Skip to main content

Adam Poliak: CV

(Last updated February 15, 2024)

Employment

Assistant Professor in Computer Science
Bryn Mawr College, Bryn Mawr, PA		 August 2022-present
Roman Family Teaching and Research Fellow
Barnard College, Columbia University, New York, NY		 July 2020-June 2022

Education

PhD in Computer Science
Johns Hopkins University, Baltimore, MD
Thesis: Revisiting Recognizing Textual Entailment for Evaluating Natural Language Processing Systems.
Advisor: Benjamin Van Durme. 		Summer 2020
M.S.E. in Computer Science
Johns Hopkins University, Baltimore, MD 		May 2019
BA in Computer Science
Johns Hopkins University, Baltimore, MD 		May 2016

Teaching

Computer Science I (CMSC B113)
Bryn Mawr College 		Fall, Spring, Fall 2022, 2023, 2023
Computational Text Analysis (COMS BC2710)
Barnard College 		Summer 2021
This research based undergraduate course will introduce students to the methods and tools used in computational text analysis, aka text as data. This course focuses on methods used to discover and measure concepts and phenomena from large amounts of text. Students will implement methods covered in class and apply these methods to texts of their choosing. Some prior programming experience is expected, though all necessary skills, including an overview of Unix and Python, will be covered in the beginning of the course.
New Directions in Computing - Applied Natural Language Processing for Semantic Evaluations (COMS BC3997)
Barnard College 		Fall 2020
This course will introduce students to the methods and tools used for developing Natural Language Processing and Machine Learning software. Students will work as a team to develop a machine learning system that can compete in a range range of increasingly challenging problems in natural language semantics. Teams will choose which challenge to tackle from a collection of tasks for computational semantic analysis. Students will have an opportunity to compare their systems against teams from other institutions and present their results. Participation requires permission of the instructor.
Introduction to Computational Thinking and Data Science (COMS BC1016)
Barnard College 		Fall B 2020
This course will introduce students to the methods and tools used in data science to obtain insights from data. Students will learn how to analyze data arising from real-world phenomena while mastering critical concepts and skills in computer programming and statistical inference. The course will involve hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. This class is ideal for students looking to increase their digital literacy and expand their use and understanding of computation and data analysis across disciplines.
Artificial Intelligence (EN 601.466.02)
Johns Hopkins University 		Spring 2020
The course situates the study of Artificial Intelligence (AI) first in the broader context of Philosophy of Mind and Cognitive Psychology and then treats in-depth methods for automated reasoning, automatic problem solvers and planners, knowledge representation mechanisms, game playing, machine learning, and statistical pattern recognition. The class is a recommended for all scientists and engineers with a genuine curiosity about the fundamental obstacles to getting machines to perform tasks such as deduction, learning, and planning and navigation. Strong programming skills and a good grasp of the English language are expected; students will be asked to complete both programming assignments and writing assignments. The course will include a brief introduction to scientific writing and experimental design, including assignments to apply these concepts.

Teaching Reviews at Barnard

Quality scale (1-5): 1=Poor, 2=Fair, 3=Good, 4=Very Good, 5=Excellent

Term Course Title (Number) Students Enrolled Course Quality Instructor Quality
Summer 2021 Computational Text Analysis (COMS BC2710) 22 4.59 4.76
Fall 2020 New Directions in Computing - Applied Natural Language Processing for Semantic Evaluations (COMS BC3997) 4 0 0
Fall B 2020 Introduction to Computational Thinking and Data Science (COMS BC1016) 36 4.18 4.11

Teaching Reviews at JHU

Quality scale (1-5): 1=Poor, 2=Weak, 3=Satisfactory, 4=Good, 5=Excellent

Term Course Title (Number) Students Enrolled Course Quality Instructor Quality
Spring 2020 Artificial Intelligence (EN 601.466.02) 23 4.35 4.32

