Ms. Shakila Rahman | Computer Engineering | Best Researcher Award

Ms. Shakila Rahman | Computer Engineering | Best Researcher Award

Ms. Shakila Rahman , American International University – Bangladesh (AIUB)

Shakila Rahman ๐Ÿ‡ง๐Ÿ‡ฉ is a Lecturer at the Department of Computer Science, AIUB ๐Ÿซ. She earned her M.Sc. in AI & Computer Engineering ๐Ÿค– from the University of Ulsan, South Korea ๐Ÿ‡ฐ๐Ÿ‡ท, with a CGPA of 4.00/4.50 ๐Ÿ“š. Her research spans UAV networking ๐Ÿš, wireless sensor networks ๐ŸŒ, and machine learning ๐Ÿง . She actively supervises undergraduate projects ๐Ÿ‘ฉโ€๐Ÿ’ป and has published in reputed journals ๐Ÿ“–. With experience in academia and applied computing, she contributes passionately to AI and emerging technologies ๐Ÿ’ก. She is also active on ResearchGate, ORCID, and LinkedIn ๐ŸŒ.

Professional Profile:

Orcid

๐Ÿ”น Education & Experienceย 

Shakila Rahman completed her M.Sc. in AI & Computer Engineering ๐ŸŽ“ from the University of Ulsan, South Korea ๐Ÿ‡ฐ๐Ÿ‡ท, and her B.Sc. in Computer Science & Engineering from IIUC ๐Ÿ‡ง๐Ÿ‡ฉ. Currently, she serves as a Lecturer ๐Ÿ‘ฉโ€๐Ÿซ at American International University-Bangladesh (AIUB), where she teaches core computing subjects ๐Ÿ’ป. She previously worked as a Graduate Research Assistant in South Korea ๐Ÿ”ฌ and as an Undergraduate Teaching Assistant at IIUC ๐Ÿ‘ฉโ€๐Ÿ”ฌ. Her experience blends international research, teaching, and real-world project supervision ๐ŸŒ. She is skilled in Python, Java, and C++ ๐Ÿง‘โ€๐Ÿ’ป, and fosters student innovation through hands-on learning ๐Ÿงช.

๐Ÿ”น Professional Development

Shakila has enhanced her technical skillset through multiple workshops and training programs ๐Ÿ› ๏ธ. She completed web development ๐Ÿ’ป at BASIS BITM, mobile game/app development ๐ŸŽฎ with Bangladesh ICT Division, and Android app development ๐Ÿค– with Affable Technology. As a competitive programmer, she participated in ICPC, NGPC, and UVA online judges ๐Ÿ†. During her B.Sc., she joined industrial events like the Tech Summit by LEADS Corporation ๐Ÿ‘ฉโ€๐Ÿ’ผ. These initiatives equipped her with a dynamic understanding of both academic theory and industry practices ๐ŸŒ. She is also an IELTS-certified English communicator with a score of 6.0 ๐Ÿ‡ฌ๐Ÿ‡ง๐Ÿ—ฃ๏ธ.

๐Ÿ”น Research Focus

Shakila Rahman’s research lies at the intersection of AI and networking technologies ๐Ÿค–๐ŸŒ. Her main focus includes UAV Networking ๐Ÿš, Wireless Sensor Networks ๐Ÿ›ฐ๏ธ, Network Systems ๐Ÿ–ง, and Optimization Algorithms ๐Ÿ”. She also works with Machine Learning ๐Ÿง , Deep Learning ๐Ÿงฌ, Image Processing ๐Ÿ–ผ๏ธ, and AR/VR applications in AI ๐Ÿฅฝ. Her work emphasizes solving real-world challenges like obstacle detection, fire/smoke recognition ๐Ÿ”ฅ๐Ÿšจ, and human activity classification ๐Ÿšถโ€โ™‚๏ธ๐Ÿšจ. She publishes in SCIE and SCOPUS-indexed journals ๐Ÿ“š, contributing to the academic community with innovations in intelligent systems and computer vision ๐Ÿ‘๏ธโ€๐Ÿ—จ๏ธ.

