(Last updated July 30, 2024)
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 |
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 |
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. |
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 |
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 |