October 17, 2021
dfried@andrew.cmu.edu
dpfried.github.io
UC Berkeley. 2015 – 2021
Ph.D. in Computer Science
Adviser: Dan Klein
Thesis: Learning Grounded Pragmatic Communication
University of Cambridge, Churchill College. 2014 – 2015
M.Phil. in Computer Science, with distinction
Adviser: Stephen Clark
Thesis: Low Rank Tensor Approximations for Compositional Distributional Semantics
University of Arizona. 2010 – 2014
B.S. in Computer Science, Mathematics, and Information Science, summa cum laude
Thesis Adviser: Mihai Surdeanu
Thesis: Predicting Community Traits Using the Language of Food on Social Media
Visiting Researcher, Facebook AI Research. 2021 – 2022
Hosts: Mike Lewis, Luke Zettlemoyer
Research Intern, Google DeepMind. Summer 2019
Hosts: Aida Nematzadeh, Stephen Clark, Chris Dyer
Project: Structured models for language-conditioned video segmentation
Research Intern, Microsoft Research. Summer 2016
Hosts: Hoifung Poon, Chris Quirk, Kristina Toutanova, Scott Wen-Tau Yih
Project: Learning to rank for personalized medicine
Undergraduate Research Assistant, University of Arizona. 2012 – 2014
Advisers: Mihai Surdeanu, Stephen Kobourov, Paul R. Cohen
Areas: Question answering, language grounding, data visualization
Research Intern, Nara Institute of Science and Technology. Fall 2013
Host: Kevin Duh
Project: Incorporating relational semantics in word embeddings
Research Intern, RWTH Aachen University. Summer 2013
Hosts: Ali Jannesari, Zhen Li
Project: Supervised learning for automatic code parallelization
Engineering Intern, Microsoft. Summer 2012
Project: Data warehousing, analytics, and visualization for Xbox Live
| Google Ph.D. Fellowship in Natural Language Processing | 2019 – 2021 |
| Outstanding Graduate Student Instructor, UC Berkeley | 2018 |
| Outstanding Reviewer, ACL | 2018, 2020, 2021 |
| Outstanding Reviewer, NeurIPS | 2019 |
| Tencent AI Lab Fellowship | 2018 – 2019 |
| Huawei & Berkeley AI Fellowship | 2017 – 2018 |
| Best M.Phil. Student Award, Cambridge Computer Laboratory | 2015 |
| Churchill Scholarship | 2014 – 2015 |
| NDSEG Fellowship | 2014 |
| Finalist, Hertz Graduate Fellowship | 2014 |
| Outstanding Senior Award in Research, U. Arizona College of Science | 2014 |
| Outstanding Senior Award in Academics, U. Arizona Computer Science | 2014 |
| Outstanding Senior Award in Academics, U. Arizona Information Science | 2014 |
| Barry M. Goldwater Scholarship | 2013 |
| National Merit Scholar | 2010 – 2014 |
| Flinn Scholarship, Flinn Foundation of Arizona | 2010 – 2014 |
| Presidential Scholar, U.S. Department of Education | 2010 |
Reference-Centric Models for Grounded Collaborative Dialogue
Daniel Fried, Justin Chiu, and Dan Klein
Empirical Methods in Natural Language Processing (EMNLP), 2021
Modular Networks for Compositional Instruction Following
Rodolfo Corona, Daniel Fried, Coline Devin, Dan Klein, and Trevor Darrell
North American Chapter of the Association for Computational Linguistics (NAACL), 2021
Interactive Assignments for Teaching Structured Neural NLP
David Gaddy, Daniel Fried, Nikita Kitaev, Mitchell Stern, Rodolfo Corona, John DeNero, and Dan Klein
Teaching NLP Workshop at NAACL, 2021
Syntactic Structure Distillation Pretraining for Bidirectional Encoders
Adhiguna Kuncoro*, Lingpeng Kong*, Daniel Fried*, Dani Yogatama, Laura Rimell, Chris Dyer, and Phil Blunsom
Transactions of the Association for Computational Linguistics (TACL), 2020
Learning to Segment Actions from Observation and Narration
Daniel Fried, Jean-Baptiste Alayrac, Phil Blunsom, Chris Dyer, Stephen Clark, Aida Nematzadeh
Annual Meeting of the Association for Computational Linguistics (ACL), 2020
Cross-Domain Generalization of Neural Constituency Parsers
Daniel Fried*, Nikita Kitaev*, and Dan Klein
Annual Meeting of the Association for Computational Linguistics (ACL), 2019
Are You Looking? Grounding to Multiple Modalities in Vision-and-Language Navigation
Ronghang Hu, Daniel Fried, Anna Rohrbach, Dan Klein, Trevor Darrell, and Kate Saenko
Annual Meeting of the Association for Computational Linguistics (ACL), 2019
Pragmatically Informative Text Generation
Sheng Shen, Daniel Fried, Jacob Andreas, and Dan Klein
North American Chapter of the Association for Computational Linguistics (NAACL), 2019
Speaker-Follower Models for Vision-and-Language Navigation
Daniel Fried*, Ronghang Hu*, Volkan Cirik*, Anna Rohrbach, Jacob Andreas, Louis-Philippe Morency, Taylor Berg-Kirkpatrick, Kate Saenko, Dan Klein**, and Trevor Darrell**
Neural Information Processing Systems (NeurIPS), 2018
Policy Gradient as a Proxy for Dynamic Oracles in Constituency Parsing
Daniel Fried and Dan Klein
Annual Meeting of the Association for Computational Linguistics (ACL), 2018
Unified Pragmatic Models for Generating and Following Instructions
Daniel Fried, Jacob Andreas, and Dan Klein
North American Chapter of the Association for Computational Linguistics (NAACL), 2018
Effective Inference for Generative Neural Parsing
Mitchell Stern, Daniel Fried, and Dan Klein
