Jixin Li

I do interdisciplinary studies of mental health and data mining. My research interests lie in creating interactive mental health intervention tools that can better serve underserved population at a low cost.

I obtained my bachelor degree, major in general psychology and minor in applied statistics, at Univeristy of Michigan, Ann Arbor, and received my master in Statistics at Columbia University. I care about people and education. I enjoy basketball, cycling, and hiking.

I am going to start my PhD in personal health informatics @ Northeastern University in Boston. If you are interested in me, don't hesitate to send me an email!

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I'm interested in psychology and statistics. I am collaborating with professors in research on psychology and social media. Representative studies and projects are listed below.

Personality Impressions of YouTubers Based on Their Trailers and Channel Sites: A Lens Model Approach
Abstraction: This study is to investigate the discrepancy between the actual personality of creators of the popular YouTube channels, the presenting personality that the YouTube channel intend to communicate with their audiences, and the personality impressions to the common audiences. A lens model analysis will be used to find what cues observers depend on when forming personality impressions of YouTubers. Consensus and accuracy of personality impressions will also be examined compared to the actual and presenting personality.
Gabriella M. Harari
In Process
Data Analysis Projects

Maths and Physics Exercises Text Classification with Concept Map
Jixin Li, Yewen (Evan) Pu, Spring 2018

Built innovated ensemble classifiers to predict the major thread of concepts of maths and physics exercise texts, given the concept maps with a multiple-layer hierarchy. The classifier features small size training set, automatic tuning with new data and good scability to enlarge concept maps.


Transportation Departure and Arrival Delay Prediction
Jixin Li, Yitong Huang, Baian Chen, Spring 2018

Web scraped weather and airport information and combined unstructured data sources including departure and destination information, GPS location, customer comments to make predictions of transportation delay. Stacking models and careful feature engineering were applied and best performance were achieved through fine-tuned neural network.


Coupon Recommendation System for Retailers (Design)
Jixin Li, Summer 2018

Designed collaborative filtering recommendation system for retailers to distribute coupon prize in a lottery game to attract customers to repurchase the products. Given no knowledge of coupon attributes, the collaborative filtering system integerates the customer demographics and historical prize redemption records and adapts to individual hidden preferences through constantly referring to the coupon redemption choices of similar customers.