Mahfooz Alam | Cloud Computing | Best Researcher Award

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Assist. Prof. Dr. Mahfooz Alam | Cloud Computing | Best Researcher Award

Assistant professor, G. L. Bajaj College of Technology and Management, Greater Noida, India

Dr. Mahfooz Alam is an esteemed academician and researcher in the field of Computer Science, specializing in Cloud Computing, Internet of Things (IoT), Workflow Scheduling, and Machine Learning 🤖. He is currently serving as an Assistant Professor in the Department of MCA at G. L. Bajaj College of Technology and Management, Greater Noida, India 🇮🇳. With a strong academic background and years of teaching experience, Dr. Alam is dedicated to advancing knowledge in secured workflow allocation and innovative computing methodologies. His research contributions are well-recognized, with publications in reputed journals, including IEEE, Springer, Elsevier, and Wiley 📚.

Publication Profile

Google Scholar

🎓 Education

Dr. Mahfooz Alam holds a Ph.D. in Computer Science from Aligarh Muslim University (AMU), Aligarh, India 🎓. He pursued his M.Tech. in Computer Science and Engineering from Dr. A. P. J. Abdul Kalam Technical University, Lucknow. Additionally, he completed his B.C.A. and M.C.A. from Indira Gandhi National Open University (IGNOU), New Delhi. His academic journey reflects his commitment to excellence in research and innovation in computing technologies 🖥️.

💼 Experience

Dr. Alam has a wealth of teaching and research experience, having served as an Assistant Professor at Al-Barkaat College of Graduate Studies (ABCGS), Aligarh, for six years 🏫. Currently, he holds the position of Assistant Professor at G. L. Bajaj College of Technology and Management, Greater Noida. His dedication to mentoring students and contributing to research has made him a respected figure in academia. His expertise extends to cutting-edge domains such as heuristic and meta-heuristic approaches in secured workflow allocation 🔬.

🏆 Awards and Honors

Dr. Mahfooz Alam has been recognized for his significant contributions to computer science research. His work has been published in high-impact international journals and conferences, gaining citations and recognition from fellow researchers worldwide 🌍. His contributions to machine learning applications and cloud computing security have positioned him as a thought leader in the field.

🔬 Research Focus

Dr. Alam’s research primarily focuses on Cloud Computing, IoT, Workflow Scheduling, and Load Balancing ☁️. He explores innovative approaches to software defect prediction, cybersecurity in IoT, and machine learning-driven optimization techniques. His research integrates heuristic, meta-heuristic, and reinforcement learning methods to address challenges in secure computing environments 🔍.

🔚 Conclusion

Dr. Mahfooz Alam is a dedicated academician and researcher contributing extensively to cloud computing, machine learning, and IoT security. His publications, teaching, and research endeavors continue to impact the field, shaping innovative solutions for complex computational challenges 🚀. With a strong passion for advancing knowledge and technology, Dr. Alam remains a prominent figure in the global research community 🌏.

📜 Publications

Comprehensive Bibliographic Survey and Forward-Looking Recommendations for Software Defect Prediction (IEEE Access, 2025) 📖 DOI: 10.1109/ACCESS.2024.3517419

Software Defects Prediction Using Generative Adversarial Network Based Data Balancing (Book Chapter, 2025) 📖 DOI: 10.1007/978-3-031-83790-6_22

Reinforcing Defect Prediction: A Reinforcement Learning Approach  (Iran Journal of Computer Science, 2025) 📖 DOI: 10.1007/s42044-024-00214-8

Ensemble Deep Learning Techniques for Time Series Analysis (Cluster Computing, 2025) 📖 DOI: 10.1007/s10586-024-04684-0

A Levelized Multiple Workflow Heterogeneous Earliest Finish Time Allocation Model  (Algorithms, 2025) 📖 DOI: 10.3390/a18020099

Cybersecurity Challenges for Social, Ad-hoc, and Sensor Networks in IoT  (Wireless Ad-hoc and Sensor Networks, 2024) 📖 DOI: 10.1201/9781003528982-12

