• Build in-house LLMs.
• Apply generative AI to legal, tax, and corporate research software.

• Developed an enterprise propensity to purchase model, involving binary classification for propensity scores and regression for ACV prediction, effectively enhancing lead prioritization and sales targeting.
• Performed extensive feature engineering and EDA on diverse data sources and natures, including firmographic, demographic, and engagement data.
• Collaborated with cross-functional teams to clarify doubts and ambiguity, communicated progress and results effectively, and embrace innovative approaches to improve model performance and business impact.

The Human Machine Lab at Ontario Tech University is an interdisciplinary research laboratory with overarching yet practical ambition: designing computer systems around human needs and capabilities while maintaining human-level intelligence. Its interdisciplinary projects fall in various fields including human-computer interaction, usable security, privacy, and artificial intelligence.

During my stay at the lab, I have the opportunity to work with Dr. Miguel Vargas Martin on various applied machine learning projects, and

• Published multiple papers in the topic of applying machine learning techniques to authentication system (see my Google Scholar).
• Created GANs-based password guessing models that surpassed the benchmark’s performance by 83%, and GPT-3-based honeyword generation technique to accelerate password breach detection process.
• Conducted literature reviews on implementations of machine learning algorithms in computer security through researching academic databases.
• Generated novel ideas for enhancing the usability and security of password authentication systems via the use of natural language processing.

Thomson Reuters (TR) is a content-driven technology company with over a century of experience curating and classifying data and supporting professionals in the legal, tax, and coporate domains. Embedded in this legacy is a team of AI-focused engineers, research, data scientists, and designers who work with some of the most comprehensive and richly enhanced legal, tax, and other professional datasets in the world.

During my 8-month internship at TR, I worked on a legal text entailment reseach project and a name entity recognition product-oriented project. My responsibilities and achivements include:

• Published two papers exploring multiple prompt engineering techiques for large language models in tackling legal reasoning tasks.
• Collaborated with other research scientists to brainstorm applications of novel techniques in NLP on legal products.
• Experimented with various state-of-the-art approaches (zero-shot, few-shot, chain-of-thought promting, fine-tuning, prompt-engineering, etc.) on different large language models (GPT-3, and T5) using Huggingface and OpenAI APIs for domain-specific reasoning.
• Experimented with baseline and cutting-edge statistical and machine learning models for a highly imbalanced name entity recognition project. Models such as spaCy, Conditional Random Field and LegalBert were used.
• Documented project updates, including literature review, data exploration and processing, experimentation and testing results in lab standards on a daily basis.

The AI Hub at Durham College offers industry partners access to AI solutions to uncover business insights and increase companys' productivity and growth.

During my stay at AI Hub, I

• Generated interactive dashboard with historical gold data using Tableau.
• Implemented machine learning models for time series data prediction and documented the project including specifications about datasets, algorithms, APIs, data flow diagrams, comparisons for different scenarios and future optimization approaches.
• Proposed data insights to business stakeholders.

Ontario Tech University (UOIT)

GPA: 4.24 on a scale of 4.30

Location: Ontario, Canada

Durham College

GPA: 4.83 on a scale of 5.00

Location: Ontario, Canada

Donghua University

Location: Shanghai, China