Mr. Siddhant Srinivas | Cyber Security | Best Researcher Award

Published on by Computer Scientists Awards

Mr. Siddhant Srinivas | Cyber Security | Best Researcher Award

California State University | United States

Siddhant Srinivas is an emerging researcher in Artificial Intelligence and Cybersecurity, currently contributing to the advancement of AI-augmented Security Operations Centers (SOC) through the integration of Large Language Models (LLMs) and AI agents. His research primarily focuses on developing intelligent frameworks that enhance the efficiency, scalability, and trustworthiness of SOC workflows. As the first author of a peer-reviewed publication in MDPI, Siddhant has presented a comprehensive taxonomy of AI-driven applications across SOC processes, highlighting their potential in transforming traditional alert triage, threat detection, and incident response systems. His work introduces a capability-maturity model that outlines the evolution from manual to autonomous SOC operations while addressing the challenges of explainability, safety, and reliability in AI deployments. Siddhant’s contributions emphasize bridging the gap between theoretical AI models and their practical implementation in cybersecurity domains. He has been recognized for his scholarly excellence through published research and active involvement in Dr. Alzahrani’s AI Research Lab. His published works are cited in indexed databases such as Scopus and Google Scholar, reflecting a growing academic footprint and influence in the emerging intersection of AI and security research. His citation records and h-index metrics from both Scopus and Google Scholar demonstrate his contributions to advancing secure, transparent, and automated AI systems.

Profile

ORCID

Featured Publication

Srinivas, S., Kirk, B., Zendejas, J., Bari, A., Dajani, K., & Alzahrani, N. (2025). AI-Augmented SOC: A survey of LLMs and agents for security automation. MDPI Informatics, 5(4), 95.

Mr. Xiang Fang | Ransomware detection | Best Researcher Award

Published on by Computer Scientists Awards

Mr. Xiang Fang | Ransomware detection | Best Researcher Award

Phd student, The City College of New York, United States.

Xiang Fang is a dedicated and technically proficient researcher in electrical and computer engineering, currently pursuing his Ph.D. at The City College of New York. With a multifaceted background combining hardware, software, cybersecurity, and AI-powered applications, Xiang stands out for his innovative approaches to problem-solving, especially in areas like control systems, image processing, and ransomware detection. His contributions span multiple international presentations, academic projects, and scholarly publications, reflecting a deep passion for research and a commitment to academic excellence. 🌍🔬

Publication Profile

Google Scholar

🎓Education Background

Xiang earned his Ph.D. in Electrical Engineering from The City College of New York (2020–2025), maintaining an impressive GPA of 3.60. Complementing his technical expertise, he acquired an MBA from Academic Europe Open University in April 2025, equipping him with valuable leadership and management skills. His journey began with an M.Sc. in Electrical and Computer Engineering from Purdue University Northwest (2017–2019), followed by a B.Sc. in Electrical and Information Engineering from Shaanxi University of Technology, China (2013–2017). 🎓📘📈

💼Professional Experience

Xiang has actively contributed to academia through teaching assistantships and tutoring roles. At The City College of New York, he worked as a Teaching Assistant for Healthcare Cybersecurity Pathways (2023) and graded Communication Theory (2025). His earlier experience includes tutoring English to middle school students (2013–2014). In research, he has led and contributed to multiple technical projects—ranging from digital image enhancement and SLAM systems to cloud-based secure applications and real-time ransomware detection using anomaly-based methods. 💡🖥️🧪

🏅Awards and Honors

Xiang’s commitment to excellence is reflected in his achievements, including the Certificate of Completion in Children and Climate Change, and the CodePath Cybersecurity Course Certificate. His research has been widely recognized through poster presentations at prestigious forums such as AFRL-CUNY, The Grove School of Engineering Expo, and the Defense & Intelligence Research Forum. 🏆📜🎖️

🔍Research Focus

Xiang’s primary research interests lie in cybersecurity, autonomous systems, signal processing, and AI-powered detection systems. His standout contributions include anomaly-based and honeyfile-based detection mechanisms for crypto ransomware, SLAM systems, and data visualization methods. Leveraging tools like MATLAB, Python, Kalman filters, and machine learning models such as LSTM, Xiang effectively bridges theoretical concepts with practical implementations. 🔐🤖📊

🔚Conclusion

A forward-thinking and results-driven scholar, Xiang Fang exemplifies the blend of innovation, technical acumen, and academic rigor. His trajectory, from hands-on hardware projects to advanced research in digital security, positions him as a rising star in the field of electrical and computer engineering. As he continues his journey, Xiang remains committed to tackling emerging global challenges with smart, scalable, and secure solutions. 🚀📡🌐

📚Top Publications Notes

  1. Crypto-Ransomware Detection Through a Honeyfile-Based Approach with R-Locker
    📅 Published: 2025 | 📘 Journal: MDPI, Mathematics | 📑 Cited by: 5 articles (as of 2025)

  2. Poster: Crypto Ransomware Detection Through a Honeyfile-Based Approach with R-Locker
    📅 Presented: Feb. 2025 | 📍 The Grove School of Engineering Expo, NY, USA

  3. Poster: Anomaly-Based Approach for Crypto Ransomware Detection
    📅 Presented: Nov. 2023 | 📍 AFRL-CUNY Technology and Workforce Development Forum

  4. Poster: Ransomware Detection Methodology
    📅 Presented: May 2022 | 📍 Defense & Intelligence Research Forum, NY, USA

  5. Thesis: Visualization of Mobile Robot Localization and Mapping
    📅 Published: April 2017 | 📍 Purdue University Northwest

  6. Thesis: Design of High-Precision Laser Engraving Platform Control System Based on STC12C5608AD
    📅 Published: June 2017 | 📍 Shaanxi University of Technology