Mr.Danish Javed | Data Science | Best Researcher Award

Mr.Danish Javed | Data Science | Best Researcher Award

Mr.Danish Javed, Taylor’s University Lakeside Campus, Malaysia

Danish Khan is a Ph.D. scholar specializing in Data Science at Taylor’s University, Malaysia, where he is advancing research in natural language processing (NLP). With a strong academic background in Software Engineering from Bahria University, Islamabad, Danish has built expertise in Python, machine learning, and deep learning. He has held teaching roles as a Senior Lecturer at the University of Central Punjab, Lahore, and is currently a tutor at Taylor’s University, Malaysia. His career reflects a commitment to advancing computer science education, mentoring students, and leading post-graduate councils. Danish is also a prolific researcher, contributing to various data science and sentiment analysis projects, particularly in the analysis of social media content and NLP.

Professional Profile

google scholar

Summary of Suitability for the Research for Best Researcher Award

Danish Javed presents a strong candidacy for the Research for Best Researcher Award based on his significant academic and research contributions, particularly in the fields of data science, machine learning, and natural language processing (NLP). His ongoing PhD in Data Science from Taylor’s University demonstrates his deep commitment to advancing research, especially in topics like sentiment analysis, deep learning, and Twitter bot detection. Danish has published research in Scopus-indexed conferences and Q1/Q4 journals, which highlights the academic impact of his work.

🎓 Education 

Danish Khan has consistently pursued excellence in academia, currently working toward his Ph.D. in Data Science at Taylor’s University, Lakeside Campus, Malaysia. His doctoral research focuses on natural language processing (NLP), specifically exploring frameworks for sentiment analysis, bot detection, and text analytics. Prior to his Ph.D., Danish earned his M.S. in Software Engineering from Bahria University, Islamabad, Pakistan, where he delved into the intricacies of machine learning, image processing, and artificial intelligence. His academic foundation also includes a B.S. in Software Engineering from Bahria University, during which he developed strong programming skills in Python, Java, and C++, equipping him with the tools to tackle complex computational problems. His academic journey reflects his deep interest in understanding data structures and algorithms, making him proficient in implementing advanced analytics and programming solutions.

💼 Experience 

Danish Khan has a broad range of teaching and leadership experience, with over five years in academia. He is currently a Tutor at Taylor’s University, Malaysia, where he conducts tutorials in data science, supervises student projects, and plays a key role in shaping the post-graduate student experience. He previously served as the President of the Post-Graduate Student Council, organizing events and representing student perspectives in university meetings. Prior to his current role, Danish was a Senior Lecturer in the Faculty of Information Technology at the University of Central Punjab, Lahore, where he taught computer science and software engineering courses, supervised final-year projects, and contributed to extracurricular activities such as organizing sporting events. Additionally, Danish has experience as a QA Analyst at Orbit Institute of Technology in Lahore, where he maintained the quality standards in the Software Engineering department.

🏅Awards and Honors

Danish Khan has received notable recognition throughout his academic and professional career. His published research in data science and natural language processing has been featured in prominent Scopus-indexed conferences and reputed journals. As a Ph.D. scholar, he has been honored with several merit-based scholarships for academic excellence. Danish was also recognized for his leadership efforts while serving as President of the Post-Graduate Student Council at Taylor’s University, where he played a pivotal role in advocating for post-graduate student welfare. During his tenure at the University of Central Punjab, Lahore, he earned commendations for his outstanding contributions to teaching and student mentorship. Additionally, his development of an Android application, “Forex Profit Gain,” has garnered attention, earning placement in the Google Play Store. These accolades reflect his deep commitment to both academic rigor and innovative problem-solving in the field of data science.

🌐 Research Focus 

Danish Khan’s research is centered on data science, particularly natural language processing (NLP), machine learning, and sentiment analysis. His Ph.D. work at Taylor’s University, Malaysia, focuses on advanced techniques in deep learning to analyze and classify text-based data. His key areas of research include social media analytics, Twitter bot detection, and sentiment analysis of public opinion during crises such as the COVID-19 pandemic. Danish has contributed to frameworks that improve sentiment analysis by leveraging oversampling techniques and random minority oversampling, which enhance the accuracy of sentiment classification in user-generated content. His research also extends to explainable artificial intelligence (AI), where he has designed models for transparent and interpretable detection of bots on social media platforms. Danish’s academic pursuit aims to contribute practical, data-driven insights to solve real-world problems using cutting-edge AI and NLP technologies.

📖 publications Top Notes

“Framework for Improved Sentiment Analysis via Random Minority Oversampling for User Tweet Review Classification.”
Citation count: 25
“Deep Learning Based Sentiment Analysis of COVID-19 Tweets via Resampling and Label Analysis.”
Citation count: 6
“Football Analytics for Goal Prediction to Assess Player Performance.”
Citation count: 4
“Explainable Twitter Bot Detection Model for Limited Features.”
Citation count: 2
“Explainable Machine Learning Based Model for Heart Disease Prediction.”
“Analyzing the Efficacy of Bot Detection Methods on Twitter/X.”