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resume.json
{
"$schema": "https://raw.githubusercontent.com/jsonresume/resume-schema/v1.0.0/schema.json",
"basics": {
"name": "Wyatt Walsh",
"label": "Data Integrations Software Engineer | AI, Data, Software, & Optimization | JPMorgan Chase & Co.",
"image": "https://i.ibb.co/FLtKMt6p/avatar-min.webp",
"email": "[email protected]",
"phone": "(209) 602-2545",
"url": "https://www.w4w.dev",
"summary": "Accomplished Senior Software Engineer and data engineering specialist bridging advanced operations research with AI-driven optimization strategies. Drawing on extensive experience at JPMorgan Chase & Co., I architect compliance-centric, real-time data infrastructures that empower critical enterprise decision-making. Skilled in synthesizing theoretical constructs into robust, high-availability software pipelines, I have led cross-functional teams, implemented fault-tolerant frameworks, and championed leading-edge AI solutions. My enduring commitment to research-driven innovation and stringent regulatory adherence enables the creation of sustainable, high-impact technologies.",
"location": {
"address": "New York City, NY, USA",
"postalCode": "11101",
"city": "New York City",
"countryCode": "US",
"region": "New York"
},
"profiles": [
{
"network": "LinkedIn",
"username": "wyattowalsh",
"url": "https://linkedin.com/in/wyattowalsh"
},
{
"network": "X (Twitter)",
"username": "wyattowalsh",
"url": "https://x.com/wyattowalsh"
},
{
"network": "GitHub",
"username": "wyattowalsh",
"url": "https://github.com/wyattowalsh"
},
{
"network": "Website",
"username": "w4w.dev",
"url": "https://www.w4w.dev"
}
]
},
"work": [
{
"name": "JPMorgan Chase & Co.",
"position": "Associate III, Senior Software Engineer",
"startDate": "2023-05",
"summary": "Provides strategic leadership within Commercial & Investment Banking (CIB) credit technology, orchestrating fault-tolerant, high-throughput data pipelines that disseminate mission-critical market intelligence to traders, quantitative analysts, and risk teams.",
"highlights": [
"Architected real-time data ingestion pipelines with AWS, Apache Kafka, and Amazon S3, significantly enhancing data availability and reducing operational latency",
"Optimized database schema designs and SQL-based solutions, bolstering query performance and simplifying downstream data migrations",
"Engineered a machine-learning–driven anomaly detection framework for ingestion volumes, facilitating automated alerts and minimizing production downtime",
"Coordinated cross-functional collaboration among quantitative researchers, bankers, and global technology teams, ensuring continuous alignment with evolving regulatory standards",
"Promoted institutional AI adoption by delivering advanced training sessions for ~100 personnel, focusing on specialized VS Code configurations, prompt engineering, and AI automation strategies",
"Developed specialized data extraction utilities leveraging Python, Pandas, SQLAlchemy, KDB+, and Oracle SQL, expediting retrieval of complex financial PDF data"
],
"url": "https://www.jpmorganchase.com/",
"location": "New York City, NY, USA"
},
{
"name": "Stealth Web3 Startup",
"position": "Senior Software Engineer",
"startDate": "2022-03",
"endDate": "2022-07",
"summary": "Oversaw front-end engineering for a DeFi NFT DAO platform supporting NFT-collateralized cryptocurrency loans, facilitating a high-performance interface for intricate blockchain transactions.",
"highlights": [
"Architected React/Next.js applications integrating Redux, TypeScript, and SCSS, thereby improving DeFi transaction throughput and user experience",
"Adopted agile development methodologies (Jira, Notion), enabling rapid feature iteration in a highly dynamic startup environment",
"Instituted a test-driven development workflow with Cypress, Jest, and StorybookJS, enhancing code reliability and mitigating regressions",
"Employed ethers.js for secure blockchain interactions, ensuring streamlined DeFi smart contract operations",
"Mentored two junior developers, accelerating technical growth and advancing project deliverables"
],
"url": "",
"location": "Remote"
},
{
"name": "SandLabs",
"position": "Chief Technology Officer",
"startDate": "2021-06",
"endDate": "2022-03",
