Allen AI Young Investigators
About the Program
Allen AI Young Investigators is a postdoctoral program offering unique benefits. The program will enable you to balance working collaboratively on an AI2 project while pursuing an independent research agenda.
- Duration: 1-3 years
- Start Date: Flexible (rolling application with no deadline)
- Candidates: Are within one year of completing their PhD, or already have a PhD.
- Dedicated AI2 mentor: Mentorship in research, grant writing, and more
- 50% collaborative work on an AI2 project
- 50% work on your own projects
- Generous travel budget
- AI2 provides support for obtaining a visa through its immigration attorney, and pays the necessary expenses
- Access to AI2’s data, AWS infrastructure, and other resources as needed
- No grant writing, teaching, or administrative responsibilities
- $100K research funding from AI2 after completion (based on proposal)
YI Program Alumni
Antoine Bosselut completed his PhD at the University of Washington. He was a young investigator at the Allen Institute for AI and a postdoctoral researcher at Stanford University. He will join the faculty at Ecole Polytechnique Fédérale de Lausanne (EPFL) in Fall 2021. His research interests are in the integration of human knowledge with modern NLP systems, with a focus on commonsense representation and reasoning, and neuro-symbolic modeling. His works at AI2 include COMET: Commonsense Transformers for Automatic Knowledge Graph Construction (ACL 2019), Dynamic Neuro-Symbolic Knowledge Graph Construction for Zero-shot Commonsense Question Answering (AAAI 2021), COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs (AAAI 2021), and “I’m Not Mad”: Commonsense Implications of Negation and Contradiction (NAACL 2021).
Gabriel Stanovsky completed his PhD in computer science at the Bar-Ilan University in Israel in 2018. Since then, he’s been a part of the Young Investigator program at AI2 and a postdoctoral researcher at the University of Washington. In the fall of 2020, he will join the faculty of the School of Computer Science and Engineering at the Hebrew University of Jerusalem. His research interests revolve around intermediate semantic representations and their application in various real-world tasks, such as machine translation, question answering, and information extraction. His works with AI2 include: DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs, Evaluating Gender Bias in Machine Translation, and Gender Trends in Computer Science Authorship.
Rachel Rudinger completed her Ph.D. in Computer Science from Johns Hopkins University in 2019. Her research interests include natural language understanding, computational semantics, commonsense reasoning, and fairness in NLP. As of summer 2020, she is an assistant professor in the Computer Science department at the University of Maryland, College Park. Her papers with AI2 include Thinking Like a Skeptic: Defeasible Inference in Natural Language and “You are grounded!”: Latent Name Artifacts in Pre-trained Language Models.
Mark Yatskar completed his PhD at the University of Washington in 2017. He will join the faculty of the University of Pennsylvania in the fall of 2020. His research interests are at the intersection of natural language processing and computer vision, as well as fairness in computing. His publications with AI2 include Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations (ICCV 2019), QuAC: Question Answering in Context (EMNLP 2018), and Neural Motifs: Scene Graph Parsing with Global Context (CVPR 2018).
Roy Schwartz completed his PhD at the School of Computer Science and Engineering at the Hebrew University of Jerusalem in 2016. He is currently a postdoctoral researcher at the University of Washington and a Young Investigator at AI2. Roy will continue working with AI2 until 2020, at which time he will join the faculty of the School of Computer Science and Engineering at the Hebrew University of Jerusalem. His research interests are semantic representation and syntactic parsing. His publications with AI2 include SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference (EMNLP 2018), Rational Recurrences (EMNLP 2018), and LSTMs Exploit Linguistic Attributes of Data (ACL RepL4NLP Workshop 2018).
Mohit Iyyer completed his PhD at the University of Maryland, College Park. He is currently an assistant professor in computer science at UMass Amherst. His research interests lie broadly in natural language processing and machine learning. His publications with AI2 include Deep Contextualized Word Representations (NAACL 2018) and QuAC: Question Answering in Context (EMNLP 2018).