AI-driven hardware optimization Professors and Supervisors | ApplySharif

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Cemil ÖZView profile

Professor

Sakarya UniversityTurkey

Prof. Cemil ÖZ is a full-time Professor at Sakarya University's Faculty of Computer and Information Sciences, where he has held academic positions since 1998. He leads research in computer vision, artificial intelligence, and medical imaging applications, with ongoing projects in virtual simulation systems and machine learning diagnostics. Education PhD in Electrical and Electronics Engineering, Sakarya University (1995-1998), specializing in neural network-based robotic trajectory control Master's in Electronics and Computer Education, Marmara University (1989-1993), focusing on adaptive robotic control systems Bachelor's in Electronics and Communication Engineering, Yıldız University (1986-1989) Research Focus Prof. ÖZ's interdisciplinary research bridges computer science with healthcare, industrial applications, and security. Primary domains include: Medical AI : Machine learning for neuroimaging diagnostics (e.g., multiple sclerosis differentiation) Virtual Simulation : Industrial and educational systems (welding/driving simulators) Computer Vision : 3D reconstruction, object detection, and facial analysis Applied ML : Hybrid algorithms for predictive modeling in security and social media Publication Trends Recent articles (2019-2024) demonstrate strong emphasis on AI-driven healthcare solutions (38% of works), particularly neurological disorder diagnostics using MRI/spectroscopy data. Virtual training systems constitute 27% of output, focusing on industrial/educational simulations. Remaining publications advance computer vision techniques (3D reconstruction, object detection) and security applications (terrorism prediction, sentiment analysis). Methodologically, deep learning (YOLO, CNN) and sensor integration (Kinect) are predominant.

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Liam McDaidView profile

Professor

Ulster UniversityUnited Kingdom

Professor Liam McDaid serves as Interim Research Director at Ulster University's School of Computing, Engineering and Intelligent Systems (Magee Campus, Derry~Londonderry). His leadership spans computational neuroscience and neuromorphic engineering projects addressing global challenges through the UN Sustainable Development Goals framework. Research focuses on computational neuroscience and neural engineering , with expertise in spiking neural networks, astrocyte modeling, hardware implementations (FPGAs/Networks-on-Chip), and biomedical applications. His work integrates computer hardware with biological systems for fault-tolerant computing and medical diagnostics. Key publications reveal trends in neuromorphic hardware for real-time fault detection (RISC-V systems), medical AI for cardiac/adrenal diagnostics, and event-driven sensing . Research bridges computer engineering with neuroscience to solve healthcare infrastructure challenges. Prize 'Biotechnology' category in Northern Ireland Science Park 25K Award (2011) IgniteNI's global Accelerator Programme (2021) Life and Health Startup Company of the Year 2019 (InventNI) Prize Asthma (2019) Leads major projects including AI-EPOCMON (cardiac diagnostics), Nervous Systems (neural interfaces), and microwave thermal therapy for hypertension. Supervises PhD researchers in neural engineering while directing industry collaborations with healthcare technology focus. Maintains active partnerships across Europe in neuromorphic computing and medical device development. Directs research teams advancing spiking neural networks for hardware security and medical applications, with recent work on adrenal segmentation pipelines and RISC-V watchdog mechanisms. Future initiatives focus on expanding AI-driven point-of-care diagnostics and neuromorphic infrastructure monitoring systems.

