Mr. Md Erfan | Machine Learning | Best Researcher Award

Mr. Md Erfan, University of Barishal, Bangladesh

Assistant Professor, Department of Computer Science and Engineering, University of Barishal, Bangladesh. His research focuses on flaky test detection, compilation error resolution, and AI applications in automation, decision-making, and problem-solving. He holds an MSSE and BSSE from the University of Dhaka. Erfan has published in Elsevier, Springer, and IEEE, exploring NLP, machine learning, and software engineering. He serves as Project Coordinator for Bangladesh’s EDGE Project and has mentored in NASA Space Apps Challenge. An athlete, he won medals in national athletic competitions.

Publication Profile

Google Scholar

Education 🎓📚

Md Erfan holds a Master of Science in Software Engineering (MSSE) 🖥️ from the Institute of Information Technology, University of Dhaka (2016), with an impressive CGPA of 3.81/4.0 (WES Equivalent: 3.97/4.00). His thesis, supervised by Dr. Md Shariful Islam, focused on an Efficient Runtime Code Offloading Mechanism for Mobile Cloud Computing ☁️💻. He also earned a Bachelor of Science in Software Engineering (BSSE) 🏆 from the same institute in 2014, achieving a CGPA of 3.80/4.0 (WES Equivalent: 3.88/4.00). His undergraduate thesis, guided by Dr. Kazi Muhaimin-us-Sakib, explored approximating social ties based on call logs 📞📊.

Research Experience 🔬📊

In Summer 2024, Md Erfan worked as a Research Student in the UIUC+/ASSIP Summer Research Program 🎓. Collaborating with Dr. Wing Lam (George Mason University) 🏛️ and Dr. August Shi (University of Texas at Austin) 🤖, he focused on automating the end-to-end reproduction of flaky test methods 🛠️. His work involved leveraging issue data, compiling code, running tests, analyzing results, and logging dependencies. Additionally, he created Dockerized environments 🐳 to ensure reproducibility, enhancing software testing efficiency and reliability. His contributions aimed at improving software quality assurance and automation in test debugging 🔍✅.

Professional Experience 💼📚

Md Erfan is an Assistant Professor (2020–Present) at the Department of Computer Science and Engineering, University of Barishal 🏛️, where he teaches Software Engineering, Software Quality Assurance, Data Structures, Algorithms, and Mathematical Analysis 📖💻. Since January 2024, he has also served as a Project Coordinator for the EDGE Project 🌐, managing a 5 crore BDT ($384,615 USD) fund 💰 to enhance digital governance and the economy in Bangladesh. Previously, he worked as a Lecturer (2016–2020) 🎓, a Trainer (2015–2016) 🖥️, and a Software Engineer Intern (2014) 🔍, focusing on testing tools and Microsoft SharePoint development.

Awards and Achievements 🏆🎖️

Md Erfan has been a Regional Mentor (2021–2023) 🌍🚀 for the NASA Space Apps Challenge, guiding innovative projects. He received the Pre-graduation Merit Award (2015) 🎓 from the University of Dhaka for outstanding academic performance. Beyond academics, he has excelled in athletics, securing 3rd place 🥉 in the 5000m and 10000m races 🏃‍♂️ at the Bangladesh Inter-University Athletic Competition (2015) and 2nd place 🥈 in multiple track events (2014–2015). Since 2016, he has been the Coach and Manager ⚽🏅 of the University of Barishal Football and Athletics teams, fostering sports excellence.

