Milad Yousefi
About Me
I am a dedicated computational researcher with a profound passion for unraveling the complexities of biological and healthcare systems through the synergistic application of mathematics, data science, and artificial intelligence. My work thrives at the nexus of biology, computer science, and applied mathematics, where I specialize in transforming intricate biological questions into structured computational problems. I excel at converting diverse, real-world biomedical data into actionable insights, building robust predictive models, and developing intelligent tools that empower clinicians and scientists.
Driven by curiosity and persistence, I focus on making complex systems understandable and clinically impactful whether analyzing genetic patterns, modeling neural systems, designing machine learning pipelines for medical diagnostics, or conducting wet-lab experiments. I am a strong advocate for teamwork and believe that ambitious scientific goals are best achieved through open collaboration, clear communication, and shared creativity. My extensive experience with interdisciplinary groups has honed my ability to bridge technical and biological domains, foster team problem-solving, and promote an inclusive research culture. I am always eager to explore new ideas, learn from peers, and contribute to projects that significantly advance human health.
Research Interests
- Biostatistics: Developing and applying advanced statistical methods for high-dimensional biological and clinical data, including causal inference.
- Topics: Survival analysis, clinical trial design, longitudinal data, genomic statistics, statistical genetics, meta-analysis.
- Systems Biology: Mathematical and computational modeling of biological networks and pathways to understand complex biological phenomena.
- Topics: Network analysis, pathway modeling, multi-omics integration, metabolic networks, gene regulatory networks.
- Computational Neuroscience: Mathematical modeling of neural systems and AI applications for understanding brain function and neurological disorders.
- Topics: Neural modeling (LIF), fuzzy logic systems, retinal biomarkers, neurodegenerative diseases.
- Healthcare AI & Medical Informatics: Applying machine learning and statistical methods to clinical data for disease prediction, diagnosis, and treatment optimization.
- Topics: Medical image analysis, predictive modeling, clinical decision support, radiomics, precision medicine.
Education
Bachelor of Science in Applied Mathematics
Tabriz University, Tabriz, Iran | September 2019 - July 2023
GPA: 17.43/20 (3.58/4.00) | Graduated with Honors
- Undergraduate Thesis: "An Investigation into Fuzzy Modeling Techniques for Input Current Conversion in LIF Neuron Models"
- Supervisor: Dr. Fariba Bahrami
- Description: Developed novel fuzzy logic approaches to model non-linear input-output relationships in Leaky Integrate-and-Fire neuron models, significantly improving biological realism in computational neuroscience simulations.
- Relevant Coursework: Advanced Calculus, Linear Algebra, Differential Equations, Probability & Statistics, Mathematical Statistics, Numerical Analysis, Operations Research, Fuzzy Mathematics, Data Structures & Algorithms, Database Systems, Computer Architecture, Software Engineering, Advanced Bioinformatics, Stochastic Processes.
Research & Laboratory Experience
Undergraduate Research Assistant - Computational Neuroscience
Mathematical Neuroscience Lab, Tabriz University | September 2022 - July 2023
Supervisor: Dr. Fariba Bahrami
- Developed fuzzy logic-based models for input current conversion in Leaky Integrate-and-Fire (LIF) neuron models, enhancing biological realism by 35% compared to traditional methods.
- Implemented computational simulations in Python and MATLAB to validate model performance against experimental data, including analysis of neuronal spike trains and membrane potential dynamics.
- Investigated AI-based approaches for detecting Alzheimer's disease using retinal imaging biomarkers, collaborating with neurologists to validate computational findings against clinical observations.
Research Collaborator - Population Genetics & Biostatistics
HLA Distribution Analysis Project, Tabriz University of Medical Sciences | January 2021 - December 2023
- Conducted comprehensive statistical analysis of HLA allele frequencies in Azeri and Kurd ethnic groups using 6 years of clinical data (N > 5,000 samples).
- Applied chi-square tests, Fisher's exact tests, and Hardy-Weinberg equilibrium analysis to identify population-specific allele distributions and performed linkage disequilibrium analysis.
- Developed R and Python pipelines for automated data cleaning, quality control, and statistical analysis of immunogenetic data, preparing visualizations and reports for manuscripts.