Publications

Refereed conference papers

  1. Dhruv Verma, Yash Kumar Lal, Shreyashee Sinha, Benjamin Van Durme and Adam Poliak (2023). Evaluating Paraphrastic Robustness in Textual Entailment Models. ACL 2023.
  2. Alexandra DeLucia, Adam Poliak, Zechariah Zhu, Stephanie R Pitts, Mario Navarro, Sharareh Shojaie, John W Ayers, Mark Dredze (2023).Automated Discovery of Perceived Health-related Concerns about E-cigarettes from Reddit.
  3. Adam Poliak, Paiheng Xu, Eric Leas, Mario Navarro, Stephanie Pitts, Andie Malterud, John W Ayers, Mark Dredze (2022). A Machine Learning Approach For Discovering Tobacco Brands, Products, and Manufacturers in the United States. Annual Meeting of the Society for Research on Nicotine and Tobacco 2022.
  4. Tuhin Chakrabarty, Debanjan Ghosh, Adam Poliak, Smaranda Muresan (2021). Figurative Language in Recognizing Textual Entailment. Findings of ACL 2021.
  5. Siddharth Vashishtha, Adam Poliak, Yash Kumar Lal, Benjamin Van Durme, Aaron Steven White (2020). Temporal Reasoning in Natural Language Inference. Findings of EMNLP 2020.
  6. Nathaniel Weir, Adam Poliak, Benjamin Van Durme (2020). Probing Neural Language Models for Human Tacit Assumptions. CogSci 2020.
  7. Tongfei Chen*, Zhengping Jiang*, Adam Poliak, Keisuke Sakaguchi, Benjamin Van Durme (2020). Uncertain Natural Language Inference. ACL 2020. 8 pages.
  8. Yonatan Belinkov*, Adam Poliak*, Stuart M. Shieber, Benjamin Van Durme, Alexander Rush (2019). Don’t Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference . ACL 2019.
  9. Yonatan Belinkov*, Adam Poliak*, Stuart M. Shieber, Benjamin Van Durme, Alexander Rush (2019). On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference. StarSem 2019.
  10. Najoung Kim, Roma Patel, Adam Poliak, Patrick Xia, Alex Wang, R. Thomas Mccoy, Ian Tenney, Alexis Ross, Tal Linzen, Benjamin Van Durme, Samuel Bowman, Ellie Pavlick (2019). Probing what different NLP tasks teach machines about function word comprehension. StarSem 2019. Best Paper Award.
  11. Ian Tenney, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, R. Thomas McCoy, Najoung Kim, Benjamin Van Durme, Samuel R. Bowman, Dipanjan Das, Ellie Pavlick (2019). What do you learn from context? Probing for sentence structure in contextualized word representations. ICLR 2019.
  12. Adam Poliak, Aparajita Haldar, Rachel Rudinger, J. Edward Hu, Ellie Pavlick, Aaron Steven White, Benjamin Van Durme (2018). Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation. EMNLP 2018.
  13. Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, Benjamin Van Durme (2018). Hypothesis Only Baselines in Natural Language Inference. StarSem 2018. Best Paper Award.
  14. Adam Poliak, Yonatan Belinkov, Jim Glass, Benjamin Van Durme (2018). On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference. NAACL 2018.
  15. Francis Ferraro, Adam Poliak, Ryan Cotterell and Benjamin Van Durme (2017). Frame-Based Continuous Lexical Semantics through Exponential Family Tensor Factorization and Semantic Proto-Roles. StarSem 2017.
  16. Adam Poliak*, Pushpendre Rastogi*, M. Patrick Martin and Benjamin Van Durme (2017). Efficient, Compositional, Order-sensitive n-gram Embeddings. EACL 2017.
  17. Ryan Cotterell, Adam Poliak, Benjamin Van Durme and Jason Eisner (2017). Explaining and Generalizing Skip-Gram through Exponential Family Principal Component Analysis. EACL 2017.
  18. Adam Teichert, Adam Poliak, Benjamin Van Durme, Matthew R. Gormley (2017). Semantic Proto-Role Labeling. AAAI 2017.

Journal articles

  1. Eric C Leas, Tomas Mejorado, Raquel Harati, Shannon Ellis, Nora Satybaldiyeva, Nicolas Morales, Adam Poliak (2023). E-commerce licensing loopholes: a case study of online shopping for tobacco products following a statewide sales restriction on flavoured tobacco in California. Tobacco Control 2023.
  2. John W Ayers, Adam Poliak, Mark Dredze, Eric C Leas, Zechariah Zhu, Jessica B Kelley, Dennis J Faix, Aaron M Goodman, Christopher A Longhurst, Michael Hogarth, Davey M Smith (2023). Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum. JAMA Internal Medicine 2023.
  3. Adam Poliak, Nora Satybaldiyeva, Steffanie A. Strathdee, Eric C. Leas, Ramesh Rao, Davey Smith, John W. Ayers (2022). Internet Searches for Abortion Medications Following the Leaked SCOTUS Draft Ruling. JAMA Internal Medicine 2022.
  4. John W. Ayers, Adam Poliak, Derek C. Johnson, Eric C. Leas, Mark Dredze, Theodore Caputi, Alicia L. Nobles (2021). Suicide-Related Internet Searches During the Early Stages of the COVID-19 Pandemic in the US. JAMA Network Open 2021.
  5. John W Ayers, Benjamin M Althouse, Adam Poliak, Eric C Leas, Alicia L Nobles, Mark Dredze, Davey Smith (2020). Quantifying Public Interest in Police Reforms by Mining Internet Search Data Following George Floyd's Death. Journal of Medical Internet Research (JMIR) 2020.
  6. John W Ayers, Eric C Leas, Derek C Johnson, Adam Poliak, Benjamin M Althouse, Mark Dredze, Alicia L Nobles (2020). Internet Searches for Acute Anxiety During the Early Stages of the COVID-19 Pandemic. JAMA Internal Medicine 2020.
  7. Pushpendre Rastogi, Adam Poliak, Vince Lyzinski, and Benjamin Van Durme. (2018). Neural Variational Entity Set Expansion for Automatically Populated Knowledge Graphs. Information Retrieval Journal 2018.

