CV
Education
- M.S. in Intelligent Information Systems, Carnegie Mellon University, 2024 (expected) (GPA: 3.92)
- B.S.E. in Computer Science, minor in Mathematics, University of Michigan, 2022 (GPA: 3.89)
- B.E. in Electrical and Computer Engineering, Shanghai Jiao Tong University, 2022
Publications
Less is Not More: Improving Findability and Actionability of Privacy Controls for Online Behavioral Advertising Download paper CHI2023 Citation: Jane Im, Ruiyi Wang, Weikun Lyu, Nick Cook, Hana Habib, Lorrie Faith Cranor, Nikola Banovic, and Florian Schaub. 2023. Less is Not More: Improving Findability and Actionability of Privacy Controls for Online Behavioral Advertising. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ‘23). Association for Computing Machinery, New York, NY, USA, Article 661, 1–33. https://doi.org/10.1145/3544548.3580773
Research experiences
- Undergraduate Research Assistant @Computational HCI Lab in Umich, Nov. 2021 – Sep. 2022
- Developed a Chrome extension using JavaScript that automatically tracks ads on Facebook and attaches novel ad privacy control interfaces to ads and menu bar to increase ad privacy settings’ findability. Played a key role in successfully deploying the system on Facebook for an online experiment.
- Processed browsing history logs and survey responses with Python to obtain meaningful research data, and analyzed users’ behavior patterns and perceptions of privacy on social media platforms
- Undergraduate Research Assistant @Machine Translation Lab in SJTU, May 2021 – Feb. 2022
- Designed and implemented a variational sequence model via PyTorch that integrates stochastic latent variables into Transformer using the Conditional-VAE framework, to improve the diversity of word choice for abstractive summarization
- Augmented the Transformer decoder with a gating mechanism as a supplementary of added latent variables to enhance the quality of generated summaries, obtaining diversity scores that outperform state-of-the-art models
Industry experiences
- Machine Learning Engineer Intern @Veytel, Jan. 2023 – present
- Design and implement Deep Learning networks via PyTorch and TensorFlow for Radiographic Assessment of Lung Edema (RALE) score prediction based on Chest X-rays, achieving more than 87% accuracy on physician-annotated images
- Software Development Engineer Intern @QAD Inc., May 2021 – July 2021
- Developed back-end code using Progress for core service that automatically calculates the potential duration of customers’ shipping requests and creates calendars in the user portal
- Implemented web pages using JavaScript and XML, monitored memory leaks of back-end programs, and provided diagnostic reports for senior software developers
Skills
- Programming Languages: Python, C, C++, JavaScript, C#, TypeScript, SQL, R, MATLAB, HTML/CSS
- Toolkits: React, Django, Angular, Linux, AWS, Git, PyTorch, TensorFlow, SciKit Learn, Pandas, PyG
- Industry Knowledge: Machine Learning, Software Engineering, Full-stack Development, Data Analytics
Teaching experiences (TA)
- Honors Mathematics: Linear Algebra and Functions of Multiple Variables @SJTU, 2020 Summer
- Honors Mathematics: Ordinary Differential Equations @SJTU, 2020 Fall
Relevant Coursework
ML:
- [CMU 11685] Intro to Deep Learning (A)
- [CMU 11777] Multimodal Machine Learning (A)
- [CMU 10708] Probabilistic Graphical Models (A)
- [UM EECS598] Reinforcement Learning Theory (A)
- [UM EECS445] Intro to Machine Learning (A)
- [UM EECS442] Computer Vision (A)
HCI:
- [UM EECS598] Human-Computer Interaction (A)
- [UM EECS498] Engineering Interactive Systems (A+)
- [UM EECS497] Human-Centered Software Design (A)
NLP:
- [CMU 11830] Computational Ethics in NLP (A)
- [CMU 11624] Human Language for AI (A-)
Math:
- [UM MATH561] Linear Programming (A)
- [UM MATH371] Numerical Methods (A)