Hire the Best Genetic Algorithms Specialists

I help research teams and technical founders build reliable, testable scientific and quantitative software — from research prototypes and backtesting frameworks to production-grade execution systems, simulation engines, and performance optimization. I also conduct independent technical review of quantitative models, frameworks, and research. If you're dealing with slow code, messy data, a model you can't trust, or a research prototype that needs to become production, I can turn it into a robust, reproducible system. Credibility: PhD Physics (NYU, 2018). Quantitative Research at JPMorgan Chase (derivative pricing, 1M+ LOC C++ library). Max Planck Institute postdoc (general relativistic hydrodynamics simulations on HPC clusters, 1000+ cores). 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 / 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 handling, quality control checks ML and statistical models for time-series signals (proper time-series CV, no leakage) ▸ Scientific Computing & Research Tooling Custom software and research tooling for research-driven teams across physics, chemistry, biology, engineering and beyond — where the underlying problem is mathematical or computational. - Custom numerical solvers (finite volume / finite difference, spectral methods, particle methods), with stability and convergence analysis - Optimization engines (Bayesian optimization, gradient-based, evolutionary) for experimental design, formulation, and parameter search - Simulation frameworks for physical, chemical, and biological systems — from prototype to production-grade - Scientific data pipelines: ingestion, transformation, quality control, reproducible workflows - Verification and validation: benchmarks, unit tests, 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 Review of implementation against specification: numerical stability, edge cases, and code-to-specification fidelity ▸ HPC & Performance Engineering Performance engineering for large-scale scientific and ML workloads Distributed computing (MPI/OpenMP/CUDA), GPU optimization, memory and I/O tuning, parallel architectures, 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 engineering: I turn research prototypes into tested, reproducible production code - De-risking: I find failure modes early (leakage, overfitting, edge cases, scaling bottlenecks) - Maintainability: clean architecture, docs, and 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 details 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 or implementations against specification - Market data pipeline and reproducibility upgrades - Performance optimization of slow Python/C++ codebases - Distributed training, GPU optimization, and inference serving for AI/ML workloads - Custom scientific or industrialized simulation or numerical software - Custom internal R&D tools for research labs and technical teams If this sounds like a fit, message me with a detailed brief with your current stack and success criteria — I'll let you know how I can help.

I am a Top Rated freelancer and Computer Science graduate bridging the gap between Biology and Data Science. I help organizations and researchers unlock hidden patterns in high-dimensional datasets (Omics, Clinical, Image data) using advanced Machine Learning and Statistical Inference. With 3+ years of experience and 24 successful projects (4.98/5 avg. rating), I specialize in: 🧬 High-Dimensional Data Analysis: Processing and normalizing massive datasets (e.g., scRNA-seq, DNA-seq) using robust pipelines to identify critical biomarkers and trends. 🤖 Machine Learning & Computer Vision: Developing predictive models and deep learning frameworks (CNNs, Transfer Learning) to solve complex diagnostic problems (e.g., Disease Classification). 📊 Statistical Modeling & Inference: Applying rigorous statistical methods to validate hypotheses and extract meaningful insights from noisy real-world data. 📈 Data Visualization & Storytelling: Creating publication-ready dashboards and plots (R/ggplot2, Python/Seaborn) to communicate findings to technical and non-technical stakeholders. ✨ Key Impact & Achievements: Computer Vision: Achieved 99% accuracy in diagnosing 5 eye diseases from retinal images by developing a Deep Learning framework (ResNet50, VGG19). Process Optimization: Reduced analysis time by 40% for large-scale genomic projects by engineering custom automated pipelines (Snakemake/Nextflow). Mentorship: Mentored researchers in computational biology and data analysis best practices. 🛠️ Tech Stack: Languages: Python (Pandas, Scikit-learn, TensorFlow), R (Tidyverse, Bioconductor), Bash, SQL. Workflow & Tools: Linux, Git, Docker, Cloud Computing. 💡 Ready to transform your raw data into discoveries? Let's talk.

