Open to AI/ML roles & research collaborations
Based in Linz, Austria
Ahmed
Mohammed
Computer Vision · Diffusion Models · OOD Detection
AI/ML engineer and entrepreneur. I build systems that work in the real world - from research to production.
4+
Years in AI/ML
99.03%
OOD Detection AUROC
+6.5pp
Gain over baseline (stable)
<1 min
Report generation
~/faultrix $ python analyze.py --input site_photos/
Loading 42 images - GPU: RTX 4090
✓ Defect detection · AUROC: 99.03% SOTA
✓ Generating ONORM report... AES-256 encrypted
→ QC report ready <1 min
~/faultrix $
PyTorchDiffusion ModelsComputer VisionOOD DetectionCUDAPython
JKU Linz, Austria
Supervised by Prof. Sepp Hochreiter
PROFACTOR / FTI_Zer0P
99.03% AUROC thesis result
Founder of Faultrix
// 01 - About
AI/ML Engineer - research that ships.
I am an AI/ML engineer who completed an M.Sc. in Artificial Intelligence at JKU Linz, working under Prof. Sepp Hochreiter, the pioneer of LSTM and deep learning.
My work spans the full stack: from class-conditional separation loss for diffusion-based OOD detection to industrial computer vision pipelines evaluated under rigorous cross-validation. 99.03% +/- 0.07% AUROC · 0.8673 AUROC.
I founded Faultrix to turn this research mindset into a real product: AI-powered construction quality control that generates compliant reports in under a minute.
Location
Linz, Austria
Supervisor
Prof. Sepp Hochreiter
Company
Faultrix.com - Founder
Open to
AI/ML Roles - Research Collaborations
Education
M.Sc. in Artificial Intelligence
Oct 2024 - Mar 2026
Johannes Kepler University Linz
Thesis: conditional diffusion models for OOD detection - 99.03% +/- 0.07% average AUROC.
B.Sc. in Mechatronics Engineering
Feb 2015 - Jan 2018
Eastern Mediterranean University, Cyprus
Thesis: SCARA robotic system for dynamic object tracking.
Skills & Stack
Deep Learning
PyTorchPyTorch LightningDiffusion ModelsCNNsTransformersRNNsLLMs
Computer Vision
OOD DetectionObject DetectionDefect DetectionImage Classification
Research & Experimentation
HydraExperiment DesignAblation StudiesAUROC/FPR95Monte CarloCross-Validation
ML Engineering
End-to-End PipelinesLLM InferencePrompt EngineeringOpenAI API
Infrastructure
DockerGitLinuxREST APIsTypeScriptNext.jsCUDAPythonOpenCV
AI Tools
GitHub CopilotChatGPTWeights & Biases
Languages
Arabic
Native
English
B2 - Professional
German
B1 CEFR - improving
// 02 - Projects
Research that ships.
Live SaaS - Founder
Faultrix
AI-powered construction quality control SaaS. Analyzes building photos and generates ONORM-compliant reports in under 1 minute - SHA-256 evidence chain, DSGVO compliant, AES-256 encryption. Built solo from zero to production.
faultrix.com →<1
min per report
ONORM · DSGVO compliant
faultrix.com →Case study: /case-studies/faultrix
Master's Thesis - JKU Linz
OOD Detection Framework
Conditional diffusion models as generative classifiers achieving 99.03% +/- 0.07% average AUROC on CIFAR-10 OOD detection. Introduced class-conditional separation loss improving average performance by 6.5 percentage points with high seed stability.
99.03% +/- 0.07% Avg AUROC
DiffusionOOD →Case study: /case-studies/diffusion-ood
Master's Thesis Track 2
InkjetOOD Defect Detection
YOLO+CDM quality classification pipeline evaluated on the public FTI_Zer0P benchmark under strict 5-fold cross-validation.
0.8673 AUROC (5-Fold CV)
InkjetOOD →Case study: /case-studies/inkjet-ood
Personal Project · Proof of Concept
Mento — AI Habits Assistant
Conversational assistant for habit-building and daily routines via a Telegram bot. Multi-provider LLM routing with escalation logic, pgvector RAG long-term memory, multimodal photo understanding, and voice replies through the Alexa Reminders API — on Supabase edge functions. Early POC, in active development.
Karunya University · BCI Research
EEG Signal Classification
LSTM, Bi-LSTM, and GRU architectures for motor imagery classification in brain-computer interface applications.
// Proof
Selected evidence.
Public artifacts, reports, and repositories behind the portfolio claims.
// 03 - Experience
Where I've worked.
Jul 2025
Present
Founder & Full-Stack Developer
Faultrix · Linz, Austria
Built an AI construction analysis platform solo from zero to production with Python, Next.js, Convex, OpenAI API, Docker, Clerk, Cloudflare R2, and Stripe.
