Hire the Best Genetic Algorithms Specialists

I'm a Top Rated freelancer and Computer Science graduate who works at the intersection of three fields most people treat separately: Statistics & Machine Learning, Bioinformatics, and AI. That combination is exactly what messy, high-dimensional data needs — the statistical rigor to trust the result, the ML to find the pattern, and the biological context to know what it means. With 3+ years of experience and 24 successful projects (4.98/5 avg. rating), here's what I bring: 📊 Statistics & Machine Learning Rigorous statistical inference and predictive modeling — hypothesis testing, causal inference (Mendelian Randomization), feature engineering, and ensemble models (XGBoost, LightGBM, Random Forest). I don't just build models that score well; I build models you can defend. Example: a coronary artery disease prediction model reaching 0.956 AUC, with feature ablation to prove what actually drives it. 🧬 Bioinformatics & Omics End-to-end analysis of complex biological data — scRNA-seq, RNA-seq, WGS/WES — using reproducible pipelines (Nextflow, Snakemake). From raw reads to normalized matrices to differential expression and biomarker discovery. Example: cut analysis time by 40% on large genomic projects through custom automated pipelines. 🤖 AI & Deep Learning Deep learning frameworks for real diagnostic problems — computer vision, medical image analysis, NLP, and LLM-based tools. ✨ What ties it together Most freelancers do one of these. I connect them — applying AI and ML to biological and clinical data with statistical discipline, then communicating the findings in publication-ready visuals that both scientists and stakeholders can act on. 🛠️ Tech Stack Python (Pandas, Scikit-learn, TensorFlow, PyTorch) · R (Tidyverse, Bioconductor) · Bash · SQL · Linux · Git · Docker · Cloud 💡 Ready to turn your raw data into discoveries you can trust? Let's talk.

I help research teams and technical founders build and validate reliable, testable scientific and quantitative systems — research prototypes, backtesting frameworks, production execution systems, simulation engines, and performance-critical pipelines. I also conduct independent technical review of quantitative models and research frameworks. Whether you are architecting a new system from the ground up, battling computational bottlenecks, auditing an unpredictable model, or scaling a fragile prototype into production, I can work with you or your team to engineer a robust, high-performance solution. Background: PhD Physics (NYU, 2018). Quantitative Research at JPMorgan Chase — derivatives pricing on a 1M+ LOC C++ library. Max Planck Institute postdoc — general-relativistic hydrodynamics on 1,000+ core HPC clusters. 13 peer-reviewed publications, 1,300+ citations, h-index 12. WHAT I DELIVER ▸ Quant & Options Engineering Backtesting frameworks (event-driven or vectorized), walk-forward, leakage checks Options analytics: Greeks, IV surfaces, Black–Scholes and numerical methods Research → production pipelines (clean architecture, tests, logging, monitoring) Execution integrations (e.g., IBKR) and robust order / risk handling Market data ingestion, cleaning, corporate actions, quality control ML and statistical time-series models with proper cross-validation (no leakage) ▸ Scientific Computing & Research Tooling For physics, chemistry, biology, engineering, and any domain where the core problem is mathematical or computational. Custom numerical solvers (finite volume / finite difference, spectral, particle methods) with stability and convergence analysis Optimization engines (Bayesian, gradient-based, evolutionary) for experimental design, formulation, and parameter search Simulation frameworks from prototype to production grade Scientific data pipelines: ingestion, transformation, quality control, reproducible workflows Verification and validation: benchmarks, unit / regression tests, convergence studies Analysis tools, dashboards, and reporting infrastructure for research workflows Air-gapped and reproducible deployments where IP sensitivity or regulatory context requires it ▸ Quantitative & Mathematical Review (NDA-protected) Independent technical review of quantitative models, frameworks, and research Verification of internal consistency, identifiability, hidden assumptions, and mathematical correctness Assessment of whether the formal structure supports the conclusions drawn from it Implementation review against specification: numerical stability, edge cases, code-to-spec fidelity ▸ HPC & Performance Engineering Distributed computing (MPI / OpenMP / CUDA), GPU optimization, memory and I/O tuning Inference and training infrastructure at scale Profiling, refactors, and speedups for codebases that need to run reliably under production load WHY CLIENTS WORK WITH ME - Trustworthy work — research prototypes turned into tested, reproducible production code; models reviewed against their own claims - De-risking — failure modes surfaced early (leakage, overfitting, edge cases, scaling bottlenecks) - Maintainability — clean architecture, docs, handover-ready delivery your team can extend - Communication — clear milestones, concise updates, realistic timelines, no surprises - Math ↔ engineering bridge — strong intuition for both theory and implementation IDEAL PROJECTS - Quant strategy development, backtesting, and research infrastructure - Options analytics and derivatives tooling - Mathematical review of quantitative manuscripts, white papers, or research frameworks - Independent validation of production models against specification - Market data pipelines and reproducibility upgrades - Performance optimization of slow Python / C++ codebases - Distributed training, GPU optimization, and inference serving for ML workloads - Custom scientific or industrial simulation and numerical software - Internal R&D tooling for research labs and technical teams If this sounds like a fit, message me with a brief on your current setup and success criteria — I'll let you know how I can help.

