Allen AI Predoctoral Young Investigators

PYI Program Alumni

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  Apoorv Khandelwal

  Apoorv graduated from Cornell University in 2020 with a BS in Computer Science. He then worked as a PYI on AI2’s PRIOR team for two years. During his time at AI2, he worked on projects such as investigating the effects of visual encoders for Embodied AI tasks and on a knowledge-based visual question answering dataset. Apoorv is now a PhD student in Computer Science at Brown University, where he is advised by Chen Sun and Ellie Pavlick.

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  Amita Kamath

  Amita graduated from Stanford University in 2020 with an MS in Computer Science. She was a PYI on the PRIOR team from October 2020 to October 2022, during which time she worked on creating General Purpose Vision Systems, and on using Web search data to greatly expand their capabilities. She is broadly interested in improving the robustness and interpretability of vision-language and NLP systems. Starting Fall 2022, Amita will be a PhD student in Computer Science at UCLA.

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  Zhaofeng Wu

  Zhaofeng graduated from the University of Washington in 2021 with a M.S. in computer science, and in 2019 with a B.S. in computer science and a B.A. in linguistics. During his one year as a PYI at AI2, he worked on projects including Transparency Helps Reveal When Language Models Learn Meaning, Continued Pretraining for Better Zero- and Few-Shot Promptability, ABC: Attention with Bounded-memory Control, and Learning with Latent Structures in Natural Language Processing: A Survey. Zhaofeng is joining the MIT NLP group in August 2022.

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  Alexis Ross

  Alexis graduated from Harvard University in 2020 with a BA in Computer Science and Philosophy. She worked as a PYI on the AllenNLP team from July 2020 to July 2022. During her time at AI2, she worked on topics such as explaining NLP systems, leveraging controlled generation for human-in-the-loop data collection, and removing dataset artifacts. Beginning in August 2022, Alexis will be a PhD student in Computer Science at MIT.

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  Jenny Liang

  Jenny Liang is a first-year Ph.D. student at Carnegie Mellon University. Inspired by her time at the AI2, she is currently researching ways for programmers to work with code-generating AI like GitHub CoPilot to output customized code.

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  Will Merrill

  Will graduated from Yale in 2019 with a B.S. in computer science and linguistics. He is broadly interested in the linguistic abilities, robustness, and interpretability of neural networks for NLP, often through the lens of theoretical tools like formal languages. At AI2, Will’s work contributed to formal papers on A Formal Hierarchy of RNN Architectures, On the Linguistic Capacity of Real-Time Counter Automata, Effects of Parameter Norm Growth During Transformer Training: Inductive Bias from Gradient Descent, Provable Limitations of Acquiring Meaning from Ungrounded Form: What Will Future Language Models Understand?, Competency Problems: On Finding and Removing Artifacts in Language Data, and CORD-19: The COVID-19 Open Research Dataset. After leaving AI2, Will will join New York University as a Ph.D. student advised by Tal Linzen.

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  Sanjay Subramanian

  Sanjay earned a B.S.E. in computer and information science and a B.S. in economics from the University of Pennsylvania in 2019 and has been a predoctoral young investigator in AI2’s AllenNLP team from June 2019 to July 2021. His research interests are in NLP and its intersection with computer vision. Through his work at AI2, he was a co-author of papers about linguistic analysis of pre-trained vision and language models, the faithfulness of neural module networks, a dataset of images and associated text from medical research papers, contrast sets for NLP evaluation, and a latent tree model for visual question answering. Beginning in August 2021, Sanjay will be a PhD student in computer science at UC Berkeley.

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  Haokun Liu

  Haokun is an NYU graduate. He worked as PYI in the Semantic Scholar team from Jan 2021 to July 2021. Haokun authored around ten papers in natural language understanding evaluation, transfer learning and probing linguistic knowledge of pretrained models. Haokun will start his PhD at UNC-Chapel Hill in fall 2021. He is interested in transfer learning and empirical analysis in NLP.

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  Suchin Gururangan

  Suchin graduated from the University of Chicago in 2014 with a bachelor’s in Mathematics, and earned a Master’s in Computational Linguistics at the University of Washington in 2018 after a few years in industry as a data scientist. He is now a PhD student at the University of Washington, advised by Noah Smith and Luke Zettlemoyer. At AI2, Suchin had the opportunity to do research in a number of different areas of NLP, including domain adaptation, safety, efficiency, and reproducibility.

