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Get to know the Amazon research community at KDD 2019!

Amazon’s research teams are looking forward to meeting you at KDD 2019. Come and visit us at the Amazon booth, and read on for more information about Amazonian involvement at KDD, academic collaboration, career opportunities, and our teams.

Publications and Talks at KDD

Chair & Committee Members

  • Derek Young - Media and Publicity Chair / Web Chair / Job Matching Chair
  • Shenghua Bao - Hands-On Tutorial Chair
  • Rajeev Rastogi - Senior Committee Member - Applied Data Science Track
  • Srinivasan Sengamedu - Program Committee - Applied Data Science Track
  • Luna Dong - Committee Member - Research Track
  • Sudipto Guha - Committee Member - Research Track
  • Eno Thereska - Committee Member - Research Track 


  • Put Deep Learning to Work: A Practical Introduction using Amazon Web Services
    • Presenters: Wenming Ye (Amazon Web Services) & Miro Enev (Amazon Web Services)
    • Timeslot: Tue, August 06, 2019 - 9:30 am - 12:30 pm (Hands on Tutorial)
  • AdKDD 2019
    • Presenters: Abraham Bagherjeiran, Nemanja Djuric, Mihajlo Grbovic, Kuang-Chih Lee, Kun Liu, Vladan Radosavljevic and Suju Rajan
  • AI for Fashion 2019: Soo-Min Pantel
  • AI4Fashion workshop: Searching for Apparel Item from Images in the Wild
    • Authors: Son Tran, Ming Du, Sampath Chanda, R Manmatha, C J Taylor
  • From Shallow to Deep Language Representations: Pre-training, Fine-tuning, and Beyond
    • Presenters: Aston Zhang (Amazon AI), Sheng Zha (Amazon AI), Haibin Lin (Amazon AI), Alexander Smola (Amazon AI), Mu Li (Amazon AI), & Chenguang Wang (Amazon AI)
    • Timeslot: Thu, August 08, 2019 - 9:30am - 12:30 pm | 1:00 pm - 4:00 pm (hands on tutorial)


  • Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
    • Authors: Namyong Park (Carnegie Mellon University); Andrey Kan (Amazon); Xin Luna Dong (Amazon); Tong Zhao (Amazon); Christos Faloutsos (Carnegie Mellon University and Amazon); (RESEARCH TRACK)
  • Scaling Multinomial Logistic Regression via Hybrid Parallelism
    • Authors: Parameswaran Raman (University of California, Santa Cruz); Sriram Srinivasan (University of California, Santa Cruz); Shin Matsushima (The University of Tokyo); Xinhua Zhang (University of Illinios, Chicago); Hyokun Yun (Amazon); Vishwanathan S.V.N. (Amazon); (RESEARCH TRACK)
  • Oral in Applied Data Science Track: Detection of Review Abuse via Semi-Supervised Binary Multi-Target Tensor Decomposition
    • Authors: Anil R. Yelundur, Vineet Chaoji, Bamdev Mishra (ADS TRACK)
  • Whole page optimization with global constraints
    • Authors: Weicong Ding, Dinesh Govindaraj, Vishy Vishwanathan (ADS TRACK)
  • DeepRoof: A Data-driven Approach For Solar Potential Estimation Using Rooftop Imagery
    • Authors: Stephen Lee, Srinivasan Iyengar, Menghong Feng, Prashant Shenoy and Subhransu Maji (ADS TRACK)

Lecture Style Tutorials:

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, 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 apply advanced computer vision and 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 and let us know if there are any specific locations, teams, or research leaders that you are interested in working with. 

Amazon Scholars

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

BAIR Lab Opening

Amazon & the University of California Berkeley ARtificial Intelligence Research (BAIR) Lab Partnered to open the BAIR Open Research Commons, a new industrial affiliate program launched to accelerate cutting-edge AI research. The BAIR Commons is designed to streamline collaborative, cutting-edge research by students, faculty, and corporate research scholars. 

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

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.

Applications will be open on September 10th, 2019 with a submission deadline of October 4, 2019. 

  • Computer vision
  • Fairness in artificial intelligence
  • Knowledge management and data quality
  • Machine learning algorithms and theory
  • Natural language processing
  • Online advertising
  • Operations research and optimization
  • Personalization
  • Robotics
  • Search and information retrieval
  • Security, privacy and abuse prevention

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

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

Questions about career opportunities or academic partnerships? Contact us at 

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.

Alexa Science

“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.

Amazon Robotics

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

The Social Shopping Team

Social Shopping is responsible for our product reviews, recommendations, Wishlist and much more.