Independent Researcher - Healthcare AI & Medical Informatics
Multiple Collaborative Projects | September 2021 - Present
- Stroke Prediction Model: Developed machine learning models (Random Forest, XGBoost, Neural Networks) achieving 87% accuracy and 0.91 AUC-ROC for stroke likelihood prediction.
- Lung Cancer Classification: Implemented deep learning models (U-Net, ResNet) for automated segmentation and classification of lung nodules from CT scans (92% sensitivity, 88% specificity).
- Thyroid Cancer Radiomics: Extracted and analyzed 400+ radiomic features from ultrasound images, applying feature selection and ML for improved diagnostic accuracy.
- Clinical Trial Analysis: Conducted statistical comparison of Gabapentin vs. Cinnarizine for tinnitus treatment using paired t-tests and effect size calculations.
- Multiple Sclerosis Biomarkers: Reviewed literature on retinal imaging biomarkers and AI applications for MS diagnosis and progression monitoring.
Laboratory Experience (Wet Lab)
Tabriz University & Collaborating Labs | 2021 - 2023
- Cell Culture: Proficient in aseptic techniques for maintaining various mammalian cell lines, including plating, passaging, and cryopreservation.
- Flow Cytometry: Experience in sample preparation, operating flow cytometers, and analyzing resulting data for cell viability, apoptosis, and cell cycle analysis.
- Cell Viability Assays: Performed assays (e.g., MTT, trypan blue exclusion) to assess cell proliferation and cytotoxicity in response to experimental treatments.
- Molecular Biology Techniques: Basic experience with DNA/RNA extraction, PCR, and gel electrophoresis for genetic analysis.
- Immunohistochemistry/Immunofluorescence: Assisted with tissue sectioning, antibody staining, and microscopy for protein localization studies.
Publications
1. Artificial Intelligence for Multiple Sclerosis Management Using Retinal Images: Pearl, Peaks, and Pitfalls
Yousefi, M., Lim, C. P., Farabi, S. (2023). Journal of Medical Artificial Intelligence.
- Contribution: Comprehensive review of AI applications for MS diagnosis and monitoring via retinal imaging, identifying challenges and proposing future directions.
2. Retinal imaging and Alzheimer's disease: an artificial intelligence-based future
Yousefi, M., Ashayeri, H., Jafarizadeh, A., Farhadi, F., Javadzadeh, A. (2023). Neuroscience Informatics.
- Contribution: Explored AI-based retinal imaging as a non-invasive biomarker for early AD detection, reviewing methodologies and discussing biological basis.
3. Advancements in Radiomics and Artificial Intelligence for Thyroid Cancer Diagnosis
Yousefi, M., Maleki, S. F., Jafarizadeh, A., Ahmadpour Youshanlui, M., et al. (2023). Submitted to Nature Communications.
- Contribution: Systematic review and meta-analysis of radiomic features and deep learning for thyroid cancer classification, proposing standardized protocols.
4. HLA distribution of Azeri and Kurd ethnic groups: 6 years investigation of Northwest Iran
Yousefi, M., Shahmohammadi-Farid, S., Farabi, S., Mandalo, L. (2023). Submitted to Immunogenetics.
- Contribution: Comprehensive statistical analysis of HLA allele frequencies, applying population genetics models to identify significant differences in immunogenetic diversity.
5. An Investigation into Fuzzy Modeling Techniques for Input Current Conversion in LIF Neuron Models
Yousefi, M., Bahrami, F., Farabi, S. (2023). Manuscript in Preparation.
- Contribution: Developed novel fuzzy inference systems for modeling input current conversion in LIF neurons, demonstrating improved accuracy in capturing non-linear neuronal dynamics.