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  Vivek Ramanujan

  Education: Past: Brown University, BSc Math and Computer Science. Graduated 2018 Current: University of Washington Computer Science PhD program, Fall 2020 Publications: What’s Hidden in a Randomly Weighted Neural Network? CVPR 2020, Soft Threshold Weight Reparameterization for Learnable Sparsity ICML 2020, Supermasks in Superposition ICML 2020 Workshop with spotlight (in submission to NeurIPS 2020), and Parameter Norm Growth during Training Transformers (in submission to NeurIPS 2020, no preprint link

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  Sarah Pratt

  Sarah Pratt completed her undergraduate studies at Brown University in Applied Mathematics and Computer Science before joining the PRIOR team at AI2. While at AI2, she worked on grounded situation recognition, which was published at ECCV 2020. She is now a PhD student at the University of Washington where she works with Prof. Ali Farhadi on computer vision and deep learning.

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  Chaitanya Malaviya

  Chaitanya graduated with a masters degree from Carnegie Mellon University and joined the Mosaic team at AI2. At AI2, he worked on Commonsense knowledge graph construction: link 1 and link 2, Generative data augmentation, and Curation of a dataset for abductive reasoning. He will join the University of Pennsylvania as a PhD student in fall 2020.

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  Isabel Cachola

  Isabel is currently attending Johns Hopkins University as a PhD student in Computer Science, and did her Undergrad at the University of Texas at Austin. Her research interests include Natural Language Processing and Computational Social Science. Her published papers with AI2 include TLDR: Extreme Summarization of Scientific Documents, Citation Text Generation, and Improving the Accessibility of Scientific Documents: Current State, User Needs, and a System Solution to Enhance Scientific PDF Accessibility for Blind and Low Vision Users.

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  Jaemin Cho

  Jaemin graduated from Seoul National University in 2018 with a BS in Industrial Engineering. He will join a PhD program in Computer Science at UNC-Chapel Hill in the fall of 2020. While at AI2 he published Mixture Content Selection for Diverse Sequence Generation for EMNLP 2019 and X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers for EMNLP 2020.

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  Mitchell Wortsman

  Mitchell is now a PhD student at the University of Washington working on neural networks with Ali Farhadi. At AI2, Mitchell explored self-adaptive navigation with Kiana Ehsani & Roozbeh Mottahgi and learning neural network wirings with Mohammad Rastegari. Mitchell is from Toronto and completed his undergraduate studies at Brown University in Applied Mathematics and Computer Science before joining AI2.

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  Kevin Lin

  Kevin Lin joined the AllenNLP team at AI2 after graduating with a BS in Computer Science from Columbia University. At AI2 he worked on structured models for code generation and reading comprehension, as well as benchmarks for situated and qualitative reasoning. He started his PhD at UC Berkeley in 2019.

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  Noah Siegel

  Noah Siegel was a Research Engineer at AI2 after receiving a BS in Computer Science and Economics from the University of Washington in 2015. He worked on computer vision and machine learning, specifically on applications to information extraction. Noah is currently a research engineer at DeepMind. He is first author on the paper FigureSeer: Parsing Result-Figures in Research Papers (ECCV 2016) and Extracting Scientific Figures with Distantly Supervised Neural Networks (JCDL 2018).

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  Roie Levan

  Roie Levin graduated from Brown University in 2015 with a BS in Applied Math/Computer Science and Math. He worked as a Research Engineer on the Euclid project at AI2. Roie accepted an offer of enrollment from CMU beginning in 2017. His publications with AI2 include FigureSeer: Parsing Result-Figures in Research Papers (ECCV 2016) and Beyond Sentential Semantic Parsing: Tackling the Math SAT with a Cascade of Tree Transducers (EMNLP 2017).

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  Chris Clark

  Chris Clark received a BS in Computer Science and a BA in Philosophy from the University of Washington, and went on to receive an MS in Artificial Intelligence from the University of Edinburgh where he studied machine learning and completed a thesis on using deep neural networks to play Go. Chris’s research at AI2 focused on information extraction, and his work was deployed as a key feature of the Semantic Scholar project. Chris enrolled in a PhD program at UW in 2015. He is first author on the papers PDFFigures 2.0: Mining Figures from Research Papers (JCDL 2016) and Looking Beyond Text: Extracting Figures, Tables and Captions from Computer Science Papers (AAAI Workshop on Scholarly Big Data 2015).