Refereed workshop papers

  1. Sanjana Marcé, Adam Poliak (2022). On Gender Biases in Offensive Language Classification Models. Workshop on Gender Bias in Natural Language Processing @ NAACL 2022.
  2. Esha Julka, Olivia Kowalishin, Jalisha B. Jenifer, Adam Poliak (2021).Characterizing Test Anxiety on Social Media.
  3. Daphna Spira, Noreen Mayat, Caitlin Dreisbach, Adam Poliak (2021). Discovering Changes in Birthing Narratives During COVID-19. WiNLP 2021.
  4. Max Fleming, Priyanka Dondeti, Caitlin Dreisbach, Adam Poliak (2021). Fine-tuning Transformers for Identifying Self-Reporting Potential Cases and Symptoms of COVID-19 in Tweets. Social Media Mining for Health Applications 2021 (Shared Task) 2021.
  5. Adam Poliak, Jalisha Jenifer (2021). An Immersive Computational Text Analysis Course for Non-Computer Science Students at Barnard College. Fifth Workshop on Teaching NLP @ NAACL 2021 2021.
  6. Adam Poliak (2020). A Survey on Recognizing Textual Entailment as an NLP Evaluation. Evaluation and Comparison of NLP Systems (Eval4NLP) 2020.
  7. Adam Poliak, Max Fleming, Cash Costello, Kenton W Murray, Mahsa Yarmohammadi, Shivani Pandya, Darius Irani, Milind Agarwal, Udit Sharma, Shuo Sun, Nicola Ivanov, Lingxi Shang, Kaushik Srinivasan, Seolhwa Lee, Xu Han, Smisha Agarwal, João Sedoc (2020). Collecting Verified COVID-19 Question Answer Pairs. NLP COVID-19 Workshop @EMNLP 2020.
  8. Adam Poliak, Benjamin Van Durme (2019). Adversarial Learning for Robust Emergency Need Discovery in Low Resource Settings. West Coast NLP (WeCNLP) 2019.
  9. Pushpendre Rastogi, Adam Poliak, Benjamin Van Durme (2017). Training Relation Embeddings under Logical Constraints. KG4IR 2017.
  10. Adam Poliak and Benjamin Van Durme (2017). Generating Automatic Pseudo-entailments from AMR Parses. MASC-SLL 2017.

Preprints

Theses

  1. Adam Poliak (2020). Revisiting Recognizing Textual Entailment for Evaluating Natural Language Processing Systems. PhD Thesis, Johns Hopkins University 2020.

Invited Talks

  1. Columbia University NLP Seminar. Exploring Reasoning Capabilities in Natura Language Processing Models. February 12, 2020.
  2. Salesforce Research (remote). Exploring Reasoning Capabilities in Natural Language Processing Models. April 21, 2020.
  3. Johns Hopkins University Applied Physics Laboratory (remote). Exploring Reasoning Capabilities in Natural Language Processing Models. March 26, 2020.
  4. Barnard College, Computer Science Seminar. Exploring Reasoning Capabilities in Natural Language Processing Models. February 18, 2020.
  5. Georgetown University, Computational Linguistics. Sentence-level Semantic Inference - From Diverse Phenomena to Applications. January 10, 2020.
  6. Stony Brook University. Sentence-level Semantic Inference - From Diverse Phenomena to Applications. January 6, 2020.
  7. University of Pennsylvania, Computational Linguistics Lunch. Sentence-level Semantic Inference - From Diverse Phenomena to Applications. December 10, 2019.
  8. Bar Ilan University. Probing for Semantic Phenomena in Neural Models via Natural Language Inference. January 8, 2019.
  9. Columbia University. Probing for Semantic Phenomena in Neural Models via Natural Language Inference. November 12, 2018.
  10. Johns Hopkins University, CLSP Seminar. Probing for Semantic Phenomena in Neural Models via Natural Language Inference. August 31, 2018.

Academic Service

Undergraduate and Masters Advising

Independent Studies and RAships

Fall 2019 & Spring 2020

  1. Rebecca Shao - Undergraduate - Independent Study

Spring 2018

  1. Aparajita Haldar - Visiting Undergraduate - Undergraduate Thesis
  2. Edward Hu - Undergraduate - Independent Study

Fall 2017

  1. Baekchun Kim - Undergraduate - Independent Study
  2. Ayush Dalmia - Undergraduate - RA