Frnaz Akbar | Neuroscience | Best Researcher Award

Dr. Frnaz Akbar | Neuroscience | Best Researcher Award

Dr. Frnaz Akbar, National University Of Modern Languages, Pakistan

Dr. Frnaz Akbar is a dedicated computer science and software engineering educationist, researcher, and web developer with over a decade of experience in teaching and web development. She has consistently contributed to building student knowledge in computer science and is committed to high-quality education. Her expertise spans artificial intelligence, data mining, precision agriculture, IoT, and blockchain. Dr. Akbar is currently pursuing a PhD in Artificial Intelligence at Air University, Islamabad, and has a strong publication record, including studies on deep learning, pattern recognition, and EEG-based Alzheimerโ€™s research. She has worked as a lecturer at NUML University and held teaching positions at prestigious institutions. Her technical proficiency includes MERN, Python, C++, and ASP.Net. Recognized for academic excellence, she has received multiple honors, including a Best Teacher Trophy and High Achiever Scholarships. Passionate about research and innovation, she continues to contribute significantly to computer science and AI.

Publication Profile

Scopus

Education ๐ŸŽ“

Dr. Frnaz Akbar is currently pursuing a PhD in Artificial Intelligence at Air University, Islamabad, focusing on machine learning and deep learning applications. She holds an MS in Software Engineering (2018-2020) from Bahria University, Islamabad, and a Master’s in Information Technology (2015-2017) from PMAS ARID University, Rawalpindi, both with first-division honors. Her Bachelor’s in Computer Science (2013-2015) was completed at Punjab University, Lahore, where she received a Role of Honor Certificate. Additionally, she earned a B.Ed. (2020-2022) from Sarhad University, Islamabad Campus, further strengthening her educational expertise. Her academic journey began with HSSC and SSC from Islamabad College F-6/2, where she maintained outstanding academic performance. Throughout her education, she has demonstrated excellence, securing High Achiever Scholarships and academic awards. With expertise in AI, IoT, and software development, her education reflects a strong foundation in research, programming, and innovative computing technologies.

Experience ๐Ÿ‘ฉโ€๐Ÿซ

Dr. Frnaz Akbar has extensive experience in academia and industry, specializing in computer science and software engineering. She is currently a Visiting Lecturer at NUML University, Islamabad, in the Department of Software Engineering. Previously, she served as a Senior Computer Science Teacher at IMCG F-10/2 (2017-2023) and ISS, G-13/1, Islamabad (2013-2017), where she mentored students and designed curricula. She has also been a Computer Tutor at Allama Iqbal Open University since 2020, providing part-time instruction. In addition to teaching, she worked as a Software Developer at K-Soft, Ministry of Defense, focusing on MIS applications. With expertise in AI, IoT, and software development, she has contributed significantly to research and education. Her teaching philosophy emphasizes innovation and hands-on learning, ensuring students acquire both theoretical and practical skills. She is passionate about integrating emerging technologies into education and has a proven track record of academic excellence.

Awards and Honors ๐Ÿ†

Dr. Frnaz Akbar has been recognized for her outstanding contributions to education and research. She received the Best Teacher Trophy from IMCG F-10/2, Islamabad, for her exemplary performance in student development and academic excellence. During her MS at Bahria University, she was appointed as a Teacher Assistant, reflecting her strong command over software engineering concepts. She secured a High Achiever Scholarship in every semester of her MSc IT at PMAS ARID University, Rawalpindi, showcasing her dedication to academic excellence. Her bachelor’s degree at Punjab University earned her a Role of Honor Certificate, highlighting her exceptional academic performance. In her early education, she was awarded a certificate for high attendance at IMCG F-7/2 and secured the second position in SSC. These accolades demonstrate her commitment to education, mentorship, and research, reinforcing her position as a leader in computer science and software engineering.

Research Focus ๐Ÿ”ฌ

Dr. Frnaz Akbar’s research interests revolve around cutting-edge technologies in artificial intelligence and data science. She specializes in AI-driven applications, including deep learning, data mining, and pattern recognition. Her work extends to precision agriculture, where she leverages AI for optimizing crop health analysis. She is also actively engaged in research on the Internet of Things (IoT) and edge computing, focusing on real-time data processing. Blockchain technology and its integration with AI for secure computing systems form another critical aspect of her research. One of her notable contributions is in healthcare AI, particularly Alzheimerโ€™s disease detection using EEG signal analysis. She has published and submitted multiple research papers on AI applications in medical diagnostics and agricultural automation. Her research is highly interdisciplinary, combining machine learning, neural networks, and computational modeling to solve real-world problems, making significant contributions to AI and software engineering domains. ๐Ÿš€

Publication Top Notes

๐Ÿ“– Optimized Approach in Requirements Change Management in Geographically Dispersed Environment (GDE), International Journal of Foundations of Computer Science, 2020

๐ŸŒฟ Identifying Lesions in Cotton Leaves Unconstrained Images using Deep Neural Network, Computers in Biology and Medicine, 2023 (Under Review)

๐Ÿง  Unlocking the Potential of EEG in Alzheimer’s Disease Research: Current Status and Pathways to Precision Detection, Foundations and Trends in Machine Learning, 2024 (Under Review)

