Software & ML Engineer|

Hey, I’m Arman — an engineer and ML researcher who loves turning hard problems into systems that work in the real world. Have a look at my research and projects below, and feel free to say hello.

LatestQuantum Angle–distance kernel for ECG classification and anomaly detection: a quantum-inspired framework for biomedical signal analysis
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About me

Hey, I'm Arman—passionate about creating intelligent, high-performance systems that make a real-world impact: As a results-driven Software Engineer and Machine Learning practitioner, I specialize in architecting high-performance backend systems and deploying intelligent solutions that solve real-world challenges. With a track record of leading startups to acquisition, scaling platforms for 20,000+ concurrent users, and mentoring underrepresented talent into tech careers, I thrive at the intersection of engineering, innovation, and social impact.

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Proficiency

Skills

Here are some key skills that define my expertise:

  1. Programming (Python, Go, C++)
  2. Machine Learning
  3. Deep Learning
  4. Software Development
  5. Data Structures and Algorithms
  6. Database Management (SQL, NoSQL)
  7. AI Frameworks (TensorFlow, PyTorch)
  8. Cloud Computing (AWS, GCP)
  9. Software Methodologies (Agile, Scrum)
  10. Problem-Solving

Achievements

  1. Built scalable backend systems handling 20,000+ concurrent users for a wearable tech startup.
  2. Founded and led a startup to profitable acquisition in 3 years.
  3. Trained 50+ underprivileged individuals in programming, with 85% job placement success.

Education

  1. B.Sc. in Computer Engineering at Sharif University of Technology
  2. M.Sc. in AI at Islamic Azad University Science and Research Branch

Publications

all publications
BioData Mining2026

Quantum Angle–distance kernel for ECG classification and anomaly detection: a quantum-inspired framework for biomedical signal analysis

A. Salehi, H. R. Goudarzi, A. Heydarian

Quantum-inspiredECGKernel Methods
1 citationRead
Health Informatics Journal2026

Interpretable heart disease risk prediction via FCA-constrained logistic regression

A. Heydarian, H. R. Goudarzi, Z. F. Rad, A. Salehi

Interpretable MLFCALogistic Regression
NewRead
2025 10th South-East Europe Design Automation, Computer Engineering Conference (SEEDA-CECNSM)2025

Meta-Optimized Risk-Aware Portfolio Management: A Hybrid Deep Reinforcement Learning and LSTM-GRU Ensemble

M. Sorouri, D. NoorMohammadzadehMaleki, A. Salehi, A. Farhadi, A. Zamanifar

Deep RLLSTM-GRUFinance
2 citationsRead

Articles

more articles
ECG Classification with Machine Learning: A Practical Overview
9 min read
SHAP, LIME, and the Case for Intrinsically Interpretable Models
7 min read
The Kernel Trick, Explained — and Why It Still Matters
8 min read
CVaR vs Value-at-Risk: Measuring the Risk That Actually Hurts
7 min read
LSTM vs GRU: Which Recurrent Net for Time-Series Forecasting?
8 min read
Why Healthcare Needs Interpretable ML — and How FCA Delivers It
7 min read
Why Quantum-Inspired Machine Learning Matters
8 min read
How Deep RL Manages Risk in Financial Markets
7 min read
Setting Up Your Deep Learning Environment and Training Your First Model
10 min read

Contact

Have a project, a question, or just want to say hi? Drop me a line — I'd love to hear from you.

Let's connect

armansal1519@gmail.com