Sitemap
A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
On Consciousness
Published:
A living list of resources I use to understand consciousness — the hard problem, the neuroscience, the philosophy, and the latest research. Updated as I go. Work in progress.
Semantic Retrieval
Published:
Every search system faces the same fundamental challenge: a user expresses an information need in a few words, and the system must find the most relevant documents from a corpus that may contain millions—or billions—of items. For decades, keyword matching was the dominant paradigm. But language is ambiguous, synonymous, and context-dependent. The query “how to fix a leaking pipe” and the document titled “plumbing repair guide for dripping faucets” share almost no tokens, yet they address the same need. This is the gap that semantic retrieval was built to close.
Clustering Algorithms and Evaluation Methods: A Practical Guide
Published:
TL;DR: Density-based clustering algorithms like DBSCAN and HDBSCAN can discover arbitrarily shaped clusters and gracefully handle noise—two properties that traditional methods like K-Means struggle with. This guide walks through how these algorithms work, when to choose one over the other, and how to rigorously evaluate clustering quality using internal metrics, external metrics, stability analysis, and practical validation techniques.
Compute Power, Geopolitics, and the AI Race
Published:
In the AI boom, the NVIDIA H100 has become more than a chip. It has become a symbol of power and an instrument of geopolitics.
Notes on NLP
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Comprehensive study notes covering foundational NLP methods from classical n-gram language models and HMMs through neural architectures including word embeddings, RNNs, and attention mechanisms.
Notes on Reinforcement Learning
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Comprehensive study notes organized by lecture topic (MDPs, planning/control, policy optimization, exploration, and imitation learning). Work In Progress.
What Buddhism Taught Me About Neuroscience
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Have you ever felt that sharp sting when reality doesn’t match what you hoped for?
Study Notes on Neuroscience
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My study notes on neuroscience, starting from molecular mechanisms and neural circuits to cognitive functions, brain disorders, and the intersection with artificial intelligence. Work In Progress.
NeuroSymbolic AI - What, Why, and How?
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I’ve been hearing the term NeuroSymbolic AI a lot these days, but really - What is it? Why was it invented? How do we use it?
The Future of Innovation & The End of Academic Monopoly
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Companies are gradually becoming the core engines of innovation. What do we do?
portfolio
Starshade Station Keeping
Published:
Orbital dynamics simulation for starshade spacecraft alignment
LLM Compression
Published:
Training-free and model-preserving transformer compression via interpolative decomposition
Knowledge Distillation: Effects of Alpha
Published:
Research on optimal alpha parameter for knowledge distillation
Parallel CNN-Based Transient Stability Analysis
Published:
Parallelized multi-channel CNN for power grid transient stability assessment (98.8% test accuracy)
Cold Diffusion
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Re-implementation of cold diffusion model for image restoration
Deep Learning Roadmap
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Interactive visualization of deep learning concepts
Multi-Agent Email Assistant
Published:
AI agent that writes emails based on natural language instructions
Exam Review Bot
Published:
A custom hybrid RAG-based chatbot for exam preparation
Awesome Math Animations
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Manim-powered animations to explain mathematical concepts
InkSight AI
Published:
AI-driven accessible note-taking platform for STEM students with disabilities WIP 
CS & Applied Math Papers
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Curated collection of ML/AI research papers with learning resources
An Efficient Sparse Kernel Generator for O(3)-Equivariant Deep Networks
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Efficient sparse kernel generation for rotation-equivariant neural networks
InkSight DataLabeler
Published:
A multimodal STEM lecture video dataset and annotation platform for AI-assisted accessibility WIP 
LeadGen.AI
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AI-powered B2B lead generation and automated outreach platform
Operator Learning in Sobolev Spaces
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Functional analysis of neural operators with quantitative error bounds
Relearn CS in One Year
Published:
Comprehensive CS fundamentals review covering 11 core topics
Little Red Rover Beta Testing
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Beta testing Cornell’s first educational robot 
publications
Option Pricing with Stochastic Volatility, Equity Premium, and Interest Rates
Published in The Joint Mathematics Meeting, 2024
This paper formulates and derives new parabolic partial differential equations using stochastic differential equations, replicating portfolio theory, and risk-neutral pricing to model time-varying volatility, equity premium, and interest rates.
Recommended citation: Nicole Hao, Echo Li, Diep Luong-Le. (2024). "Option Pricing with Stochastic Volatility, Equity Premium, and Interest Rates." The Joint Mathematics Meeting.
