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C4AI Roads to Research - Research Question Ideation Panel


Date: May 21, 2024

Time: 3:00 PM - 4:00 PM

Location: Online

Let's demystify ML research. This panel hosted by Marzieh Fadaee will focus on how to ask research questions, featuring insight and advice from experienced researchers.

Discussion to include:
-some practical strategies or techniques you use for generating and refining research ideas in the field of ML
-common pitfalls or mistakes that ML researchers should avoid when formulating research questions
-focus on a specific niche or sub-field of ML versus exploring a variety of topics
Look forward to a lively discussion, followed by audience questions.

About the speakers
- Stella Biderman: I am a mathematician and theoretical computer scientist interested in a variety of types of computational research, including artificial intelligence, combinatorics, and data science. Currently, I do research developing new techniques and new applications of established techniques in AI and machine learning to data analysis, especially social network analysis. Recently I have been focusing on sociopolitical and ethical implications of computational decision making.

- Tim Bettmers: I am a graduating PhD student at the University of Washington advised by Luke Zettlemoyer working on efficient deep learning at the intersection between machine learning, natural language processing, and computer systems with a focus on quantization and sparsity. My main research goal is to empower everyone to make AI their own. I do this by making large models accessible through my research (QLoRA, LLM.int8(), k-bit inference scaling laws, Petals, SWARM) and by developing software that makes it easy to use my research innovations (bitsandbytes).

- Mostafa: I'm a Research Scientist at Google Brain, where I work on machine learning, in particular, deep learning. My areas of interest include self-supervised learning, generative models, training giant models, and sequence modeling. Before Google, I was doing a PhD at the University of Amsterdam. My PhD research was focused on improving the process of learning with imperfect supervision. I explored ideas around using injecting inductive biases into algorithms, incorporating prior knowledge, and meta-learning the properties of the data using the data itself, in order to help learning algorithms to better learn from noisy or/and limited data.

- Eugene Cheah: I am currently the CEO / Co-Founder at Recursal AI and currently work on: RWKV : Opensource foundation model under the Linux Foundation, : Low-Code UI Testing, various other opensource projects, most notable : GPU.JS. In the past, I worked as a developer / project manager at multiple banks and insurance companies and developer at multiple startup

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