29 Amazon Research Awards recipients announced

Awardees, who represent 25 universities in seven countries, have access to Amazon public datasets, along with AWS AI/ML services and tools.

Amazon Research Awards (ARA) provides unrestricted funds and AWS Promotional Credits to academic researchers investigating various research topics in multiple disciplines. This cycle, ARA received many excellent research proposals from across the world and today is publicly announcing 29 award recipients who represent 25 universities in seven countries.

This announcement includes awards funded under five call for proposals during the spring 2022 cycle: AI for Information Security, Alexa – Fairness in AI, Amazon Advertising, Amazon Science Community and Machine Learning University, and AWS AI: Human-in-the-loop machine learning and annotation.

Proposals were reviewed for the quality of their scientific content and their potential to impact both the research community and society.

Recipients have access to more than 300 Amazon public datasets and can utilize AWS AI/ML services and tools through their AWS Promotional Credits. Recipients also are assigned an Amazon research contact who offers consultation and advice, along with opportunities to participate in Amazon events and training sessions.

Additionally, Amazon encourages the publication of research results, presentations of research at Amazon offices worldwide, and the release of related code under open-source licenses.

“Scientists and engineers are at their best when they’re inventing on behalf of customers," said Brent Werness, manager of applied science with Machine Learning University. "But how does that invention happen? And what can we do to help scientists and engineers do their best work? Answering these questions requires a sustained, interdisciplinary research agenda, and our 2022 Amazon Research Award recipients will take one more step toward understanding.”

Top row, left to right: Vardan Avagyan, Yakov Bart, Stevie Chancellor, Muhao Chen, Bas Donkers, Chuang Gan, Diego Gomez-Zara; second row, left to right: Omer Levy, Zhou Li, Vidya Muthukumar, Gijs Overgoor, Ashwin Pananjady, Xiao Qiao, Christian Schlereth; third row, left to right: Shuba Srinivasan, Damien Teney, Misha Teplitskiy, Berk Ustun, Dashun Wang, Xiaolong Wang, Yang Weng; and bottom row, left to right: Eric Xing, Diyi Yang, Gokhan Yildirim, Heng Yin, and Hanzhe Zhang are among the recipients from the Amazon Research Awards Spring 2022 call for proposals.
Top row, left to right: Vardan Avagyan, Yakov Bart, Stevie Chancellor, Muhao Chen, Bas Donkers, Chuang Gan, Diego Gomez-Zara; second row, left to right: Omer Levy, Zhou Li, Vidya Muthukumar, Gijs Overgoor, Ashwin Pananjady, Xiao Qiao, Christian Schlereth; third row, left to right: Shuba Srinivasan, Damien Teney, Misha Teplitskiy, Berk Ustun, Dashun Wang, Xiaolong Wang, Yang Weng; and bottom row, left to right: Eric Xing, Diyi Yang, Gokhan Yildirim, Heng Yin, and Hanzhe Zhang are among the recipients from the Amazon Research Awards Spring 2022 call for proposals.

ARA funds proposals two times a year in a variety of research areas. Applicants are encouraged to visit the ARA call for proposals page for more information or send an email to be notified of future open calls.

The table below lists, in alphabetical order, spring 2022 cycle call-for-proposal recipients.