**************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. 🔍💻🧬

I'm a PhD engineer who helps businesses automate complex processes and extract insights from their data using Python. With a research background in robotics and control systems, I bring a systematic, analytical approach to solving technical challenges. I specialize in: Python Development - Building efficient scripts and applications for data processing, automation, and analysis Data Analysis - Transforming messy data into actionable insights using pandas, NumPy, and visualization tools Algorithm Development - Creating optimized solutions for complex computational problems Automation - Turning repetitive manual tasks into reliable automated workflows Technical Expertise - System integration, coordinate transformations, and sensor calibration from robotics PhD My experience includes developing software for a DARPA robotics competition (where our team won 3rd place), building real-time control systems, and processing large-scale sensor data. I've worked with everything from embedded systems to 100GB+ datasets. I'm passionate about writing clean, well-documented code that solves real business problems. Whether you need to automate a workflow, analyze your data, or optimize an algorithm, I'd love to help. Currently accepting new projects with quick turnaround times. Let's discuss how I can help streamline your technical challenges.

I work on genomics and biology projects, the kind where you have raw sequencing data, mass spec output, or protein sequences and you need someone to turn it into results you can publish or act on. I've done this across more than 67 projects on Upwork and through my PhD and postdoc, so I know what a clean analysis looks like and I know the shortcuts that cause problems later. Quick background: Actually postdoc in population genetics. PhD in bioinformatics and molecular evolution at Paris-Saclay (CNRS), where I worked on plant-bacteria interactions. Master's in bioinformatics from Sorbonne, and bachelor's in computer science from Descartes. I've been programming for over 10 years, mostly Python, R, and Bash, though I can work in Java, C, or JavaScript if a project calls for it. Eight years of building data pipelines specifically. What I do: Genomics and population genetics. GWAS, co-GWAS (PLINK2, Firth regression), population structure analysis (PCA, ADMIXTURE, pairwise FST), ancient DNA work (qpAdm, qpWave, ADMIXTOOLS2), variant calling and annotation. I've run these on human, plant, pathogen, and ancient samples. Genome assembly and annotation: De novo assembly from Nanopore or Illumina, gene prediction, functional annotation, genome comparison. Bacteria, plants, insects, human. I've assembled all of them at some point. RNA-seq: Differential expression from raw reads, co-expression networks, pathway enrichment. Full pipeline or just the analysis part, depending on what you need. Structural bioinformatics: AlphaFold2 batch runs, FoldSeek for structural comparisons, molecular docking with ClusPro, AutoDock Vina, or HADDOCK. I do the visualization too (PyMOL, ChimeraX), and create publication-ready figures, not just screenshots. Phylogenetics and molecular evolution: IQ-TREE2, RAxML, selection tests, gene family evolution, protein evolution. Proteomics and lipidomics: Mass spec data processing through to statistical analysis and biological interpretation. Pipelines: I build workflows, documented and tested with example data. Your team should be able to rerun them without me. Writing and code review: Methods sections, technical reports, manuscript figures. I also debug and review existing pipelines. If your code or script doesn't work, I'll fix it and tell you why it broke. Everything comes with commented code and R Markdown or Jupyter reports. Figures are made in ggplot2 or matplotlib and ready for submission. 100% Job Success score across 65+ projects. I reply the same day. "If you need a bioinformatics expert who's not only insanely skilled but also great to work with, Dr. Amira is the one. Would 100% work with her again!" (Client) "Amira delivered a thorough and well-structured technical review, including worked examples and code snippets that were very useful for demonstrating enablement. Her analysis was detailed, thoughtful, and delivered on time. I highly recommend her for bioinformatics and technical review projects." (Recent client) Drop me a message with your data and what you're trying to do. I'll get back to you with a plan and timeline.