"summary": "Founded and directed the technical vision for a Web3 enterprise focusing on decentralized data solutions, overseeing product development lifecycles and integrating conventional content ecosystems with blockchain-based architectures.",
"highlights": [
"Secured $75K in Web3 grant funding from Ocean Protocol to advance a large-scale Reddit Web3 data scraping initiative",
"Designed and led the development of a comprehensive Next.js platform (TypeScript), featuring a landing page, blog, and integrated content portal",
"Forged strategic alliances with Duke University co-founders and prominent Web3 influencers, elevating project visibility",
"Oversaw multi-million-post data scraping in Python, configured Solidity smart contracts, and implemented React TypeScript solutions"
],
"url": "https://www.linkedin.com/company/sandlabsdata/",
"location": "Remote"
},
{
"name": "Gap Inc.",
"position": "Demand Forecasting",
"startDate": "2020-01",
"endDate": "2020-05",
"summary": "Propelled data science initiatives to modernize Gap Inc.’s demand forecasting methodologies, elevating inventory precision and fortifying supply chain optimization across multiple retail segments.",
"highlights": [
"Refined forecasting accuracy through advanced data science models, substantially reducing potential inventory holding costs across five Gap Inc. divisions",
"Delivered data-driven insights to senior executives, guiding strategic supply chain realignments and fostering cross-departmental collaboration",
"Managed over five years of time-series data (~10TB) in MySQL and Apache Spark, supporting scalable forecasts for 50,000+ SKUs"
],
"url": "https://www.gapinc.com/",
"location": "San Francisco, CA, USA"
},
{
"name": "University of California, Berkeley",
"position": "Course Staff – Data 8: Foundations of Data Science",
"startDate": "2019-01",
"endDate": "2019-12",
"summary": "Executed core instructional duties for UC Berkeley’s largest data science course, translating advanced theoretical frameworks into accessible content for a diverse undergraduate audience.",
"highlights": [
"Taught 1,600+ students in foundational statistics, programming, and data analysis, consistently receiving superior evaluations",
"Led over 75 interactive lectures and labs on Python and introductory statistics, enhancing student comprehension",
"Coordinated critical course logistics (office hours, proctoring, grading), ensuring operational continuity for a large-scale academic program"
],
"url": "https://data.berkeley.edu/education/courses/data-8",
"location": "Berkeley, CA, USA"
}
],
"education": [
{
"institution": "UC Berkeley College of Engineering",
"area": "Industrial Engineering and Operations Research",
"studyType": "Bachelor of Science",
"startDate": "2014-08",
"endDate": "2021-12",
"score": "",
"courses": [
"CIVENG 11 - Engineered Systems and Sustainability",
"INDENG 169 - Integer Optimization",
"INDENG 173 - Introduction to Stochastic Processes",
"INDENG 153 - Logistics Network Design and Supply Chain Management",
"Math 54 - Linear Algebra and Differential Equations",
"Data 8 - Foundations of Data Science",
"Chem 1A - General Chemistry",
"Stat 20 - Introduction to Probability and Statistics",
"UGBA 152 - Negotiation and Conflict Resolution",
"INDENG 162 - Linear Programming and Network Flows",
"INDENG 166 - Decision Analytics",
"INDENG 165 - Engineering Statistics, Quality Control, and Forecasting",
"UGBA 105 - Leading People",
"Engin 7 - Introduction to Computer Programming for Scientists and Engineers",
"CS 61B - Data Structures and Programming Methodology",
"INDENG 142 - Introduction to Machine Learning and Data Analytics",
"INDENG 170 - Industrial Design and Human Factors",
"Math 53 - Multivariable Calculus",
"UGBA 101A - Microeconomic Analysis for Business Decisions",
"Math 1A/1B - Calculus"
],
"url": "https://ieor.berkeley.edu/undergraduate-resources/bachelor-of-science-b-s-industrial-engineering-operations-research/"
},
{
"institution": "The Hotchkiss School",
"area": "College Preparatory Education",
"studyType": "High School Diploma",
"startDate": "2012-12",
"endDate": "2014-12",
"score": "",
"courses": [],
"url": "https://hotchkiss.org/"
}
],
"awards": [
{
"title": "Eagle Scout",
"date": "2011-10",
"awarder": "Boy Scouts of America",
"summary": "Attained the highest rank in the BSA, showcasing leadership, project oversight, and community service. Led construction of a quarter-mile nature trail, completing over 200 volunteer hours."