RecipientUniversityResearch title
Vardan AvagyanErasmus University RotterdamRole of consumer mindset metrics in optimal ad decisions
Yakov BartNortheastern UniversityUsing video summarization for generating effective short video ads
Amrit Singh BediUniversity of Maryland, College ParkEnsuring fairness via federated learning beyond consensus
Stevie ChancellorUniversity of Minnesota, Twin CitiesCollaborative and socially translucent task instructions for emotionally heavy and subjective annotation tasks
Muhao ChenUniversity of Southern CaliforniaOn faithfulness of information extraction
Bas DonkersErasmus University NetherlandsReal-time personalization in dynamic environments
Chuang GanUMass AmherstAuto-labeling through neuro-symbolic learning for visual and text data
Diego Gomez-ZaraUniversity Of Notre DameCreating and designing disruptive teams: Experiments and models for assessing teams’ disruption
Pallassana (P. K.) KannanUniversity of Maryland College ParkMeasuring the synergy across marketing touchpoints using transformers
Omer LevyTel Aviv UniversityExplaining and mitigating adverse biases in large language models via natural language instructions
Zhou LiUniversity Of California, IrvineAccurate, scalable and robust attack provenance on discrete temporal graph
Dinesh ManochaUniversity of Maryland, College ParkEnsuring fairness via federated learning beyond consensus
Vidya MuthukumarGeorgia Institute of TechnologyFramework for learning from online bidding
Gijs OvergoorRochester Institute Of TechnologyUsing video summarization for generating effective short video ads
Ashwin PananjadyGeorgia Institute of TechnologyFramework for learning from online bidding
Xiao QiaoCity University of Hong KongPredicting successful scientific collaborations
Christian SchlerethWHU Germany Otto Beisheim School of ManagementThe power of the climate friendly badge
Shuba SrinivasanBoston UniversityRole of consumer mindset metrics in optimal ad decisions
Damien TeneyIdiap Research InstituteAddressing underspecification for improved fairness and robustness in conversational AI
Misha TeplitskiyUniversity of MichiganLearning by reviewing
Berk UstunUniversity of California, San DiegoParticipatory personalization in machine learning
Dashun WangNorthwestern UniversityCreating and designing disruptive teams: Experiments and models for assessing teams’ disruption
Xiaolong WangUniversity of California, San DiegoOpen world object discovery and tracking with grouping vision transformers
Yang WengArizona State UniversityReinforcement learning twins: granular level recommendations with causal interpretations on amazon assortment via limited tests
Eric XingCarnegie Mellon UniversityA faster and more accurate secure model serving framework on the cloud
Diyi YangStanford UniversityHuman-in-the-loop for long text generation
Gokhan YildirimImperial College LondonRole of consumer mindset metrics in optimal ad decisions
Heng YinUniversity of California, RiversideNext-generation AI-powered binary diffing
Hanzhe ZhangMichigan State UniversityPredicting successful scientific collaborations
Research areas
  • Applied machine learning
  • Fairness in artificial intelligence
  • Online advertising

