Lihao Liu Graduate Data Research Assistant - The Impact of Daylight Saving on

DS Project - Ischemia & Cognition Proteomics

Yale University

Jun 2025 - Current (1 year 3 months)

Conducted differential expression and pathway analyses on proteomics data to identify biomarkers linked to cognitive function across ischemic phenotypes. Generated IPA-ready inputs, volcano plots, and Venn diagrams using R and filtered phenotype-specific protein signatures to uncover cognition-related pathways.

Graduate Data Research Assistant - Ischemia & Cognition Proteomics

Yale University

Jun 2025 - Current (1 year 3 months)

● Conducted differential expression and pathway analyses on proteomics data to identify biomarkers linked to cognitive function across ischemic phenotypes. Generated IPA-ready inputs, volcano plots, and Venn diagrams using R and filtered phenotype-specific protein signatures to uncover cognition-related pathways.

Graduate Data Research Assistant - Zebrafish CHD8 RNA Pipeline (Bulk & TRAP-seq)

Yale University

May 2025 - Current (1 year 4 months)

● Built CHD8/TP53 zebrafish RNA/TRAP-seq pipeline (R/DESeq2/Shiny): interactive volcano plots, heatmaps, automated Excel with exon/UTR lengths ● Led UTR motif analysis (HOMER + MEME zebrafish) with reciprocal FG/BG QC; delivered best-match known motifs and a compact QC summary

Full Stack Developer - Recipes Sharing Platform

Yale University

Feb 2025 - May 2025 (4 months)

● Full-stack development of a recipe-sharing web application using Flask, Jinja (Python), and SQLite, implemented secure authentication (JWT cookies, password hashing), RESTful endpoints, and a normalized relational schema to ensure data integrity, scalability, and maintainability ● Built responsive, form-driven UI components with client-side JavaScript (dynamic ingredient rows, progressive features), enforced server-side validation and CSRF protection, and collaborated on blueprint/app-factory architecture to deliver a stable MVP demonstrating clean, tested code and robust client–server interactions

DS Project - The Impact of Daylight Saving on NBA Free Throw Percentage

Yale University

Jan 2025 - Current (1 year 8 months)

● Analyzed 29 seasons of NBA and 14 seasons of NCAA game data from ESPN and HoopR to assess the impact of Daylight-Saving Time (DST), using hypothesis testing (paired and independent t-tests) and exploratory modeling. ● Performed data cleaning and feature engineering to examine DST effects by player/team/league tertiles and time zone travel; derived insights on circadian disruption in athletic performance. Additionally, conducted causal inference analyses to strengthen the validity of findings. Utilized Microsoft Power BI and Tableau to create dashboards that effectively presented insights to my professor, enhancing the communication of complex data visualizations and fostering a deeper understanding of the social impact of DST on athleti

Graduate Data Research Assistant - The Impact of Daylight Saving on NBA Free Throw Percentage

Yale University

Jan 2025 - Current (1 year 8 months)

● Analyzed 10 seasons of NBA free throw data from ESPN to assess the impact of Daylight Saving Time (DST), utilizing hypothesis testing (paired and independent t-tests) and exploratory modeling. ● Cleaned and performed feature engineering to examine DST effects by player skill, time zone travel, and game timing; derived insights on circadian disruption in athletic performance. Additionally, employed JavaScript and HTML to create high-quality visualizations that effectively communicated the research findings, enhancing the overall presentation of the data analysis.

Full Stack Developer - YUAG Collection Explorer - CLI, Desktop, Web

Yale University

Jan 2025 - May 2025 (5 months)

● Built a multi-interface search system over a normalized SQLite dataset, implementing case-insensitive filters, stable ordering, and multi-valued aggregation for clean, deduplicated results ● Developed CLI (argparse + reusable table renderer), desktop app (PySide6 GUI + JSON/TCP socket server), and web app (Flask/Jinja with cookies, editable labels, thumbnails, and HTML/CSS/JS UI) ● Delivered end-to-end ownership across design, implementation, testing, documentation, and releases, ensuring performance, usability, and consistent formatting

Capstone Project - Dissecting Heritability and Causal Variants in Cancer Genomics

UC San Diego

Dec 2023 - Mar 2024 (4 months)

● In charge of generating heritability scores of all genes from 1000 Genomes, used Plink and Python to filter, clean, and transform data and eventually mapped cancer information data from sample populations with the heritability scores to generate models. Details of the project is at https://github.com/AntonBeliakovUCSD/Capstone

Capstone Project - Genetic Risk Prediction of Gene Expression, Complex Traits, and Polygenic Disease

UC San Diego

Sep 2023 - Dec 2023 (4 months)

● Collaborated on a capstone project using population genetics and genomics data to assess individual risk for disease outcomes and transcriptomic measurements. Worked with genotype data from 1000Genomes and genetic association data from genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) to construct Polygenic Risk Scores. ● Practiced performing cis eQTL analysis and generating PRS, with the use of Plink to manipulate genotype data and Python and R to visualize and analyze the statistics.

Data Science and Processing Intern

Chinese Academy of Sciences

Jul 2023 - Sep 2023 (3 months)

As a Data Science and Processing Intern, I assisted in the large language models and data processing components of the lab, primarily focusing on acquiring knowledge of Hugging Face and fine-tuning pre-trained models such as T5 and BERT, utilizing insights from bioinformatics papers on Pub Med. In addition to my core responsibilities, I conducted causal inference analyses, which enhanced my understanding of data relationships and improved the overall quality of our projects. Furthermore, I leveraged Microsoft Power BI to create dashboards that effectively presented our findings to stakeholders, facilitating informed decision-making and showcasing the impact of our work in a visually engaging manner.