Research Interests 🔍💻

Md Erfan’s research primarily focuses on Software Engineering, specializing in flaky test detection and mitigation as well as compilation error resolution to enhance software reliability and development efficiency. Additionally, he explores the applications of Artificial Intelligence (AI), leveraging Machine Learning (ML) 🤖, Natural Language Processing (NLP) 🗣️, and Computer Vision 👀 to tackle real-world challenges. His work aims to improve automation, decision-making, and problem-solving across various domains, ensuring smarter and more efficient technological advancements. Through his research, Erfan contributes to optimizing software development and AI-driven innovations for practical applications. 🚀

Research Focus Areas 🧑‍💻📡

Md Erfan’s research spans multiple domains in Software Engineering and Artificial Intelligence. His work focuses on Mobile Cloud Computing ☁️📱, including task allocation and code offloading for performance optimization. He explores Machine Learning 🤖 applications, such as flaky test detection, compilation error resolution, and autism spectrum disorder detection 🧠. His contributions in Natural Language Processing (NLP) 🗣️ involve cyberbullying classification and user similarity computation. Additionally, he applies Computer Vision 👁️ techniques for mosquito species identification and assistive robotics. His interdisciplinary approach integrates automation, decision-making, and problem-solving in real-world applications.

Publication Top Notes

Mobility aware task allocation for mobile cloud computing
Cited by: 8
Year: 2016 📱☁️
Task allocation for mobile cloud computing: State-of-the-art and open challenges
Cited by: 4
Year: 2016 📊
Identification of Vector and Non-vector Mosquito Species Using Deep Convolutional Neural Networks with Ensemble Model
Cited by: 2
Year: 2022 🦟🤖
Recurrent neural network based multiclass cyber bullying classification
Cited by: 1
Year: 2024 💻🗣️
User Similarity Computation Strategy for Collaborative Filtering Using Word Sense Disambiguation Technique
Cited by: 1
Year: 2023 🔍📚
Approximating Social Ties Based on Call Logs: Whom Should We Prioritize?
Cited by: 1
Year: 2015 📱📞
An exploration of machine learning approaches for early Autism Spectrum Disorder detection
Year: 2025 🧠🤖
Experimental Study of Four Selective Code Smells Declining in Real Life Projects
Year: 2024 🧑‍💻🔧
Autism Spectrum Disorder Detecting Mechanism on Social Communication Skills Using Machine Learning Approaches
Year: 2023 🧠💡
Dynamic Method Level Code Offloading for Performance Improvement and Energy Saving
Year: 2017 ⚡💻
A comparative study of early autism spectrum disorder detection using deep learning based models
Year: 2017 🧠🔍
An Optimal Task Scheduling Mechanism for Mobile Cloud Computing
Year: 2016 ☁️📊
WVGM: Water View Google Map, Introducing Water Paths on Rivers to Reach One’s Destination using Various Types of Vehicles
Year: 2016 🌍🚗
A comprehensive survey of code offloading mechanisms for mobile cloud computing
Year: 2016 ☁️🔄
MICROCONTROLLER BASED ROBOTICS SUPPORT FOR BLIND PEOPLE
Year: 2016 🤖👨‍🦯

Conclusion 🌟

Mr. Md Erfan is a highly suitable candidate for the Research for Best Researcher Award due to his strong academic background, impactful research in software engineering and AI, extensive publications, leadership in digital governance projects, and active contributions to global research collaborations. His work demonstrates innovation, technical expertise, and a commitment to advancing knowledge in his field.

Prof Dr. Qibin Zhao | Machine Learning Award | Best Researcher Award

Prof Dr. Qibin Zhao, RIKEN, Japan

👨‍💼 Dr. Qibin Zhao is a prominent figure in the field of machine learning and deep learning, serving as the Team Leader at RIKEN Center for Advanced Intelligence Project in Tokyo, Japan. With a Ph.D. in Computer Science and Engineering from Shanghai Jiao Tong University, China, his expertise spans across tensor networks, computer vision, and brain imaging/signal processing. Dr. Zhao has received numerous research grants and awards, including the ICASSP Best Student Paper Award in 2019. He actively contributes to academic activities as an area chair and organizer in prestigious conferences like NeurIPS and ICML, while also serving as a reviewer for leading journals.