Related content

US, WA, Virtual Contact Center-WA
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python is necessary, and experience with SQL and UNIX would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. About the team The Selling Partner Fees team owns the end-to-end fees experience for two million active third party sellers. We own the fee strategy, fee seller experience, fee accuracy and integrity, fee science and analytics, and we provide scalable technology to monetize all services available to third-party sellers. Within the Science team, our goal is to understand the impact of changing fees on Seller (supply) and Customers (demand) behavior (e.g. price changes, advertising strategy changes, introducing new selection etc.) as well as using this information to optimize our fee structure and maximizing our long term profitability.
US, WA, Seattle
This is a unique opportunity to build technology and science that millions of people will use every day. Are you excited about working on large scale Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL)? We are embarking on a multi-year journey to improve the shopping experience for customers globally. Amazon Search team creates customer-focused search solutions and technologies that makes shopping delightful and effortless for our customers. Our goal is to understand what customers are looking for in whatever language happens to be their choice at the moment and help them find what they need in Amazon's vast catalog of billions of products. As Amazon expands to new geographies, we are faced with the unique challenge of maintaining the bar on Search Quality due to the diversity in user preferences, multilingual search and data scarcity in new locales. We are looking for an applied researcher to work on improving search on Amazon using NLP, ML, and DL technology. As an Applied Scientist, you will lead our efforts in query understanding, semantic matching (e.g. is a drone the same as quadcopter?), relevance ranking (what is a "funny halloween costume"?), language identification (did the customer just switch to their mother tongue?), machine translation (猫の餌を注文する). This is a highly visible role with a huge impact on Amazon customers and business. As part of this role, you will develop high precision, high recall, and low latency solutions for search. Your solutions should work for all languages that Amazon supports and will be used in all Amazon locales world-wide. You will develop scalable science and engineering solutions that work successfully in production. You will work with leaders to develop a strategic vision and long term plans to improve search globally. We are growing our collaborative group of engineers and applied scientists by expanding into new areas. This is a position on Global Search Quality team in Seattle Washington. We are moving fast to change the way Amazon search works. Together with a multi-disciplinary team you will work on building solutions with NLP/ML/DL at its core. Along the way, you’ll learn a ton, have fun and make a positive impact on millions of people. Come and join us as we invent new ways to delight Amazon customers.
US, WA, Seattle
This is a unique opportunity to build technology and science that millions of people will use every day. Are you excited about working on large scale Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL)? We are embarking on a multi-year journey to improve the shopping experience for customers globally. Amazon Search team creates customer-focused search solutions and technologies that makes shopping delightful and effortless for our customers. Our goal is to understand what customers are looking for in whatever language happens to be their choice at the moment and help them find what they need in Amazon's vast catalog of billions of products. As Amazon expands to new geographies, we are faced with the unique challenge of maintaining the bar on Search Quality due to the diversity in user preferences, multilingual search and data scarcity in new locales. We are looking for an applied researcher to work on improving search on Amazon using NLP, ML, and DL technology. As an Applied Scientist, you will lead our efforts in query understanding, semantic matching (e.g. is a drone the same as quadcopter?), relevance ranking (what is a "funny halloween costume"?), language identification (did the customer just switch to their mother tongue?), machine translation (猫の餌を注文する). This is a highly visible role with a huge impact on Amazon customers and business. As part of this role, you will develop high precision, high recall, and low latency solutions for search. Your solutions should work for all languages that Amazon supports and will be used in all Amazon locales world-wide. You will develop scalable science and engineering solutions that work successfully in production. You will work with leaders to develop a strategic vision and long term plans to improve search globally. We are growing our collaborative group of engineers and applied scientists by expanding into new areas. This is a position on Global Search Quality team in Seattle Washington. We are moving fast to change the way Amazon search works. Together with a multi-disciplinary team you will work on building solutions with NLP/ML/DL at its core. Along the way, you’ll learn a ton, have fun and make a positive impact on millions of people. Come and join us as we invent new ways to delight Amazon customers.
US, WA, Seattle
The retail pricing science and research group is a team of scientists and economists who design and implement the analytics powering pricing for Amazon’s on-line retail business. The team uses world-class analytics to make sure that the prices for all of Amazon’s goods and services are aligned with Amazon’s corporate goals. We are seeking an experienced high-energy Economist to help envision, design and build the next generation of retail pricing capabilities. You will work at the intersection of economic theory, statistical inference, and machine learning to design new methods and pricing strategies to deliver game changing value to our customers. Roughly 85% of previous intern cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. Key job responsibilities Amazon’s Pricing Science and Research team is seeking an Economist to help envision, design and build the next generation of pricing capabilities behind Amazon’s on-line retail business. As an economist on our team, you will work at the intersection of economic theory, statistical inference, and machine learning to design new methods and pricing strategies with the potential to deliver game changing value to our customers. This is an opportunity for a high-energy individual to work with our unprecedented retail data to bring cutting edge research into real world applications, and communicate the insights we produce to our leadership. This position is perfect for someone who has a deep and broad analytic background and is passionate about using mathematical modeling and statistical analysis to make a real difference. You should be familiar with modern tools for data science and business analysis. We are particularly interested in candidates with research background in applied microeconomics, econometrics, statistical inference and/or finance. A day in the life Discussions with business partners, as well as product managers and tech leaders to understand the business problem. Brainstorming with other scientists and economists to design the right model for the problem in hand. Present the results and new ideas for existing or forward looking problems to leadership. Deep dive into the data. Modeling and creating working prototypes. Analyze the results and review with partners. Partnering with other scientists for research problems. About the team The retail pricing science and research group is a team of scientists and economists who design and implement the analytics powering pricing for Amazon’s on-line retail business. The team uses world-class analytics to make sure that the prices for all of Amazon’s goods and services are aligned with Amazon’s corporate goals.
