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Daniel Khashabi
Assistant Professor of Computer Science
Center for Language and Speech Processing
Data Science and AI Institute
Whiting School of Engineering
Johns Hopkins University

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

Research Themes

My research is motivated by understanding the computational foundations of intelligent behavior, often through the lens of natural language. I am excited about intelligence amplification — building computational models that would augment human experience in a mutually interdependent fashion. The dominant majority of my research is aligned with natural language processing (ACL, NAACL, EMNLP), machine learning (ICLR, NeurIPS, ICML) and artificial intelligence (AAAI, IJCAI).

Here are several themes I am interested in:

  • General-purpose models: AlphaGo may be the world champion at Go, although it can't solve any other problem! How can we build models that generalize a broader scope of tasks, abilities, modalities, or environments?

  • Self-supervised representation learning: The AI literature has found powerful ways to build rich representations of the world by utilizing cheap signals available in the wild (web data, physical environment, etc.). How can we make these algorithms more effective and efficient (in terms of data or computation cost)? How can we make them robust to distributional drifts in data, e.g., low-data regimes or adversarial settings? How can we scale them up to various modalities or forms of communication/interaction?

  • Reasoning and problem-solving: I view “reasoning” as the process of using “reasons” to explain or justify decisions. How can we enable machines to communicate via reasons, for a broad-ranging spectrum of tasks? How can we make this process “verifiable” or “explainable” to humans? Can we build systems that can recourse upon a mistake?

  • Interaction, communication, and coordination: Can we engineer AI systems to effectively engage, interact, and communicate with humans and other AI systems for the purpose of, for example, coordination?

  • AI ↔ humans: The ultimate goal of our work is to benefit humans! How should we engineer the interface between AI and machines? What forms of interaction are most effective and meaningful for humans? How can we make AI systems more transparent and accountable to humans? Can we turn such transparency into a truly democratic oversight of systems, their algorithmic biases and mistakes? How should we think about personalizing AI systems to their users?

If you are an undergraduate or masters student and would like to work on research with my group, please fill out this form.

Select Talks

Teaching

Publication

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  • 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.