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Kovalchuk Myroslav LyubomyrovychView profile

Associate Professor

St. Cyril and St. Methodius University of Veliko TarnovoBulgaria

Myroslav Lyubomyrovych Kovalchuk serves as an Associate Professor in the Department of Computer Science at Yuriy Fedkovych Chernivtsi National University. His academic foundation includes degrees in Electronics and Microelectronics, culminating in a Candidate of Physical and Mathematical Sciences qualification. His educational background comprises: Bachelor of Electronics, Yuriy Fedkovych Chernivtsi National University (2001) Engineer-physicist in Microelectronics and Semiconductor Devices, Yuriy Fedkovych Chernivtsi National University (2002) Candidate of Physical and Mathematical Sciences, Specialty 01.04.01 (2010) Kovalchuk's research centers on neural networks, database systems, information system development, and electronics engineering. His work bridges theoretical computer science with practical engineering applications, particularly in AI-driven solutions for real-world problems. He has developed specialized systems for image analysis, financial forecasting, and tourism infrastructure, demonstrating consistent innovation in computational methodologies. Over the past five years, his publication record reveals a strategic focus on neural network optimization, distributed database systems, and intelligent information architectures. His textbooks and research articles show increasing integration of cloud computing with traditional AI techniques, reflecting industry trends toward scalable, accessible computational solutions across finance, healthcare, and tourism sectors. Kovalchuk actively contributes to the Bukovina Information Technology Cluster (since 2019) and provides scientific consulting to KM TRADE LLC on security systems. He maintains professional currency through Sigma Software University's Teachers' Smart Up program and international training initiatives in Warsaw and Suceava, emphasizing modern pedagogical approaches for technical education.

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Leonard Ng Wei TatView profile

Assistant Professor

Nanyang Technological UniversitySingapore

Leonard Ng Wei Tat serves as an Assistant Professor at Nanyang Technological University's School of Materials Science & Engineering, where he directs the NGenuity Lab focused on data-driven roll-to-roll printed flexible electronics and self-driving laboratories for renewable energy systems. His work bridges advanced manufacturing, nanomaterials, and artificial intelligence to develop sustainable energy solutions. Ng's academic foundation includes a PhD in Engineering from the University of Cambridge (2019), where he pioneered low-cost printed electronics using graphene and 2D materials. This was followed by a prestigious NTU International Postdoctoral Scholarship with CSIRO and a Royal Academy of Engineering Enterprise Fellowship for renewable energy commercialization. His research spans seven core areas: Additive and Advanced Manufacturing, Nanomaterials and Nanotechnology, Smart Cities infrastructure, Surfaces and Coatings engineering, Artificial and Augmented Intelligence, Biomedical Informatics, and 2D Materials applications. Current projects emphasize machine-learning enabled inverse design for high-throughput manufacturing of photovoltaics, with particular focus on stability enhancement and efficiency optimization of printed solar cells. Analysis of Ng's recent publications reveals a clear trajectory toward integrating AI with laboratory automation, with dominant themes in self-driving laboratories (35% of output), printed photovoltaics (40%), and stability enhancement techniques (25%). His work demonstrates exceptional translational impact, with multiple industry features including PV Magazine coverage of his perovskite solar cell breakthroughs. Scientific recognition includes: NTU International Postdoctoral Scholar Royal Academy of Engineering Enterprise Fellowship Ng actively mentors eight PhD students and multiple postdoctoral researchers in organic and perovskite photovoltaics, self-driving lab development, and AI-driven materials design. His group secures substantial funding through competitive mechanisms including Nanyang PhD Graduate Scholarships, SINGA Scholarships, and industry partnerships, supporting both fundamental research and commercial translation efforts. The NGenuity Lab operates as a dynamic hub for innovation, featuring specialized facilities for roll-to-roll printing, photovoltaic characterization, and AI-driven experimental automation. Current initiatives focus on democratizing laboratory robotics through low-cost 3D printing and developing closed-loop optimization systems for industrial-scale renewable energy manufacturing.

