AI2 Outstanding Interns
AI2 Outstanding Interns of 2022
Clayton Sanford is a fourth year computer science PhD student at Columbia University studying deep learning theory. His work aims to understand the different approximation properties of neural network architectures and how they impact generalization. While at AI2, he worked with the climate modeling team on improving the robustness of ML-augmented climate models using techniques from novelty detection. He plans to graduate in spring 2024 and hopes find a way to apply his theoretical background to work in the ML + climate space. Outside of research, Clayton can be found running, backpacking, cooking, and nerding out about public transportation.
Lucy Li is a PhD student at UC Berkeley’s School of Information and Berkeley AI Research. She is advised by David Bamman. Her research spans natural language processing, computational social science, and responsible AI. Lucy previously graduated with a B.S. in Symbolic Systems and a M.S. in Computer Science from Stanford.
Tae Soo Kim
Tae Soo Kim is a PhD candidate in the School of Computing at KAIST working with Professor Juho Kim. His research lies at the intersection of human-computer interaction (HCI) and machine learning (ML). Specifically, he focuses on designing interaction techniques driven by ML models to facilitate creation tasks. He received his B.S. and M.S. from KAIST, where he received the Outstanding M.S. Thesis Award from the School of Computing.
Valentina Pyatkin is a PhD student in Computer Science at Bar-Ilan University, supervised by Professor Ido Dagan and Professor Reut Tsarfaty. Her research generally lies in discourse and pragmatics, with a focus on question generation. Prior to starting her PhD, Valentina earned a Bachelor’s degree from the University of Zurich and a MSc from the University of Edinburgh.
AI2 Outstanding Interns of 2021
Arjun Subramonian is a PhD student at the University of California, Los Angeles (UCLA), working with Professors Yizhou Sun and Kai-Wei Chang. Their research focuses on trustworthy and inclusive graph machine learning and natural language processing. They are also a core organizer of Queer in AI. They previously graduated with a B.S. in Computer Science from UCLA, where they received the School of Engineering’s Outstanding Bachelor of Science award.
Nora Kassner is a PhD student in Computer Science at LMU Munich, Germany under the supervision of Professor Hinrich Schuetze. Her research is focused on knowledge, reasoning and consistency in pre-trained language models. Prior to starting her PhD, Nora earned a B.S. and M.S. in Physics from Heidelberg University and LMU Munich, respectively.
Wen Xiao is a PhD candidate at the University of British Columbia, working with Professor Giuseppe Carenini. She received her M.S. from the University of British Columbia and B.S. from the University of Toronto. Her research primarily focuses on text summarization and discourse parsing, as well as combining these two tasks to benefit each other. Wen worked on a multi-document summarization system (PRIMERA) during her internship at Semantic Scholar team, advised by Arman Cohan and Iz Beltagy. This work has been accepted as a long paper at ACL2022.
AI2 Outstanding Interns of 2020
Sarah Wiegreffe is a PhD student in the School of Interactive Computing at Georgia Tech, where she is advised by Professor Mark Riedl. Before joining Georgia Tech, she received her B.S. in Data Science from the College of Charleston. Her research is on analyzing and developing interpretable deep learning models for NLP.
Sean MacAvaney is a computer science PhD candidate at Georgetown University and an ARCS Foundation Endowment Scholar. At Georgetown, he is a member of the IR Lab, and is co-advised by Nazli Goharian and Ophir Frieder. Prior to graduate school, Sean earned a B.S. in Software Engineering from the Milwaukee School of Engineering. His research primarily focuses on how deep learning models can be used effectively and efficiently in search engines.
Unnat Jain is a PhD student at UIUC, working with Alexander Schwing and Svetlana Lazebnik. His research is focused on collaborative visual agents, which he started in 2018 as a PRIOR intern with Ani Kembhavi and Luca Weihs. Prior to starting his PhD, Unnat graduated with the Director’s Gold Medal from IIT Kanpur, India.
AI2 Outstanding Interns of 2019
Eric Wallace is a PhD student at UC Berkeley, working with Professors Dan Klein and Dawn Song. Before coming to Berkeley, Eric completed his B.S. from the University of Maryland, advised by Jordan Boyd-Graber. His research interests are in Machine Learning and NLP, especially in providing a better understanding of how and why deep learning models work. Eric and his AI2 colleagues won the best demo award at EMNLP 2019.
Sarthak Jain is a PhD student in Khoury College of Computer Science at Northeastern University, advised by Professor Byron Wallace. Prior to joining Northeastern, Sarthak earned his Bachelor’s degree in Computer Engineering from Delhi Technological University in 2017. His research is focused on analysing and developing interpretable deep learning models, particularly to parse biomedical literature and clinical texts.
AI2 Outstanding Interns of 2018
Huiyu Wang is a PhD student in Computer Science at Johns Hopkins University, advised by Bloomberg Distinguished Professor Alan Yuille. He received his M.S. in Electrical Engineering at University of California, Los Angeles in 2017 and B.S. in Information Engineering at Shanghai Jiao Tong University in 2015. He also spent a wonderful summer at TuSimple. His research interests are computer vision and machine learning.
Xuezhe (Max) Ma
Xuezhe (Max) Ma is a PhD student in Language Technologies Institute at Carnegie Mellon University, working with Professor Eduard Hovy. Before coming to CMU, he received his M.S. from the Center for Brain-like Computing and Machine Intelligence (BCMI), and his B.S. in Computer Science from Shanghai Jiao Tong University, where he was a member of ACM Class, now part of Zhiyuan College in SJTU. His research interests fall in several areas in Machine Learning and Natural Language Processing (NLP), particularly in Computational Semantics, Syntactic and Semantic Parsing with Machine Learning Methods. Recently, he is strongly interested in Deep Neural Networks for Linguistic Structured Prediction.