I’m an incoming MSCS Student @ Stanford University. Previously, I received my Bachelor of Electronics and Computer Science (Honours) degree from the University of Edinburgh 📖, where I was proud to rank 2nd in my programme.I had study at the University of Texas at Austin in 2022 as an exchange student. I’m a cat person with a ten-year-old cat🐱.
- Short bio:
I completed my bachelor’s thesis on structure prediction supervised by Professor Hakan Bilen. Previously, I had the privilege of working with Bonnie Webber and Lori Levin on f Information Status and Discourse Relation (NLP). Before that, I spent the summer of 2022 as a research assistant at the University of Texas at Austin, leaded a COVID-19 misinformation detection project, under the supervision of Professor Dhiraj Murthy. The prior summer in 2021 saw me working as a Machine Learning Intern (CQC Analyst Intern) at NXP Semiconductors, focusing on anomaly detection tasks.
Throughout my academic journey, I’ve honed my skills in various areas, including computer vision, natural language processing, anomaly detection and software development.
My current research endeavors are geared towards building more explainable ML and applying AI for social good. I always maintain an open mind and am constantly eager to venture into new fields of study and work!!
- Long bio:
My previous experiences include working in fields such as anomaly detection, computer vision, natural language processing, and software development. I have completed an internship at NXP Semiconductor as a Machine Learning Intern where I collaborated with the NXP Product and Test team in the development of AI solutions to anomaly detection, and also interned at a startup where I was responsible for hyperparameter optimization for improving Wind Farm Performance. Furthermore, I also participated in a project about Covid-19 Misinformation Detection where I collected and preprocessed data, designed and optimized deep learning models, and proposed approaches for enhancing the ability of models to do misinformation detection by augmenting the model with crowdsourced data. This work was published by JMIR Infodemiology in August.
I am also well-versed in data annotation and collection, and have hands-on experience with deep learning frameworks such as PyTorch. Additionally, I have experience in studying of Information Status and Discourse Relations, where I performed data processing for PTB, PDTB, ISNotes, and OntoNotes data, and helped to expand the number of annotated units of PDTB-3 and ISNotes using MMAX. My current research interests include trustworthy and robust machine learning, interpretable Natural Language Processing (NLP), connecting deep learning with mathematical structures in decision-making, adding causal reasoning to build robust models, and applying AI for social good. I’m always happy to explore new areas!