2023-2024 Amazon ML Fellows
USC and Amazon have created a joint research center focused on development of new approaches to machine learning (ML) privacy, security, and trustworthiness. The Center for Secure and Trusted Machine Learning (in short, Trusted AI), which will be housed at the USC Viterbi School of Engineering, will support USC and Amazon researchers in the development of novel approaches to privacy-preserving ML solutions.
FEI WANG
FEI WANG
Biography
Fei Wang is a Ph.D. candidate in Computer Science at University of Southern California, advised by Prof. Muhao Chen. He is supported by an Annenberg Fellowship. Previously, he was a research intern at Amazon AWS, Amazon Alexa, and Tencent AI Lab (Seattle).
Research Interests
My research interests lie in natural language processing and machine learning. My long-term goal is to build robust, controllable, and accountable large language model-based systems. My recent work spans across (1) mitigating knowledge conflicts and spurious correlations, (2) guiding text generation using constraints, and (3) enhancing model privacy and security against information leakage and backdoor attacks.
What does being an Amazon ML Fellow mean to you?
Being an Amazon Fellow is a great honor for me. This fellowship not only recognizes my past achievements, but also motivates me to engage in transformative research with broader impacts
BRIHI JOSHI
BRIHI JOSHI
Biography
Brihi Joshi is a third-year Computer Science Ph.D. student at the University of Southern California, advised by Prof. Xiang Ren as a part of the INK Lab and the broader NLP Group. In the past, she has worked with Amazon Alexa AI, Snap Research and Goldman Sachs on fundamental research and engineering problems. On the side, she is passionate about inclusivity in CS, and volunteers her time in various organizations like NLPWithFriends and Women Who Code.
Research Interests
My work focuses on human-grounded explainability - where explanations generated by NLP systems align with that of humans, and humans are involved in designing the way models explain. Motivated by this, my broad research goal is to incorporate human-centered explainability in NLP systems, inspired by how humans interact amongst themselves and with these systems, while also borrowing insights from other disciplines. I believe that this is a crucial foundation for establishing trust in NLP systems and operationalizing explanations for real-world use cases.
What does being an Amazon ML Fellow mean to you?
I am extremely honored to be named an Amazon ML Fellow. To be working on NLP in this atmosphere is challenging — not only because of the competition that it entails, but also because finding impactful problems to work on gets harder. The fellowship has motivated me to double down on my research goals of human-grounded explainability and focus on the impact that trustworthy systems in this age can bring.