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Blair A. WinegarView profile

Associate Clinical Professor

University of UtahUnited States

Blair A. Winegar, MD, is a Clinical Associate Professor in the Department of Radiology & Imaging Sciences at the University of Utah School of Medicine. He holds dual board certifications in Diagnostic Radiology and Neuroradiology from the American Board of Radiology, reflecting his specialized expertise in advanced imaging techniques. Education: Fellowship: Neuroradiology, University of Utah School of Medicine Residency: Radiology, University of Texas Health Science Center at San Antonio Transitional Residency: University of Texas Health Science Center at Houston Medical Degree: University of Texas Medical Branch at Galveston Undergraduate: Physics (minor in Mathematics), Texas A&M University Research Focus: Dr. Winegar's work spans neuroradiology, musculoskeletal imaging, and innovative applications of AI in radiology. His primary interests include spine MRI optimization, orbital pathology quantification using deep learning, and advancing diagnostic protocols for infections and oncologic conditions. His research emphasizes interdisciplinary collaboration to enhance imaging accuracy and clinical outcomes. Publication Trends: Recent articles (2016-2023) demonstrate consistent themes: spine and musculoskeletal imaging techniques, orbital disorders, AI-driven diagnostics, and multidisciplinary approaches to complex pathologies. His scholarship frequently bridges clinical radiology with emerging technologies like ultrafast MRI sequences and automated segmentation algorithms.

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Stephen OjoView profile

Assistant Professor

Fresno Pacific UniversityUnited States

Stephen Ojo serves as Assistant Professor of Electrical and Computer Engineering at Anderson University's College of Engineering, where he integrates Christian values with technical education. A senior IEEE member and NIH R25 research mentor, he leads the MDPI special issue on AI for Network Management while directing over 60 publications in wireless and biomedical AI systems. His educational foundation includes: BS in Electrical and Electronics Engineering, Federal University of Technology, Nigeria MS in Electrical and Electronics Engineering, Girne American University, Cyprus PhD in Information Systems, Girne American University, Cyprus Dr. Ojo's research pioneers AI-driven medical diagnostics and 5G/6G network optimization , developing systems like LesionNet for skin cancer detection and HO-SsNF for cervical cancer classification. His work spans cardiac signal analysis, neurological disorder prediction, and UAV network modeling with emphasis on explainable AI and real-world implementation. Recent publications reveal strong interdisciplinary trends merging deep learning with healthcare applications (65% of 2024 works), particularly in medical image analysis and physiological signal processing. Wireless networking research focuses on energy-efficient routing and adversarial attack defense in critical infrastructure. His scientific recognition includes: Elevation to IEEE Senior Member grade As NIH R25 mentor, Dr. Ojo guides biomedical research while developing active learning frameworks for student anxiety prediction and elderly activity monitoring. His teaching philosophy emphasizes hands-on problem solving with ethical engineering practice, reflected in AU's Christ-centered curriculum since his 2021 appointment. His laboratory work focuses on wireless sensor networks and medical AI prototyping, with ongoing projects in Parkinson's detection via voice analysis and myocardial infarction prediction using multi-label classification systems.

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David Christopher Balderas-SilvaView profile

Research Professor

Monterrey Institute of TechnologyMexico

David Christopher Balderas-Silva serves as a Research Professor at Tecnológico de Monterrey's Institute for Advanced Materials for Sustainable Manufacturing in Mexico City. His interdisciplinary work bridges biomedical engineering, computer science, and advanced manufacturing with emphasis on sustainable industrial solutions and accessibility. His educational credentials include: B.Eng. in Mechatronics Engineering from Universidad Panamericana MSc in Biomedical Engineering from Delft University of Technology PhD in Engineering Sciences from Tecnológico de Monterrey Dr. Balderas-Silva's research centers on computer vision , artificial intelligence , and brain-computer interfaces , with applications in healthcare accessibility, robotics, and Industry 4.0. His work on EEG-based speech decoding and 3D-printed assistive devices demonstrates commitment to inclusive technology, while metaheuristic optimization research advances sustainable manufacturing. Recent publications reveal strong focus on neural signal processing (40% of 2022-2024 output) and computer vision for robotics (30%), with growing emphasis on UN Sustainable Development Goals related to disability inclusion and clean energy. His recognition includes: Mexican Researcher Certification - Level 1 As a member of the Mexican National Researchers System, he has co-authored over 20 publications and multiple inventions. His Education 4.0 pedagogy initiatives for machine learning and neurotechnology training highlight academic leadership. While specific grant details aren't provided, his Institute affiliation indicates active participation in collaborative research addressing sustainable manufacturing and assistive technology gaps. He contributes to the Institute for Advanced Materials for Sustainable Manufacturing through projects integrating brain-computer interfaces with industrial robotics, developing low-cost eye-tracking systems, and optimizing PCB manufacturing via digital twins. His lab work emphasizes cross-disciplinary teams focused on translating AI research into practical solutions for Industry 4.0 challenges.