Active - faultrix.com
Dec 2024
Mar 2026
ML Researcher
JKU Machine Learning Institute · Linz
Master thesis on conditional diffusion models as generative classifiers for out-of-distribution detection under Prof. Sepp Hochreiter.
Apr 2024
Nov 2024
Machine Vision Researcher
PROFACTOR GmbH · Steyr, Austria
YOLO + diffusion pipeline for industrial defect detection in zero-defect inkjet printing on building components.
98.4% accuracy
Aug 2023
Oct 2023
AI Research Intern
Karunya University · India (Remote)
RNN/CNN architectures for EEG motor imagery classification with hyperparameter optimization.
Jan 2021
Present
AI & Programming Tutor
Freelance
Tailored training in Python, ML, and deep learning, plus mentoring on portfolio projects and GitHub practice.
// 04 - Research
Reports & Thesis
Problem
How can a model recognize when an image does not belong, instead of acting overconfident?
Contribution
A separation loss that forces the model s competing class explanations to move apart.
Result
99.03% +/- 0.07% AUROC on CIFAR-10, with a +6.5pp gain over the non-separated baseline.
Why it matters
The score became not only higher, but dramatically more stable across seeds and easier to trust.
Master's Thesis (Track 1) - JKU Linz - 2026
Conditional Diffusion Models as Generative Classifiers for Out-of-Distribution Detection
A diffusion model compares how well different class explanations reconstruct an image. The new separation loss pushes those explanations apart, making the anomaly score clearer and more stable. 99.03% +/- 0.07% AUROC, +6.5pp gain, baseline 92.52% +/- 11.07%, external OOD 90.50%-96.97%.
99.03%
AUROC - CIFAR-10 Average
+/-0.07% variance (3 seeds)
Separation weight sweep: best stability at lambda=0.02
Score distributions at calibrated operating threshold
Reconstruction error gap peaks at intermediate noise
Novel contribution - separation loss
Plain idea: train the model to make its two explanations visibly different. The gap becomes the anomaly signal.
1Learn to denoiseReconstruct images under each class condition.
2Push explanations apartIncrease the distance between conditional predictions.
3Score the gapUse the reconstruction-error difference as the OOD signal.
Total loss=denoising loss+lambda * separation loss
Latest thesis result: lambda = 0.02 reached 99.03% +/- 0.07% AUROC, a +6.5pp gain over no separation loss.
Master's Thesis (Track 2) - JKU Linz - 2026
Rigorous Cross-Domain Transfer & FTI_Zer0P Benchmark
The public YOLO+CDM print-quality pipeline was evaluated on FTI_Zer0P with strict 5-fold cross-validation, showing where separation loss transfers and where small manufacturing textures behave differently. 0.8673 +/- 0.0230 AUROC.
0.8673
AUROC - 5-Fold CV Baseline
Rigorous Public Evaluation
Cross-domain impact: CIFAR-10 gain vs. Inkjet QC variance
Per-feature AUROC across separation weights
// 05 - Writing
Thinking out loud.
Deep-dives on research decisions, production lessons, and the gap between papers and products.
How I Reached 99.03% AUROC on OOD Detection with Conditional Diffusion Models
My master's thesis at JKU Linz introduced class-conditional separation loss into conditional diffusion models used as generative classifiers...
Shipping Faultrix: What I Learned Building an AI SaaS in 5 Months
What changed when I moved from research into product building: Faultrix, an AI SaaS for construction quality control that generates ONORM-al...
Diffusion Models for Industrial Defect Detection at PROFACTOR GmbH
How I evaluated a public YOLO + conditional diffusion pipeline on the FTI_Zer0P benchmark under strict 5-fold cross-validation, reaching a 0...
// 06 - News
Tracking the field.
Curated AI news with a short take on each — with a bias toward agentic AI, where I spend most of my attention.
Claude Mythos 5 and Fable 5 Explained: What Anthropic Actually Shipped
Anthropic’s new Mythos-class tier above Opus is significant for agentic coding, but at double Opus’s price, the production economics need sc...
Alibaba's model never trained as an agent — and improved agent performance across seven benchmarks
Training agents to predict environment states rather than actions is a clever inversion, and controlled simulation that injects edge cases r...
Multiverse Computing Launches Pulsar 16B in collaboration with NVIDIA: Frontier-Grade Reasoning at Half the Parameters
Achieving 30B-class reasoning at 16B parameters through compression is meaningful for deployment cost, but "no retraining from scratch" clai...
// 06 - Contact
Let's build something extraordinary
Open to AI/ML roles, research collaborations, and product partnerships.
ahmed@faultrix.com