Jiying Zou Email & Phone Number

Stanford, Ca, Us

South San Francisco, California, Us

Roche Advanced Analytics Network (RAAN) SSF Chapter Co-Founding Member, RWDS AI/ML Taskforce, Education Pillar Co-Lead, SSF RWDS Team Site LeadBringing insights from real-world data (i.e. non-clinical trial data, such as insurance claims, EMR, EHR, registries, etc.) to enable and improve the end-to-end drug development process. Experience in building patient cohorts, analyzing cohort attrition, understanding patient characteristics, and analyzing treatment patterns, with a specialization in ophthalmology (retinal diseases), respiratory, breast cancer, and nephrology disease areas.Bridging the gap between traditional and novel methodologies and bringing new technologies into the healthcare analytics space. Currently exploring applications of AI and ML to deliver foundational insights towards improving drug development in a trustworthy, fit-for-purpose, and effective way.

Stanford, Us

Mentor high school and college student independent research projects in Core program, and provide guidance for project topic exploration through Launchpad program

San Jose, Ca, Us

Host talks from health and biostatistics leaders from all over the world to spread data science education and build a community around learning and growth in the health data space.

Co-mentored a team of 2 high school students on a project using ML and AI to predict molecule candidacy for breast cancer drug discovery.

Menlo Park, Ca, Us

Trailblazed team strategy and approach to health-related content on Meta platforms, with aims to protect people from harm and promote the betterment of public health.Guided strategic launch of FB Search intervention to bring relevant authoritative resources and information to people as a part of the company's monkeypox response.

Menlo Park, Ca, Us

Worked on the Health team to improve the state of global public health messaging and encourage users to adopt healthy behaviors in real life through interactions with social media. Served as lead DS for COVID-19 related public health messaging campaigns. Worked with global health organizations (WHO/CDC/JHUSPH/UNICEF/etc.) and collaborated with leadership to direct strategy, optimize product, and drive prioritization decisions to help company towards its public commitment of bringing 50M people one step closer to getting vaccinated (and beyond).Analyses and recommendations contributed heavily to several of the team's strategic pivots.

South San Francisco, California, Us

Focus: Clinical Trials, Drug DevelopmentI led the statistical analysis of a retrospective external control study for a clinical trial exploring novel follicular lymphoma treatments. We aimed to evaluate whether real-world data could be leveraged to produce an external control cohort having similar characteristics and outcomes as those of the control arm from the randomized-controlled trial, thereby lending evidence to the possibility of reducing time and costs of future clinical trials. A manuscript is in the works.

Palo Alto, Ca, Us

Focus: Clinical trials, Bayesian statistics, simulation studiesDerived statistical theory/infrastructure & ran simulations establishing the feasibility of taking into account patient preferences in clinical trials with composite endpoints. The method is an improvement over previous composite endpoint evaluation methods because it factors in patient voices while retaining statistical rigor, helping take another step towards truly personalized healthcare. Paper published in Statistics in Biopharmaceutical Research: https://www.tandfonline.com/doi/full/10.1080/19466315.2022.2085783Designed a novel Phase I clinical trial design based on Bayesian inference for transplant-induced chimerism radiation therapy and related treatments with a monotonic dose-response relationship. Aimed to create an interpretable design that is easily to implement and also has sufficient backing in statistical theory, helping introduce more data-driven procedures into medicine. This work won the Top Poster award at the Stat4Onc 2021 conference.Led statistical analyses for a behavioral clinical trial regarding sun protective behaviors in young children. In collaboration with Professors Ying Lu and John Tamaresis.

Focus: Building the story behind data through data aggregation, analysis, visualization, and presentationAs a DS intern on the FB Marketplace Ads Delivery Team, I explored how to leverage user behavior and intent signals on our platform to serve them a more relevant and personalized ad experience. My analyses presented clear and actionable insights from engineering, financial, statistical, and product-side perspectives, helping my team better understand user behavior and inspiring engineers to run experiments to test some of my hypotheses. I figured out stories behind counterintuitive results, and my findings were backed by and supported other full-timers' independent analyses. Briefly, the technical bits involved aggregating data from 10+ different tables using complex SQL queries, analyzing/visualizing them in SQL, R, and Excel, and designing/writing a data pipeline in Python to facilitate future analyses (cut original query stream workload in half).

Boston, Ma, Us

Focus: AB testing, Gradient Boosted Tree methods (XGBoost, LightGBM), Machine learning researchIn this position, I performed rigorous testing to prove that an idea resulted in statistically significant model improvement, resulting in my findings being moved into production. I also presented an in-depth understanding of XGBoost and various black-box algorithm prediction explainers that helped guide project trajectories across teams company-wide. With extra time, I helped uncover the causes of some algorithmic inefficiencies and push the research regarding implementing batch learning with gradient-boosted tree algorithms.

Berkeley, Ca, Us

Focus: Secondhand smoke exposure effects on epigenetic differential methylation, Machine LearningMy research under Professor Lisa Barcellos regards using statistical models to evaluate the relationship between secondhand smoke (SHS) exposure and epigenetic alterations. A paper is on the way, which discusses the potential of using ML algorithms to predict patients' long-term SHS exposure status in a more reliable way than existing clinical methods: “A machine learning approach to predicting secondhand smoke exposure in multiple sclerosis patients using DNA methylation”.

Berkeley, Ca, Us

Focus: Multivariate statistics, biostatisticsI was nominated by Professor Lexin Li as an undergraduate TA for a graduate multivariate statistics course. Operating under administrative limitations, I helped lead office hours, gave 1-on-1 homework assistance, and graded papers for ~100 students.

Focus: AB testing, Product optimization, Quantitative market researchMy first project concerned using collected user experience data to optimize product features through AB testing, sparking engineers' interest in incorporating my findings into future production releases. My second project regarded helping guide global product expansion in directions as to have the most impact using data-backed comparisons. My results effectively supported marketing team trajectories, and some of my presentation material was incorporated into their reports.

Berkeley, Ca, Us

Focus: Data analytics, Web scraping, Medical billingI discovered anomalies in health insurance data and pursued them using business- and context-related expertise in order to identify inefficiencies, fraud, waste, and abuse in the medical billing system. I then developed these findings into 3 algorithms, catching over $12+ million in misbilled claims for one client.

Master Of Science - Ms, Statistics

Bachelor Of Arts - Ba, Statistics