I'm a Cybersecurity Machine Learning Engineer based in Tampa, Florida — working at the crossroads of AI, security, and blockchain infrastructure. I build systems that detect, classify, and respond to threats using machine learning, and I design decentralized token infrastructure aligned with the latest advances in AI inference computing. [cite:3]
- 🎯 Currently preparing for the CompTIA Security+ exam (target: June 2026) [cite:3]
- 🧠 Self-teaching Deep Learning through structured Cornell note-taking + spaced repetition [cite:3]
- 🔬 Building a portfolio around Quantum ML, threat detection, and blockchain token infrastructure [cite:3]
- ⚡ Aligned with NVIDIA GTC 2026 themes: token factory efficiency, inference scaling, and deep learning augmentation [cite:3]
- 📚 Studying from: Hands-On ML with Scikit-Learn, Keras & TensorFlow · Andrew Ng's Deep Learning Specialization [cite:3]
- 🎓 Enrolled at Purdue Global — Cybersecurity focus [cite:3]
"The token is the basic unit of modern AI." — Jensen Huang, NVIDIA GTC 2026 [cite:3]
I work with CEOs and technical leaders who are exploring machine learning and quantum‑inspired products with a strong cybersecurity and data analysis foundation. [file:107][cite:3]
- Penetration testing briefs – concise executive summaries of vulnerabilities, business impact, and remediation priorities.
- Security posture & gap analyses – mapping current environments to best‑practice frameworks and highlighting critical risks.
- Incident response playbooks – roles, runbooks, and communication trees tailored to high‑impact scenarios.
- Risk assessment reports – quantitative/qualitative risk scoring using data analysis in Excel/R/Tableau.
- ML/quantum concept notes – 2–4 page documents framing use cases, data requirements, and constraints.
- Threat models for AI/quantum systems – attack surfaces, abuse cases, and mitigations.
- Data pipeline & logging specs – what to collect, how to structure it, and how to use it for detection and metrics.
- Security architecture outlines – how crypto, key management, and access control wrap around ML/quantum workloads.
- Technical white papers – APA‑style write‑ups connecting product ideas with current research.
- Architecture diagrams & network schemas – Visio diagrams for systems, networks, and security zones.
- Metrics dashboards & reports – KPI definitions and starter Tableau/Excel views for security and product health.
- Project scopes & timelines – Gantt‑style scopes with milestones, dependencies, and QA checkpoints.
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A production-grade token factory built on Solana using the Token-2022 program — a full-stack dApp that mints, augments, and deploys on-chain tokens through a guided multi-step interface. [cite:3] Directly aligned with NVIDIA GTC 2026:
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What makes this a strong portfolio piece — click to expand
| Skill | Demonstrated By |
|---|---|
| Token factory design | Minting pipeline with throughput and cost optimization |
| Scale up | On-chain metadata, Token-2022 extensions, IPFS URI |
| Scale out | Multi-wallet ATA deployment, parallel token distribution |
| Deep learning analogy | Inference tokens mapped to blockchain tokens (NVIDIA GTC 2026) |
| Smart contract security | Ethereum escrow with Checks-Effects-Interactions pattern |
| Authority revocation | Immutable supply — analogous to frozen neural network weights |
| Web3 UX/UI | Responsive multi-step wizard with wallet integration |
| Unit testing | 3 Jest test cases covering mint, metadata, and revocation |
| Cross-chain | Both Solana (Token-2022) and Ethereum (Solidity/ERC-20) |
Why it matters now: At NVIDIA GTC 2026, Jensen Huang projected $1 trillion in revenue through 2027, driven by the shift from training to large-scale inference. Token throughput is now directly linked to revenue — and understanding token infrastructure at this level is the skill set companies are hiring for. [cite:3]
An autonomous shopping and delivery vehicle concept for grocery logistics — quantum AI navigation, post-quantum encryption, and a 5-phase development roadmap through 2030.