Technical Skills
Statistical Methods:
- Hypothesis Testing (t-tests, ANOVA, Chi-square, non-parametric)
- Regression Analysis (Linear, Logistic, Poisson, Cox, mixed-effects)
- Survival Analysis (Kaplan-Meier, log-rank)
- Multivariate Analysis (PCA, Factor, Discriminant)
- Bayesian Statistics
- Experimental Design
- Meta-analysis
Programming & Software:
- Python: NumPy, Pandas, SciPy, Statsmodels, Matplotlib, Seaborn (Advanced)
- R: dplyr, ggplot2, survival, Bioconductor (Advanced)
- MATLAB: Signal processing, numerical analysis, optimization (Intermediate)
- SQL: MySQL, PostgreSQL, complex queries (Intermediate)
- C++: OOP, data structures, algorithms (Intermediate)
- Version Control: Git, GitHub
Machine Learning & AI:
- Frameworks: TensorFlow, PyTorch, Keras, Scikit-learn, XGBoost
- Deep Learning: CNN, RNN, LSTM, U-Net, ResNet, Autoencoders, Transfer Learning
- Classical ML: Random Forest, SVM, Gradient Boosting, Ensemble Methods
- Computer Vision: Image preprocessing, segmentation, feature extraction, medical image analysis
- Model Evaluation: Cross-validation, ROC/AUC, Confusion Matrices, Calibration
Bioinformatics & Systems Biology:
- Sequence Analysis (BLAST, alignment)
- Genomic Analysis (Variant calling, population genetics, HLA typing)
- Pathway Analysis (GSEA, network analysis)
- Systems Modeling (ODEs, metabolic/regulatory networks)
- Multi-omics Integration
Mathematical Modeling:
- Linear Algebra (SVD, PCA)
- Differential Equations (Dynamical Systems, numerical methods)
- Optimization (Linear, Nonlinear, Gradient methods)
- Fuzzy Logic (Fuzzy sets, inference systems)
- Numerical Methods
Laboratory Skills (Wet Lab):
- Cell Culture (aseptic technique, passaging, cryopreservation)
- Flow Cytometry (sample prep, analysis)
- Cell Viability Assays (MTT, trypan blue)
- DNA/RNA Extraction, PCR, Gel Electrophoresis
- Immunohistochemistry/Immunofluorescence
Professional Skills & Competencies
- Communication: Scientific writing, manuscript preparation, data visualization, presenting complex technical concepts to diverse audiences, interdisciplinary collaboration.
- Problem Solving: Deconstructing complex biological challenges, creative methodological design, debugging, cross-domain method adaptation.
- Research & Learning: Rapid assimilation of new tools/concepts, critical evaluation of literature, self-directed learning, attention to detail.
- Collaboration & Leadership: Effective multidisciplinary teamwork, mentoring, project management, open to feedback.
- Organization & Management: Project planning, time management, organized code repositories, systematic experimental design, meeting deadlines.
- Personal Qualities: Persistent, curious, ethical, committed to reproducible and transparent research.
Teaching Experience
Teaching Assistant - Linear Optimization
Department of Mathematics and Computer Science, Tabriz University | 2022
Instructor: Dr. Vakili
- Conducted weekly problem-solving sessions for undergraduate students.
- Prepared and administered midterm and final exams.
- Provided personalized guidance on optimization techniques and applications.
- Developed supplementary materials to enhance student understanding.
Teaching Assistant - Combinatorics
Department of Mathematics and Computer Science, Tabriz University | 2021
Instructor: Dr. Behmaram
- Led weekly sessions explaining complex combinatorial concepts.
- Graded student assignments and provided constructive feedback.
- Developed supplementary learning materials for challenging topics.
- Assisted students with problem-solving strategies and exam preparation.
Teaching Assistant - Differential Equations
Department of Mathematics and Computer Science, Tabriz University | 2020
Instructor: Dr. Bahrami
- Facilitated weekly sessions focusing on problem-solving and theory applications.
- Designed and supervised class projects connecting differential equations to real-world problems.
- Provided individualized support to students struggling with complex concepts.
- Created practice problems and study guides for exams.
Certifications & Professional Development
- Deep Learning Specialization (deeplearning.ai, All 5 courses completed)
- Machine Learning (Stanford University, Coursera)
- Advanced Bioinformatics (Specialized coursework covering genomic data analysis and structural bioinformatics)
- Computational Neuroscience (Focused study of neural modeling and dynamical systems)
- Data Structures and Algorithms (Comprehensive university coursework)
- Database Systems (University-level training in database design and management)
Languages
- Persian (Farsi): Native proficiency - Speaking, reading, and writing
- English: Professional proficiency - Academic writing and technical communication
- Azerbaijani (Turkish): Native proficiency - Speaking and conversational fluency
References
Available upon request