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Tayab Din MemonView profile

Senior Lecturer

Torrens University AustraliaAustralia

Tayab Din Memon serves as Senior Lecturer and Senior Learning Facilitator at Torrens University Australia's Design and Creative Technology school, where he leads initiatives in the Centre for Artificial Intelligence Research and Optimisation (AIRO). With over 15 years of academic experience spanning teaching, research, and management, he actively supervises PhD candidates in AI-driven sustainable infrastructure and education technology projects. Education PhD in Computer Engineering from Royal Melbourne Institute of Technology University (awarded July 16, 2012) Research Focus Dr. Memon's work bridges technical AI systems and educational innovation , with core expertise in machine learning for fault detection in railway infrastructure and generative AI applications in learning analytics. His signal processing research targets FPGA/SoC implementations for sustainable engineering solutions, while his education technology work pioneers student-teacher-GenAI interaction frameworks using Bloom's Taxonomy to enhance engagement and retention. Current projects include AI-powered condition monitoring systems and flipped learning reimagined through AI collaboration. Research Trends Analysis of his 2024-2025 publications reveals two converging streams: (1) AI in Education focusing on generative AI's role in future pedagogy through collaborative teacher-GenAI models, and (2) Embedded Systems Engineering developing sigma-delta modulation techniques for railway monitoring and image processing. Both streams emphasize sustainable infrastructure solutions and real-world hardware implementation on FPGA platforms. Academic Leadership As former Head of Electronic Engineering Department (implementing Washington Accord standards), Dr. Memon has coordinated online curricula using flipped learning pedagogy and served as Co-Principal Investigator for the Haptics, Human-Robotics, and Condition Monitoring System Lab under Pakistan's National Center of Robotics and Automation. His current grants focus on AI-driven sustainable infrastructure and educational technology innovation. Research Environment He leads the Centre for Artificial Intelligence Research and Optimisation (AIRO) at Torrens University, directing interdisciplinary teams that combine robotics, haptics, and machine learning. Previously, he spearheaded the 11-lab NCRA consortium developing condition monitoring solutions for railway wheelsets and track surfaces, with ongoing work extending to three-phase induction motors and wireless communication systems.

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Sayed Amir HoseiniView profile

Lecturer

University of New South WalesAustralia

Dr. Sayed Amir Hoseini is a Lecturer at the University of New South Wales Canberra, specializing in Computer Science and Engineering. He previously served as a sessional lecturer at institutions including UNSW Sydney, University of Sunshine Coast, and Central Queensland University. His research focuses on Wireless Communication, 5G/6G networks, Physical Layer Security, and UAV-assisted Communications. Dr. Hoseini holds a BSc in Electronic Engineering (2008, Isfahan University of Technology), an MSc in Electronics and Communication Engineering (2011, Amirkabir University of Technology), and a PhD in Computer Science and Engineering (2017, UNSW Sydney). His work bridges theoretical advancements and practical applications in secure wireless systems and IoT integration. He has secured grants including a $550k project on private 5G radio access (2023–2025) and a $47k initiative for self-forming research groups (2022–2023). His research trends emphasize AI-driven smart home security, physical-layer security protocols, and UAV trajectory optimization for improved communication reliability. His publications span machine learning applications, network security, and MIMO systems in high-frequency bands. Dr. Hoseini advises students in Wireless Communication and is affiliated with UNSW Canberra’s Department of Computer Science and Engineering.

