I am a passionate data scientist with a solid foundation in mathematics and statistics, skilled in machine learning modeling (PyTorch, Spark), data/product analytics (SQL, Pandas) and software development (Python, Java). I excel at leveraging big data to enhance business efficiency, operational performance, and decision-making.With two years of research experience in deep learning and Generative AI at NYU and MBZUAI, I have honed my expertise in advanced machine learning techniques. Last summer, I interned at PetSmart as a Data Scientist, where I contributed to building automated ML pipelines and developing large language models (LLMs).I am driven by a love for technology and a commitment to solving real-world problems through data-driven insights.
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Data ScientistKizzy LabsSeattle, Wa, Us -
Data ScientistPetsmart Jun 2023 - Sep 2023Developed a CI/CD pipeline in PyTorch to hierarchically categorize pet products based on departments and features, reducing manual labor by 60 hours per week and minimizing misclassifications costsTrained a random forest model that achieves 98.5% accuracy in VP-level classification and fine-tuned BART to create an MLflow LLM class, generating refined features and allowing auto-classifications in MLOps pipelinesCollaborated with market research team to specify user needs and worked with data engineers to streamline data handling processes for large-scale complex datasets by standardizing a GCP product description dataset into Spark RDDs -
Research Assistant (Machine Learning)Mbzuai (Mohamed Bin Zayed University Of Artificial Intelligence) Jun 2022 - Sep 2022Abu Dhabi, United Arab EmiratesRA in the Department of Machine Learning; Studied Variational Autoencoder (VAE) and Stable Diffusion in Music AIPublished and presented “AccoMontage2: A Complete Harmonization and Accompaniment Arrangement System” in International Society for Music Information Retrieval 2022https://github.com/JohnnyHHU/Polydis-ARG -
Honors ThesisNyu Shanghai Sep 2021 - May 2022New York, New York, United StatesIn my thesis "Implicit Regularization towards Minimum-rank Solutions in Matrix Factorization", I studied gradient descent on a factorization of a square matrix and the implicit regularization phenomenon that prefer the minimum-rank solutions over all the other optimums. I illustrated theoretically that with commutative factor matrices that satisfy Restricted Isometry Property, gradient descent on an under-determined matrix factorization converges to the minimum-rank solution.Empirically, I showed that initialization can drastically affect the performance, and that implicit regularization can be sensitive to several other factors, step size, sample size, etc. -
Research AssistantNyu Shanghai Jan 2021 - May 2022Shanghai, ChinaML Research at MusicX Lab: Designed an algorithmic harmonization generating system; Music information retrieval (MIR)https://github.com/JohnnyHHU/ChorderatorReceived Dean's Undergraduate Research Fund 2021
Haochen H. Education Details
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Honors Mathematics -
Honors Mathematics; Data Science
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High School Diploma
Frequently Asked Questions about Haochen H.
What company does Haochen H. work for?
Haochen H. works for Kizzy Labs
What is Haochen H.'s role at the current company?
Haochen H.'s current role is Data Scientist.
What schools did Haochen H. attend?
Haochen H. attended University Of Washington, New York University, Nyu Shanghai, Beijing National Day School.
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