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Seven Amazon scientists shaping the future of AI

To commemorate International Women’s Day, we spoke to women scientists across a variety of research areas at Amazon.

By Arun Krishnan
March 8, 2020
11 min read
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To commemorate International Women’s Day (IWD), during Women's History Month, we asked scientists across a variety of Amazon research areas about their backgrounds, and the most exciting innovations in their fields. Here’s what they had to say.

Xin Luna Dong, principal scientist

Xin Luna Dong
Xin Luna Dong, principal scientist

Dong is a principal scientist, leading the efforts to develop the Amazon Product Knowledge Graph. Dong received her PhD in computer science, with a focus on data integration, from the University of Washington. The personal information management system in Dong’s dissertation (which won the Best Demo award in Sigmod’ 2005), is a personal knowledge graph developed at least five years before the phrase “knowledge graph” was coined. After graduation, Dong led the development of the Knowledge-based Trust project at Google. Dong has co-authored the book Big Data Integration. She is an ACM Distinguished Member, and has received the VLDB Early Career Research Contribution Award for "advancing the state of the art of knowledge fusion”. Dong was program committee co-chair for Sigmoid 2018, and is program committee co-chair for VLDB 2020. She also serves on the VLDB endowment and PVLDB advisory committees.

Innovations I find exciting

A recent innovation that I’m most excited about is graph neural network (GNN). Unlike recurrent neural networks (RNNs) and convolutional neural networks (CNNs), which focus much more on regular data such as word sequences, 2-D images, and 3-D videos, GNNs allow us to leverage graphs to capture much more complex relationships. These include elements in the graph, represented by nodes in the graph, and their relationships, represented by edges between the nodes. Examples of graphs influenced by GNNs include social networks, world wide web (WWW) topology, knowledge graphs, and molecular graphs. As we build knowledge graphs for products, it is amazing how many different ways we can benefit from GNNs.

Naturally, we can apply GNNs on the knowledge graphs we have built, to discern interesting patterns to find popular artists in the music domain. We also model webpage layouts as graphs, and model customer behaviors as graphs, so GNNs help us extract relevant knowledge and enrich our knowledge graphs. This new technique enables us to be so much more creative in the practice of constructing knowledge graphs, and applying the findings to real-world applications.

Claire Law, senior technical program manager

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Claire Law, senior technical program manager

Law is senior technical program manager on Amazon’s physical retail team, the team behind the Just Walk Out technology used in Amazon Go and Amazon Go Grocery stores. She studied nanotechnology engineering at the University of Waterloo. Early on, she realized that she didn’t enjoy the type of lab work expected from a researcher in material science. She leveraged a university program, and interned as a software developer, marketer, hardware test engineer, project control officer in consulting, and software program manager. These experiences, coupled with work experiences at Microsoft and Research in Motion, led Law to pursue a career in software.

After a stint in Amazon’s international organization, Law joined the physical retail team to work on machine vision initiatives. On this team, Law is able to leverage her experience in cloud computing and knowledge of optics and photography to build new experiences for physical retail.

Innovations I find exciting

We are only now reaching a level where computer vision can solve real-world problems in a meaningful way. While we still need to be creative in where we look for simplifiers, algorithms are able to solve more and more problems every day. Challenges that looked insurmountable just a couple years ago are now part of production systems across the industry. Checkout-free stores seemed like science fiction before Amazon Go was launched, and now customers are loving this effortless shopping experience in the 25 Amazon Go stores, and the new Amazon Go Grocery store we have open today.

Yoelle Maarek, vice president

Yoelle Maarek, vice president of research and science for Alexa Shopping
Yoelle Maarek, vice president of research and science for Alexa Shopping

Maarek is vice president of research and science, Alexa Shopping. Prior to Amazon, Maarek served in engineering and research leadership roles at Yahoo, Google and IBM. Maarek has been regularly serving as program committee (PC) chair and senior PC committee member at leading academic research conferences related to Web search and data mining, such as SIGIR, The Web Conference, and Web Search and Data Mining (WSDM). She is currently serving on the steering committees of WSDM and the Web Conference series.

