The first conference on industry applications of machine learning in climate tech
June 19-23, 2022, Caesar’s Palace, Las Vegas
Discover how machine learning can be a vital component in effective climate tech.
PART OF MACHINE LEARNING WEEK
There is a wealth of expertise, passion, and money pouring into climate tech as both startups and established industrial players seek to address one of the most important challenges facing humanity. Machine learning can be an important component in tech for addressing the climate crisis. Join PAW Climate to explore how companies apply machine learning to problems such as smart electrical grids, supply chain optimization, building energy efficiency, industrial control, precision agriculture, climate risk assessment, weather forecasting, ecosystem monitoring, and disaster response.
The PAW Climate Conference Chairs
Eugene Kirpichov, Work On Climate
Eugene is an expert in the large-scale data processing and machine learning infrastructure space. With over 13 years of experience under his belt, he had spent the last 8 as a Staff Software Engineer at Google Cloud and Google AI, before fully realizing the urgency and opportunity of climate change mitigation and leaving in Aug 2020 with his friend Cassandra Xia to pivot into climate.
Currently he’s mobilizing professionals to work on climate long-term as part of the Work On Climate community and exploring other ways to accelerate the climate solutions ecosystem.
Sasha Luccioni, Postdoctoral Researcher, Université de Montréal and Mila
Sasha Luccioni is a Postdoctoral Researcher working with Yoshua Bengio at Université de Montréal and Mila. Her work sits at the intersection of AI and the environment, and her goal is to find ways to maximize the positive impacts of AI while minimizing the negative ones, be it from a research or application perspective. She is a 2020 National Geographic Explorer and holds an IVADO postdoctoral fellowship.
David Rolnick, Assistant Professor, School of Computer Science, McGill University
David Rolnick is Assistant Professor in the School of Computer Science at McGill University and at the Mila Quebec AI Institute. He is co-founder and chair of Climate Change AI and serves as scientific co-director of Sustainability in the Digital Age. Dr. Rolnick has also worked at Google and DeepMind, and is a former NSF Mathematical Sciences Postdoctoral Research Fellow, NSF Graduate Research Fellow, and Fulbright Scholar. He received his Ph.D. in Applied Mathematics from MIT.
PAW Climate is part of Machine Learning Week — the facts:
Predictive Analytics World is the leading cross-vendor conference series covering the commercial deployment of machine learning and predictive analytics. Hear from the horse’s mouth precisely how Fortune 500 analytics competitors deploy machine learning and the kind of business results they achieve.
COMPANIES ON THE MACHINE LEARNING WEEK 2020 AGENDA INCLUDED
Witness how practitioners at these leading enterprises apply machine learning:
Previous attendees describe what they found most valuable at PAW:
James McCaffrey- Senior Scientist Engineer, Microsoft
The bottom line: the event was really good — I give it an overall grade of an A- which is (tied for) the best grade I’ve ever given to any conference.
Indu Sriram - Digital Marketing Analytics Manager, Staples
I'm happy we have a conference like Predictive Analytics World - where practitioners like myself can meet other professionals and learn all the latest and greatest. It's a go-to resource and I often attend - hats off to this conference's producers!
Allison Gonzalez - Decision Science Analyst, USAA
Just do it! Everybody is doing it! I attended PAW San Francisco 2016 and I come back with many new contacts, new friends, and more knowledgeable.
Kenton - Economist, Nike
The emphasis on practical application of analytics to real world business problems and decision making is just right at this conference!
Jason King - Principal Scientist, Procter and Gamble
A 360 degree event - great for anyone who wants to know where data analytics is at and where it's going.