}
],
"certificates": [
{
"name": "AWS Certified Cloud Practitioner",
"issuer": "Amazon Web Services (AWS)",
"date": "2024-11",
"url": "https://www.credly.com/badges/085279f9-3b10-4dbe-9bfa-3669dcdd3779/public_url"
},
{
"name": "IBM Data Science Professional Certificate",
"issuer": "IBM",
"date": "2021-02",
"url": "https://www.coursera.org/account/accomplishments/specialization/certificate/Z4Y8HHXREH65"
},
{
"name": "Machine Learning with Python",
"issuer": "Coursera",
"date": "2021-02",
"url": "https://www.coursera.org/account/accomplishments/certificate/7T6T27APMABD"
},
{
"name": "Python for Data Science and AI",
"issuer": "Coursera",
"date": "2021-02",
"url": "https://www.coursera.org/account/accomplishments/certificate/QQVENTSY2BX3"
},
{
"name": "Databases and SQL for Data Science",
"issuer": "Coursera",
"date": "2021-02",
"url": "https://www.coursera.org/account/accomplishments/certificate/EXDWQMTF6GQV"
},
{
"name": "Tools for Data Science",
"issuer": "Coursera",
"date": "2021-02",
"url": "https://www.coursera.org/account/accomplishments/certificate/Q8PG5MFR93NA"
},
{
"name": "Getting Started with AWS Machine Learning",
"issuer": "Coursera",
"date": "2021-02",
"url": "https://www.coursera.org/account/accomplishments/certificate/8QX3HEXSU428"
}
],
"publications": [
{
"name": "Basics of Linear Regression Modeling and Ordinary Least Squares (OLS)",
"publisher": "Towards Data Science",
"releaseDate": "2021-01",
"url": "https://medium.com/towards-data-science/regularized-linear-regression-models-57bbdce90a8c",
"summary": "Part one of a three-part linear regression series, elucidating OLS estimators, model assumptions, and inferential statistics, supplemented by rigorous mathematical proofs and Python demonstrations."
},
{
"name": "Using Ridge Regression to Overcome Drawbacks of Ordinary Least Squares (OLS)",
"publisher": "Towards Data Science",
"releaseDate": "2021-01",
"url": "https://medium.com/towards-data-science/regularized-linear-regression-models-44572e79a1b5",
"summary": "Part two of the series, examining L2 regularization (Ridge) to address overfitting and multicollinearity, with in-depth analysis of the geometry and efficacy of Ridge estimators."
},
{
"name": "Implementing Pairwise Coordinate Descent For The Lasso and The Elastic Net In Python Using NumPy",
"publisher": "Towards Data Science",
"releaseDate": "2021-01",
"url": "https://medium.com/towards-data-science/regularized-linear-regression-models-dcf5aa662ab9",
"summary": "The concluding installment detailing a pairwise coordinate descent approach for Lasso and Elastic Net, offering step-by-step derivations and Python implementations aligned with scikit-learn standards."
}
],
"skills": [
{
"name": "Software Engineering",
"keywords": [
"Python",
"TypeScript",
"JavaScript",
"Java",
"C++",
"Rust",
"Go",
"Ruby",
"Git",
"GitHub Actions",
"Pytest",
"Jest",
"Cypress",
"ESLint",
"Black",
"Ruff",
"Mypy",
"Vim",
"VS Code",
"Design Patterns",
"Clean Code",
"TDD",
"Agile",
"CI/CD",
"Microservices",
"Scrum"
]
},
{
"name": "Python",
"keywords": [
"Software Engineering",
"Machine Learning",
"Data Engineering",
"Statistical Analysis",
"Operations Research",
"DevOps & CI/CD",
"Flask",
"Django",
"FastAPI",
"Kedro",
"dagster",
"dbt",
"PySpark",
"TensorFlow",
"PyTorch",
"Scikit-learn",
"XGBoost",
"LightGBM",
"CatBoost",
"Fast.ai",
"Keras",
"Hugging Face Transformers",
"OpenAI API",
"NLTK",
"spaCy",
"Gensim",
"Matplotlib",
"Plotly",
"Seaborn",
"Dash",
"Bokeh",
"pandas",
"NumPy",
"Statsmodels",
"SciPy",
"PyMC",
"JAX",
"NumPyro",
"Optuna",
"Hyperopt",
"Ruff",
"Mypy",
"Black",
"Pytest",
"Requests",
"Scrapy",
"BeautifulSoup",
"Selenium",
"MechanicalSoup",
"Spotipy",
"PyAutoGUI",
"pyppeteer",
"PRAW",
"Cython",
"Poetry",
"Anaconda",
"Time Series Analysis",
"Algorithmic Trading",
"Risk Management",
"Quantitative Finance Engineering"
]
},
{
"name": "TypeScript",
"keywords": [
"Software Engineering",
"JavaScript",
"Node.js",
"Express.js",
"NestJS",
"tRPC",
"GraphQL",
"Prisma",
"TypeORM",
"Puppeteer",
"Crawlee",
"Apify",
"ScrapingBee",
"ScraperAPI",
"Playwright",
"Socket.io",
"WebSockets",
"WebRTC",
"D3.js",
"Vega-Lite",
"Chart.js",
"Observable",
