Get to know the Amazon research community at AAAI 2019!
Amazon’s research teams are looking forward to meeting you at AAAI 2019. Come and visit us at the Amazon booth, and read on for more information about academic collaboration, career opportunities, and our teams.
Amazonians with AAAI Publications
- Mono3D++: Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses and Task Priors: Tong He (UCLA); Stefano Soatto (UCLA/Amazon)*
Transfer Learning Only Using the Source Model and the Target Data for Sequence Labeling Tasks: Lingzhen Chen (University of Trento)*; Alessandro Moschitti (Amazon)
Online Embedding Compression for Text Classification using Low Rank Matrix Factorization: Anish Acharya (Amazon)*; Rahul Goel (Amazon); Angeliki Metallinou (Amazon); Inderjit Dhillon (University of Texas at Austin)
Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents: Aditya Siddhant (Carnegie Mellon University)*; Anuj Kumar Goyal (Amazon); Angeliki Metallinou (Amazon)
Kernelized Hashcode Representations for Biomedical Relation Extraction: Sahil Garg (USC)*; Aram Galstyan (USC Information Sciences Institute); Greg Ver Steeg (USC Information Sciences Institute); Irina Rish (IBM Research); Guillermo Cecchi (IBM); Shuyang Gao (ISI USC/Amazon)
Visual Place Recognition via Simultaneously Minimizing and Maximizing p-Order L2-Norm Distances with Non-greedy Strictly Orthogonal Solutions: Kai Liu (Colorado School of Mines); Hua Wang (Colorado School of Mines)*; Fei Han (Colorado School of Mines/Amazon); Hao Zhang (Colorado School of Mines)
Generating Character Descriptions for Automatic Summarization of Fiction: Weiwei Zhang (McGill University/Amazon)*; Jackie Chi Kit Cheung (); Joel Oren (Yahoo! Research)
Balanced Linear Contextual Bandits: Maria Dimakopoulou (Stanford University)*; Zhengyuan Zhou (Stanford University); Susan Athey (Stanford University); Guido Imbens (Stanford University/Amazon)
Efficient Online Learning For Mapping Kernels On Linguistic Structures: Giovanni Da San Martino (Qatar Computing Research Institute)*; Fabio Aiolli (Universita' di Padova); Alessandro Sperduti (University of Padova); Alessandro Moschitti (Amazon)
LabelForest: Non-Parametric Semi-Supervised Learning for Activity Recognition: Yuchao Ma (Washington State University/Amazon)*; Hassan Ghasemzadeh (Washington State University)
AAAI-Related Blog Posts
Interested in learning more about one of our papers? Check out our blog posts related to papers being presented at the conference.
- New Method for Compressing Neural Networks Better Preserves Accuracy
- Leveraging Unannotated Data to Bootstrap Alexa Functions More Quickly
- More Efficient "Kernel Methods" Dramtically Reduce Training Time for Natural-Language-Understanding Systems
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, Seattle, Shanghai, Sunnyvale, Tel Aviv, Tübingen, Turin, and Vancouver. To apply, email your resume to AAAI2019@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 AAAI2019@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.
Meet a few Amazonians working in AI
Meet the Alexa AI Team
Meet the science team that created Alexa.
What does customer-obsessed science look like at Alexa AI?
We asked Alexa AI scientists what customer-obsessed science means at Alexa AI.
What are scientists at Alexa AI working on?
Learn more about the work some of our team members are leading, and how this will improve the Alexa experience for customers.
The people and teams behind Alexa AI
Learn more about the people and teams that are innovating on new capabilities for Alexa.
How Alexa AI is engaging with the broader science community
Learn how Alexa AI scientists are encouraged to publish research that will contribute to a future of more natural and engaging computing experiences.
Advice for scientists considering a role in Alexa AI
Five Alexa AI scientists share their tips for any scientists interested in joining us to create a better experience for Alexa’s customers.