Empirical Methods in Natural Language Processing (EMNLP), 2017
Improving Neural Parsing by Disentangling Model Combination and Reranking Effects
Daniel Fried*, Mitchell Stern*, and Dan Klein
Annual Meeting of the Association for Computational Linguistics (ACL), 2017
Towards Using Social Media to Identify Individuals at Risk for Preventable Chronic Illness
Dane Bell, Daniel Fried, Luwen Huangfu, Mihai Surdeanu, and Stephen Kobourov
Language Resources and Evaluation Conference (LREC), 2016
Low-Rank Tensors for Verbs in Compositional Distributional Semantics
Daniel Fried, Tamara Polajnar, and Stephen Clark
Annual Meeting of the Association for Computational Linguistics (ACL), 2015
Higher-Order Lexical Semantic Models for Non-Factoid Answer Reranking
Daniel Fried, Peter Jansen, Gustave Hahn-Powell, Mihai Surdeanu, and Peter Clark
Transactions of the Association for Computational Linguistics (TACL), 2015
Incorporating both Distributional and Relational Semantics in Word Representations
Daniel Fried and Kevin Duh
International Conference on Learning Representations (ICLR) Workshop, 2015
Analyzing the Language of Food on Social Media
Daniel Fried, Mihai Surdeanu, Stephen Kobourov, Melanie Hingle, and Dane Bell
International Conference on Big Data, 2014
Maps of Computer Science
Daniel Fried and Stephen Kobourov
Pacific Visualization Symposium (PacificVis), 2014
Predicting Parallelization of Sequential Programs Using Supervised Learning
Daniel Fried, Zhen Li, Ali Jannesari, and Felix Wolf
International Conference on Machine Learning and Applications, 2013
A Generative Probabilistic Framework for Learning Spatial Language
Colin Dawson, Jeremy Wright, Antons Rebguns, Marco Valenzuela Escárcega, Daniel Fried, and Paul Cohen
International Conference on Development and Learning, 2013. Best Paper Award
Bayesian Geometric Modeling of Indoor Scenes
Luca Del Pero, Joshua Bowdish, Daniel Fried, Bonnie Kermgard, Emily Hartley, and Kobus Barnard
Conference on Computer Vision and Pattern Recognition (CVPR), 2012
*,**: equal contribution
Modularity in Grounded Interaction. ViGIL Workshop, NAACL 2021. (with Rudy Corona)
Learning Grounded Pragmatic Communication. University of Arizona, TTI-Chicago, University of Southern California, Purdue, Carnegie Mellon University, UC Irvine, Université de Montréal/Mila, Allen Institute for Artificial Intelligence, Facebook AI Research, Google Research, Stanford Cognition and Language Workshop. Spring 2021
Pragmatic Models for Generating and Following Grounded Instructions. Stanford NLP Seminar, University of Arizona, USC ISI. Fall 2018–Spring 2019
Instructor: CS188, Artificial Intelligence, UC Berkeley, Summer 2018
Co-taught (with Anwar Baroudi) a 160-student upper-division undergraduate introduction to AI.
Topics: search, games, Markov decision processes, reinforcement learning, graphical models, and machine learning.
Prepared and delivered lectures; co-managed an 8-person course staff; designed exams;
co-supervised redesign of a course project; managed course logistics; held office hours and graded.
Received UC Berkeley’s Outstanding Graduate Instructor Award.
Teaching effectiveness rating: 6.2 / 7. The department average is 5.9 / 7. Rated in the top 25% of instructors for this course in the last 10 years by teaching effectiveness.
Teaching Assistant: CS188, Artificial Intelligence, UC Berkeley, Fall 2017
Teaching assistant for a 600-student upper-division undergraduate course.
Taught a weekly section of students; helped design a machine learning project which has been completed by over 4,000 students at Berkeley in semesters since; helped write course notes, exams, and section problems; held office hours and graded.
Teaching effectiveness rating: 4.6 / 5. The department average is 4.3 / 5.
Teaching Assistant: CS245, Intro to Discrete Structures, University of Arizona, Spring 2012
Teaching Assistant: ISTA100, Great Ideas of the Information Age, University of Arizona, Fall 2011
Guest Lecturer: CS288, Natural Language Processing, Grounded Semantics, UC Berkeley, Spring 2020 & Spring 2021
Project Design: CS288, Natural Language Processing, UC Berkeley, Spring 2020
Workshop co-organizer: 2nd Advances in Language and Vision Workshop, NAACL 2021
Outstanding reviewer awards: ACL 2018, NeurIPS 2019, ACL 2020, ACL 2021
Program committee (reviewer): ACL Rolling Review 2021; ACL 2018–2021; EMNLP 2016–2021; NAACL 2019, 2021; AACL-IJCNLP 2020; EACL 2017, 2021; NeurIPS 2019–2021; ICML 2019; ICLR 2021; AKBC 2021; COLING 2018; *SEM 2016–2018; NAACL-SRW 2016, 2018; ACL-SRW 2020; IJCAI 2016; SpLU-RoboNLP 2019, 2021; NeuralGen 2019; ViGIL 2019, 2021; DeepLo 2019; ALVR 2020, 2021; LaReL 2020; Cooperative AI 2021; Meaning in Context 2021
EECS Peer Advisor, UC Berkeley, 2019–2021
Treasurer, CS Graduate Student Association, UC Berkeley, 2016–2018
EECS Graduate Admissions Committee, UC Berkeley, 2017 & 2019
Student Ambassador, College of Science, University of Arizona, 2011–2014
Vice President, Association for Computing Machinery, University of Arizona, Fall 2011