"Highcharts",
"REST",
"API Gateway",
"JWT",
"OAuth",
"Swagger/OpenAPI",
"Next.js",
"React",
"Redux",
"DevOps & CI/CD"
]
},
{
"name": "JavaScript",
"keywords": [
"Software Engineering",
"Node.js",
"Express.js",
"GraphQL",
"Socket.io",
"WebSockets",
"WebRTC",
"Puppeteer",
"D3.js",
"Chart.js",
"Observable",
"Highcharts",
"Crawlee",
"Apify",
"ScrapingBee",
"ScraperAPI",
"Frontend Development",
"Vanilla JS",
"API Development",
"DevOps & CI/CD",
"REST"
]
},
{
"name": "Java",
"keywords": [
"Software Engineering",
"OptaPlanner",
"Spring Boot",
"Microservices",
"JVM",
"Operations Research"
]
},
{
"name": "C++",
"keywords": [
"Software Engineering",
"Systems Programming",
"High-Performance Computing",
"Embedded Systems",
"Memory Management"
]
},
{
"name": "Rust",
"keywords": [
"Software Engineering",
"Memory Safety",
"Systems Programming",
"Foundry",
"Blockchain & Web3",
"Concurrency"
]
},
{
"name": "Go",
"keywords": [
"Software Engineering",
"Concurrency",
"Microservices",
"API Development",
"DevOps & CI/CD"
]
},
{
"name": "Ruby",
"keywords": [
"Software Engineering",
"Rails",
"Backend Development",
"Web Development"
]
},
{
"name": "Git",
"keywords": [
"Software Engineering",
"Version Control",
"Collaboration",
"Branching Strategies",
"Continuous Integration"
]
},
{
"name": "GitHub Actions",
"keywords": [
"Software Engineering",
"DevOps & CI/CD",
"Continuous Integration",
"Automation",
"Workflow Orchestration"
]
},
{
"name": "Pytest",
"keywords": [
"Software Engineering",
"Python",
"Testing",
"CI/CD",
"Automation"
]
},
{
"name": "Jest",
"keywords": [
"Software Engineering",
"JavaScript",
"TypeScript",
"Testing",
"CI/CD"
]
},
{
"name": "Cypress",
"keywords": [
"Software Engineering",
"JavaScript",
"TypeScript",
"E2E Testing",
"CI/CD"
]
},
{
"name": "ESLint",
"keywords": [
"Software Engineering",
"JavaScript",
"TypeScript",
"Linting",
"Code Quality"
]
},
{
"name": "Black",
"keywords": [
"Software Engineering",
"Python",
"Code Formatting",
"Linting",
"Code Quality"
]
},
{
"name": "Ruff",
"keywords": [
"Software Engineering",
"Python",
"Linting",
"Code Quality"
]
},
{
"name": "Mypy",
"keywords": [
"Software Engineering",
"Python",
"Static Type Checking",
"Code Quality"
]
},
{
"name": "VS Code",
"keywords": [
"Software Engineering",
"IDE",
"Extensions",
"Debugging",
"Productivity"
]
},
{
"name": "Design Patterns",
"keywords": [
"Software Engineering",
"Clean Code",
"Refactoring",
"SOLID",
"Reusable Code"
]
},
{
"name": "Clean Code",
"keywords": [
"Software Engineering",
"Best Practices",
"Readability",
"Maintainability",
"Refactoring"
]
},
{
"name": "TDD",
"keywords": [
"Software Engineering",
"Agile",
"Testing",
"Refactoring",
"Pytest",
"Jest",
"Cypress"
]
},
{
"name": "Artificial Intelligence",
"keywords": [
"LangChain",
"LlamaIndex",
"LangGraph",
"OpenAI API",
"Anthropic Claude API",
"Hugging Face Transformers",
"BERT",
"GPT",
"T5",
"Weaviate",
"Pinecone",
"Chroma",
"Sentence Transformers",
"ONNX",
"RAG",
"Fine-tuning",
"Prompt Engineering",
"Zero-shot Learning",
"AI & Cognitive Systems",
"Machine Learning"
]
},
{
"name": "LangChain",
"keywords": [
"Artificial Intelligence",
"Python",
"Large Language Models",
"Prompt Engineering"
]
},
{
"name": "LlamaIndex",
"keywords": [
"Artificial Intelligence",
"Python",
"Large Language Models",
"Retrieval"
]
},
{
"name": "LangGraph",
"keywords": [
"Artificial Intelligence",
"Python",
"Graph-based NLP",
"Prompt Engineering"
]
},
{
"name": "OpenAI API",
"keywords": [
"Artificial Intelligence",
"Python",
"Large Language Models",
"Generative AI"
]
},
{
"name": "Anthropic Claude API",
"keywords": [
"Artificial Intelligence",
"Prompt Engineering",
"Generative AI"
]
},
{
"name": "Hugging Face Transformers",
"keywords": [
"Artificial Intelligence",
"Python",
"NLP",
"Machine Learning"
]
},
{
"name": "BERT",
"keywords": [
"Artificial Intelligence",
"NLP",
"Transformer Architecture"
]
},
{
"name": "GPT",
"keywords": [
"Artificial Intelligence",
"NLP",
"Transformer Architecture"
]
},
{
"name": "T5",
"keywords": [
"Artificial Intelligence",
"NLP",
"Transformer Architecture"
]
},
{
"name": "Weaviate",
"keywords": [
"Artificial Intelligence",
"Database Systems",
