135 - PhD Application Series: After Submitting Applications

135 - PhD Application Series: After Submitting Applications

Author: Allen Institute for Artificial Intelligence March 3, 2022 Duration: 36:53
This episode is the third in our current series on PhD applications. We talk about what the PhD application process looks like after applications are submitted. We start with a general overview of the timeline, then talk about how to approach interviews and conversations with faculty, and finish by discussing the different factors to consider in deciding between programs. The guests for this episode are Rada Mihalcea (Professor at the University of Michigan), Aishwarya Kamath (PhD student at NYU), and Sanjay Subramanian (PhD student at UC Berkeley). Homepages: - Aishwarya Kamath: https://ashkamath.github.io/ - Sanjay Subramanian: https://sanjayss34.github.io/ - Rada Mihalcea: https://web.eecs.umich.edu/~mihalcea/ The hosts for this episode are Alexis Ross and Nishant Subramani.

While NLP Highlights is currently on hiatus, its archive remains a compelling snapshot of conversations from the front lines of computational linguistics. Produced by the Allen Institute for Artificial Intelligence, this science podcast carved out a space for deep, researcher-led discussions about natural language processing. Each episode functions as an informal seminar, where the people actively designing algorithms and pushing the field forward explain their work in their own words. You'll hear about the nuanced challenges behind making machines understand, generate, and reason with human language, from foundational theories to unexpected applications. The dialogue in this podcast often delves into the "why" behind the research, not just the results, offering clarity on complex topics like machine translation, sentiment analysis, or large language models. It’s a chance to listen as experts articulate their thought processes, debates, and moments of insight, with all the candidness that comes from a conversation between peers. The views shared are personal perspectives from the hosts and their guests, independent of their affiliated institutions. For anyone curious about how machines learn to parse meaning, the archived episodes of NLP Highlights provide a thoughtful and accessible entry point.
Author: Language: English Episodes: 100

NLP Highlights
Podcast Episodes
103 - Processing Language in Social Media, with Brendan O'Connor [not-audio_url] [/not-audio_url]

Duration: 43:12
We talked to Brendan O’Connor for this episode about processing language in social media. Brendan started off by telling us about his projects that studied the linguistic and geographical patterns of African American Eng…
101 - The lottery ticket hypothesis, with Jonathan Frankle [not-audio_url] [/not-audio_url]

Duration: 41:16
In this episode, Jonathan Frankle describes the lottery ticket hypothesis, a popular explanation of how over-parameterization helps in training neural networks. We discuss pruning methods used to uncover subnetworks (win…
100 - NLP Startups, with Oren Etzioni [not-audio_url] [/not-audio_url]

Duration: 30:55
For our 100th episode, we invite AI2 CEO Oren Etzioni to talk to us about NLP startups. Oren has founded several successful startups, is himself an investor in startups, and helps with AI2's startup incubator. Some of ou…
98 - Analyzing Information Flow In Transformers, With Elena Voita [not-audio_url] [/not-audio_url]

Duration: 37:05
What function do the different attention heads serve in multi-headed attention models? In this episode, Lena describes how to use attribution methods to assess the importance and contribution of different heads in severa…
95 - Common sense reasoning, with Yejin Choi [not-audio_url] [/not-audio_url]

Duration: 35:29
In this episode, we invite Yejin Choi to talk about common sense knowledge and reasoning, a growing area in NLP. We start by discussing a working definition of “common sense” and the practical utility of studying it. We…
94 - Decompositional Semantics, with Aaron White [not-audio_url] [/not-audio_url]

Duration: 27:50
In this episode, Aaron White tells us about the decompositional semantics initiative (Decomp), an attempt to re-think the prototypical approach to semantic representation and annotation. The basic idea is to decompose co…