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Katalin FazekasView profile

Assistant Professor

Vienna University of TechnologyAustria

Katalin Fazekas is an Assistant Professor in the Formal Methods in Systems Engineering group at TU Wien. Her research focuses on improving incremental reasoning methods for SAT/SMT solvers and advancing formal verification techniques. She holds a PhD from the Johannes Kepler University Linz, supervised by Armin Biere. Education: PhD in Logical Methods in Computer Science (LogiCS), JKU Linz (2016–2021) Research Interests: Incremental SAT/SMT solving Formal verification of distributed systems Algorithm optimization for constraint solving Automated reasoning and proof generation Key Projects: INCR (2021–2024) : Austrian Science Fund (FWF) project on scalable verification via incremental reasoning REVEAL-AI and SLIM : Collaborative projects on AI-driven formal methods Awards: Hertha Firnberg Fellowship (FWF), 2021–2024 Tools Developed: CaDiCaL 2.0: Advanced SAT solver QSM: Quantified symmetric minimization framework for distributed protocols Lab/Affiliations: FORSYTE research group, TU Wien.

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Elias KougianosView profile

Professor

University of North TexasUnited States

Elias Kougianos is a Professor in the Department of Electrical Engineering at the University of North Texas. His research focuses on the intersection of cybersecurity, quantum computing, blockchain technology, and edge computing, with applications in smart grids, healthcare systems, and agricultural technology. His work explores Security-by-Design principles using Quantum Physical Unclonable Functions (PUF) and Quantum Key Distribution for robust cybersecurity in energy systems. He develops blockchain-based frameworks for secure EHR management, crop insurance, and pharmaceutical supply chains, emphasizing scalability and privacy preservation . In edge computing, he investigates machine learning-driven authentication and lightweight models for IoT devices. The recent articles highlight his contributions to Cybersecurity (quantum PUF, deepfake detection) Healthcare (seizure detection, EHR management) Agriculture (plant disease classification, precision spraying) Blockchain (secure ledgers, consensus algorithms) His work integrates hardware security with distributed systems to address challenges in smart cities and CPS.

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Saman AmarasingheView profile

Professor

Massachusetts Institute of TechnologyUnited States

Professor Saman Amarasinghe is a leading academic in the MIT Electrical Engineering and Computer Science Department, specializing in compiler design, high-performance computing, and programming languages. His research bridges theoretical computer science with practical applications, focusing on optimizing compilers for modern hardware architectures. He holds a professorship within the School of Engineering at MIT. His research interests span artificial intelligence, machine learning integration into compilers, sparse tensor algebra optimizations, and domain-specific languages (DSLs) for high-performance computing. He has pioneered frameworks like TACO (Tensor Algebra Compiler) and GraphIt, which enable efficient handling of complex data structures across diverse computational domains. Recent work emphasizes compiler-driven optimizations for sparse data formats, machine learning models for code optimization, and unified interfaces for large language models. His projects often address scalability challenges in exascale computing and bioinformatics applications. Publications highlight advancements in compiler techniques for sparse tensor operations, graph analytics, and memory-efficient algorithms. His contributions to Halide, Simit, and Weld further demonstrate his impact on DSLs for image processing and physical simulation. Awards and recognitions are integral to his career, though specific personal accolades are not detailed here. His work is characterized by a strong focus on practical compiler solutions for emerging computational challenges.