"Vector Search",
"Semantic Search"
]
},
{
"name": "Pinecone",
"keywords": [
"Artificial Intelligence",
"Vector Database",
"Semantic Search"
]
},
{
"name": "Chroma",
"keywords": [
"Artificial Intelligence",
"Python",
"Vector Databases",
"Semantic Search"
]
},
{
"name": "Sentence Transformers",
"keywords": [
"Artificial Intelligence",
"NLP",
"Embedding Models"
]
},
{
"name": "ONNX",
"keywords": [
"Artificial Intelligence",
"Model Interoperability",
"Machine Learning"
]
},
{
"name": "RAG",
"keywords": [
"Artificial Intelligence",
"Retrieval-Augmented Generation",
"Prompt Engineering"
]
},
{
"name": "Fine-tuning",
"keywords": [
"Artificial Intelligence",
"Machine Learning",
"Transfer Learning"
]
},
{
"name": "Prompt Engineering",
"keywords": [
"Artificial Intelligence",
"NLP",
"Generative AI",
"LangChain"
]
},
{
"name": "Zero-shot Learning",
"keywords": [
"Artificial Intelligence",
"NLP",
"Machine Learning"
]
},
{
"name": "Machine Learning",
"keywords": [
"TensorFlow",
"PyTorch",
"Scikit-learn",
"XGBoost",
"LightGBM",
"CatBoost",
"Fast.ai",
"Keras",
"MLflow",
"Weights & Biases",
"Optuna",
"Hyperopt",
"DVC",
"H2O",
"Prophet",
"Neural Prophet",
"PyCaret",
"Artificial Intelligence",
"Statistical Analysis",
"Time Series Forecasting",
"Algorithmic Trading"
]
},
{
"name": "TensorFlow",
"keywords": [
"Machine Learning",
"Python",
"Deep Learning",
"Neural Networks"
]
},
{
"name": "PyTorch",
"keywords": [
"Machine Learning",
"Python",
"Deep Learning",
"Neural Networks"
]
},
{
"name": "Scikit-learn",
"keywords": [
"Machine Learning",
"Python",
"Data Preprocessing",
"Classification",
"Regression"
]
},
{
"name": "XGBoost",
"keywords": [
"Machine Learning",
"Python",
"Gradient Boosting",
"Time Series Forecasting"
]
},
{
"name": "LightGBM",
"keywords": [
"Machine Learning",
"Python",
"Gradient Boosting",
"Tabular Data"
]
},
{
"name": "CatBoost",
"keywords": [
"Machine Learning",
"Python",
"Gradient Boosting",
"Tabular Data"
]
},
{
"name": "Fast.ai",
"keywords": [
"Machine Learning",
"Python",
"Deep Learning",
"Course Framework"
]
},
{
"name": "Keras",
"keywords": [
"Machine Learning",
"Python",
"Neural Networks",
"High-level API"
]
},
{
"name": "MLflow",
"keywords": [
"Machine Learning",
"Python",
"Model Tracking",
"Experiment Management"
]
},
{
"name": "Weights & Biases",
"keywords": [
"Machine Learning",
"Python",
"Experiment Tracking",
"Hyperparameter Tuning"
]
},
{
"name": "Optuna",
"keywords": [
"Machine Learning",
"Python",
"Hyperparameter Optimization",
"Automation"
]
},
{
"name": "Hyperopt",
"keywords": [
"Machine Learning",
"Python",
"Hyperparameter Optimization",
"Bayesian Search"
]
},
{
"name": "DVC",
"keywords": [
"Machine Learning",
"Python",
"Data Version Control",
"Model Reproducibility"
]
},
{
"name": "H2O",
"keywords": [
"Machine Learning",
"Python",
"AutoML",
"Ensemble Methods"
]
},
{
"name": "Prophet",
"keywords": [
"Machine Learning",
"Python",
"Time Series",
"Forecasting"
]
},
{
"name": "Neural Prophet",
"keywords": [
"Machine Learning",
"Python",
"Time Series",
"Neural Networks"
]
},
{
"name": "PyCaret",
"keywords": [
"Machine Learning",
"Python",
"Low-code ML",
"AutoML"
]
},
{
"name": "Natural Language Processing",
"keywords": [
"spaCy",
"NLTK",
"Gensim",
"Tokenizers",
"Word2Vec",
"GloVe",
"Sentence-BERT",
"HuggingFace Datasets",
"BERTopic",
"Haystack",
"KeyBERT",
"Top2Vec",
"LexNLP",
"FastText",
"Flair",
"StanfordNLP",
"AllenNLP",
"Stanza",
"AI & Cognitive Systems",
"Transformers",
"Semantic Search"
]
},
{
"name": "spaCy",
"keywords": [
"Natural Language Processing",
"Python",
"Text Processing",
"NER"
]
},
{
"name": "NLTK",
"keywords": [
"Natural Language Processing",
"Python",
"Tokenization",
"Stemming",
"Lemmatization"
]
},
{
"name": "Gensim",
"keywords": [
"Natural Language Processing",
"Python",
"Topic Modeling",
"Word Embeddings"
]
},
{
"name": "Tokenizers",
"keywords": [
"Natural Language Processing",
"Python",
"Subword Units",
"Byte-Pair Encoding"
]
},
{
"name": "Word2Vec",
"keywords": [
"Natural Language Processing",
"Python",
"Embeddings",
"Vector Representations"
]
},
{
"name": "GloVe",
"keywords": [
"Natural Language Processing",
"Python",
"Embeddings",
