Saeed Rafieyan

Curriculum vitae

Saeed Rafieyan

Also listed as Saeed Rafieyan Boroujeni

PhD researcher, Digital Manufacturing of Biomimetic Systems group, Institute of Physical Chemistry, Polish Academy of Sciences, Warsaw
Raf.Biomed@gmail.com · rafieyan.xyz · ORCID 0000-0002-3018-3683

Updated

Download CV (PDF, 309 KB)

Short bio

Saeed Rafieyan is a biomedical engineer and machine-learning researcher at the Institute of Physical Chemistry, Polish Academy of Sciences, building AI tools such as MLATE for tissue engineering and biofabrication.

Education

  1. PhD

    Institute of Physical Chemistry, Polish Academy of Sciences (IChF PAN) · Warsaw, Poland

    Thesis: Inverse design-assisted biodegradable modular dressings for negative pressure wound healing

    • Digital Manufacturing of Biomimetic Systems group.

  2. MSc in Chemical Engineering – Biomedical Engineering

    Tarbiat Modares University · Tehran, Iran

    Thesis: Predicting cell behavior on cardiac tissue engineering scaffolds using machine learning algorithms

    • GPA: 3.78 / 4

  3. BSc in Chemical Engineering

    Tafresh University · Tafresh, Iran

    Thesis: Simulation of the biodiesel production process with Aspen HYSYS

Research experience

  1. Research assistant – data scientist

    Jiangnan University, Dr. Madadi’s group · Wuxi, China

    • Machine-learning and deep-learning models for biofuel production and process optimisation, resulting in peer-reviewed publications.

  2. Research assistant – data scientist

    Tarbiat Modares University, Dr. Fakhroleslam’s group · Tehran, Iran

    • Machine-learning and deep-learning models for distillation and separation processes; chemical-engineering text mining.

  3. Research assistant – data scientist

    Tarbiat Modares University, Prof. Vasheghani-Farahani’s group · Tehran, Iran

    • Developed and led AI-driven research projects, including MLATE (three versions, web app at mlate.ir) and TissueGPT, a language model fine-tuned on tissue-engineering literature.

  4. Data scientist

    Men’s Health & Reproductive Health Research Centre, Shohadaye Tajrish Hospital · Tehran, Iran

    • Multimodal deep learning for semen analysis and drug-effectiveness prediction from clinical data and imaging.

Industry experience

  1. Data scientist

    HiWEB · Tehran, Iran

    • Churn, next-purchase and purchase-value models for marketing; ASR and LLM-based quality control for Persian call-centre audio; dashboards in Flask and Plotly Dash.

  2. Data scientist

    YecomSoft · Tehran, Iran

    • Persian NLP and text-to-speech for audiobook production.

  3. Django web developer (intern)

    MAPSA · Tehran, Iran

    • Backend and REST APIs for an online retail platform.

Publications

  2. MLATE V3: An Open-Source Multi-Tissue AI Framework for Data-Driven Optimization of 3D-Printed and Bioprinted Scaffolds

    Rafieyan S, Partovi Nasr M, Ansari E, Kheradvar Kolour A, Banimohamad-Shotorbani B, Vasheghani-Farahani E

    Biofabrication · · doi:10.1088/1758-5090/aeb162 In press

  5. Acetone-butanol-ethanol fermentation products recovery: Challenges and opportunities

    Rafieyan S, Boojari MA, Setayeshnia A, et al.

    Chemical Engineering Research and Design · · doi:10.1016/j.cherd.2024.04.021

  6. MLATE: Machine learning for predicting cell behavior on cardiac tissue engineering scaffolds

    Rafieyan S, Vasheghani-Farahani E, Baheiraei N, Keshavarz H

    Computers in Biology and Medicine · · doi:10.1016/j.compbiomed.2023.106804

  7. A Review of Recent Advances in Natural Polymer-Based Scaffolds for Musculoskeletal Tissue Engineering

    Fan J, Abedi-Dorcheh K, Sadat Vaziri A, Kazemi-Aghdam F, Rafieyan S, et al.

    Polymers · · doi:10.3390/polym14102097

  8. Predicting cell behavior on cardiac tissue engineering scaffolds using machine learning algorithms

    Rafieyan S

    MSc thesis, Tarbiat Modares University, Tehran ·

  9. Process Simulation of Biodiesel Production from Soybean Oil with Aspen HYSYS: A Comparative Study of two different processes

    Rafieyan S, Bayat A

    11th International Chemical Engineering Congress & Exhibition (IChEC 2020), Fouman, Iran ·

Software and data

  • MLATE — Machine Learning Applications in Tissue Engineering: publicly available models and data that predict how a scaffold will print and how cells will respond to it, rank candidate formulations, and suggest what to fabricate next.
  • TissueGPT (doi:10.57967/hf/8917) — A BioGPT language model fine-tuned on tissue-engineering literature, for summarising papers, generating domain text and supporting scientific writing.
  • ECM Program Atlas — A reproducible, Matrisome-based framework for mapping extracellular-matrix gene programs across human tissues, validated on external transcriptomic and protein-level data, to support ECM-informed biomaterial design.

Service

  • Peer reviewer for IOP Publishing journals (IOP Trusted Reviewer), including Physics in Medicine and Biology, Biomedical Materials, Biomedical Physics & Engineering Express, Engineering Research Express and Progress in Biomedical Engineering; also Results in Surfaces and Interfaces (Elsevier).

Skills

Machine learning
Regression, classification, clustering, feature engineering, model selection, Bayesian optimisation, gradient boosting (XGBoost, LightGBM, CatBoost), scikit-learn
Deep learning and NLP
PyTorch, TensorFlow, Hugging Face Transformers, CNNs, RNNs, multimodal learning, LLM fine-tuning, text mining, topic modelling
Software and data
Python, NumPy, pandas, Matplotlib, Plotly, Dash, Streamlit, Flask, FastAPI, Docker, GitHub Actions, PostgreSQL, MySQL, SQLite
Laboratory
Cell culture, MTT assay, decellularisation, hydrogel preparation, scaffold fabrication, 3D printing, freeze-drying

Languages

  • English · TOEFL iBT 90/120
  • Persian · Native

Additional training

Show 6 certificates

References

Available on request.