Scuba_Diving_Hawaii
Ruby on Rails 7 web app for scuba diving enthusiasts: location & tour listings, photo sharing, posts with comments/likes, and Devise-based authentication.
I'm Md Julfiker Ali Jewel, a Software Programmer Analyst at the West Virginia Department of Health and Human Resources (DHHR). I design and ship secure, high-performance C#/.NET web applications, tune SQL Server for reliability at scale, and modernize enterprise systems with a strong focus on cybersecurity and operational resilience.
Previously I contributed to the State Highway Crash Database at the West Virginia Department of Transportation, automating analytics pipelines, and served as a Graduate Research & Teaching Assistant at West Virginia State University, conducting research in AI/ML, Cybersecurity, and HCI.
I hold an MS in Computer Science from West Virginia State University (USA) and a BS in CSE from Daffodil International University (Bangladesh), including an academic exchange at SIAS International University (China).
Years of Experience
Projects Completed
Research Publications
C#, C++, Java, Python
PyTorch, TensorFlow, NumPy, Ruby on Rails, .NET
HTML, CSS, JavaScript, PHP, UI/UX, MySQL, VBA
IDS/IPS, Vulnerability Scanning, Network Traffic Analysis, Incident Response
West Virginia State University โ Institute, WV, USA
Academic Advisor & Research Supervisor: Dr. Ali Al-Sinayyid
Co-Supervisor: Dr. Heng Wu (in memoriam)
Internship in Computer Science (3 terms): Spring 2023, Summer 2024, Fall 2024
Daffodil International University โ Dhaka, Bangladesh
Total: 16 credits accepted
West Virginia Department of Health and Human Resources
Department of MIS
West Virginia State University
Department of Math & Computer Science
West Virginia Department of Transportation
Traffic Engineering Division
Abstract: Recently, the advent of multimodal large language models (MLLMs) which integrate text with other modalities such as images, audio, and video, promise to revolutionize various fields ranging from natural language processing to computer vision and beyond. However, while MLLMs have shown remarkable performance across a wide range of tasks, their inner workings remain largely opaque, presenting significant challenges in terms of interpretability, robustness, and ethical considerations. This paper investigates the next frontier in artificial intelligence research: understanding multimodal large language models. We explore the architecture and applications of MLLMs, shedding light on their capabilities and limitations. By delving into the intricacies of multimodal large language models, this paper aims to show the potential use of LLM in biomedical and advanced machine learning algorithms to extract valuable features and improve the prediction accuracy of clinical analysis. Therefore, it will pave the way for future research directions and facilitate the development of more transparent, equitable, and trustworthy AI systems.
๐ฝ Read LessPresented on Critical Infrastructure in cybersecurity, covering phishing detection, defending attacks on IoT, and characteristics of attacks and defenses.
Delivered an in-depth analysis on โPhishing Cyber Attacks Characteristics and Attribution.โ
Showcased research on EMG pattern recognition control method for prosthetic applications.
Mentored undergraduate students โ Summer Undergraduate Research Experience (SURE) Program 2023.
Mentored undergraduate students in AI for cybersecurity problem-solving โ SURE Program 2024.
Volunteered at the CS_for_all Conference, Memphis, 2022.
WVOT Cybersecurity, July 17, 2025
WVOT Cybersecurity, July 16, 2025
WVOT Cybersecurity, July 16, 2025
WVOT Cybersecurity, July 16, 2025
WVOT Cybersecurity, July 17, 2025
WVOT Cybersecurity, July 16, 2025
WVOT Cybersecurity, July 17, 2025
WVOT Cybersecurity, July 16, 2025
Coursera / Google, May 23, 2025
Coursera / Google, May 23, 2025
Coursera / Google, May 23, 2025
Coursera / Google, May 23, 2025
Coursera / Google, May 23, 2025
Coursera / Google, May 23, 2025
FITX, July 12, 2025
Coursera Project Network, July 17, 2025
Coursera Project Network, July 17, 2025
Coursera Project Network, May 21, 2025
Coursera / Google, March 19, 2025
Coursera / Google, March 19, 2025
Coursera / Google, March 20, 2025
Coursera / Google, April 12, 2025
Coursera / Google, April 12, 2025
Coursera / Google, March 19, 2025
Coursera / Google, April 12, 2025
Coursera / Google, April 12, 2025
IBM SkillsBuild, September 8, 2024
IBM SkillsBuild, September 26, 2023
IBM, April 24, 2024
IBM, September 16, 2023
IBM, September 21, 2023
Software AG, August 14, 2024 (valid until August 14, 2026)
BITM (SEIP Program), August 24, 2019
Candidate ID: ....749 (2025โ2026) โ Global cybersecurity professional community affiliation
In good standing through December 2025
West Virginia Section (Member #...5875)
Association for Computing Machinery (Member #....742)
Member, volunteer, awarded for contribution
โCompTIA Network+ (N10-009) Certification Companionโ โ Editing all 14 chapters for technical accuracy
in cybersecurity and networking (4 chapters completed, 10 chapters in progress).
July 2025 โ Present
Peer-reviewed a technical paper in the area of adaptive power system protection (July 2025).
Asia Pacific Academy of Science and PTE. LTD
Selected participant with full support (travel, hotel, and meals covered).
Ruby on Rails 7 web app for scuba diving enthusiasts: location & tour listings, photo sharing, posts with comments/likes, and Devise-based authentication.
Python notebook that generates colorful turtle-inspired mandalas using overlapping circles and rotational symmetry.
Priority-based round-robin OS scheduler simulation: create, block, fork, terminate, and replace programs via interactive commands.
Classic recursive Tower of Hanoi implementation with step-by-step solution logic.
Simulates First Fit, Next Fit, Best Fit, and Worst Fit strategies; compares efficiency and fragmentation.
QuickSort implementation with benchmarking and performance visualization (Matplotlib); includes comparison against binary search.
Simple daily calorie tracker to log foods and compare intake against personal goals.
Long-term flood forecasting for the Kanawha River (WV) using LSTM, BiLSTM, GRU, and ARIMA for multi-level predictions.
Project related to dental record management or analysis. Details not publicly available.
Built linear & logistic regression models to predict house prices from real-estate features.
Used classification and dimensionality reduction to evaluate expression regression performance.
Recurrent Neural Network trained on historical market data to forecast Google stock prices.
Clustering and feature embeddings to visualize and group unlabeled samples.
Interactive click-through demos for usability testing informed by HCI principles.
Reservation site with integrated search and booking for ship trips.
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