About Me

I am a Ph.D. student advised by Prof. Linglong Kong and Prof. Bei Jiang at the University of Alberta (UofA). I obtained my M.S. degree at the University of Minnesota with Prof. Yang Li and Prof. Haiyang Wang and B.S. degree at Beijing University of Posts and Telecommunications(BUPT).

I am especially passionate about research in Statistics, Machine learning, Deep learning, and Natural language processing and their application and impact on society. As such, I work at the intersection of applying statistical machine learning methods and natural language processing algorithms to investigate social impact.

Interview:

University of Alberta Folio

Academic Research and Conference Papers:

Debiasing with Sufficient Projection: A General Theoretical Framework for Vector Representations (NAACL 2024)

• We propose a novel framework to reduce bias by transforming vector representations to an unbiased subspace using sufficient projection.

Gaussian differential privacy on Riemannian manifolds (NeurIPS 2023)

• We develop an advanced approach for extending Gaussian Differential Privacy (GDP) to general Riemannian manifolds.

Local Differential Privacy for Population Quantile Estimation (ICML 2023)

• Developed a novel approach for estimating population quantiles with Local Differential Privacy.
• Accepted at ICML 2023.

Quantile Fairness Regression with Conformalized Prediction Intervals (NeurIPS 2022)

• Presented a pioneering study on quantile fairness regression at NeurIPS 2022.
• Proposed a novel conformed prediction interval to assess fairness algorithm uncertainty and provide ‘fair’ prediction intervals.

Reducing Gender Bias in GloVe Word Embeddings Using Causal Inference (AAAI 2022)

• Led a team in reducing gender bias in GloVe word embeddings while retaining semantics information.
• Successfully presented the findings at AAAI 2022 conference.

AI-driven Combat Against Bias in Job Recruitment (Canada-UK AI Initiative)

• Led an international project to identify and mitigate gender and ethnic bias in the job market using AI.
• Managed a diverse team of postdocs and Ph.D. students from Canada and the UK.
• Collaboration with sociology teams for interdisciplinary work.
• Published outcomes in reputable journals and conferences.

Synthetic Health Data Generation with Deep Learning (Replica Analytics)

• Internship at Replica Analytics, working on synthesizing structured health data.
• Developed state-of-the-art deep learning models to generate longitudinal synthetic data.
• Contributed to critical modeling discussions and utility evaluation methods.
• Research paper accepted at BMC Medical Research Methodology 2023.

Predicting Carbon Percentage in Alberta Soil (Alberta Biodiversity Monitoring Institute)

• Collaborated on an interdisciplinary project for predicting carbon soil percentage.
• Developed a novel screening variable method with bootstraps group lasso for analysis.
• Submitted research paper to Science of the Total Environment in 2021.

Reducing Selection Bias in Counterfactual Reasoning (NeurIPS 2019 Workshop)

• Contributed to a research project addressing selection bias in counterfactual reasoning.
• Proposed a novel synthetic data-generating mechanism.
• Paper presented at NeurIPS 2019 Workshop on “Do the right thing.”

Cloud User Interest Analysis in P2P Systems (2017)

• Conducted measurements and analysis of cloud peers’ interest in P2P systems.
• Built web scrapers and performed data analysis and visualization.
• Utilized machine learning (clustering algorithm) to gain insights.
• Paper presented at IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops

Invited Talk:

Ding, L, (Author & Presenter), Society for Industrial and Applied Mathematics (SIAM) Spring 2023, “Word embeddings via causal inference: Gender bias reducing and semantic information preserving” the University of Texas at Arlington, Arlington, TX. (May 2nd, 2023)

Internships and Personal Projects:

1. Augmented Decision-making for Innovation Management (Human Resources Company)

• Led a project that utilized various language modeling methods to cluster ideas and comments efficiently.
• Implemented NLP models including TF-IDF, averaging word vectors, doc2vec, and RNN with BERT embeddings.
• Successfully improved innovation management and resource allocation.

1. Medical Insurance Cost Prediction using Recurrent Neural Networks

• Internship at Guangzhou Huazi Software Technology Co in 2018.
• Built RNN models to predict medical insurance costs for government authorities.
• Worked on NLP word correction for OCR with Kneser-Ney smoothing and noisy channel model.

2. Named Entity Recognition with RNN and Tensorflow (2018)

• Developed a bi-directional LSTM model with character embeddings for Named Entity Recognition.
• Utilized Glove word embeddings and conditional random field for accurate predictions.

4. Neural-based Dependency Parser Implementation (2017)

• Implemented a neural-based dependency parser using fully connected dense layers.
• Incorporated word embeddings and POS tag features for improved accuracy.

5. Cooperation of Clustering and Differential Evolution Algorithm (2015)

• Explored the combination of different clustering algorithms with Differential Evolution.
• Improved searching ability and convergence speed on challenging optimization problems.

6. 3D Human Faces Modeling at SAMSUNG Advanced Institute of Technology (2015)

• Internship focused on 3D human faces modeling.
• Developed Matlab and C++ programs for face point cloud data processing and ECG signals analysis.