Get to know the Amazon research community at ICML 2019!
Amazon’s research teams are looking forward to meeting you at ICML 2019. Come and visit us at the Amazon booth, and read on for more information about academic collaboration, career opportunities, and our teams.
- Bernhard Schoelkopf
- Thorsten Joachims
• ICML 2019 Time Series Workshop - Cheng Tang, Yuyang Wang, Vitaly Kuznetsov, Scott Yang, Rose Yu
• ICML Workshop on Imitation, Intent, and Interaction (I3) - Nicholas Rhinehart, Sergey Levine, Chelsea Finn, He He, Ilya Kostrikov, Justin Fu, Siddharth Reddy
• Machine Learning for Music Discovery (ML4MD) - Erik Schmidt, Oriol Nieto, Fabien Gouyon, Katherine Kinnaird, Gert Lanckriet, Kat Ellis (invited speaker)
"Bayesian Counterfactual Risk Minimization" - Ben London, Ted Sandler
"A Self-supervised Approach to Hierarchical Forecasting with Applications to Groupwise Synthetic Controls" - Mallory Montgomery, Federico Vaggi
"Learning the Learning Rate for Gradient Descent by Gradient Descent" - Orchid Majumder, Michele Donini, Pratik Chaudhari
"P3O: Policy-on Policy-Off Policy Optimization" - Rasool Fakoor, Pratik Chaudhari, Alex Smola
"Deep Generative Quantile-Copula Models for Probabilistic Forecasting" - Ruofeng Wen, Kari Torkkola
"AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss" - Kaizhi Qian, Yang Zhang, Shiyu Chang, Xuesong Yang, Mark Hasegawa-Johnson
"Categorical Feature Compression via Submodular Optimization" - Mohammad Hossein Bateni, Lin Chen, Hossein Esfandiari, Gang Fu, Vihab Mirrokni, Afshin Rostamizadeh
"Deep Factors for Forecasting" - Danielle Robinson, Jan Gasthaus, Tim Januschowski, Yuyang Wang, Alex Smola, Dean Foster
"A Tutorial on Attention in Deep Learning" - Alex Smola, Aston Zhang
"Learning Context-dependent Label Permutations for Multi-label Classification" - Jinseok Nam, Young-Bum Kim, Eneldo Loza Mencia, Sunghyun Park, Ruhi Sarikaya, Johannes Furnkranz
"Open Vocabulary Learning on Source Code with a Graph-Structured Cache" - Milan Cvitkovic, Badal Singh, Anima Anandkumar
"Adaptive Neural Trees" - Ryutaro Tanno, Kai Arulkumaran, Daniel Alexander, Antonio Criminisi, Aditya Nori
"Compressing Gradient Optimizers via Count-Sketches" - Ryan Spring, Anastasios Kyrillidis, Vijai Mohan, Anshumali Shrivastava
"Distribution calibration for regression" - Hao Song, Tom Diethe, Meelis Kull, Peter Flach
Internships for PhD Students
We offer 3-6 month internships year-round, with opportunities in Aachen, Atlanta, Austin, Bangalore, Barcelona, Berlin, Boston, Cambridge, Cupertino, Graz, Haifa, Herzliya, Manhattan Beach, New York, Palo Alto, Pasadena, Pittsburgh, San Francisco, Shanghai, Seattle, Sunnyvale, Tel Aviv, Tübingen, Turin, and Vancouver. To apply, email your resume to ICML2019@amazon.com, and let us know if there are any specific locations, teams, or research leaders that you are interested in working with.
Interested in learning more about research at Amazon? Check out our blog page here.
Amazon and NSF Collaborate to Accelerate Fairness in AI Research
NSF and Amazon are partnering to jointly support computational research focused on fairness in AI, with the goal of contributing to trustworthy AI systems that are readily accepted and deployed to tackle grand challenges facing society. NSF has long supported transformative research in artificial intelligence (AI) and machine learning (ML). The resulting innovations offer new levels of economic opportunity and growth, safety and security, and health and wellness.
Check out the details here.
Artificial intelligence is changing every industry. Join us at Amazon re:MARS, a new global AI event on Machine Learning, Automation, Robotics, and Space, to learn why and how to apply the latest AI advances in your business and work. re:MARS is inspired by MARS, an event hosted by Jeff Bezos that brings together leading minds to advance a golden age of innovation. re:MARS is your opportunity to participate, combining the latest in forward-looking science with practical applications.
Job Opportunities for Graduating Students and Experienced Researchers
We are looking for results-driven individuals who can apply advanced machine learning techniques, love to work with data, are deeply technical, and highly innovative. If you long for the opportunity to invent and build solutions to challenging problems that directly impact the way Amazon transforms the consumer experience, we are the place for you. To apply, email your resume to ICML2019@amazon.com, and let us know if there are any specific locations, teams, or research leaders that you are interested in working with.
Amazon Web Services (AWS) Research Grants
In partnership with Machine Learning@Amazon, AWS offers up to $20,000 in compute tokens each quarter to professors and students. Academics have used these grants for projects ranging from Hack End weekends to massive MRI imaging projects. AWS provides building blocks for developing applications ranging from Elastic MapReduce for Hadoop analytics to fast and scalable storage with Amazon DynamoDB. Learn more & apply here.
Amazon Research Awards
ARA is an unrestricted gift to recognize exceptional faculty, and fund projects leading toward a PhD degree or conducted as a part of post-doctoral work. Each selected proposal is assigned an Amazon research contact, as we believe that both sides benefit from direct interaction on the topic of their research. We invite ARA recipients to visit Amazon offices worldwide to give talks related to their work and meet with our research groups face-to-face. We encourage ARA recipients to publish the outcome of the project and commit any related code to open source code repositories. Learn more here.
The Alexa Prize is an annual competition for university students dedicated to accelerating the field of conversational AI. Learn more at alexaprize.com.
Amazon Scholars is a program for academic leaders to work with Amazon in a flexible capacity, ranging from part-time to full-time research roles. Learn more at: amazon.jobs/scholars.
Diversity at Amazon
We are a company of builders working on behalf of a global customer base. Diversity is core to our leadership principles, as we seek diverse perspectives so that we can be “Right, A Lot”. We welcome people from all backgrounds and perspectives to innovate with us. Learn more at amazon.com/diversity.
Publishing at Amazon
Amazon is committed to innovating at the frontiers of machine learning and artificial intelligence. Our scientists are encouraged to engage in the research community in the form of written publications, open source code and public datasets. We have instituted a new, fast-track publication approval process, to help share our research efforts as quickly as possible, while maintaining the highest standards of quality. Check out some of our most recent publications here.
Amazon is an Equal Opportunity Employer.
Meet a few leaders in our research community
Learn more about Amazon's research teams:
Customer-obsessed Science at Amazon
Scientists at Amazon explain the customer-obsessed science they're tackling to bring Amazon products and services to life.
“I spoke to the future and it listened.” - Gizmodo. Meet the team of world-class scientists behind Alexa.
The history of Amazon's recommendation algorithm
Consumer division CEO, Jeff Wilke, discusses the history of Amazon's recommendation algorithm at re:MARS 2019.
Ever wonder how Amazon delivers your packages so quickly? In some cases, robots.
The Shopping Core Team
Shopping Core owns many of the technical features and functionality for Amazon.com
The Social Shopping Team
Social Shopping is responsible for our product reviews, recommendations, Wishlist and much more.