CV

Education

M.Eng. in Computer Science (Artificial Intelligence Concentration), Cornell University, 2025
B.A. in Mathematics (Computer Science Concentration), Cornell University, 2024
- Certificates in Cognitive Science, Neuroscience

Relevant Courses

Graduate-Level: Robot Learning, Foundations of Robotics, Parallel Computing, Applied Functional Analysis, Reinforcement Learning, Natural Language Processing, Measure Theory, Probability, and Stochastic Processes (OSU), Advanced Stochastic Calculus (OSU)
Undergraduate: Numerical Analysis, Computational Linear Algebra, Deep Learning, Machine Learning, Object-Oriented Programming and Data Structures, Applicable Algebra, Real Analysis, Number Theory, Advanced Linear Algebra, Probability & Statistics, Multivariable Calculus
Independent: Neuroscience, Cognitive Science, Functional Programming, Databases, Computer Organization and Architecture, Algorithms, Operating Systems

Work Experience

Jul 2025 - Present: Software Engineer
- Artian AI, New York, NY
Jun 2024 - Present: Founder, Lead AI Engineer
- InkSight, Cornell eLab Startup Accelerator, Ithaca, NY
- Prototyped an AI-powered note-taking tool integrating real-time speech-to-text transcription and multimodal data processing
- Managed and led a 3-person team selected from 200+ applied teams to Cornell’s entrepreneurship program
Nov 2024 - Jun 2025: Software Engineer
- Cornell AI Innovation Lab, Ithaca, NY
- Developed a second-opinion chatbot for the Cornell College of Veterinary Medicine and a RAG-based analytics dashboard for the College of Agriculture and Life Sciences
Jun 2024 - Aug 2024: AI Engineer & Strategist Intern
- Ascot Group, Stamford, CT
- Designed & developed NLP-based (NER, OCR) software to extract PDF data using internal databases and online sources for insurance market trends
- Built a scoring system assessing risk of broker submissions by fine-tuning pre-trained LLMs (DistilBERT, RoBERTa)
- Led a remote team of engineers to deliver a proof-of-concept product to senior leadership (projected 86% efficiency improvement)

Research Experience

Nov 2024 - Present: Independent Researcher, Multimodal Learning
- Department of Computer Science, Cornell University
- Advisor: Prof. Jennifer Sun
- Researching multimodal learning and representation alignment across audio, vision, and text
- Developed a multimodal STEM lecture video dataset and annotation framework for training/evaluating vision-language models
- Designed the InkSight Data Labeler, an AI-assisted labeling tool enabling frame-level diagram/handwriting/speech alignment
Nov 2024 - May 2025: Research Assistant, Robot Learning
- EmPRISE Lab, Department of Computer Science, Cornell University
- Beta tested Little Red Rover (LRR), Cornell’s educational rover platform used in CS 5700
- Implemented ROS-based motion-planning pipelines in Python, including A* path-finding for autonomous navigation
- Ran quantitative performance experiments and documented bugs and edge cases
Aug 2023 - Aug 2024: Research Assistant, Computational Linear Algebra, LLM Compression
- Department of Computer Science, Cornell University
- Advisor: Prof. Anil Damle
- Researched model-preserving compression techniques for neural networks, focusing on eigen-estimation and randomized numerical linear algebra
- Analyzed BERT hidden-state and attention outputs for low-rank structure
- Implemented and evaluated customized low-rank decomposition/compression pipelines
Aug 2023 - Jan 2024: Researcher, Applied Math, Deep Learning
- Department of Mathematics, Cornell University
- Advisor: Prof. Yunan Yang
- Formulated and tested response-based Knowledge Distillation objectives, analyzing how alpha weighting impacts CNN student accuracy under noisy labels
- Authored paper on Sobolev approximation bounds for neural operators with empirical validation on Burgers’ Equation
Jun 2023 - Aug 2023: Researcher, Analysis of PDEs, Numerical Analysis
- Department of Mathematics, Ohio State University
- Advisor: Prof. John Holmes
- Formulated and derived new parabolic PDEs using stochastic differential equations, replicating portfolio theory, and risk-neutral pricing
- Implemented finite difference schemes (Forward Euler, Backward Euler, Crank-Nicolson) in MATLAB for European and barrier options
- Research selected for presentation at 2023 Young Mathematicians Conference and 2024 Joint Mathematics Meetings
May 2022 - Aug 2022: Nexus Research Scholar, Applied Machine Learning
- Department of Astronomy, Cornell University
- Advisors: Prof. Ray Jayawardhana and Dr. Laura Flagg
- Created a standardized procedure for classifying solar flares using supervised machine learning
- Developed full data pipeline, including processing HARPS-N spectra, implementing PCA for dimensionality reduction
- Trained and optimized C-Support Vector Classification (SVC) model with RBF kernel
Dec 2021 - May 2022: Undergraduate Researcher, Space Systems & Orbital Mechanics
- Cornell Space Imaging and Optical Systems Lab (SIOS Lab), Cornell University
- Advisors: Prof. Dmitry Savransky, Prof. Jackson Kulik, Dr. Grace Genszler
- Implemented orbital dynamics simulation and control algorithms for starshade station-keeping, replicating methodology from Kulik et al. (arXiv:2105.05898)
- Modeled gravitational forces, orbital perturbations (J2 perturbation), and third-body interactions using Newton’s law of gravitation and two-body problem equations
- Developed proportional-derivative (PD) control algorithm to compute thruster firings and minimize differential lateral acceleration while maintaining precise telescope alignment
- Used numerical integration techniques (Runge-Kutta method) to simulate starshade trajectory and optimize fuel consumption

