I am a senior research scientist at Google DeepMind in New York. I work on natural language processing — for most of the last decade on learning representations of meaning, and more recently on watermarking language models and on what makes long-form generated text good or bad. I have also contributed to the Gemini, Gemma, and PaLM model families.
I did my Ph.D. at Carnegie Mellon, advised by Taylor Berg-Kirkpatrick and Graham Neubig, working closely with Kevin Gimpel at TTIC. Before that I did an MS with Dan Roth at Illinois, and undergraduate degrees in mathematics and chemistry at Wisconsin.
The best way to reach me is by email.
News
- New preprint: POLARIS, on guiding small models to write long stories.
- New preprint: StoryScope, investigating idiosyncrasies in AI fiction.
- Two watermarking papers accepted to TMLR.
Publications
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51 · 2026
POLARIS: Guiding Small Models to Write Long Stories
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50 · 2026
StoryScope: Investigating Idiosyncrasies in AI Fiction
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49 · 2026
Improving Detection of Watermarked Language Models
Tackles the detection side of watermarking, showing that combining watermark-based detection with model-based detectors outperforms either alone — including on text from unwatermarked models.
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48 · 2026
A Watermark for Black-Box Language Models
A watermarking scheme requiring only sampling access to a model — no logits — making watermarking practical for API-only settings while retaining distortion-free guarantees.
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47 · 2026
Gemma 4 Technical Report
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46 · 2025
A Gold Standard Dataset for the Reviewer Assignment Problem
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45 · 2025
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
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44 · 2025
Gemma 3 Technical Report
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43 · 2024
PostMark: A Robust Blackbox Watermark for Large Language Models
- 42 · 2024
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41 · 2024
Multiple References with Meaningful Variations Improve Literary Machine Translation
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40 · 2024
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
- 39 · 2023
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38 · 2023
Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense
Showed that a discourse-level paraphraser (DIPPER) evades watermarking, GPTZero, DetectGPT, and OpenAI's own classifier — and that retrieval over previously generated text is the one defense that holds up. Widely cited in the debate over whether AI-text detection is possible at all.
- 37 · 2023
- 36 · 2023
- 35 · 2023
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34 · 2023
FIAT: Fusing Learning Paradigms with Instruction-Accelerated Tuning
Fused the two dominant learning paradigms — in-context instruction and parameter tuning — into a single method that outperforms both ICL and fine-tuning alone across model scales.
- 33 · 2023
- 32 · 2023
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31 · 2023
Gemini: A Family of Highly Capable Multimodal Models
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30 · 2023
PaLM 2 Technical Report
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29 · 2022
Canine: Pre-training an Efficient Tokenization-Free Encoder for Language Representation
The first pre-trained tokenization-free encoder: CANINE operates directly on characters, sidestepping the brittleness of fixed subword vocabularies while outperforming a comparable mBERT model with fewer parameters.
- 28 · 2022
- 27 · 2022
- 26 · 2022
- 25 · 2022
- 24 · 2022
- 23 · 2021
- 22 · 2021
- 21 · 2020
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20 · 2020
Reformulating Unsupervised Style Transfer as Paraphrase Generation
Reframed unsupervised style transfer as paraphrase generation (STRAP): normalize the style away with a paraphraser, then style it back. No parallel data or style-specific engineering, with large gains over prior systems across formality, Shakespeare, and social-media styles.
- 19 · 2020
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18 · 2020
Learning and Applications of Paraphrastic Representations for Natural Language
- 17 · 2019
- 16 · 2019
- 15 · 2019
- 14 · 2019
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13 · 2018
ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations
Showed that back-translating a bitext yields tens of millions of paraphrase pairs essentially for free, and that simple embeddings trained on them outperform far more sophisticated architectures — a baseline the Sentence-BERT paper later acknowledged as remarkably strong. The dataset became a standard resource for paraphrase generation and semantic similarity.
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12 · 2018
Adversarial Example Generation with Syntactically Controlled Paraphrase Networks
Introduced syntactically controlled paraphrase networks, which rewrite a sentence into a specified syntactic form — and showed these adversarial rephrasings break models that word-level perturbations leave intact.
- 11 · 2018
- 10 · 2018
- 9 · 2017
- 8 · 2017
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7 · 2016
Towards Universal Paraphrastic Sentence Embeddings
The first work to evaluate sentence embeddings directly on semantic textual similarity — a protocol later adopted by SentEval and its successors — it found that simply averaging word embeddings beat LSTMs for general-purpose sentence representation. This result became the starting point for Arora et al.'s (2017) famous “tough-to-beat baseline.”
- 6 · 2016
- 5 · 2016
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4 · 2015
From Paraphrase Database to Compositional Paraphrase Model and Back
The Paragram vectors trained here — including the 300d Paragram-SL999 set released in the appendix — were the first to reach human-level agreement on SimLex-999, and became a standard initialization for later similarity models.
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3 · 2015
Clustering With Side Information: From a Probabilistic Model to a Deterministic Algorithm
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2 · 2014
Tiered Clustering to Improve Lexical Entailment
A sole-authored early paper: tiered clustering over distributional contexts to sharpen lexical entailment detection.
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1 · 2013
Illinois Cognitive Computation Group UI-CCG TAC 2013 Entity Linking and Slot Filler Validation Systems
Software & data
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data
ParaNMT-50M
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models
Paraphrastic representations at scale
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tool
compare-mt
Service
Area Chair
- AAAI2022, 2023, 2026 · Senior Program Committee, 2027
- NeurIPS2023, 2024, 2025
- ICML2025
- ACL2020, 2024
- NAACL2021, 2024
- EMNLP2024
- EACL2024
- ARRongoing
Reviewing
- TMLRongoing
Other
- ACL 2020Paper Matching System — designed and trained the abstract similarity model used to assign reviewers to papers, and helped build the system with Graham Neubig
Invited talks
- Georgia Tech — CS 4650, Natural Language Understanding
- Google — NLX invited talk · Google — Lux Leads meeting
- Ohio State University · UMass Amherst · Allen Institute for AI · IBM
- Midwest Speech and Language Days, University of Chicago
- Midwest Speech and Language Days, University of Chicago · CCICADA Research Retreat, Carnegie Mellon