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Daniel Khashabi
Assistant Professor, Department of Computer Science, Johns Hopkins University
Office: Hackerman Hall 316B
Email: danielkjhu.edu

Other affiliations:

Research Themes

Broadly, my research is anchored around the use of natural language as a key medium for communication between humans and AI systems and is driven by the goal of making these systems more helpful, reliable, and efficient.

In pursuit of these objectives, my team and I have made various endeavors toward the following more specific themes:

  • Augmentation: Designing models that enhance human experience—our aim is to build AI that genuinely benefits people.

  • Generality: Building models that generalize across tasks, modalities, and environments, with attention to the long tail of edge cases.

  • Specificity: Tailoring models to specific users or domains to improve efficiency and real-world utility.

  • Reasoning: Strengthening models’ ability to communicate via reasons, both for creative problem-solving and for communicating decisions through clear explanations.

  • Interpretability: Understanding and monitoring how models behave; e.g., how in-context learning emerges, when it works, and when it fails.

  • Safety & Transparency: Advancing reliability and transparency in AI—essential for responsible deployment in high-stakes settings.

  • Applications: Exploring high-impact uses of AI, especially in advancing scientific discovery.

Most of my research is aligned with the following research communities: natural language processing (ACL, NAACL, EMNLP), machine learning (ICLR, NeurIPS, ICML), and artificial intelligence (AAAI, IJCAI).

Information for Prospective Students and Visitors

Due to the large number of emails I receive, I cannot respond to every email individually. Please review the information below before contacting me.

  • Current JHU students: If you are an undergraduate or masters student and would like to work on research with my group, please fill out this form. The minimum time commitment is 15 hours per week for six months.
  • Prospective visiting students: Please fill out the above form. For visiting graduate students, the minimum length of a visit is six months.
  • Prospective postdocs: Please email me directly with your CV and I will get back to you if there is an opportunity that is a good fit.
  • Prospective graduate students: Please apply through the system and list me as a potential advisor in your application. There is no need to contact me.

Recent Talks

  • 2025, University of Pennsylvania Computational Linguistics lunch (slides)

  • 2024, University of Cambridge the Language Technology Lab seminar (slides)

  • 2024, Oracle Labs ML seminar (slides)

  • 2024, Tel Aviv NLP seminar (slides)

  • 2024, Forum on ‘‘Engineered AI Systems’’ (slides)

  • 2024, Keynote at ‘‘Engineering for Professionals’’ quarterly meeting (slides)

  • 2024, Workshop on ‘‘LLMs for Healthy Aging’’ (slides)

  • 2023, NYU ‘‘Text-as-Data’’ talk series (slides)

  • 2023, Hopkins Center for Language and Speech Technologies seminar (video)

  • 2023, Hopkins Electrical Engineering department seminars (slides)

  • 2023, Amazon ‘‘Human in the Loop’’ seminar

  • 2023, Hopkins Center for Health Security seminars (slides)

  • 2023, UMD Computational Linguistics seminar (slides)

  • 2023, Applied Physics Lab, Intelligent Systems Center seminars

  • 2022, University of Tehran NLP seminar

  • 2021, University of Glasgow IR seminar (slides)

  • 2021, Johns Hopkins University (slides)

  • 2021, Google AI (slides)

  • 2021, UCLA Big Data and ML seminar (slides)

  • 2021, USC NLP seminar (slides)

  • 2020, Tel Aviv University NLP seminar (slides)

  • 2019, Workshop on Progress Towards the Holy Grail, Conference on Constraint Programming (CP), 2019. (slides)

  • 2019, CMU LTI seminar (slides)

  • 2018, NYU NLP seminar Reasoning-Driven Question Answering.

  • 2018, Stanford NLP seminar (slides)

  • 2018, Mid-Atlantic Student Colloquium on Speech, Language and Learning (slides)

Teaching

Intelligence Amplification Lab (IALab)

PhD students:

We’re also grateful to collaborate with a number of exceptional PhD, MS and undergraduate students who are not listed here.

Publication

Disclaimer: This material is presented to ensure the timely dissemination of scholarly works. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms invoked by each author's copyright.

  • Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.
    Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adri`{a} Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, Aman Hussain, Amanda Askell, Amanda Dsouza, Ambrose Slone, Ameet Rahane, Anantharaman S. Iyer, Anders Andreassen, Andrea Madotto, Andrea Santilli, Andreas Stuhlm"{u}ller, Andrew Dai, Andrew La, Andrew Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta and others.
    Transactions on Machine Learning Research (TMLR), 2023. Finalist for outstanding certification.🏆 [data]

  • Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks.
    Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, A. Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Gary Lai, Ishan Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Maitreya Patel, Kuntal Kumar Pal, M. Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Shailaja Keyur Sampat, Savan Doshi, Siddharth Deepak Mishra, Sujan Reddy, Sumanta Patro, Tanay Dixit, Xudong Shen, Chitta Baral, Yejin Choi, Noah A. Smith, Hanna Hajishirzi and Daniel Khashabi.
    , 2022.

  • Findings of the 2021 Conference on Machine Translation (WMT21).
    Farhad Akhbardeh, Arkady Arkhangorodsky, Magdalena Biesialska, Ond{v{r}}ej Bojar, Rajen Chatterjee, Vishrav Chaudhary, Marta R. Costa-jussa, Cristina Espa{~n}a-Bonet, Angela Fan, Christian Federmann, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Barry Haddow, Leonie Harter, Kenneth Heafield, Christopher Homan, Matthias Huck, Kwabena Amponsah-Kaakyire, Jungo Kasai, Daniel Khashabi, Kevin Knight, Tom Kocmi, Philipp Koehn, Nicholas Lourie, Christof Monz, Makoto Morishita, Masaaki Nagata, Ajay Nagesh, Toshiaki Nakazawa, Matteo Negri, Santanu Pal, Allahsera Auguste Tapo, Marco Turchi, Valentin Vydrin and Marcos Zampieri.
    Conference on Machine Translation (WMT), 2021.

  • CogCompNLP: Your swiss army knife for nlp.
    Daniel “Khashabi, Mark Sammons, Ben Zhou, Tom Redman, Christos Christodoulopoulos, Vivek Srikumar, Nicholas Rizzolo, Lev Ratinov, Guanheng Luo, Quang Do, Chen-Tse Tsai, Subhro Roy, Stephen Mayhew, Zhili Feng, John Wieting, Xiaodong Yu, Yangqiu Song, Shashank Gupta, Shyam Upadhyay, Naveen Arivazhagan, Qiang Ning, Shaoshi Ling and Dan” Roth.
    International Conference on Language Resources and Evaluation (LREC), 2018. [poster] [code]

  • Image demosaicing.
    Reinhard Sebastian Bernhard Nowozin, Danyal Khashabi, Jeremy Martin Jancsary, Bruce Justin Lindbloom and Andrew William Fitzgibbon.
    US Patent 9,344,690 - Google Patents, 2016.