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Daniel Khashabi
Assistant Professor of Computer Science
Whiting School of Engineering
Johns Hopkins University

Office: Hackerman Hall 316B | Email: danielk ¯\_(ツ)_/¯ cs.jhu.edu

Research Themes

My research focuses on the computational foundations of intelligent behavior, through the lens of natural language. My goal is to explore the different ways in which we can develop and evaluate systems that understand and reason with (and about) natural language in different contexts. I mostly identify with natural language processing (ACL, NAACL, EMNLP) and artificial intelligence communities (AAAI, IJCAI) and, to some extent, with the machine learning community (ICLR, NeurIPS, ICML).

Here are several themes I am interested in:

  • Generalism*: AlphaGo may be the world champion at Go, although it can't solve any other problem! How can we incentivize building models that can address a broader scope of tasks and abilities? (papers: ACL’22, EMNLP-Findings’20)

  • Self-supervised learning: How can we build task-independent representations that utilize cheap signals available in-the-wild (web data or physical environment) and support better model generalization? (papers: ACL’20, EMNLP’18)

  • Reasoning: I view “reasoning” as the process of using “reasons” for explaining or justifying decisions. How can we enable machines to communicate via reasons, for a broad-ranging spectrum of tasks? (papers: TACL’21, NAACL’21)

  • AI + Humans: With the increased deployment of AI we need to better understand their impacts on humans and foresee the unintended harms that may result: How can we bake in transparency in models so human operators can contextualize system output? Can we make such transparency into a truly democratic oversight of systems and their algorithmic biases? Can we build systems that can recourse when there is harm to those that are marginalized? Can we build systems that reduce our socio-political divisions? (papers: EMNLP’20, NAACL’19)

Select Talks

Teaching

Publication

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  • 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.
    arXiv preprint arXiv:2206.04615, 2022. [data]

  • Benchmarking generalization via in-context instructions on 1,600+ language tasks.
    Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana 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, Mehrad Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Shailaja Keyur Sampat, Savan Doshi, Siddhartha Mishra, Sujan Reddy, Sumanta Patro, Tanay Dixit, Xudong Shen, Chitta Baral, Yejin Choi, Noah A. Smith, Hannaneh Hajishirzi and Daniel Khashabi.
    arXiv preprint arXiv:2204.07705, 2022. [data] [project]

  • 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.