal a strong trend toward integrating artificial intelligence with edge and cloud infrastructure, particularly focusing on privacy-preserving techniques and resource-efficient algorithms. His work spans applications from intelligent transportation systems to healthcare monitoring and meteorological services, demonstrating the versatility and real-world impact of his research. Dr. Bilal has established a prolific publication record with numerous high-impact papers in top-tier journals and conferences, reflecting his active engagement in the academic community. His collaborative approach is evident through extensive co-authorship networks, particularly with researchers in China, Pakistan, and internationally.

• Hardware Security
• Machine Learning for Security
• VLSI Design
• +7 more

Sai Manoj Pudukotai Dinakarrao is a Professor in the Department of Computer Engineering at Rochester Institute of Technology's College of Engineering. He is an active researcher with numerous publications spanning hardware security, machine learning applications, VLSI design, and IoT security. His work focuses on developing innovative solutions for security challenges in hardware and embedded systems. Dr. Dinakarrao's research interests center around hardware security, machine learning for security applications, VLSI design methodologies, and IoT security frameworks. His work has pioneered approaches to hardware Trojan detection, processing-in-memory architectures, and energy-efficient security mechanisms for resource-constrained devices. He has made significant contributions to the understanding of hardware vulnerabilities and the development of robust countermeasures. His recent publications demonstrate a clear trend toward integrating machine learning techniques with hardware security solutions, particularly focusing on processing-in-memory architectures for efficient security implementations. His work spans both theoretical foundations and practical implementations, with applications in IoT security, malware detection, and hardware verification. The research shows increasing sophistication in addressing security challenges through novel hardware-software co-design approaches. Dr. Dinakarrao has mentored numerous graduate students who have co-authored publications with him, indicating an active research group focused on cutting-edge security and hardware design topics. His collaborations span multiple institutions and research groups, demonstrating his integration within the broader academic community.

• 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.

• Machine Learning
• Optimization
• Computer Vision
• +5 more

Tom Goldstein is the Volpi-Cupal Endowed Professor of Computer Science at the University of Maryland, with appointments in Applied Mathematics and Electrical and Computer Engineering. His research focuses on AI system development, optimization methods, and their applications in computer vision and signal processing. He emphasizes the intersection of theoretical foundations and practical hardware implementations. Education: PhD in Applied Mathematics from UCLA (2010), BA from Washington University (2006). Prior to UMD, he held postdoctoral positions at Stanford University and Rice University. Research interests include AI security/privacy, algorithmic bias, diffusion models, and large language model robustness. His work addresses challenges in model watermarking, adversarial attacks, and ethical AI deployment. Notable awards include the Sloan Research Fellowship (2017), DARPA Young Faculty Award, and JPMorgan Faculty Research Award. He directs the Maryland Center for Machine Learning and has advised over 20 graduate students in AI-related disciplines. Recent research trends emphasize multimodal systems, generative model analysis, and scalable training techniques. His work on diffusion models explores style similarity and content authenticity, while watermarking studies address anti-plagiarism and data provenance. Labs/Teams: Leads the Maryland Center for Machine Learning, collaborating with UMIACS and ECE departments. Active in open-source AI initiatives and supercomputing applications for large model training.