**************Genomics & Informatics Lab (GIL) ***************** **************Your Trusted Partner in Advanced Bioinformatics & AI-Driven Multi-Omics****** I lead a distinguished team at Genomics & Informatics Lab (GIL), specializing in Biotechnology, Bioinformatics, Computational Biology, and AI-powered Multi-Omics. At GIL, we offer a comprehensive suite of cutting-edge services tailored to meet the evolving demands of genomics, transcriptomics, proteomics, metagenomics, pharmacogenomics, and precision medicine. Our Expertise: AI-Powered Bioinformatics & Multi-Omics Data Analysis 🔬 1. Bioinformatics & Genomics: AI/ML-powered Next-Generation Sequencing (NGS) data analysis Comparative & Population Genomics for evolutionary insights Genetic variant detection & annotation Functional Genomics (Gene Ontology, KEGG Pathway analysis) Phylogenetics & Evolutionary Genomics miRNA structure prediction & target analysis 🧬 2. AI-Driven Transcriptomics & Epigenomics: RNA-Seq & Single-Cell Transcriptomics Differential Expression & Alternative Splicing Analysis Epigenomics: DNA Methylation & Histone Modification Analysis 💊 3. Computational Pharmacogenomics & Precision Medicine: AI-assisted drug-gene interaction prediction Pharmacogenomic modeling for personalized medicine Toxicogenomics & Drug Response Prediction 🦠 4. Metagenomics & Microbiome Data Science: Whole-genome & 16S rRNA sequencing-based microbiome analysis Microbiome-host interaction modeling AI-powered taxonomic & functional profiling ⚛ 5. AI in Structural & Systems Biology: Protein-Protein Interaction Analysis & Docking Molecular Dynamics (MD) & Simulation Studies AI-driven protein structure prediction (AlphaFold, Rosetta, etc.) 🤖 6. AI & Machine Learning in Multi-Omics Integration: Deep learning for biomarker discovery Multi-omics data integration (Genomics, Proteomics, Metabolomics, Epigenomics) Network-based systems biology approaches 🚀 State-of-the-Art Computing Infrastructure GIL is equipped with high-performance computing (HPC) clusters, multi-core processing, and cloud-based analytics, ensuring scalable, fast, and accurate bioinformatics solutions. 💡 Why Choose GIL? ✅ Expert Team: Decades of experience in computational biology & AI ✅ Cutting-Edge Technologies: AI, ML, and HPC-powered analytics ✅ Proven Track Record: Successful projects in academia & industry ✅ Custom Solutions: Tailored pipelines for diverse research needs Let GIL be your trusted partner in advancing your research! Contact us today to explore how we can support your next breakthrough in computational biology and AI-driven bioinformatics. 🔍💻🧬

Research, development and consulting in projects involving Artificial Intelligence, Neural networks, Statistics, Computer vision, Signal and Image processing, Operation research, and Algorithm design. Ph.D. in Computer Science and Applied Mathematics

Ph.D. AI Researcher Deep Learning & Generative AI | Bioinformatics, Energy & Environmental Science I am a Ph.D. researcher specializing in the application of Artificial Intelligence (AI) and Computer Science across bioinformatics, environmental science, and energy systems. My work focuses on leveraging Machine Learning (ML), Deep Learning (DL), Large Language Models (LLMs), and Generative AI to address complex, real-world scientific challenges. My research includes: Protein–peptide interaction modeling and binding site prediction Molecular modeling and computational biology Environmental monitoring and data-driven sustainability solutions Energy system optimization and intelligent forecasting I aim to develop interpretable, scalable, and data-driven AI frameworks that bridge scientific research and practical applications in healthcare, sustainability, and clean energy. Core Disciplines Artificial Intelligence Bioinformatics Environmental Engineering Materials & Energy Engineering

I have experience in: -Research in numerical solution of variety of nonlinear equations. -Statistical analysis of data. -Scientific Computing Machine Learning. -Optimization (Linear, Mixed, Heuristics). Most of my work was academical research in my PhD years (nonlinear dynamics in quantum and classical systems), but I also worked as a freelancer with several Machine Learning projects both in Scientific Computing as in Generative AI. I also have experience in a variety of Optimization problems (With heuristics, or exact solutions). Nowadays I do research in the place where Machine Learning mixes with Natural Evolution.