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Fall 2023

Omarov, Talgat

Computational lexical semantics is a subfield of natural language processing (NLP) that deals with the study of meaning in language at the level of individual words or phrases using computational models and algorithms. Despite the recent success of large language models and contextualized word...

Fall 2023

Chui, Daniel CY

Modelling agent preferences has applications in a range of fields including economics and increasingly, artificial intelligence. These preferences are not always known and thus may need to be estimated from observed behavior, in which case a model is required to map agent preferences to...

Fall 2021

Ayoub, Alex

This thesis proposes novel algorithmic ideas in reinforcement learning for regret minimization. These algorithmic ideas enjoy nice theoretical guarantees and are more practical in large problems than their alternatives. We focus on finite-horizon episodic RL. We propose model-based and model-free...

Fall 2021

Nuryyev, Batyr

Application Programming Interfaces (APIs) allow developers to reuse existing functionality without knowing the implementation details. However, developers might make mistakes in using APIs, which are known as API misuses. One way to detect and prevent API misuses is to encode usage specifications...

Fall 2021

Campbell, Hazel V

Many software engineering problems have traditionally been approached by applying techniques based on static analysis and fixed sets of rules. I created two novel techniques to tackle three software engineering problems: typo location, fix suggestion, and crash report bucket creation. However,...

2006

Qin, Xuejie

Fall 2021

Yang, Yuchen

Deep neural network (DNN) has been developed rapidly in years. While it shows promising results in various tasks of computer vision, DNN typically suffers from accuracy loss due to the domain shift from a source domain to a target domain. To mitigate the accuracy loss without the label from...

1982

Sacharuk, Edward, 1948-

Spring 2024

Alikhasi, Mahdi

In reinforcement learning, agents solve problems through interactions with the environment. However, when faced with intricate environmental dynamics, learning can become challenging, resulting in sub-optimal policies. A potential remedy to this situation lies in the transfer of knowledge from...

Spring 2024

Mohammed, Hayat S

Trajectory data analysis refers to the systematic exploration of spatial and temporal movement patterns in trajectory datasets. Missing trajectory points pose a challenge as they affect downstream tasks that rely on these datasets, such as public transportation management, wildlife monitoring,...