She is a member of the Technion Board of Governors and was inducted as an ACM Fellow in 2013. Maarek obtained a PhD in computer science from the Technion, Israel in 1989. She holds an engineering degree from the Ecole des Ponts et Chaussées, and a DEA (graduate degree) in computer science from Paris VI university. Maarek completed her PhD at the Technion in Israel and was a visiting student at Columbia University. She played a pioneering role within industry in researching the field of information retrieval, the computer science discipline behind search, in the pre-Web era, and led the launch of Google Suggest, the query auto-completion capability. As such, she jokingly refers to herself as a “search dinosaur”.

Innovations I find exciting

We are on the verge of making ambient computing happen, and Alexa is pioneering this long-awaited revolution. It forces us to revisit all our assumptions across multiple domains. I see this prevalent especially in search and question answering. These are topics close to my heart. I have been following progress in these areas since I got my PhD thirty years ago. The focus on ambient computing is also a unique opportunity for us at Amazon to demonstrate what we mean by customer-obsessed science. As humans are learning to interact with machines, their behavior is evolving and we need to follow suit. It not only challenges scientists to keep inventing on behalf of customers but also forces all of us to remain humble. We are not here to teach customers how to speak to a machine, but rather to do everything in our power to understand, satisfy and predict their needs so as to constantly wow and delight them.

Angeliki Metallinou, applied science manager

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Angeliki Metallinou, applied science manager

Metallinou is an applied science manager within the Amazon Alexa AI Natural Understanding group. She received both her PhD, and master’s degree in electrical engineering from the University of Southern California. Her interests and experience lie in the areas of spoken and natural language understanding, dialogue systems, machine learning, deep learning, affective computing and applications for education and healthcare.

She has published papers in the areas of speech, language, dialogue, artificial intelligence and multimodal human computer interaction at leading science conferences such as Interspeech, the AAAI Conference on Artificial Intelligence, and the Association for Computational Linguistics (ACL), has served as an area chair for Interspeech 2016, and as a reviewer of papers for several science conferences.

Innovations I find exciting

It is exciting to see how new techniques in deep learning continuously push the boundaries of the state of the art in the fields of dialogue and spoken language processing. I’m very interested in advances around unsupervised, semi-supervised and transfer learning, which allow deep learning models to leverage the power of large corpora without relying on costly and time-consuming manual annotations. Pre-trained language models like BERT and GPT-2 and their use in downstream applications are just a few examples. These innovations are particularly relevant for industry applications where scalability is key.

I am also excited about recent literature in deep learning that is allowing us to develop models to perform complex tasks like higher-level reasoning, for example, over the contents of a document or an image or both, as opposed to simpler classification tasks. I’m also excited to see how these methods can have a positive impact on people through their deployment in products, especially in applications of healthcare, accessibility and education.

Priya Ponnapalli, principal deep learning scientist

Priya Ponnapalli
Priya Ponnapalli, principal deep learning scientist

Ponnapalli is a senior manager and principal deep learning scientist within the Amazon ML Solutions Lab, where she leads a global team of data scientists that help AWS customers accelerate their adoption of ML and cloud technologies across industries, from healthcare and finance to sports. As the leader of Amazon ML Solutions Lab’s sports business, Ponnapalli works with customers including National Football League (NFL), Six Nations Rugby, and Formula 1 (F1), just to name a few, to enhance the fan experience and transform sports using ML.

Ponnapalli is also a senior research affiliate at the Genomic Signal Processing Lab at the University of Utah, and a faculty member at Rutgers Business School, where she teaches ML to business leaders, and works to inspire the next generation of leaders. Prior to joining AWS, she co-founded Eigengene, a data-driven personalized medicine startup and has helped companies like Genentech and Roche establish and build data science teams. For her PhD in electrical and computer engineering at the University of Texas at Austin, Ponnapalli defined and demonstrated the higher-order generalized singular value decomposition (HO GSVD), the only framework that can create a single coherent model from multiple two-dimensional datasets by extending the GSVD from two to more than two matrices.

Innovations I find exciting

As an Amazon ML Solutions Lab scientist, I’m most excited about real-world applications of ML across industries. I’m interested in innovations to overcome challenges with small, limited datasets that companies often have to contend with. I’m also intrigued by model interpretability and explainability which are key to earning trust and spurring broad adoption. I’m passionate about making ML accessible to all, so it can be used to solve some of the most important problems we are facing, from fighting climate change to treating cancer.