Performance Evaluation on Detection of Phishing Websites Using Machine Learning Techniques (ICEECT, 2024) 📖 DOI: 10.1109/iceect61758.2024.10739275

Empowering IoT Security (Book Chapter, 2024) 📖 DOI: 10.1201/9781003460367-11

A Trustworthy Hybrid Model for Transparent Software Defect Prediction: SPAM-XAI (PLOS ONE, 2024) 📖 DOI: 10.1371/journal.pone.0307112

Security Challenges for Workflow Allocation Model in Cloud Computing Environment: A Comprehensive Survey, Framework,
Taxonomy, Open Issues, and Future Directions  (Journal of Supercomputing, 2024) 📖 DOI: 10.1007/s11227-023-05873-1

Mr. Mingi Kwon | Computer Engineering | Best Researcher Award

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Mr. Mingi Kwon | Computer Engineering | Best Researcher Award

University of California San Diego, United States

Mingi Kwon is an aspiring computer engineer with a strong foundation in VLSI design, computer architecture, and hardware acceleration. 🎓 Currently pursuing an MS in Electrical and Computer Engineering at the University of California, San Diego, he previously earned his BS in Electrical Engineering from Hanyang University, South Korea. With a deep interest in optimizing hardware for AI acceleration, he has worked on advanced projects involving reconfigurable systolic arrays, low-power circuit design, and RISC-V processor architectures. His dedication to high-performance computing and low-power hardware systems is evident through his research contributions and hands-on experience with industry-standard tools. 🚀

Publication Profile

ORCID

🎓 Education:

Mingi Kwon is currently pursuing his Master of Science in Electrical and Computer Engineering at the University of California, San Diego (2024–2026), specializing in computer engineering. He completed his Bachelor of Science in Electrical Engineering from Hanyang University, South Korea (2019–2024), graduating with an impressive GPA of 3.97/4.5. 📚 His academic journey has been focused on advanced coursework, including computer architecture, low-power VLSI design, and deep learning accelerators, equipping him with a strong foundation in hardware and system design.

💼 Experience:

Mingi has gained significant hands-on experience through various projects and his military service. During his undergraduate studies, he developed a Cyclone IV GX-Based Reconfigurable 2D Systolic Array for AI Acceleration, optimizing power consumption and chip area. He also worked on a RISC-V 5-stage Pipeline Processor with an advanced branch predictor, significantly improving execution efficiency. 🔧 Additionally, he served as a cybersecurity specialist and squad leader in the Republic of Korea Army (2020–2022), where he managed encrypted communications and network security while leading a team of 20 soldiers, earning a Distinguished Service Award. 🏅

🏆 Awards and Honors:

Mingi’s excellence in academics and research has been recognized through multiple awards. He was named to the Dean’s List (2022) with a perfect GPA of 4.5/4.5. 🎖️ He also received the National Logic Chip Design Track Scholarship (2023–2024), awarded by the South Korean government for outstanding achievements in electrical engineering. His leadership and dedication in the military earned him a Distinguished Service Award (2021–2022) for enhancing work efficiency and team collaboration.

🔬 Research Focus:

Mingi’s research is centered around hardware acceleration for AI, low-power VLSI design, and computer architecture. 🖥️ His work on systolic arrays focuses on optimizing deep learning computations with reconfigurable architectures, improving efficiency in sparse neural networks. He has also explored low-power circuit design, reducing leakage power and optimizing combinational logic for improved energy efficiency. His expertise extends to processor architecture, particularly RISC-V pipeline design and branch prediction, enhancing execution speed and minimizing stalls.

🔚 Conclusion:

Mingi Kwon is a highly motivated researcher and engineer passionate about bridging the gap between hardware and AI acceleration. 🚀 With extensive experience in VLSI design, digital systems, and processor architecture, he is committed to advancing high-performance, energy-efficient computing systems. His technical expertise, research achievements, and leadership skills position him as a promising innovator in the field of computer engineering. 💡

📄 Publication:

Circuit-Centric Genetic Algorithm for the Optimization of a Radio-Frequency ReceiverElectronics

DOI: 10.3390/electronics14040770