Faisal Ahmed
Assistant Professor of Mathematics & Data Science
Embry-Riddle Aeronautical University — faculty profile ↗I'm a Data Scientist with a strong foundation in mathematics, machine learning, computer vision, and NLP, building predictive models that turn complex data into decisions. I am building hybrid and interpretable models by combining TDA features with deep learning and transformer models, and my focus is healthcare — transforming complex medical imaging data into models that improve diagnosis and patient outcomes.
About
Data scientist, machine learning engineer, and researcher with 9+ years across academia and industry. I bring a unique blend of academic research and industry experience — from applied mathematics and topological data analysis to computer vision, NLP, generative AI, and large language models — turning complex data into models that drive real decisions. My primary focus has been healthcare, using deep learning to make medical imaging models both accurate and mathematically interpretable. Alongside research, I teach and mentor students through end-to-end machine learning projects, from problem formulation to deployment.
Technical Skills
Programming & Tools
Languages: Python, R, SQL, MATLAB, Java, C++
Libraries: NumPy, Pandas, SciPy, Scikit-learn, Keras, PyTorch, TensorFlow
BI & Visualization: Matplotlib, Seaborn, Tableau, Power BI
Machine Learning
Supervised: Linear & Logistic Regression, Decision Trees, SVM, KNN, Naive Bayes, LDA/QDA, Random Forest, XGBoost, Gradient Descent & Stochastic Gradient Descent
Unsupervised: K-Means & Hierarchical Clustering, PCA
Model Practices: Cross-Validation, Bagging & Boosting, Regularization, ETL Pipelines
Deep Learning & NLP
Deep Learning: Neural Networks, MLP, CNN, RNN, LSTM, Vision Transformers, Transfer Learning
Generative AI & NLP: LLMs, GPT, Fine-Tuning LLMs, spaCy, NLTK, Gensim, Transformers, TextBlob, Hugging Face Datasets
Data Engineering & Cloud
Databases: SQL Server, PostgreSQL, MongoDB, NoSQL
Cloud & Big Data: AWS, Hadoop, Apache Spark, MLlib, Hive, Scala, Kafka, Impala
Web Scraping: BeautifulSoup, Scrapy
End-to-End ML Workflow
From raw data to a deployed, production-ready model — the pipeline I follow on real-world projects.
Data Collection
Pulling structured data via SQL and unstructured data via web scraping.
Data Preprocessing
Cleaning, handling missing values, and manipulating raw data into an analysis-ready form.
Feature Engineering
Selecting and transforming the most predictive features for the model.
Model Training & Validation
Training and validating classical ML and deep learning models.
Model Deployment
Serving the trained model behind a REST API for real-world use.
Experience
Education
(Data Science Concentration)
Funded by the National Science Foundation (Grants #DMS-2202584, #DMS-2220613) and the Simons Foundation (Grant #579977).
Publications & Research
Peer-reviewed work in topological data analysis, deep learning, and medical image analysis, published in IEEE, ICCV, MIUA, and leading imaging-informatics journals.
- 2023Presented Retinal Image Screening with Topological Machine Learning at the Workshop on Imaging and Data Science, University of Texas at Dallas, Richardson, Texas, USA, on October 27, 2023.
- 2023Showcased a machine learning model, Topo-CXR, focused on computer vision for automated medical diagnosis at the ICCV CVAMD 2023 workshop in Paris, France, on October 2, 2023.
- 2022Exhibited a machine learning model, Topo-ML, at the Conference on Advances in Data Science – Theory, Methods, and Computation, Texas A&M University, Commerce, Texas, USA, from October 21–22, 2022.
- 2025Presented the paper titled “Topo-CNN: Retinal Image Analysis with Topological Deep Learning” as a speaker at the NxT AI Conference, held in San Francisco, CA, USA, on November 4, 2025.
- 2026Presented the paper titled “PseudoColorViT-CXR: Colormap-Enhanced Vision Transformers for Tuberculosis and Pneumonia Detection from Grayscale Chest X-ray Images” at the International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA 2026), IEEE.
- 2026Presented the paper titled “RepViT-CXR: A Channel Replication Strategy for Vision Transformers in Chest X-ray Tuberculosis and Pneumonia Classification” at the AABIP 2026 Annual Conference in Denver, Colorado, USA.
- 2026Presented my paper titled “Addressing High Class Imbalance in Multi-Class Diabetic Retinopathy Severity Grading with Augmentation and Transfer Learning” at the 6th International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME 2026).
Certifications
- Data Science Certificate — The University of Texas at Dallas (2022)
- Graduate Teaching Certificate — The University of Texas at Dallas