Invited Speakers
University of Southern California
Title: Contextualizing Representations in Varied Annotator Perspectives
Bio: Swabha Swayamdipta is an Assistant Professor of Computer Science and a Gabilan Assistant Professor at the University of Southern California. Her research interests are in natural language processing and machine learning, with a primary interest in the estimation of dataset quality, the semi-automatic collection of impactful data, and evaluating how human biases affect dataset construction and model decisions. At USC, Swabha has launched the Data, Interpretability, Language and Learning (DILL) Lab. She received her PhD from Carnegie Mellon University, followed by a postdoc at the Allen Institute for AI. Her work has received outstanding paper awards at ICML 2022, NeurIPS 2021 and an honorable mention for the best paper at ACL 2020.
Apple
Title: Adaptive computation for generalisation and efficiency in neural networks
Bio: I am currently a research scientist at the machine learning research team at Apple and my research is focused on understanding and improving reasoning processes in neural networks at different scales. Before joining Apple, I was a PhD candidate at the Institute for Logic, Language, and Computation at University of Amsterdam, where I worked on my thesis titled “Inductive biases for learning natural language”.
University of Washington and Allen AI
Title: Augmenting Parametric Language Models with Retrieval-based Non-Parametric Representations
Bio: Hanna Hajishirzi is a Torode Family Associate Professor at UW CSE and a Senior Director of NLP at AI2. Her research spans different areas in NLP and AI, focusing on developing machine learning algorithms that represent, comprehend, and reason about diverse forms of data at large scale. Honors include the NSF CAREER Award, Sloan Fellowship, Allen Distinguished Investigator Award, Intel rising star award, UIUC alumni award, a best paper and honorable mention paper awards, and several industry research faculty awards. Hanna received her PhD from University of Illinois at Urbana-Champaign and spent a year as a postdoc at Disney Research and CMU.
Tel Aviv University and Meta AI
Title: LIMA: Less Is More for Alignment.
Bio: Omer is a research scientist at Meta AI and an assistant professor at Tel Aviv University. He is interested in all aspects of large language models, from pretraining through architecture, alignment, and evaluation.