Skills

Programming Languages: Python, Java, C/C++, MATLAB, Julia, SQL, Bash/Unix/Linux Shell, TypeScript/JavaScript
Front End: React
ML/AI: Multimodal learning, representation alignment, LLM/VLM evaluation, knowledge distillation, model compression, retrieval-augmented generation, information extraction, agentic workflows
Frameworks & Libraries: PyTorch, TensorFlow, Keras, Transformers (HF), Scikit-learn, NumPy, Pandas, Matplotlib, OpenCV, SpaCy, NLTK, Gensim, CVXPY, Pydantic, MPI, OpenMP, CUDA
Systems & DevOps: Git/GitHub, Docker, Linux, CI/CD, REST APIs, AWS, Azure (VMs/DevOps/Storage)
Data & Tooling: Excel, data cleaning/ETL, annotation tooling, dataset curation, evaluation/benchmarking
Robotics: ROS, motion planning, A* path planning, trajectory generation, simulation/experimentation
Product & Collaboration: Figma, Linear, Jira, technical writing, stakeholder communication, demos & presentations

Publications

Quantitative Sobolev Approximation Bounds for Neural Operators with Empirical Validation on Burgers’ Equation

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

InkSight: A Multimodal STEM Lecture Video Dataset and Data Labeling Tool

Nicole Hao. (2025). "InkSight: A Multimodal STEM Lecture Video Dataset and Data Labeling Tool." Work In Progress.

Detecting and Classifying Solar Flares in High-Resolution Solar Spectra using Supervised Machine Learning

Nicole Hao, Laura Flagg, Ray Jayawardhana. (2024). "Detecting and Classifying Solar Flares in High-Resolution Solar Spectra using Supervised Machine Learning." The Astrophysical Journal.

Option Pricing with Stochastic Volatility, Equity Premium, and Interest Rates

Nicole Hao, Echo Li, Diep Luong-Le. (2024). "Option Pricing with Stochastic Volatility, Equity Premium, and Interest Rates." The Joint Mathematics Meeting.

Talks

InkSight: Inspiring All Learners using AI

April 01, 2024

Entrepreneurship Showcase at Cornell Tech Entrepreneurship Showcase, New York City Metropolitan Area

Option Pricing with Stochastic Volatility, Equity Premium, and Interest Rates

January 05, 2024

Research Presentation at Joint Mathematics Meeting (JMM), San Francisco, California

Option Pricing with Stochastic Volatility

August 20, 2023

Conference Talk at Young Mathematicians Conference (YMC), Columbus, Ohio

Financial Mathematics: Stochastic Models for Option Pricing

July 28, 2023

Research Presentation at Consortium of Summer Undergraduate Research Experiences (CSURE), Columbus, Ohio

Solar Flare Classification using Machine Learning

April 15, 2023

Conference Talk at Rochester Symposium for Physics Students (RSPS), Rochester, New York

Supervised Machine Learning for Solar Flare Classification

March 15, 2023

Symposium Talk at Cornell University Exoplanet Symposium, Ithaca, New York

Using Supervised Machine Learning to Detect and Classify Solar Flares

January 20, 2023

Conference Presentation at Conferences for Undergraduate Women in Physics (CUWiP), Ithaca, New York

Detecting Solar Flares in High-Resolution Spectra using Supervised Machine Learning

August 20, 2022

Research Symposium at Nexus Research Scholars Symposium, Ithaca, New York

Teaching Experience

Jul 2025 - Present: Python Tutor
- Coding4Youth, Remote
- Teaching Python programming to K-12 students
Aug 2021 - Jan 2025: Programming & Mathematics Tutor
- GoPeer, Remote
- Tutored mathematics to K-12 students from low-income families

Honors & Awards

Cornell Tech Entrepreneurship Showcase (2025) - Pitched venture InkSight; raised $10,000 from alumni donations and microgrants
Cornell eLab Student Startup Accelerator (2024) - Selected out of 200+ applicants for Cornell’s most competitive venture accelerator
NSF LEAPS-MPS Grant (2023) - Awarded $9,000 in research support from the National Science Foundation for research on stochastic PDEs
Nexus Research Scholar (2022) - Selected from 300+ applicants; awarded $7,000 summer research grant