๐Ÿ“Š Assessing the Effects of Alzheimer Disease on EEG Signals using the Entropy Measure: A Meta-analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024 (Under Review)

Linjing Mu | Brain PET Imaging Award | Best Researcher Award

Dr. Linjing Mu | Brain PET Imaging Award | Best Researcher Award

Dr. Linjing Mu, ETH Zurich, Switzerland

๐Ÿ”ฌ Dr. Linjing Mu pursued doctoral research at Nankai University, followed by a post-doctoral fellowship at Basel University, Switzerland. With expertise in radiopharmaceutical sciences, Dr. Mu led research teams at University Hospital Zurich and ETH Zurich, focusing on lead structure discovery, ligand synthesis, and radiolabeling method development. A prolific author, Dr. Mu has 106 peer-reviewed publications, including 10 as last and corresponding author, and 19 as the first author. Co-investigator on 10 patents, Dr. Mu has presented internationally and collaborated with industry leaders like Bayer Schering, Novartis, and Roche, contributing significantly to drug discovery and clinical translations.

 

Publication Profile:

Scopus

Orcid

Education:

Doctoral Research in Chemistry Department, Nankai University (1993-1996)

Post-Doctoral Fellow, Basel University, Switzerland (1996-2001)

Experience:

Team Leader in the field of Radiopharmaceutical Sciences, Group of Prof. Schubiger (2001-2010)

Head of Research, Radiopharmacy, University Hospital Zurich (2010-2021)

Senior Scientist, Prof. Schibli’s Group, ETH Zurich (2021-Present)

Research Expertise:

Lead Structure Finding based on Literature and Patents

Design and Synthesis of Novel Ligands

Structure-Activity Relationship Studies

Radiolabeling Method Development

In Vitro and In Vivo Evaluation

Academic Achievements:

Co-authored 106 Peer-Reviewed Publications

Last and Corresponding Author on 10, First Author on 19 (H-Factor 29, Scopus)

Co-Investigator on 10 Patent Applications

Contributed to International Conferences as Invited Speaker, Oral and Poster Presenter

Research Focus:

๐Ÿ”ฌ Dr. Linjing Mu’s research focus lies at the intersection of neuroimaging and drug development, particularly in the field of molecular imaging for neurodegenerative diseases like Alzheimer’s. Their work encompasses the development and optimization of radioligands targeting specific molecular pathways implicated in neurodegeneration. Through PET/MRI studies and the exploration of novel PET tracers, such as (R)-[18F]YH134 and (R)-[18F]PSS232, Dr. Mu aims to deepen our understanding of disease mechanisms and identify potential therapeutic targets. This research not only sheds light on disease pathology but also paves the way for the development of new diagnostic tools and pharmacological interventions.

 

Publication Top Notes:

๐Ÿ“„ Wang, J. et al. Metabotropic glutamate receptor 5 (mGluR5) is associated with neurodegeneration and amyloid deposition in Alzheimerโ€™s disease: A [18F]PSS232 PET/MRI study. Published in Alzheimer’s Research and Therapy, 2024. Cited by: 0.

๐Ÿ“„ He, Y. et al. Identification of (R)-[18F]YH134 for Monoacylglycerol Lipase Neuroimaging and Exploration of Its Use for Central Nervous System and Peripheral Drug Development. Published in Journal of Nuclear Medicine, 2024. Cited by: 0.

๐Ÿ“„ Boccalini, C. et al. The impact of tau deposition and hypometabolism on cognitive impairment and longitudinal cognitive decline. Published in Alzheimer’s and Dementia, 2024. Cited by: 1.

๐Ÿ“„ Trachsel, B. et al. Reducing kidney uptake of radiolabelled exendin-4 using variants of the renally cleavable linker MVK. Published in EJNMMI Radiopharmacy and Chemistry, 2023. Cited by: 0.

๐Ÿ“„ Lu, Y. et al. Proof-of-concept optimization of a copper-mediated 18F-radiosynthesis of a novel MAGL PET tracer on a high-throughput microdroplet platform and its macroscale translation. Published in Lab on a Chip, 2023. Cited by: 1.

๐Ÿ“„ Ni, R. et al. Imaging increased metabolism in the spinal cord in mice after middle cerebral artery occlusion. Published in Photoacoustics, 2023. Cited by: 0.

๐Ÿ“„ Zechner, M. et al. In Vitro and In Vivo Evaluation of ABCG2 (BCRP) Inhibitors Derived from Ko143. Published in Journal of Medicinal Chemistry, 2023. Cited by: 1.

๐Ÿ“„ Bengs, S. et al. Rest/stress myocardial perfusion imaging by positron emission tomography with 18F-Flurpiridaz: A feasibility study in mice. Published in Journal of Nuclear Cardiology, 2023. Cited by: 3.

๐Ÿ“„ Peretti, D.E. et al. ATN profile classification across two independent prospective cohorts. Published in Frontiers in Medicine, 2023. Cited by: 0.

๐Ÿ“„ He, Y. et al. Multi-parameter optimization: Development of a morpholin-3-one derivative with an improved kinetic profile for imaging monoacylglycerol lipase in the brain. Published in European Journal of Medicinal Chemistry, 2022. Cited by: 2.