Four labs, each tied directly to one research question in the PhD thesis statement. They run on classical hardware today and now include machine learning baselines for routing, encryption, error correction, and hardware selection; quantum extensions are the PhD research targets. [cite:3]
| Lab | Research Question (Thesis) | Key Result |
|---|---|---|
| Lab 1 — Routing | Can hybrid quantum-classical optimization and ML improve retail routing over classical heuristics? | SA beats NN by 5.0% — QAOA requires 12 qubits on IonQ Forte |
| Lab 2 — Encryption | What cryptographic architecture protects retail data in a post-quantum threat landscape? | Kyber-768 + AES-256 hybrid (NIST FIPS 203) — similar latency to RSA, quantum-safe |
| Lab 3 — Error Correction | How can QEC principles and ML error modeling enable fault-tolerant fleet communication? | Steane [7,1,3] reduces logical error by 79% at 1% physical noise |
| Lab 4 — Hardware Spec | What is the minimum viable quantum hardware spec for GlideCart's algorithms? | IonQ Forte — all-to-all, 0.07% gate error — only system where all 3 algorithms are viable today |
Honest framing: These labs establish the classical baseline a PhD program would extend. Hardware experiments (QAOA, Steane code, QSVM) require PhD lab access at UMD QuICS, Harvard HQI, or U. Chicago CQE — PhD targets for 2027. [cite:3]
An interactive CompTIA Security+ SY0‑701 study site built for exam prep, with 4 modules, each containing visual concept explainers and a 12‑question scored quiz. Content is grounded in official SY0‑701 objectives, and the platform is being extended toward CompTIA PenTest+ scenarios. [cite:3]
| Module | Domain | Topics | Questions |
|---|---|---|---|
| Threats & Attacks | Domain 1 (~22%) | Threat actors, malware types, social engineering, attack frameworks, vulnerability categories | 12 |
| Cryptography & PKI | Domain 2 (~15%) | Symmetric/asymmetric, hashing, PKI, TLS, digital signatures, certificate management | 12 |
| Network Security | Domain 3 (~24%) | Firewalls, IDS/IPS, VPNs, DMZ, segmentation, wireless security, protocols | 12 |
| Identity & Access | Domain 4 (~16%) | Auth factors, MFA, SSO/federation, Zero Trust, PAM, access control models, directory services | 12 |
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A visual gallery of 29 graduate portfolio pages from Purdue Global's MS Cybersecurity program — covering applied statistics, risk analysis, blockchain development, platform security, and client design work. [cite:3] Courses covered:
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A visual gallery of 58 portfolio pages from Rasmussen University's Computer Science BS program — browsable by course with lightbox preview. [cite:3] Courses covered:
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Your contribution funds prototyping, pilot deployments, curriculum development, and open-source education tools.
| Project | Description | Stack | Status |
|---|---|---|---|
| ⭐ LogikaQBits-dApp | Token factory dApp — Solana Token-2022, on-chain metadata, authority revocation, Ethereum escrow. NVIDIA GTC 2026 aligned. | Solana, Solidity, JS | 🟢 Live |
| ⭐ purdue-grad-samples-2026 | MS Cybersecurity graduate portfolio — 29 pages, 6+ courses covering threat analysis, blockchain dev, risk assessment, and platform security | Python, R, Security | 🟢 Live Gallery ↗ |
| ⭐ rasmussen-cs-portfolio | Computer Science BS portfolio — 58 pages, 15+ courses covering Java, databases, software engineering, networks, and QA | Java, MySQL, Python | 🟢 Live Gallery ↗ |
| portfolio-samples | Interactive portfolio showcase with project index | Markdown | 🟢 Public |
My background spans graphic design, software engineering, and cybersecurity. I hold a BFA in Graphic Design and a BS in Computer Science, and I am currently completing an MS in Cybersecurity Management. I founded Logika Coders — a technology company focused on machine learning, quantum computing research, and ethical retail automation.
All content in this repository and across my GitHub profile is the original work of Anél Henning, created and documented through timestamped, verifiable Git commits.
- License: CC BY-NC-ND 4.0 — You may view and share with attribution, but may not modify or use commercially.
- Copyright: © 2026 Anél Henning / Logika Coders, Tampa, Florida. All rights reserved.
- Legal protections: U.S. Copyright Law (17 U.S.C.), Florida Right of Publicity (§ 540.08), DMCA (17 U.S.C. § 512)
- AI/Deepfake notice: Unauthorized AI-generated derivatives, synthetic media, or digital impersonation of the author will be pursued under applicable state and federal law.
Unauthorized reproduction, modification, or distribution of this work — including plagiarism, AI-generated copies, or identity impersonation — will result in DMCA takedown notices and legal action where applicable.
To report unauthorized use: anelhenning2@student.purdueglobal.edu