I have Expert-Vetted Talent (EVT) badge - it's Upwork's top 1% freelancers — pre-screened by Upwork Talent Managers and experts in their field. Out of ~30 million programmers worldwide, only a few thousand know Algorithms & Data Structures better than I do, which is proven by programming competitions. Please, contact me if you need that skill level (top 0.01%). I can do algorithmic/performance work in C/C++, Python, SQL, Java, MQL4, MQL5, C#, Assembly, JavaScript, Julia, Rust, and probably other languages - learning them rapidly. I also work with AI, mostly in NLP and NLU: large language models including OpenAI GPT-3, Bloom, BloomZ, GPT-J 6B, LLaMA, Alpaca, etc.; HuggingFace Transformers, Accelerate; Petals, Deepspeed, zfp/zfpy; CUDA, CPU, and MPS (AArch 64 M2 MacOS Metal GPU) backends. I have some work experience with Apple Neural Engine (ANE). In AI, I also worked with XGBoost for predictions (including trading), LibSVM, TensorFlow, PyTorch, Scikit-learn, etc. I have the hardware in my home office for training and inference with large language models and other AI. English: C1 (Grammarly plugin says I use more unique words than 95% of other users, native speakers included). Polish: B1 (86%). Russian, Belarusian: Native. - With unique skills in Algorithms & Data Structures, I improve programs asymptotically (often 100 or more times on large input data). - 29 years of programming (started Basic and assembler on ZX Spectrum), 24 years of C/C++, 16 years of commercial work experience + 3 years of research projects. - Contributed to widely used Open Source projects: LLVM/Clang (my contribution is XRay profiler on ARM32 and AArch64 systems), Katana Graph (multiple small contributions mostly driven by the proprietary part where I do GPU/CUDA), CBMC "C Bounds Model Checking" (I contributed parallelized output of DIMACS formatted Boolean Satisfiability formula), oatpp (C++ web framework, I contributed bug-fixes), OWL (OptiX Wrapper Library, I contributed build fixes for Ubuntu), Galois (research project for distributed computations on graphs, I contributed GPU improvements) - Actively participated in bug reporting and reproduction (for NVIDIA CUDA, Cadical&kissat boolean satisfiability solver, JBOSS, MariaDB, Tensorflow, Linux, etc) - Led several open-source projects of my own: ProbQA (a video game recommendation system based on a high-performance Bayesian inference engine with CUDA, SIMD, and multi-threading); InSoAr (automatic reconstruction of software architecture from source code ), a multi-threaded Boolean Satisfiability solver, etc. Working for hire, implemented: - efficient multi-threading, scaling real-world workloads almost linearly with the number of CPU cores (128x for AMD Ryzen Threadripper 3990X) - SIMD vectorization (SSE/AVX), up to 8x improvement in computing thread or even copying (see my "Faster alternatives to memcpy" answer on Stackoverflow, URL upon request). - cache-aware algorithms: up to 50x improvement on some workloads - up to 20 trillion operations/second in CUDA (thousands of times faster than CPU) - up to the theoretical limit (6.8 Gigarays/second on RTX 2080 laptop GPU) in ray-tracing with OWL and OptiX - AVX512 and RTM (Restricted Transactional Memory) based acceleration, 16x improvement for float numbers -up to 20x improvements to cryptocurrency miners on CPU using AVX512 and cache-friendly algorithms - crypto-miners for Ethereum, Bittensor, Qubic 15K reputation on Stackoverflow: (1915854/serge-rogatch) Topcoder SRM score: 1480 - among top 5K programmers in the world - top 0.02% (rSerge) I developed all kinds of networking applications, from Linux Kernel modules up to Web applications. The majority of work was, of course, done at TCP/IP level with socket calls like send/recv/select. Programming languages: C++, C++11/14/17/20, C, Python, x86/x64/ARM/AArch64 assembly, SQL, C# .NET, JavaScript, HTML, CSS, Java, MQL4, MQL5, XML, Cypher, Rust. Libraries/Frameworks: PyTorch, Tensorflow, HuggingFace Transformers/Accelerate/Safetensors, Hivemind/Petals, OpenAI, tiktoken, Django, Flask, STL, LibSVM, XGBoost, libcurl, Selenium, PyTorch, Transformers. Technologies: OpenMP, CUDA, SIMD (AVX&SSE, RTM), Linux Kernel Modules, OptiX/OWL, RTX, raytracing. Theory/Principles/Know-how/Methodologies: Algorithms & Data Structures, Performance Optimization, Artificial Intelligence, Multithreading, Vectorization, OOP, Low-latency, High-frequency, Blockchain. Open source code: Clang, LLVM, LLVM's compiler-rt library, Linux Kernel, a few of my own repositories, contributions to AI and Algorithm open-source projects such as Petals and CBMC. Tools/APIs/Architectures/Platforms: PostgreSQL, MSSQL, MySQL, Neo4j, MATLAB, CMake, GIT, MT4, MetaTrader 5, Conda, PyCharm. Virtual Machines / Containers: Docker, VMWare, VirtualBox, QEmu, Hyper-V. OSes: Windows, Linux, Android, macOS