Fang Hou | Computer Science | Best Researcher Award

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Fang Hou | Computer Science | Best Researcher Award

Ms Fang Hou,Utrecht university,Netherlands

🌟 Fang Hou is a dedicated Ph.D. candidate specializing in empirical software engineering, focusing on software ecosystem trust and quality. With over a decade of experience in international banking IT, she brings a wealth of practical knowledge to her research. Fang’s work on the TrustSECO project aims to establish trust within software frameworks through sentiment analysis and opinion mining. She has presented her findings at various prestigious conferences and contributed to notable publications. Fang’s commitment to enhancing software security and trustworthiness underscores her passion for improving global software systems. As a teaching assistant at Utrecht University, she shares her expertise in software ecosystem security. 🖥️🔍

Publication profile

google scholar

Research focus

Fang Hou is deeply immersed in the realm of Empirical Software Engineering, with a specific focus on understanding and enhancing trust within software ecosystems. Through extensive research and practical experience, Fang delves into the technical intricacies of software trust and quality assurance. Their dedication to this field is evident through a comprehensive exploration of trust evaluation methodologies and sentiment analysis tools development. Fang’s work not only contributes to the theoretical understanding of software ecosystems but also strives to implement practical solutions for securing and fortifying global software systems. 🌐🔍

Publication top notes

A systematic literature review on trust in the software ecosystem

Trustseco: A distributed infrastructure for providing trust in the software ecosystem

TrustSECO: an interview survey into software trust

Analyzing the Performance of the Unmanned Bank to Explore the Failure Reasons for AI Projects

The role of software trust in selection of open-source and closed software

Sentiment analysis for software quality assessment

A survey of the state‐of‐the‐art approaches for evaluating trust in software ecosystems

Md Habibur Rahman | Computer Science Award | Best Researcher Award

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Mr. Md Habibur Rahman | Computer Science Award | Best Researcher Award

Mr. Md Habibur Rahman, Dept. of Computer Science and Engineering, Islamic University, Kushtia 7003, Bangladesh

Dr. Md Habibur Rahman is an accomplished researcher and educator specializing in bioinformatics and machine learning. With a PhD from the Chinese Academy of Sciences, his expertise lies in computational bioinformatics, pattern recognition, and disease comorbidities. He’s served as a faculty member at Islamic University, Bangladesh, while leading groundbreaking research projects funded by organizations like the UGC and Ministry of Science and Technology. Dr. Rahman’s extensive training in AI, genomics, and data analysis, coupled with his publication record and grant success, showcases his commitment to advancing knowledge in understanding complex diseases like Guillain-Barre Syndrome and their genetic underpinnings. 🧬🔬🎓

 

Publication Profile

Google Scholar

Educational Background

Dr. Md Habibur Rahman embarked on an academic odyssey, culminating in a PhD in Pattern Recognition and Intelligent Systems from the Chinese Academy of Sciences. His earlier pursuits include an MSc in Computer Science & Engineering from Islamic University and a BSc in the same discipline. Noteworthy is his thesis on securing information sharing within wireless sensor networks and his groundbreaking research on fingerprint recognition.

Professional/Teaching Experiences

Since 2013, Dr. Md Habibur Rahman has been an esteemed faculty member at Islamic University, Bangladesh. His role extends beyond teaching to encompass curriculum development and research in burgeoning fields like bioinformatics and machine learning. With a robust publication record and active participation in conferences, Dr. Rahman fosters a dynamic academic environment. He not only guides students through their academic journey but also collaborates with colleagues to secure external funding for groundbreaking research initiatives. His commitment to excellence is exemplified by his continuous engagement in professional networking and service to the academic community. 🌱📚🤝

 

Research Experience

Dr. Md Habibur Rahman has spearheaded groundbreaking research projects, showcasing his expertise in bioinformatics and machine learning. As Project Director, he led initiatives funded by prestigious organizations like the UGC and Ministry of Science and Technology. His innovative work includes identifying cell-specific biomarkers of Guillain-Barre Syndrome and exploring the synergistic effects of COVID-19 on Autism Spectrum Disorder. Dr. Rahman’s extensive experience encompasses the development of computational methods to analyze diverse genomic data types, from microarray to RNA sequencing. His dedication to advancing medical knowledge is underscored by his contributions to predicting cancer patient survival through machine learning. 🌟🔬🚀

 

Awards & Honors

Dr. Md Habibur Rahman has garnered esteemed awards, affirming his exceptional contributions to academia. His noteworthy achievements include the prestigious CAS-TWAS President’s Fellowship during his tenure at the University of Chinese Academy of Sciences. Additionally, his academic prowess earned him the University Grants Commission (UGC) scholarship in Bangladesh for securing a yearly merit position in the B.Sc. honours program at Islamic University. These accolades not only recognize Dr. Rahman’s scholarly achievements but also highlight his commitment to academic excellence and his impactful contributions to the field. 🎓🌟🏅

 

Research Focus 🧬

Dr. Md Habibur Rahman’s research endeavors span across various domains within bioinformatics and system biology. His primary focus lies in leveraging advanced computational techniques, including machine learning and network-based approaches, to unravel complex biological phenomena. With a keen interest in disease pathogenesis, Dr. Rahman explores the interplay between infectious agents like SARS-CoV-2 and chronic conditions such as diabetes and neurological disorders. His contributions extend to identifying biomarkers, understanding disease progression, and uncovering potential therapeutic targets, all crucial steps towards advancing healthcare and medical interventions. Dr. Rahman’s dedication to cutting-edge research epitomizes the intersection of technology and biology in tackling global health challenges. 🌐🔬📊

 

Publications Top Notes 📚

  1. Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients 🦠
    • Cited by: 77
    • Year: 2021
  2. A network-based bioinformatics approach to identify molecular biomarkers for type 2 diabetes that are linked to the progression of neurological diseases 🧠
    • Cited by: 54
    • Year: 2020
  3. Prevalence and Early Prediction of Diabetes Using Machine Learning in North Kashmir: A Case Study of District Bandipora🩺
    • Cited by: 49
    • Year: 2022
  4. PreDTIs: prediction of drug–target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques 💊
    • Cited by: 46
    • Year: 2021
  5. Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19
  6. Bioinformatics Methodologies to Identify Interactions Between Type 2 Diabetes and Neurological Comorbidities
  7. Bioinformatics and system biology approaches to identify pathophysiological impact of COVID-19 to the progression and severity of neurological diseases.
  8. A system biological approach to investigate the genetic profiling and comorbidities of type 2 diabetes
  9. Genetic effect of type 2 diabetes to the progression of neurological diseases
  10. Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression🧠
    • Cited by: 37
    • Year: 2021