Research Projects Portfolio

AI/ML Bioinformatics & Precision Medicine - Integrated, Multi-Omics Project Portfolio

A curated multi-project lab of reproducible AI/ML and statistical genomics workflows. Each project is a complete pipeline focused on a specific precision medicine problem—from variant pathogenicity prediction and transcription-factor binding modeling to gene expression inference and biomarker discovery.

This portfolio integrates:

• Deep learning architectures (CNNs, RNNs, Transformers) for genomic sequence analysis
• Statistical genetics and biostatistical modeling for population-level insights
• Explainable machine learning to interpret biological mechanisms
• Cloud-ready, scalable bioinformatics pipelines
• Clinical data science methodologies aligned with real-world healthcare applications

Together, these projects demonstrate an end-to-end approach to next-generation precision medicine—combining data-driven insight, biological interpretability, and clinical relevance.

Data Science & Predictive Analytics - Multi-Domain Project Portfolio

A collection of end-to-end analytics projects spanning real-world domains including marketing analytics, customer intelligence, cybersecurity, NLP, HR insights, fraud detection, energy analysis, ML education, compensation science, and startup analytics.

Each project is built using a consistent, reproducible workflow:

1. Data Cleaning & Feature Engineering
2. Exploratory Data Analysis (EDA)
3. Predictive Modeling (ML, statistical modeling, or NLP)
4. Insight Generation & Communication

The structure makes it easy to open any folder, understand the problem, run the notebook/script, and reproduce the full workflow. It’s both a practical learning resource and a demonstration of applied data science across diverse, high-impact domains.

Clinical Data Science & Health Analytics - Translational AI in Public Health Portfolio

A curated set of clinical data science and health analytics projects demonstrating real-world applications of machine learning, statistical modeling, and evidence-based analysis across healthcare. Projects span disease prediction and risk stratification, treatment adherence modeling, patient behavior analysis, and population-level health insights.

The portfolio includes:

• End-to-end predictive modeling pipelines for chronic disease, metabolic conditions, and clinical outcomes
• Risk scoring systems and stratification frameworks inspired by real-world clinical decision support tools
• Behavioral and lifestyle analytics that surface patient-level patterns influencing disease progression
• Treatment initiation and adherence modeling, plus data cleaning, feature engineering, and statistical validation for replicable, interpretable insights

Each project is organized for reuse and reproducibility, with clear structure, well-documented analysis steps, and modular code aligned with best practices in clinical AI and health informatics.

Infectious Disease Modelling & Epidemiology - Computational Transmission & Intervention Analytics Portfolio

A focused infectious disease modelling portfolio that turns transmission questions into reproducible code—pairing compartmental and stochastic perspectives with intervention-style analyses so dynamics can be explored before real-world deployment.

Key capabilities:

• Python, R, shell, and Jupyter-friendly workflows sized for transparent assumptions and iterative experimentation
• Transmission modelling motifs suited to vector-borne contexts such as malaria and related scenario exploration
• Modular repository layout so methods can extend to additional pathogens or geographical settings
• Open, version-controlled artifacts aligned with reproducibility expectations in computational epidemiology

The portfolio bridges mechanistic intuition with simulation discipline—stress-testing hypotheses with documented pipelines rather than one-off scripts.

Computational Biomedical Research - Integrated Imaging, Sequence & Oncology AI Portfolio

A multi-project computational biomedical laboratory spanning neurological signal analytics from eye-movement data, retinal-image cardiovascular risk stratification, DNA sequence–based gene expression modeling, oncology genomics and radiomics, ultrasound-informed hepatology analytics, microscopy cell phenotyping, and AI-guided drug and cancer-target discovery.

Key capabilities:

• Deep learning and classical ML stacks with diagnostics oriented toward interpretability and clinical relevance
• Imaging and sequence pipelines—from CNN-style fundus workflows to radiomics, ultrasound tasks, and sequence encoders
• Robust feature engineering, statistical validation, and artifact exports that keep experiments auditable
• Standalone project folders with notebooks and scripts that preserve reproducible structure end-to-end

Together these workflows show how modern ML can stay disciplined for biomedical rigor—from discovery through transparent evaluation and reporting.

Climate, Energy & Green Microbiology - Forecasting, Renewables & Sustainable Systems Portfolio

A climate and energy intelligence portfolio spanning AI-assisted global weather forecasting, renewable optimization under climate variability, electricity pricing strategies, load and demand forecasting, extreme-event risk modeling, reinforcement learning for energy systems control, and sustainable transition scenarios—including datasets and narratives aligned with green biotechnology contexts where climate intersects microbiology.

Key capabilities:

• Time-series and probabilistic forecasting layered with deep learning and graph-inspired atmospheric models
• Climate-informed renewable yield and planning analytics grounded in realistic variability assumptions
• Electricity market–aware modeling for price, dispatch, and volatility-aware decision support
• Scenario-centric tooling for decarbonization pathways and resilient infrastructure planning

The collection frames sustainability challenges as forecasting and operations problems—pairing disciplined data governance with models stakeholders can inspect and extend.