Ana Pinheiro Privette, senior program manager

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Ana Pinheiro Privette, senior program manager

Ana Pinheiro Privette is a senior program manager with Amazon's Sustainability group. She joined the Sustainability Science and Innovation team in September 2017 as the program lead for the Amazon Sustainability Data Initiative (ASDI), a program that seeks to leverage Amazon’s scale, technology, and infrastructure to help create more global innovation for sustainability. ASDI is a Tech-for-Good project and is a joint effort between Amazon Sustainability and the AWS Open Data team focusing on democratizing access to key data and analytical capabilities to anyone working in the sustainability space.

Privette was trained as an environmental engineer and as an earth sciences researcher at the New University of Lisbon (Portugal) and at MIT. She did her doctoral research work at NASA in the Washington D.C. area and as part of her project, she spent a couple of years running scientific field work sites in Africa to support a NASA international field campaign. After spending most of her career at NASA and NOAA as a scientist, Privette led projects for the White House climate portfolio, including the Obama Climate Data Initiative and the Partnership for Resilience and Preparedness (PREP), both focused on delivering better access and use of US Federal climate data to support decision makers.

Innovations I find exciting

As part of ASDI, I work very closely with AWS customers developing applications in the space of sustainability to understand what challenges they may be experiencing and how we may accelerate sustainability research and innovation by minimizing the cost and time required to acquire and analyze large sustainability datasets. The ASDI currently works with scientific organizations like NOAA, NASA, the UK Met Office and Government of Queensland to identify, host, and deploy key datasets on the AWS Cloud, including weather observations, weather forecasts, climate projection data, satellite imagery, hydrological data, air quality data, and ocean forecast data. These datasets are publicly available to anyone.

In addition, ASDI provides cloud grants to those interested in exploring the use of AWS’ technology and scalable infrastructure to solve big, long-term sustainability challenges with this data. The dual-pronged approach allows sustainability researchers to analyze massive amounts of data in mere minutes, regardless of where they are in the world or how much local storage space or computing capacity they can access.

Nashlie Sephus, manager, applied science

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Nashlie Sephus, applied scientist, Amazon Web Services machine learning team.
Credit: Terrence Wells@PoetWilliamsPhotography

Sephus is an applied scientist on AWS’s artificial intelligence team, focusing on computer vision. In this role, Sephus focuses on the fairness and accuracy of the team’s algorithms. Sephus formerly led the Amazon Visual Search team in Atlanta, which launched visual search for replacement parts on the Amazon Shopping app in June 2018. This technology was a result of former startup Partpic (Atlanta) being acquired by Amazon, for which she was the chief technology officer (CTO). Prior to working at Partpic, she received her PhD in 2014 from the School of Electrical and Computer Engineering at the Georgia Institute of Technology. She received her bachelor’s degree in computer engineering in 2007 from Mississippi State University.

Innovations I find exciting

Since the onset of machine learning and artificial intelligence, neural networks (such as convolutional neural networks (CNNs), and generative adversarial networks (GANs), etc.) and learning algorithms have always excited me. It’s being able to quickly and automatically draw patterns from data, whether it be images, video, or audio at scale, that fascinates me. Since music was my first love (along with karaoke!), music information retrieval has always been a passion of mine. These innovations, when used responsibly and fairly, are able to benefit people in their everyday activities.

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About the Author
Arun Krishnan
Arun Krishnan is a contributor to Amazon Science and the editor-in-chief for Alexa Events.

