Uncover the secrets of successful scientific communication at our upcoming panel discussion. Join Marzieh Fadaee with Jay Alammar and Shayne Longpre as they share their insights on the art of translating scientific concepts.

Join us for an panel discussion exploring the role of mentorship in the dynamic field of machine learning research. Our panelists will explore the impact of mentorship on shaping the careers, from fostering innovation to navigating pivotal moments, and considering when one is ready to become a mentor themself. Following the discussion, there will be an allocated Q&A session, offering attendees the opportunity to engage directly and seek clarification on any aspect of mentorship in ML research.

Join us for a panel discussion with Bryan Li as part of our 'Roads to Research' series on the different pathways to graduate studies in ML research. You'll hear from speakers who have all had different journeys through ML grad programs. Following the discussion, there will be an allocated Q&A session, offering attendees the opportunity to engage directly and seek clarification on any aspect of grad studies in ML research.

Prof. Dr. Max Welling is a research chair in Machine Learning at the University of Amsterdam and a Distinguished Scientist at MSR. He is a fellow at the Canadian Institute for Advanced Research (CIFAR) and the European Lab for Learning and Intelligent Systems (ELLIS) where he also serves on the founding board. His previous appointments include VP at Qualcomm Technologies, professor at UC Irvine, postdoc at U. Toronto and UCL under supervision of prof. Geoffrey Hinton, and postdoc at Caltech under supervision of prof. Pietro Perona. He finished his PhD in theoretical high energy physics under supervision of Nobel laureate prof. Gerard ‘t Hooft.

Max Welling has served as associate editor in chief of IEEE TPAMI from 2011-2015, he serves on the advisory board of the Neurips foundation since 2015 and has been program chair and general chair of Neurips in 2013 and 2014 respectively. He was also program chair of AISTATS in 2009 and ECCV in 2016 and general chair of MIDL 2018. Max Welling is recipient of the ECCV Koenderink Prize in 2010 and the ICML Test of Time award in 2021. He directs the Amsterdam Machine Learning Lab (AMLAB) and co-directs the Qualcomm-UvA deep learning lab (QUVA) and the Bosch-UvA Deep Learning lab (DELTA).

Cohere For AI Fireside Chats bring together leading researchers and rising stars in the field of machine learning to discuss their research learning journeys. Research is inherently a human endeavour, and this discussion series provides insights from beginning to breakthrough.

Join us for a panel discussion with industry experts as we delve into the world of applying for ML research roles in industry. You'll hear directly from industry ML researchers at various stages of their careers, and the perspective of a talent recruiter, as they share their insights, experiences, and advice on navigating the job application process. We hope to provide valuable insights for ML researchers looking to transition into industry research roles.

Cohere For AI Fireside Chats bring together leading researchers and rising stars in the field of machine learning to discuss their research learning journeys. Research is inherently a human endeavour, and this discussion series provides insights from beginning to breakthrough.

This Fireside Chat features Dr.Alice Oh, a professor at KAIST, School of Computing and Director of the MARS AI Research Center. She will be diving into Cross Cultural Aspects of LLMs with our very own Beyza Ermis, Research Scientist, at Cohere For AI!

Cohere For AI Fireside Chats bring together leading researchers and rising stars in the field of machine learning to discuss their research learning journeys. Research is inherently a human endeavour, and this discussion series provides insights from beginning to breakthrough.

This Fireside Chat features Zeynep Akata, Professor at TUM & Director at Helmholtz Munich. Beyza Ermis, Research Scientist at Cohere For AI, will sit down with Zeynep for a conversation on "Interpretable and Reliable Machine Learning via Communication."

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

Discussion includes:
-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, uilicious.com : 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 startu

The Roads to Research Program aims to showcase everything it takes to be an ML researcher, with a particular focus on elements of the process that are less often spotlighted.

To kick it off, head of C4AI, Sara Hooker will give her presentation “Your journey into research: lessons to live by.” This interactive session will help you think about the research process anew, covering such topics as how to find collaborators, consideration for your first research endeavor, and ways to know whether you are working on something that is scientifically meaningful.

Look forward to future Roads to Research sessions on:
💡 research ideation,
🤝 applying to research roles,
✨ connecting with mentors,
💪 and interactive calls to leverage the strength of our collective expertise

Cohere For AI’s series on AI & Technical Governance explores the landscape of AI policy and governance with experts and leaders from industry, government, academia, and civil society. Join us as we gather different perspectives on topics ranging from translating between research and policy to designing sociotechnical safety evaluations.


In this session, C4AI’s Policy & Responsible AI Lead, Aidan Peppin, is joined by Bran Knowles, Professor of Sociotechnical Systems, Lancaster University and Shahar Avin, Senior Research Associate at Centre for the Study of Existential Risk. Bran and Shahar will sit down with Aidan for a conversation on "Tangible trustworthiness for AI systems"