๐Ÿ”น Awards & Honorsย 

Shakila Rahman has received prestigious academic honors ๐Ÿ…, including the Brain Korea Scholarship (BK21) ๐Ÿ’ฐ during her M.Sc. at the University of Ulsan ๐Ÿ‡ฐ๐Ÿ‡ท. She was also awarded the fully funded AF1 Scholarship worth approx. $21,000 USD ๐ŸŽ“๐Ÿ’ต. Her research was supported by the National Research Foundation of Korea (NRF) ๐Ÿ‡ฐ๐Ÿ‡ท for multiple publications ๐Ÿ“‘. These recognitions highlight her academic excellence ๐ŸŒŸ, research potential ๐Ÿ”ฌ, and commitment to global scientific collaboration ๐ŸŒ. Her accolades stand as a testament to her dedication and passion in the field of Artificial Intelligence and networked systems ๐Ÿš€๐Ÿ“ก.

Publication Top Notes:

๐Ÿง  Bilingual Sign Language Recognition: A YOLOv11-Based Model for Bangla and English Alphabets

  • Journal: Journal of Imaging

  • Year: 2025

  • Citations: 0

  • Summary: This study presents a YOLOv11-based model designed for real-time recognition of Bangla and English sign language alphabets. The model aims to bridge communication gaps for the hearing-impaired by accurately detecting and classifying sign language gestures.


๐Ÿ™๏ธ Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11

  • Journal: Future Internet

  • Year: 2025

  • Citations: 0

  • Summary: This research introduces an AI-driven approach utilizing YOLOv11 for detecting infrastructure faults in urban environments. The model enhances city safety by enabling prompt identification and maintenance of structural issues.


๐Ÿงด A Hybrid CNN Framework DLI-Net for Acne Detection with XAI

  • Journal: Journal of Imaging

  • Year: 2025

  • Citations: 0

  • Summary: The study proposes DLI-Net, a hybrid CNN framework combining DeepLabV3 for segmentation and InceptionV3 for classification, to detect and classify acne lesions. Achieving a 97% accuracy rate, the model incorporates Grad-CAM for explainable AI, enhancing transparency in dermatological diagnostics.


๐Ÿš A Deep Q-Learning Based UAV Detouring Algorithm in a Constrained Wireless Sensor Network Environment

  • Journal: Electronics

  • Year: 2025

  • Citations: 0

  • Summary: This paper presents a Deep Q-Learning algorithm for unmanned aerial vehicles (UAVs) to navigate efficiently within constrained wireless sensor networks. The approach optimizes detouring strategies, enhancing UAV performance in complex environments.


๐Ÿ›ก๏ธ A Deep Learning Model for YOLOv9-based Human Abnormal Activity Detection: Violence and Non-Violence Classification

  • Journal: Iranian Journal of Electrical and Electronic Engineering

  • Year: 2024

  • Citations: 0

  • Summary: The research develops a YOLOv9-based deep learning model to classify human activities as violent or non-violent. Aimed at enhancing surveillance systems, the model contributes to improved public safety through accurate activity detection.

โœ… Conclusion

Shakila Rahman is a highly suitable candidate for the Best Researcher Award due to her blend of academic excellence, innovative research, international exposure, and impactful contributions to AI and networking fields. Her work not only advances fundamental knowledge but also targets practical challenges, demonstrating a research trajectory with strong future promise. Recognizing her with this award would celebrate her achievements and encourage continued excellence in emerging technology domains. ๐Ÿš€๐Ÿค–๐Ÿ“ก

Ms. Farzaneh Rastegari | Bioinformatics | Best Researcher Award

Ms. Farzaneh Rastegari | Bioinformatics | Best Researcher Award

Ms. Farzaneh Rastegari | University of Connecticut | United States

Farzaneh (Dana) Rastegari is a Ph.D. student in Computer Science and Engineering at the University of Connecticut, specializing in computational genomics and microbiome research. With a strong background in bioinformatics, machine learning, and robotics, she applies statistical and computational techniques to analyze microbiome data. As a Predoctoral Associate at The Jackson Laboratory, she has gained extensive experience in both wet lab methodologies and computational research. Passionate about integrating AI with biological data, she has contributed to multiple high-impact research projects and publications, making strides in genomics, healthcare, and environmental studies. ๐ŸŒฑ๐Ÿ“Š๐Ÿ”ฌ

Professional Profile:

SCOPUS

Suitability for Best Researcher Award:

Farzaneh (Dana) Rastegari is a highly qualified researcher specializing in computational genomics and microbiome research. Her interdisciplinary expertise spans bioinformatics, machine learning, and robotics, making her a strong contender for a Best Researcher Award. Her ability to integrate artificial intelligence with biological data showcases innovation and a forward-thinking approach to scientific discovery.

