Milad Yousefi

Computational Researcher · Biostatistics & Systems Biology

Dedicated computational researcher specializing in the synergistic application of mathematics, data science, and artificial intelligence to biological and healthcare problems. My work spans biostatistics, systems biology, computational neuroscience, and medical AI—transforming complex biological questions into structured computational frameworks and converting diverse biomedical data into actionable clinical insights. Currently seeking PhD or Master's positions to contribute to rigorous interdisciplinary genomics and healthcare AI research.

Research & Laboratory Experience

Visiting ResearcherCanan Atilgan Lab
Current
Sabancı University, Istanbul, Türkiye. Contributing to advanced molecular dynamics, computational biology pipelines, and structural bioinformatics frameworks.
Research CollaboratorPopulation Genetics & Biostatistics
Jan 2021 – Dec 2023
Tabriz University of Medical Sciences
  • Comprehensive statistical analysis of HLA allele frequencies in Azeri and Kurd populations using 6 years of clinical datasets (N > 5,000).
  • Applied chi-square tests, Fisher's exact tests, Hardy–Weinberg equilibrium, and linkage disequilibrium mapping.
  • Built reproducible R and Python pipelines for automated data cleaning, structural quality control, and population genetics evaluation.
Undergraduate Research AssistantComputational Neuroscience
Sep 2022 – Jul 2023
Mathematical Neuroscience Lab, Tabriz University | Supervisor: Dr. Fariba Bahrami
  • Developed novel fuzzy logic-based models for input current conversion in LIF neuron models, enhancing baseline biological realism by 35% over traditional static systems.
  • Executed simulations in Python/MATLAB; validated variants against empirical neuronal spike trains.
Independent ResearcherHealthcare AI & Medical Informatics
Sep 2021 – Present
  • Stroke Prediction: Built ML architectures (Random Forest, XGBoost, MLPs) hitting an 87% predictive accuracy and 0.91 AUC-ROC.
  • Lung Cancer Classification: Built Deep Learning pipelines (U-Net, ResNet) for CT nodule segmentation yielding 92% sensitivity and 88% specificity.
  • Thyroid Cancer Radiomics: Handled extraction matrices for 400+ ultrasound features utilizing downstream feature selection algorithms for diagnostic optimization.

Education

Bachelor of Science in Applied Mathematics | Honours
Sep 2019 – Jul 2023
Tabriz University, Tabriz, Iran  •  GPA: 17.43/20 (3.58/4.00)
Thesis: "An Investigation into Fuzzy Modeling Techniques for Input Current Conversion in LIF Neuron Models" — Supervisor: Dr. Fariba Bahrami.
Coursework: Advanced Calculus, Linear Algebra, Mathematical Statistics, Differential Equations, Probability, Numerical Analysis, Fuzzy Mathematics, Data Structures, Stochastic Processes, Advanced Bioinformatics.

Research Interests

Biostatistics

Survival analysis, longitudinal metrics, causal inference, statistical genetics, clinical trials, Bayesian models.

Systems Biology

Network analysis, multi-omics integration, gene regulatory networks, metabolic pathways modeling.

Computational Neuroscience

Neural modelling (LIF), fuzzy inference systems, retinal biomarkers, neurodegenerative mapping.

Healthcare AI

Medical image segmentation, predictive modeling, clinical decision support, radiomics features.

Technical Skills

Statistical Methods

Hypothesis testing, Regression (Cox, Mixed-effects, Poisson), Survival analysis (KM, Log-rank), PCA, Factor analysis.

Programming

Python (NumPy, Pandas, SciPy, Statsmodels, Matplotlib), R (dplyr, ggplot2, Bioconductor), MATLAB, SQL, C++, Git/GitHub.

Machine Learning

TensorFlow, PyTorch, Scikit-learn, XGBoost, CNNs, LSTMs, U-Net, ResNet, Transfer Learning, ROC/AUC curves.

Bioinformatics

Sequence Analysis (BLAST), Genomics (Variant calling, HLA typing), Pathway/GSEA metrics, Systems modeling (ODEs).

Publications

06 The Effects of Optic Nerve Sheath Fenestration on Visual Function and OCT Metrics in Idiopathic Intracranial Hypertension: A Retrospective Study
Farabi, S., Yousefi, M., Fekrazad, S., Asiayi, A., Nabie, R., Kostic, M. · Frontiers in Ophthalmology, Vol. 6, 1752218 (Frontiers).
05 An Investigation into Fuzzy Modeling Techniques for Input Current Conversion in LIF Neuron Models
Yousefi, M., Bahrami, F., Farabi, S. · Manuscript in Preparation.
04 HLA Distribution of Azeri and Kurd Ethnic Groups: A 6-Year Investigation of Northwest Iran
Yousefi, M., Shahmohammadi-Farid, S., Farabi, S., Mandalo, L. · Submitted to Immunogenetics.
03 Advancements in Radiomics and Artificial Intelligence for Thyroid Cancer Diagnosis
Yousefi, M., Maleki, S.F., Jafarizadeh, A., Ahmadpour Youshanlui, M., et al. · Submitted to Nature Communications.
02 Retinal Imaging and Alzheimer's Disease: An Artificial Intelligence-Based Future
Yousefi, M., Ashayeri, H., Jafarizadeh, A., Farhadi, F., Javadzadeh, A. (2023). · Neuroscience Informatics.
01 Artificial Intelligence for Multiple Sclerosis Management Using Retinal Images: Pearls, Peaks, and Pitfalls
Yousefi, M., Lim, C.P., Farabi, S. (2023). · Journal of Medical Artificial Intelligence.

Additional Research Experience (Wet Lab)

Tabriz University & Collaborating Laboratories
2021 – 2023
  • Cell Culture: Maintained sterile execution pipelines, passage protocols, and complex cryopreservation.
  • Assays & Flow Cytometry: Run routine sample preps tracking apoptosis, viability via MTT/Trypan Blue and cell cycle checkpoints.
  • Molecular Targets: Executed DNA/RNA extraction protocols, classic PCR setups, gel electrophoresis, IHC, and structural immunofluorescence mapping.

Teaching Experience

Teaching Assistant — Linear Optimization · Dr. Vakili
2022
Teaching Assistant — Combinatorics · Dr. Behmaram
2021
Teaching Assistant — Differential Equations · Dr. Bahrami
2020

Certifications

  • Deep Learning Specialization
    deeplearning.ai (5 Courses)
  • Machine Learning
    Stanford University / Coursera
  • Advanced Bioinformatics
    Genomic & Structural Analysis
  • Computational Neuroscience
    Neural Modelling Systems

Professional Competencies

  • Communication: Technical writing, clean data visualization, and interdisciplinary translation.
  • Problem Solving: Formulating analytical frameworks for complex clinical and biological phenomena.
  • Collaboration: Thriving in integrated medical-engineering cohorts; eager to incorporate active feedback metrics.

Languages

  • Persian (Farsi): Native
  • Azerbaijani (Turkish): Native
  • English: Professional (Academic)

References

Available upon request.