Data Scientist Internship

ByteDance

Apr 2023 - Jun 2023 (3 months)

● Assisted in defining data processing protocols and performed preliminary data analysis. Spearheaded feature construction and implemented predictive models like linear, Lasso, and Ridge Regression. Conducted Time Series Analysis and employed machine learning methodologies for predictions and sales forecasting. Conducted model optimization and eventually produced a comprehensive analytical report. ● Built a 28-day, creator-level segmentation for TikTok Live using robust features (enter rate, median watch time per impression, engagement and gifts per 100 enters, sessions per week, median session length, stutter rate, violations) and K-means (k=3-4); mapped Growth / Stable / At-risk clusters to targeted actions (prompt templates, go-live time

Neurobiology Laboratory Technical Assistant

UC San Diego

Jan 2023 - Apr 2024 (1 year 4 months)

● Assisted in the coding tasks of VR implementation of the study on zebra fish neuroscience and big data analysis for studying the neural basis of flexible behavior and internal states. ● Assisted in fitting models like Linear regression and Neural networks using PyTorch to predict movements of zebra fish in VR environment. Analyzed the movement data, including tail, heading, frontal, and lateral movement, using Python (OpenCV) and plotted trajectory to analyze the behavior of zebra fish. Also worked on VR input and output data synchronization to reduce noises when fitting models.

Data Scientist Intern

Proximo Inc

Jan 2023 - Apr 2023 (4 months)

As a Data Scientist Intern, I collaborated with the team on various projects for clients, including major telecommunication companies like AT&T. My responsibilities included researching and analyzing new product releases and customer insights, while also managing data entry, data cleaning, data analysis, and data mining to support the creation of dashboards and comprehensive reports for management. Additionally, I utilized AWS for cloud access and computing, which enhanced our data processing capabilities and facilitated more efficient analysis and reporting. This experience allowed me to leverage tools such as Microsoft Power BI and Tableau to visualize data effectively and present actionable insights to stakeholders.

Data Scientist Internship

Thaddeus Resource Center

Nov 2022 - Jan 2023 (3 months)

● Sift through data points to create organized categories, comparing data points to current organization processes and writing reports outlining business predictions or proposals. ● Oversee the collection, storage and interpretation of beneficiary’s data, and making modifications to current data storage formats through 4 weekly department and all staff meetings.

Individual Researcher (Rental Prices Prediction)

UC San Diego

Oct 2022 - Dec 2022 (3 months)

● Worked in a group of 3 on predicting the rental prices of CA housings between 2019 and 2021 using Python packages like Sklearn, Scipy, and Statsmodels. ● Employed uses of OneHotEncoder, TfidfVectorizer, and Bags of Words to build models including Lasso, Ridge Regression, and Linear Regression. Selected the model that gave the best prediction accuracy and eventually visualized our model results using Matplotlib. Additionally, utilized JavaScript and HTML to create high-quality visualizations, enhancing the presentation of our findings and making the data more accessible to stakeholders. This experience also strengthened my skills in data analysis and causal inference, allowing for a deeper understanding of the factors influencing rental

Individual Researcher (US House of Representatives’ Investments During COVID)

UC San Diego

Apr 2022 - Jun 2022 (3 months)

● Guided a research group of 2 undergraduates to research stock types made by Uthe S House of Representatives from 2020 to 2022 using Python. Employed techniques like permutation test, data cleaning, and data visualization using NumPy, Pandas, Seaborn, and Matplotlib. ● Concluded that the US House of Representatives purchased more and sold fewer stocks. Leading to our deduction that they were gaining confidence in investments from 2020 to 2021-2022.

Digital Token Design

Self Employed

Feb 2022 - Mar 2022 (2 months)

● Automated design of an original digital token using self-designed features with Jupyter Notebook. ● Minted over 400 NFTs on OpenSea.

Academic Internship

Qualcomm Institute UC San Diego Division of Calit

Jan 2022 - Jun 2022 (6 months)

● Collected geographical and weather data of past wildfires from over 50 locations in Southern California; analyzed the correlational relationship between wildfire locations and aquatic species, and visualized results with Python Seaborn, aiming to lower the cost of wildfire precautionary measures through more accurate predictions. Additionally, utilized JavaScript and HTML to create high-quality visualizations, enhancing the presentation of data findings and making complex information more accessible to stakeholders.

Individual Researcher (Housing market analysis)

UC San Diego

Jan 2022 - Mar 2022 (3 months)

● Directed a research group of 5 undergraduates to research California's latest housing crisis using quantitative methods such as hypothesis testing and chi-squared tests, visualizing results in graphs like Q-Q plots, swarm plots, and boxplots with Seaborn and Matplotlib via Python. ● Debunked common misconceptions about surging housing prices, demonstrating that a P-value of approximately 0.97 indicated no significant correlation between factors of Parking/Pets/Laundry and rental rates in California. Additionally, utilized JavaScript and HTML to create high-quality visualizations, enhancing the presentation of research findings and making complex data more accessible to a broader audience.

Lihao Liu

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