Education & Experience ๐ŸŽ“๐Ÿ”

โœ… Ph.D. in Computer Science & Engineering โ€“ University of Connecticut, USA (Ongoing)
โœ… Predoctoral Associate in Genomics Medicine โ€“ The Jackson Laboratory ๐Ÿฅ๐Ÿงฌ
โœ… M.S. in Electrical & Computer Engineering (Communications) โ€“ University of New Haven, USA
โœ… B.S. in Computer Science (Robotics) โ€“ Amirkabir University of Technology, Iran ๐Ÿค–

๐Ÿ“Œ Research & Work Experience:
๐Ÿ”น Microbiome research on mice & human samples ๐Ÿญ๐Ÿงช
๐Ÿ”น Developing computational pipelines for microbiome analysis ๐Ÿ’ป๐Ÿ”ฌ
๐Ÿ”น Applying Bayesian ML & AI in genomics ๐Ÿค–๐Ÿ“ˆ
๐Ÿ”น Signal processing & digital communications ๐Ÿ“ก๐ŸŽถ

Professional Development ๐Ÿš€๐Ÿ“š

Farzaneh actively enhances her expertise by engaging in diverse research projects, applying machine learning and computational methods in genomics. At The Jackson Laboratory, she mastered microbiome data processing and wet lab techniques, contributing to high-impact publications. Her research at UConn focuses on developing algorithms for phylogenetic networks, Bayesian classification of TCRs, and reinforcement learning. She has also worked on digital signal processing, bioinformatics tools, and robotics. Passionate about interdisciplinary innovation, she continuously integrates AI, statistics, and biology to tackle critical challenges in genomics and healthcare. ๐ŸŒ๐Ÿ”—

Research Focus ๐Ÿงช๐Ÿ“Š

Farzaneh’s research bridges computational biology, AI, and genomics. She specializes in microbiome analysis, applying statistical and machine learning models to understand microbial communities and interactions. Her projects involve phylogenetic inference, Bayesian ML, reinforcement learning, and genomic signal processing. With expertise in bioinformatics tools, she develops pipelines to analyze 16S rDNA and metagenomic data. She also explores disease prediction models, evolutionary genomics, and biomedical applications of AI. Her work contributes to precision medicine, healthcare analytics, and environmental studies, driving breakthroughs in genomics and biotechnology. ๐ŸŒฟ๐Ÿ”ฌ๐Ÿง 

Awards & Honors ๐Ÿ†๐ŸŽ–๏ธ

๐Ÿ… Predoctoral Associate Position โ€“ The Jackson Laboratory
๐Ÿ… Published in “Cancer” Journal for Metagenomics & Chemotherapy Study
๐Ÿ… Multiple IEEE Publications in Genomics, AI, & Environmental Studies
๐Ÿ… Outstanding Research Contributions in Computational Biology & Bioinformatics
๐Ÿ… Certified in Working with Mice for Biomedical Research ๐Ÿญ๐Ÿงช

Publication Top notes:

  • A Comprehensive Simulation of Electric Vehicle Energy Consumption: Incorporating Route Planning and Machine Learning-Based Predictions ๐Ÿš—๐Ÿ”‹๐Ÿ“Š
  • Predictive Modeling of Chronic Kidney Disease Progression Using Longitudinal Clinical Data and Deep Learning Techniquesย  ๐Ÿฉบ๐Ÿง ๐Ÿค–
  • Exploring the Relationship Between Air Pollution and CNS Disease Mortality in Italy: A Forecasting Study with ARIMA and XGBoost ๐ŸŒซ๏ธ๐Ÿง ๐Ÿ“ˆ