Researched on Natural Language Processing; Dense Retrieval, the optimization and the evaluation oflanguage models.

Researched on applying offline reinforcement learning to manufacturing processes, as well as adapting transformers to reinforcement learning. Researched on black-box optimization, especially Bayesian optimization.

Worked on a multi-agent reinforcement learning project under the supervision of professor Animesh Garg.

Researched on multi-language paraphrasing and its evaluation. Also helped on QA systems. Under the supervision of professor Jimmy Lin and Animesh Garg.

https://guanghua-rotman.work/tdResearched on market analysis and companies' credibility check with natural language processing models.

CSC2547H1S: Seminar in Machine Learning for Machine Vision as Inverse GraphicsThis is an advanced graduate course in machine learning. It is primarily a seminar course in which students will read and present papers from the literature. The goal is to bring students to the state of the art in this exciting field. Tentative topics include generative and discriminative models for vision, convolutional and deconvolutional neural nets, variational inference and autoencoders, capsule networks, group symmetries and equivariance, visual attention mechanisms, differentiable renderers, and applications.CSC321H5S: Intro to Neural Networks and Machine LearningThis course gives an overview of both the foundational ideas and the recent advances in neural net algorithms. Roughly the first 2/3 of the course focuses on supervised learning – training the network to produce a specified behaviour when one has lots of labelled examples of that behaviour. The last 1/3 focuses on unsupervised learning.CSC411H5F: Machine Learning and Data MiningThis course is a broad introduction to machine learning. It will start with basic methods of regression and classification and problems of over fitting and the evaluation of learning algorithms, and then move on to more sophisticated methods such as neural networks. As part of the course, you will expand your Python skills to include numerical and scientific programming. As a fringe benefit, the students will also find out what all that math they learned is actually used for.CSC108H1F: Introduction to Computer ProgrammingIntroduction to Computer Programming. By the end of this course, the students should be comfortable with procedural programming in Python and will have been exposed to software development topics like testing, design, and documentation. The students will also be exposed to some core computer science ideas, such as complexity, abstraction, and the use of algorithms.

Used Various NLP and deep learning methods to analyze the relationship between millions of news articles and the credibility of listed companies.Under the supervision of professor Hai Lu.

Reproduced the latest publicized papers in natural language processing to present proof of concept solutions to potential business problems. Rebuilt models in PyTorch according to the architecture specified in papers.Met with different vendors to decide whether they are capable of providing machine learning and AI solutions to business problems encountered by other departments of Exxon Mobil.

Developed new functionalities with the agile approach for Oracle Eloqua; Helped to manage a large quantity of data in a distributed system. Also developed backend functionalities with C\# and various kinds of database systems, as well as QA automation.