US, CA, San Francisco
The retail pricing science and research group is a team of scientists and economists who design and implement the analytics powering pricing for Amazon's on-line retail business. The team uses world-class analytics to make sure that the prices for all of Amazon's goods and services are aligned with Amazon's corporate goals. We are seeking an experienced high-energy Economist to help envision, design and build the next generation of retail pricing capabilities. You will work at the intersection of statistical inference, experimentation design, economic theory and machine learning to design new methods and pricing strategies for assessing pricing innovations. Roughly 85% of previous intern cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. Key job responsibilities Amazon's Pricing Science and Research team is seeking an Economist to help envision, design and build the next generation of pricing capabilities behind Amazon's on-line retail business. As an economist on our team, you will will have the opportunity to work with our unprecedented retail data to bring cutting edge research into real world applications, and communicate the insights we produce to our leadership. This position is perfect for someone who has a deep and broad analytic background and is passionate about using mathematical modeling and statistical analysis to make a real difference. You should be familiar with modern tools for data science and business analysis. We are particularly interested in candidates with research background in experimentation design, applied microeconomics, econometrics, statistical inference and/or finance. A day in the life Discussions with business partners, as well as product managers and tech leaders to understand the business problem. Brainstorming with other scientists and economists to design the right model for the problem in hand. Present the results and new ideas for existing or forward looking problems to leadership. Deep dive into the data. Modeling and creating working prototypes. Analyze the results and review with partners. Partnering with other scientists for research problems. About the team The retail pricing science and research group is a team of scientists and economists who design and implement the analytics powering pricing for Amazon's on-line retail business. The team uses world-class analytics to make sure that the prices for all of Amazon's goods and services are aligned with Amazon's corporate goals.
US, WA, Bellevue
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python is necessary, and experience with SQL and UNIX would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of interns from previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US
The Amazon Supply Chain Optimization Technology (SCOT) organization is looking for an Intern in Economics to work on exciting and challenging problems related to Amazon's worldwide inventory planning. SCOT provides unique opportunities to both create and see the direct impact of your work on billions of dollars’ worth of inventory, in one of the world’s most advanced supply chains, and at massive scale. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. We are looking for a PhD candidate with exposure to Program Evaluation/Causal Inference. Knowledge of econometrics and Stata/R/or Python is necessary, and experience with SQL, Hadoop, and Spark would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US, WA, Seattle
The Selling Partner Fees team owns the end-to-end fees experience for two million active third party sellers. We own the fee strategy, fee seller experience, fee accuracy and integrity, fee science and analytics, and we provide scalable technology to monetize all services available to third-party sellers. We are looking for an Intern Economist with excellent coding skills to design and develop rigorous models to assess the causal impact of fees on third party sellers’ behavior and business performance. As a Science Intern, you will have access to large datasets with billions of transactions and will translate ambiguous fee related business problems into rigorous scientific models. You will work on real world problems which will help to inform strategic direction and have the opportunity to make an impact for both Amazon and our Selling Partners.
US, WA, Bellevue
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. We are looking for a PhD candidate with exposure to Program Evaluation/Causal Inference. Some knowledge of econometrics, as well as basic familiarity with Stata or R is necessary, and experience with SQL, Hadoop, Spark and Python would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time scientist employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US, MA, Boston
Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun. Amazon Robotics, a wholly owned subsidiary of Amazon.com, empowers a smarter, faster, more consistent customer experience through automation. Amazon Robotics automates fulfillment center operations using various methods of robotic technology including autonomous mobile robots, sophisticated control software, language perception, power management, computer vision, depth sensing, machine learning, object recognition, and semantic understanding of commands. Amazon Robotics has a dedicated focus on research and development to continuously explore new opportunities to extend its product lines into new areas. AR is seeking uniquely talented and motivated data scientists to join our Global Services and Support (GSS) Tools Team. GSS Tools focuses on improving the supportability of the Amazon Robotics solutions through automation, with the explicit goal of simplifying issue resolution for our global network of Fulfillment Centers. The candidate will work closely with software engineers, Fulfillment Center operation teams, system engineers, and product managers in the development, qualification, documentation, and deployment of new - as well as enhancements to existing - operational models, metrics, and data driven dashboards. As such, this individual must possess the technical aptitude to pick-up new BI tools and programming languages to interface with different data access layers for metric computation, data mining, and data modeling. This role is a 6 month co-op to join AR full time (40 hours/week) from July – December 2023. The Co-op will be responsible for: Diving deep into operational data and metrics to identify and communicate trends used to drive development of new tools for supportability Translating operational metrics into functional requirements for BI-tools, models, and reporting Collaborating with cross functional teams to automate AR problem detection and diagnostics