Download Paper
Detecting and Classifying Solar Flares in High-Resolution Solar Spectra using Supervised Machine Learning
Published in The Astrophysical Journal, 2024
This paper presents a novel, standardized procedure for classifying solar flares using supervised machine learning, with implications for both solar physics and exoplanet research.
Recommended citation: Nicole Hao, Laura Flagg, Ray Jayawardhana. (2024). "Detecting and Classifying Solar Flares in High-Resolution Solar Spectra using Supervised Machine Learning." The Astrophysical Journal.
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InkSight: A Multimodal STEM Lecture Video Dataset and Data Labeling Tool
Work In Progress, 2025
This work develops a multimodal STEM lecture video dataset and annotation framework for training and evaluating vision-language models on educational understanding.
Recommended citation: Nicole Hao. (2025). "InkSight: A Multimodal STEM Lecture Video Dataset and Data Labeling Tool." Work In Progress.
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Quantitative Sobolev Approximation Bounds for Neural Operators with Empirical Validation on Burgers’ Equation
Machine Learning: Science and Technology (In Submission), 2025
This paper derives a complexity-error scaling law in Sobolev norms and validates it empirically with Fourier Neural Operators.
Recommended citation: Nicole Hao. (2025). "Quantitative Sobolev Approximation Bounds for Neural Operators with Empirical Validation on Burgers Equation." In submission to Mach. Learn.: Sci. DOI: 10.13140/RG.2.2.32739.62248
Download Paper
Emotionally Aware AI Models: An Evaluation-and Safety-Centric Survey
ResearchGate Preprint, 2026
This survey reframes emotionally aware AI around evaluation and safety, analyzing existing generative models, datasets, and benchmarks for evaluating emotional intelligence in affective computing systems.
Recommended citation: Nicole Hao. (2026). "Emotionally Aware AI Models: An Evaluation-and Safety-Centric Survey." ResearchGate Preprint.
Download Paper
talks
Detecting Solar Flares in High-Resolution Spectra using Supervised Machine Learning
Published:
Presented summer research findings from the Nexus Research Scholars program on using supervised machine learning to detect and classify solar flares in HARPS-N spectra. This work was conducted under the supervision of Prof. Ray Jayawardhana and Dr. Laura Flagg.
Using Supervised Machine Learning to Detect and Classify Solar Flares
Published:
Presented early research findings on machine learning approaches for solar flare detection and classification in high-resolution spectra at Cornell’s CUWiP conference.
Supervised Machine Learning for Solar Flare Classification
Published:
Presented research on supervised machine learning methods for detecting and classifying solar flares, with implications for exoplanet research and stellar contamination modeling.
Solar Flare Classification using Machine Learning
Published:
Presented research on detecting and classifying solar flares in high-resolution solar spectra using supervised machine learning techniques. Discussed the development of a full data pipeline including PCA dimensionality reduction and SVC model optimization.
Financial Mathematics: Stochastic Models for Option Pricing
Published:
Presented summer research on stochastic partial differential equations and numerical methods for option pricing. Discussed the implementation of finite difference schemes (Forward Euler, Backward Euler, Crank-Nicolson) and their application to pricing European and barrier options.
Option Pricing with Stochastic Volatility
Published:
Presented research on stochastic partial differential equations for option pricing. Selected from 250+ applicants and 60+ presenters for this recognition.
Option Pricing with Stochastic Volatility, Equity Premium, and Interest Rates
Published:
Presented research on formulating and deriving new parabolic partial differential equations using stochastic differential equations, replicating portfolio theory, and risk-neutral pricing to model time-varying volatility, equity premium, and interest rates in a complete market. Implemented finite difference schemes in MATLAB for European and barrier option pricing.
InkSight: Inspiring All Learners using AI
Published:
Presented InkSight AI, an AI-driven accessible note-taking platform for STEM students with disabilities, at the 2024 Cornell Tech Entrepreneurship Showcase. The presentation highlighted how InkSight uses AI to improve lecture accessibility for blind/low-vision, hard-of-hearing, and neurodivergent learners; market opportunities, and more. I also connected with investors and industry executives at the event for fundraising.
teaching
Programming & Mathematics Tutor
Volunteer Teaching, GoPeer, 2021
Tutored mathematics and programming to K-12 students from low-income families. Provided personalized one-on-one instruction covering topics from basic arithmetic to calculus, as well as introductory computer science concepts. Focused on making STEM education accessible to underrepresented students.
Python Tutor
Volunteer Teaching, Coding4Youth, 2024
Teaching Python programming to K-12 students. Focus on building foundational programming skills and computational thinking through interactive lessons and hands-on projects.