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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.

By Amazon Research Awards team
January 30, 2023
4 min read
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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.

Recipient

University

Research title

Vardan Avagyan

Erasmus University Rotterdam

Role of consumer mindset metrics in optimal ad decisions

Yakov Bart

Northeastern University

Using video summarization for generating effective short video ads

Amrit Singh Bedi

University of Maryland, College Park

Ensuring fairness via federated learning beyond consensus

Stevie Chancellor

University of Minnesota, Twin Cities

Collaborative and socially translucent task instructions for emotionally heavy and subjective annotation tasks

Muhao Chen

University of Southern California

On faithfulness of information extraction

Bas Donkers

Erasmus University Netherlands

Real-time personalization in dynamic environments

Chuang Gan

UMass Amherst

Auto-labeling through neuro-symbolic learning for visual and text data

Diego Gomez-Zara

University Of Notre Dame

Creating and designing disruptive teams: Experiments and models for assessing teams’ disruption

Pallassana (P. K.) Kannan

University of Maryland College Park

Measuring the synergy across marketing touchpoints using transformers

Omer Levy

Tel Aviv University

Explaining and mitigating adverse biases in large language models via natural language instructions

Zhou Li

University Of California, Irvine

Accurate, scalable and robust attack provenance on discrete temporal graph

Dinesh Manocha

University of Maryland, College Park

Ensuring fairness via federated learning beyond consensus

Vidya Muthukumar

Georgia Institute of Technology

Framework for learning from online bidding

Gijs Overgoor

Rochester Institute Of Technology

Using video summarization for generating effective short video ads

Ashwin Pananjady

Georgia Institute of Technology

Framework for learning from online bidding

Xiao Qiao

City University of Hong Kong

Predicting successful scientific collaborations

Christian Schlereth

WHU Germany Otto Beisheim School of Management

The power of the climate friendly badge

Shuba Srinivasan

Boston University

Role of consumer mindset metrics in optimal ad decisions

Damien Teney

Idiap Research Institute

Addressing underspecification for improved fairness and robustness in conversational AI

Misha Teplitskiy

University of Michigan

Learning by reviewing

Berk Ustun

University of California, San Diego

Participatory personalization in machine learning

Dashun Wang

Northwestern University

Creating and designing disruptive teams: Experiments and models for assessing teams’ disruption

Xiaolong Wang

University of California, San Diego

Open world object discovery and tracking with grouping vision transformers

Yang Weng

Arizona State University

Reinforcement learning twins: granular level recommendations with causal interpretations on amazon assortment via limited tests

Eric Xing

Carnegie Mellon University

A faster and more accurate secure model serving framework on the cloud

Diyi Yang

Stanford University

Human-in-the-loop for long text generation

Gokhan Yildirim

Imperial College London

Role of consumer mindset metrics in optimal ad decisions

Heng Yin

University of California, Riverside

Next-generation AI-powered binary diffing

Hanzhe Zhang

Michigan State University

Predicting successful scientific collaborations
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US, MA, Boston
**This is an experimental role to support a business pilot and can potentially span up to 12 months** Embark on a transformative journey as our Sr. Domain Expert Lead, where intellectual rigor meets technological innovation. As a Sr. Domain Expert Lead, you will blend your advanced analytical skills and domain expertise to provide strategic oversight to our human-in-the-loop and model-in-the-loop data pipelines. You will also provide mentorship and guidance to junior team members. Your responsibilities will ensure data excellence through strategic oversight of high-quality data output, while delivering expert consultation throughout the pipeline and fostering iterative development. This position directly impacts the effectiveness and reliability of our AI solutions by maintaining the highest standards of data quality throughout the development process while building capability within the broader team. Key job responsibilities • Serve as a trusted domain advisor to cross-functional teams, providing strategic direction and specialized problem-solving support • Champion domain knowledge sharing across multiple channels and teams to maintain data quality excellence and standardization • Drive collaborative efforts with science teams to optimize output of complex data collections in your domain expertise, ensuring data excellence through iterative feedback loops • Foster team excellence through mentorship and motivation of peers and junior team members • Make informed decisions on behalf of our customers, ensuring that selected code meets industry standards, best practices, and specific client needs • Collaborate with AI teams to innovate model-in-the-loop and human-in-the-loop approaches, to ensure the collection of high-quality data, safeguarding data privacy and security for LLM training, and more. • Stay abreast of the latest developments in how LLMs and GenAI can be applied to your area of expertise to ensure our evaluations remain cutting-edge. • Develop and write demonstrations to illustrate "what good data looks like" in terms of meeting benchmarks for quality and efficiency • Provide detailed feedback and explanations for your evaluations, helping to refine and improve the LLM's understanding and output
Senior Domain Expert Lead- STEM (Contract), AGI - Data Services
US, MA, Boston
**This is an experimental role to support a business pilot and can potentially span up to 12 months** Embark on a transformative journey as our Sr. Domain Expert Lead, where intellectual rigor meets technological innovation. As a Sr. Domain Expert Lead, you will blend your advanced analytical skills and domain expertise to provide strategic oversight to our human-in-the-loop and model-in-the-loop data pipelines. You will also provide mentorship and guidance to junior team members. Your responsibilities will ensure data excellence through strategic oversight of high-quality data output, while delivering expert consultation throughout the pipeline and fostering iterative development. This position directly impacts the effectiveness and reliability of our AI solutions by maintaining the highest standards of data quality throughout the development process while building capability within the broader team. Key job responsibilities • Serve as a trusted domain advisor to cross-functional teams, providing strategic direction and specialized problem-solving support • Champion domain knowledge sharing across multiple channels and teams to maintain data quality excellence and standardization • Drive collaborative efforts with science teams to optimize output of complex data collections in your domain expertise, ensuring data excellence through iterative feedback loops • Foster team excellence through mentorship and motivation of peers and junior team members • Make informed decisions on behalf of our customers, ensuring that selected code meets industry standards, best practices, and specific client needs • Collaborate with AI teams to innovate model-in-the-loop and human-in-the-loop approaches, to ensure the collection of high-quality data, safeguarding data privacy and security for LLM training, and more. • Stay abreast of the latest developments in how LLMs and GenAI can be applied to your area of expertise to ensure our evaluations remain cutting-edge. • Develop and write demonstrations to illustrate "what good data looks like" in terms of meeting benchmarks for quality and efficiency • Provide detailed feedback and explanations for your evaluations, helping to refine and improve the LLM's understanding and output
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