Publication Profile:

Scopus

Education

📚 Dr. Qibin Zhao’s academic journey is marked by excellence and dedication. He earned his Ph.D. in Computer Science and Engineering from Shanghai Jiao Tong University, China, from 2004 to 2009, laying the foundation for his future contributions to the field. Prior to this, he obtained his M.S. in Computer Science at Guangxi University, China, from 2001 to 2004, and his B.S. in Computer Science at Henan University of Science and Technology, China, from 1996 to 2000. This comprehensive educational background equipped him with the necessary skills and knowledge to excel in his career in research and academia. 🎓

Working Experience

👨‍💼 Dr. Qibin Zhao’s professional journey reflects a commitment to advancing the fields of artificial intelligence and computer science. Since 2020, he has held the position of Team Leader at the Tensor Learning Team within the RIKEN Center for Advanced Intelligence Project in Tokyo, Japan, guiding cutting-edge research initiatives. Concurrently, he serves as a Visiting Professor at Tokyo University of Agriculture and Technology and was a Part-time Lecturer at Waseda University, both in Tokyo. His leadership roles include being the Unit Leader of the Tensor Learning Unit at RIKEN from 2017 to 2020. Dr. Zhao’s international influence extends to his visiting professorships in China and Japan, alongside his impactful research scientist roles at RIKEN. 🌐

Awards and Honors:

🏆 Dr. Qibin Zhao’s contributions to signal processing and artificial intelligence have garnered significant recognition. Notable among his accolades is the 2019 ICASSP Best Student Paper Award for groundbreaking work presented by L. Yuan. His research excellence was further acknowledged with the 2018 IEEE Signal Processing Magazine Best Paper Award, authored by A. Cichocki and team. Dr. Zhao’s impact extends to Japan, where he received the 3rd IEEE Signal Processing Society Japan Best Paper Award in 2018. Additionally, he has been honored with the 5th Research Incentive Award by the RIKEN President in 2014, among other prestigious recognitions for his pioneering research in brain signal decoding and affective brain-computer interfaces. 🌟

Research Focus:

🔬 Dr. Qibin Zhao’s research primarily focuses on advanced techniques in tensor decomposition and multiway data analysis, leveraging the power of tensor networks in various applications. His work encompasses areas such as semi-supervised multi-view concept decomposition, robust kernel PCA for multidimensional data, Bayesian tensor factorization for scalable analysis, and noisy tensor completion methods. With expertise in tensor ring factorization, he explores innovative approaches for image completion, fusion, and analysis in hyperspectral and multispectral domains. Dr. Zhao’s contributions extend to exclusive and consistent NMF for multi-view representation learning, deep matrix factorization with hypergraph regularization, and novel tensorized transformer networks for medical image segmentation. 🧠

Publication Top Notes:

Semi-supervised multi-view concept decomposition – Jiang, Q., Zhou, G., Zhao, Q. (2024) Expert Systems with Applications 📝
- Citations: 0
Noisy Tensor Completion via Low-Rank Tensor Ring – Qiu, Y., Zhou, G., Zhao, Q., Xie, S. (2024) IEEE Transactions on Neural Networks and Learning Systems 📝
- Citations: 8, Cited by: Unknown
Exclusivity and consistency induced NMF for multi-view representation learning – Huang, H., Zhou, G., Zheng, Y., Yang, Z., Zhao, Q. (2023) Knowledge-Based Systems 📝
- Citations: 0, Cited by: Unknown
Diverse Deep Matrix Factorization with Hypergraph Regularization for Multi-View Data Representation – Huang, H., Zhou, G., Liang, N., Zhao, Q., Xie, S. (2023) IEEE/CAA Journal of Automatica Sinica 📝
- Citations: 3, Cited by: Unknown
TT-Net: Tensorized Transformer Network for 3D medical image segmentation – Wang, J., Qu, A., Wang, Q., Liu, J., Wu, Q. (2023) Computerized Medical Imaging and Graphics 📝
- Citations: Unknown, Cited by: Unknown

Md Erfan | Machine Learning | Best Researcher Award