Get to know the Amazon research community at ICML 2018!
Amazon’s research teams are looking forward to meeting you at ICML 2018. Come and visit us at the Amazon booth, and read on for more information about academic collaboration, career opportunities, and our teams.
- “Structured Variationally Auto-encoded Optimization” | Authors: Xiaoyu Lu (University of Oxford) · Javier González (Amazon) · Zhenwen Dai (Amazon) · Neil Lawrence (Amazon)
- “Semi-Supervised Learning on Data Streams via Temporal Label Propagation” | Authors: Tal Wagner (MIT) · Sudipto Guha (Amazon) · Shiva Kasiviswanathan (Amazon) · Nina Mishra (Amazon)
- “Detecting non-causal artifacts in multivariate linear regression models” | Authors: Dominik Janzing (Amazon) · Bernhard Schölkopf (MPI for Intelligent Systems & Amazon)
- “Detecting and Correcting for Label Shift with Black Box Predictors” | Authors: Zachary Lipton (Amazon & CMU) · Yu-Xiang Wang (Amazon & UCSB) · Alexander Smola (Amazon)
- “Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising” | Authors: Borja de Balle Pigem (Amazon) · Yu-Xiang Wang (Amazon & UCSB)
- “signSGD: compressed optimisation for non-convex problems” | Authors: Jeremy Bernstein (Caltech) · Yu-Xiang Wang (Amazon & UCSB) · Kamyar Azizzadenesheli (UC Irvine & Stanford) · Anima Anandkumar (Amazon & Caltech)
- “Born Again Neural Networks”| Authors: Tommaso Furlanello (University of Southern California) · Zachary Lipton (Amazon & CMU) · Michael Tschannen (ETH Zurich) · Laurent Itti (University of Southern California) · Anima Anandkumar (Amazon & Caltech)
- “Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization” | Authors: Jiong Zhang (University of Texas at Austin) · Qi Lei (University of Texas at Austin) · Inderjit Dhillon (UT Austin & Amazon)
- “Learning long term dependencies via Fourier recurrent units” | Authors: Jiong Zhang (University of Texas at Austin) · Yibo Lin (UT Austin) · Zhao Song (UT-Austin) · Inderjit Dhillon (UT Austin & Amazon)
- “Towards Fast Computation of Certified Robustness for ReLU Networks” | Authors: Tsui-Wei (Lily) Weng (MIT) · Huan Zhang (UC Davis) · Hongge Chen (MIT) · Zhao Song (UT-Austin) · Cho-Jui Hsieh (University of California, Davis) · Luca Daniel (MIT) · Duane Boning (MIT) · Inderjit Dhillon (UT Austin & Amazon)
- “Learning Steady-States of Iterative Algorithms over Graphs” | Authors: Hanjun Dai (Georgia Tech) · Zornitsa Kozareva (Google) · Bo Dai (Georgia Institute of Technology) · Alex Smola (Amazon) · Le Song (Georgia Institute of Technology)
- Workshop on CausalML
- “Bayesian Counterfactual Risk Minimization” | Authors: Ben London (Amazon) · Ted Sandler (Amazon)
- Workshop on Theoretical Foundations and Applications of Deep Generative Models
- "Airline Passenger Name Record Generation using Generative Adversarial Networks" | Authors: Alejandro Mottini (Amazon) · Alix Lhéritier (Amadeus SAS) · Rodrigo Acuna-Agost (Amadeus SAS)
- "Sample Path Generation for Probabilistic Demand Forecasting" | Authors: Dhruv Madeka (Amazon) · Lucas Swiniarski (NYU) · Dean Foster (Amazon) · Leonid Razoumov (Amazon) · Ruofeng Wen (Amazon) · Kari Torkkola (Amazon)
- Workshop on Privacy in Machine Learning and Artificial Intelligence
- Co-organized by Borja Balle (Amazon), Kamalika Chaudhuri (UCSD), Beyza Ermis (Amazon), Antti Honkela (University of Helsinki), Mijung Park (MPI), and Jose Such (King's College London)
Internships for PhD Students
We offer 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, Seattle, Sunnyvale, Tel Aviv, Tübingen, Turin, and Vancouver. To apply, email your resume to ICML2018@amazon.com, and let us know if there are any specific locations, teams, or research leaders that you are interested in working with.
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. Apply to one of the job postings below or send your resume directly to ICML2018@amazon.com.
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.
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.
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.
Amazon Scholars is a new 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.
Amazon is an Equal Opportunity Employer.
Meet a few leaders in our research community
Learn more about Amazon's research teams:
Graduate Research Careers
Learn more about graduate research careers for PhD and Masters students.
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