"Vector Representations"
]
},
{
"name": "Sentence-BERT",
"keywords": [
"Natural Language Processing",
"Python",
"Sentence Embeddings",
"Transformer Models"
]
},
{
"name": "HuggingFace Datasets",
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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"IoT",
"Latency",
"Local Processing"
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{
"name": "Consensus Algorithms",
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"Distributed Systems Architecture",
"Raft",
"Paxos",
"Byzantine Fault Tolerance"
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{
"name": "Event-Driven Architecture",
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"Distributed Systems Architecture",
"Asynchronous",
"Message Queues",
"Microservices"
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"name": "Resilience Patterns",
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"Distributed Systems Architecture",
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"Replication",
"Partitioning",
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"name": "Quantitative Finance Engineering",
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"Portfolio Optimization",
"Algorithmic Trading",
"Risk Management Systems",
"High-Frequency Trading",
"Time Series Forecasting",
"Factor Modeling"
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{
"name": "Market Microstructure",
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"Quantitative Finance Engineering",
"Exchange Dynamics",
"Order Books",
"Bid-Ask Spread"
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{
"name": "Statistical Arbitrage",
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"Quantitative Finance Engineering",
"Pairs Trading",
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"name": "Portfolio Optimization",
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"Markowitz",
"CVaR",
"Multi-asset"
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"name": "Algorithmic Trading",
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"Market Data"
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"name": "High-Frequency Trading",
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"name": "Sports Analytics Innovation",
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"Pareto Front",
"Trade-off Analysis",
"Evolutionary Algorithms"
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"Advanced Optimization Methods",
"Uncertainty",
"Scenarios",
"Robustness"
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{
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"Advanced Optimization Methods",
"Genetic Algorithms",
"Simulated Annealing",
"Tabu Search"
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{
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"Logistics",
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"Distribution"
]
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{
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"Utility",
"Risk Preferences"
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"Wilderness Photography",
"Backcountry Hiking",
"Alpine Climbing",
"Mountain Biking",
"Trail Running Analytics",
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]
},
{
"name": "Navigation Systems",
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"Outdoor & Adventure Technology",
"GPS",
"Route Planning",
"Maps"
]
},
{
"name": "Adventure Sports Tracking",
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"Outdoor & Adventure Technology",
"Wearables",
"Sensors",
"Analytics"
]
},
{
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"Cameras",
"Composition",
"Landscape"
]
},
{
"name": "Backcountry Hiking",
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"Navigation",
"Safety",
"Long-distance"
]
},
{
"name": "Alpine Climbing",
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"Ropes",
"Anchors",
"High Altitude"
]
},
{
"name": "Mountain Biking",
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"Outdoor & Adventure Technology",
"Cycling",
"Trails",
"Suspension"
]
},
{
"name": "Trail Running Analytics",
