From long runs to code sprints
As a former runner, I relentlessly pursue my goals. My finish line isn't a medal, it's a solution. My experiments aren't for headlines, but a better tomorrow. I want to fill the cracks in the world rather than widen them.
I like to craft sustainable solutions tailored to specific challenges. Explore my snippets and mini-projects that serve as components of sustainable solutions.
Web/App Development
I craft dynamic web experiences from the ground up using Python, PHP, and Java to sculpt robust backends. I use ReactJS and AngularJS to build apps that work even when the wifi doesn’t.
Data Analysis
I translate data into data-driven intelligence. Python helps me create wrangle complex datasets and create anomaly detectors and personalized recommendations systems, while ReactJS visualizes my creations.
Machine Learning
I bring languages and visions to life – building and deploying Natural Language Processing and Computer Vision models used in real-world applications. My toolkit includes crafting tailored loss functions like hierarchical cross-entropy for nuanced learning and classification.
Mechanism Design
I craft economic mechanisms that align individual actions with collective goals, like efficient markets or sustainable resource use. Think puzzles with purpose, building systems that guide behavior without coercion.
Operations Research
Unraveling the tangled threads of resource allocation through operations research, I wield linear and combinatorial tools to sculpt streamlined, cost-optimal solutions.
System Architecture/Design
I architect resilient, scalable systems using Docker orchestration, microservices via REST APIs, and Gitlab CI/CD pipelines for robust development - I build the foundation for smooth, high-traffic experiences.
My blog offers a glimpse into my mind, where I explore messy, real-world problems problems and their solutions. I look into sustainable solutions, impact-driven design, and practical approaches that go beyond the next shiny trend.
Balancing LLM Prompt Analytics and User Privacy
Learn how to implement effective prompt analytics for large language models while safeguarding user privacy. Discover key strategies, best practices, and ethical considerations in this guide.
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Neural Variational Document Models with PyTorch for Topic Extraction
Discover how Neural Variational Document Models, implemented using PyTorch, improve topic modeling and unsupervised learning in natural language processing. Learn about the architecture, training process, and applications of these latent variable models for text analysis and beyond.
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Harnessing X-Means Clustering and CIE2000 for Visually Striking Dominant Color Extraction
Discover how to harness the power of the X-Means clustering algorithm and CIE2000 color distance metric to accurately extract dominant colors from images. This advanced technique combines unsupervised machine learning with human color perception principles to generate visually appealing and representative color palettes. Perfect for data-driven design, image analysis, and computer vision applications. Dive into the world of color science and elevate your image processing skills with this comprehensive guide.
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Boosting Web Performance: Implementing K-Means Clustering with WebAssembly and Emscripten
Exploring complexities of optimizing web performance by implementing K-Means clustering algorithms using WebAssembly and Emscripten.
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Fair Division of Goods
How to use algorithms in FairPy to efficiently find fair allocations of goods and create fair systems.
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2 Trustworthy Alternatives to Improving Performance Lists in Track & Field
Improving meet performance lists in track & field using the previous performances of the competitors
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