Nasrin Mostafazadeh

New York, NY
Email: me [at] nasrin [dot] info

I am a senior AI research scientist at Elemental Cognition where I work on AI systems that not only comprehend stories, but also explain their reasoning and answer 'why' (we are hiring!). I was selected as a Forbes 30 Under 30 in Science in 2019. I am active in the AI startup community and I am a big believer in the power of sustainable startups disrupting different sectors through AI, something that is harder to achieve in big tech companies. I got my PhD at the Computer Science department of University of Rochester, with my thesis titled "From Event to Story Understanding". At Rochester, I worked under James F. Allen in Conversational Interaction and Spoken Dialogue Research Group. After graduation, I joined BenevolentAI labs in NYC as a senior research scientist to use NLP and AI for accelerating scientific discovery for major diseases. Before that, I spent about a year at Microsoft Research working on various Vision & Language problems and one summer at Google NYC working on Sentiment Analysis. I've got my bachelor's degree from Sharif University of Technology in 2012 where I was a member of NLP lab. I graduated from Farzanegan high school where I started my work in NLP and AI.

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I'm interested in building intelligent systems that can comprehend language (in a deep manner, where they can "explain" themselves!). I mainly model language in terms of 'events' and how they interact with each other in terms of temporal and causal relations, with applications ranging from storytelling to vision & language. Deep language understanding, which enables inference and commonsense reasoning, requires systems that have large amounts of knowledge enabling them to connect natural language to the concepts of the world. A part of my work concerns developing approaches for learning knowledge bases on semantically-rich narrative structures.

Recent Invited Talks & Interviews

Interview with Forbes, The Art Of AI Storytelling: How One 30 Under 30 Scientist Is Teaching Devices To Make Assumptions , December 2018 [Interview]
Interview with This Week in Machine Learning (TwiML), "Contextual Modeling for Language and Vision", August 2018 [Interview]
Speaker at ACM NYC, "Language Understanding and Language Generation in Eventful Contexts", July 2018
Keynote Speaker at the Storytelling Workshop at NAACL 2018, "Event-centric Context Modeling: The Case of Story Comprehension and Story Generation", June 2018 [Slides]
Interview with the Computer Vision & Pattern Recognition (CVPR) Magazine, featured in the Best of CVPR, June 2018, [Interview]
Speaker and Panelist at the VQA and the Visual Dialogue Workshop at CVPR 2018, "Event-centric Multimodal Context Modeling", June 2018, [Slides]
Speaker at Cognitive AI NYC, "Language Understanding and Language Generation in Eventful Contexts", April 2018
Speaker at Columbia Lean-in Conference, "Perseverance & Personal Power", March 2018
Speaker at the NLP Seminar at the Stanford University, "Language Comprehension and Language Generation in Eventful Contexts", November 2018, [Slides]
Speaker at CS Seminar Series at the Columbia University, "Language Comprehension and Language Generation in Eventful Contexts", October 2018

Publications

Organization

NAACL 2019

I will be co-chairing the system demonstration track at NAACL 2019. The Call for Papers to be announced soon.

LSDSem 2017

I co-organized Linking Models of Lexical, Sentential and Discourse-level Semantics (LSDSem) workshop, with the ***Story Cloze Test*** as the shared task. Visit the Shared Task page for more details.

QA TempEval 2015

I co-organized Question Answering Temporal Evaluation (QA TempEval) as the task 5 at SemEval-2015, continuing the TempEval series. Visit the page for more details.

Thought bubble! Quite a long time ago, I got interested in Computational Linguistics when I read about Noam Chomsky's theory of Innate Universal Grammar, when I was amazed by how language capability (seemingly the easiest for human) is the hardest to achieve for a robot (which could readily prove things like geometry theorems, which seemed harder to me at the time!). After you watch this video, don't you wish we had NLP systems which could actually tell us 'why', going beyond recognizing patterns and classifying things? and of course the question is how...