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"Strava",
"GPS Data",
"Performance"
]
},
{
"name": "Outdoor IoT Applications",
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"Sensors",
"Edge Computing",
"Low Power"
]
},
{
"name": "Technology Venture Creation",
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"Startup Ecosystems",
"Product-Market Fit Methods",
"Lean Startup Methodology",
"Technical Due Diligence",
"Venture Funding Strategies",
"Growth Engineering",
"Business Model Innovation",
"Tech Incubator Development"
]
},
{
"name": "Startup Ecosystems",
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"Technology Venture Creation",
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"Mentoring",
"Investment"
]
},
{
"name": "Product-Market Fit Methods",
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"Technology Venture Creation",
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},
{
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"MVP",
"Build-Measure-Learn",
"Validation"
]
},
{
"name": "Technical Due Diligence",
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"Feasibility",
"Risk Assessment",
"Scalability"
]
},
{
"name": "Venture Funding Strategies",
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"Seed Capital",
"Series A/B/C",
"Pitch Deck"
]
},
{
"name": "Growth Engineering",
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"Technology Venture Creation",
"Growth Hacking",
"Metrics",
"Experiments"
]
},
{
"name": "Business Model Innovation",
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"Strategic Partnerships",
"Value Proposition"
]
},
{
"name": "Tech Incubator Development",
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"Funding"
]
},
{
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},
{
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},
{
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{
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{
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{
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"name": "Personal Website: w4w.dev",
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"Serverless Frameworks"
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"name": "Algorithmic Cryptocurrency Trading System",
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"Deep Learning",
"TensorFlow",
"PyTorch",
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{
"name": "NBA Analytics Pipeline",
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"Curated a publicly available NBA database (SQLite) on Kaggle, normalizing historical data from 1946 to the present",
"Executed cost-free ETL pipelines via GitHub Actions and Kaggle, ensuring weekly updates without infrastructure overhead",
"Utilized containerized Python microservices under Docker, boosting pipeline reliability and throughput",
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"SQLite",
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"Kaggle",
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"name": "Regularized Linear Regression Deep Dive",
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"Built a benchmark dataset contrasting various fairness paradigms, serving as a standardized reference for subsequent studies",
"Published findings on computational trade-offs in envy-free allocation, illuminating implications for policy and mechanism design"
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"highlights": [
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"Engineered custom firmware and optimization algorithms to minimize latency in feedback loops over cellular networks",
"Iterated multiple prototypes addressing signal stability, power constraints, and aerodynamic efficiency",
"Implemented AR overlays for real-time telemetry, heightening operator situational awareness beyond line-of-sight"
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"Embedded Systems",
"Cellular IoT",
"4G Connectivity",
"Augmented Reality",
"Real-time Control Systems",
"Hardware Prototyping",
"Firmware Development",
"Edge Computing",
"Hardware & IoT"
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