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Bechir HamdaouiView profile

Professor

Oregon State UniversityUnited States

Bechir Hamdaoui is a Professor at the School of Electrical Engineering and Computer Science, Oregon State University. He leads the Networked Systems and Technology Advanced Research (NetSTAR) Laboratory, focusing on advancing technologies for future networked information, computing, and communication systems. His affiliations include IEEE, where he holds senior membership, and he has served as Chair of the IEEE Communication Society’s Wireless Technical Committee. Ph.D., Electrical & Computer Engineering, University of Wisconsin at Madison (2005) M.S., Computer Sciences, University of Wisconsin at Madison (2004) M.S., Electrical & Computer Engineering, University of Wisconsin at Madison (2002) Hamdaoui's research expertise spans intelligent networked systems, wireless and network cyber security, AI-driven networking, and protocol design for next-generation systems. His work addresses challenges in spectrum availability, energy-efficient cloud datacenters, and edge cloud caching for smart cities. Publications highlight innovative approaches to blockchain-enabled IoT resource sharing, machine learning for spectrum sensing, and virtualized network architectures. His scientific accolades include the NSF CAREER Award (2009), ISSIP Distinguished Recognition Award (2020), and multiple best paper awards at ICC and IWCMC conferences. He has served on editorial boards for IEEE Transactions on Mobile Computing and IEEE Network, among others. NSF CAREER Award (2009) ISSIP Distinguished Recognition Award (2020) ICC Best Paper Award (2017) IWCMC Best Paper Award (2017) EECS Outstanding Research Award (2016) Hamdaoui mentors graduate students in the NetSTAR Lab, which focuses on wireless privacy, AI-driven networking, and resource optimization. The lab engages in theoretical and experimental research with applications to 6G networks, autonomous systems, smart agriculture, and cloud datacenters. Current projects include AI-driven hardware fingerprinting for IoT security, spectrum availability recovery, and energy-efficient resource utilization frameworks.

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Ron C. ChiangView profile

Associate Professor

Thomas CollegeUnited States

Ron C. Chiang is an Associate Professor at the University of St. Thomas, specializing in software engineering, distributed systems, and cloud computing. His research focuses on optimizing virtualization, resource management, and high-performance architectures. He holds a Ph.D. in Computer Engineering from George Washington University and has contributed to advancements in cloud security, I/O prefetching, and precision agriculture through machine learning. Education: Ph.D. in Computer Engineering (George Washington University). Research Interests: Distributed systems, cloud computing, virtualization systems, and data-intensive applications. He has developed innovative solutions for container placement strategies, interference-aware scheduling, and energy-efficient memory management. Awards include the Best Student Paper Finalist at SC11, a 'Best of HotPower' award, and grants from IBM and Amazon. He teaches courses on cloud computing, database systems, and infrastructure as code. His work integrates theoretical computer science with practical applications, such as sustainable farming through AI and healthcare platforms in cloud environments. Grants: IBM Academic Initiative (2015–2017), Amazon AWS Educator Credits (2015–2016). He has published extensively in journals like IEEE Transactions and at conferences including SC, ICAC, and ICPA.

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Azalia MirhoseiniView profile

Assistant Professor

Stanford UniversityUnited States

Azalia Mirhoseini is an Assistant Professor of Computer Science at Stanford University and founder of the Scaling Intelligence Lab. She also serves as a Senior Staff Scientist at Google DeepMind. Her research focuses on scalable and self-improving AI systems, including advancements in chip design automation, large language models, and reinforcement learning applications. Prior roles include co-founding the ML for Systems team at Google Brain. Education: BSc in Electrical Engineering from Sharif University of Technology; PhD in Electrical and Computer Engineering from Rice University. Awards include MIT Technology Review’s 35 Under 35 and Best ECE Thesis Award. Research highlights include AlphaChip (deep RL for chip layout optimization), Mixture-of-Experts (MoE) architectures, and Constitutional AI frameworks for alignment. Her work bridges AI and systems, addressing real-world challenges in hardware design and scalable AI. Key contributions span over 50 publications in top venues like Nature , NeurIPS , and ICLR . Media coverage includes MIT Tech Review, IEEE Spectrum, and WIRED. Teaching includes CS229s: Systems for Machine Learning. Labs/Teams: Scaling Intelligence Lab (Stanford), collaborations with Google Research/DeepMind. Advising/Grants: Mentors students through Stanford; her work is funded via industry-academic partnerships.

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