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Data Scientist, WWGS SSG Strategic Initiative Development
CN, 31, Shanghai
As WWGS is committed to building an AI-native, agentic organization, the heart of SSG's evolution is to reinvent how we engage sellers with an AI-first mindset. Delivering that ambition at scale demands breakthrough innovation in our foundational capabilities, at ever-faster speed. To meet these priorities with the pace and focus they require, we are standing up a dedicated team, organized as a POD model - a lean, cross-functional team led by a single owner who holds end-to-end decisions and the business outcome, so we can move at the speed the business demands. Our priority is to build the capability to reach the right sellers at scale, anchored on two pillars: Seller Tagging and Good Contact. Through Seller Tagging, we will turn today's fragmented, siloed seller signals into a unified seller intelligence capability that deepens and broadens how well we truly understand every seller. Through Good Contact, we will resolve the seller identity gaps across our channels and systems, so that every engagement and every system that depends on it, can consistently reach the right seller contact. The Data Scientist will be part of the POD team and support the team to establish structured data governance and model training framework, and trail predict models with real business use cases. Key job responsibilities Design and deliver seller segmentation and propensity models at scale — incorporating GMS, category, growth trajectory, engagement signals, and lifecycle stage. Build contact quality scoring and lifecycle management systems (coverage optimization, dormancy detection, reactivation modeling). Define success metrics, experimentation frameworks (A/B, causal inference), and measurement methodology for seller engagement interventions. Productionize ML models and data products — partner with engineering to deploy seller scores, contact quality indices, and recommendation signals. Explore LLM/GenAI applications: automated insight generation from seller data, contact intent classification, and intelligent report synthesis. Serve as the science representative in bi-weekly NBS theme reviews; present findings and proposals to theme Bar Raisers and leadership. Collaborate with BIE team members to democratize analytical outputs via dashboards and self-serve tools. Contribute to cross-marketplace seller behavior analysis supporting Global Expansion strategy (IN, KR, VN, LATAM). Evaluate, integrate, and iterate on AI systems — assess new AI/ML tools, frameworks, and third-party models for applicability to seller intelligence use cases.
2027 Applied Science Internship - Robotics - United States
US, MA, North Reading
Robotics Applied Scientist II Intern/Co-op (2027) Are you passionate about robotics and research? Do you want to solve real customer problems through innovative technology? Do you enjoy working on scalable research and projects in a collaborative team environment? Do you want to see your science solutions directly impact millions of customers worldwide? We’re looking for current PhD students with a passion for robotic research and applications to join us in 2027 as Robotics Applied Scientist II Interns/Co-ops to shape the future of robotics and transform automation at Amazon’s scale. By applying to this role, you will be considered for Robotics Applied Scientist II Intern/Co-op (2027) opportunities across various Robotics teams at Amazon with different robotics research focus. Internship positions are available for multiple locations, durations (3 to 6+ months), and year-round start dates (winter, spring, summer, fall). Join us at the forefront of Applied Science, where your contributions will shape the future of Robotics! Key job responsibilities As a Robotics Applied Scientist II Intern/Co-op, you will work alongside global experts to advance the state of the art in robotics and artificial intelligence, tackling innovative problems that bridge the gap between research and real-world robotics implementation to innovate and build solutions on behalf of our customers. We are particularly interested in students with specialization and expertise in one or more of the following research areas: - Robotics - Artificial Intelligence - Robotics Manipulation (e.g., robot arm, grasping, dexterous manipulation, end of arm tools/end effector) - Autonomous Mobile Robots - Mobile Manipulation - Multi-Robot Coordination - Autonomous Navigation - Locomotion - Motion/Path Planning - Controls - Perception - Sensing - Robot Learning - Machine Learning - Computer Vision - Large Language Models (LLM), Vision-Language-Action Models (VLA), Vision-Language-Models (VLM) - World Models - Human-Robot Interaction - Robotics Simulation - Optimization - And related! Key responsibilities may include: - Develop novel, scalable algorithms and/or modeling techniques that advance the state-of-the-art in science areas such as robotics, artificial intelligence, or related - Tackle challenging, innovative research problems within robotics, artificial intelligence, or related that bridge the gap between theoretical advancement and practical robotics implementation - Collaborate with cross-functional teams to solve complex problems, and to communicate research findings clearly, leveraging your expertise in robotics, artificial intelligence, or related - Demonstrate the ability to work independently, thrive in a fast-paced, ever-changing environment - Leverage AI-powered tools where applicable to accelerate research, experimentation, and prototyping. - Critically review and validate outputs from AI tools and automated systems. About the team Explore Robotics Teams at Amazon At Amazon, robotics is where innovative research meets real-world deployment. There are many different specialized robotics teams, including the few highlighted below. Learn more about Robotics at Amazon here: https://www.amazon.science/research-areas/robotics Fulfillment Technologies and Robotics We build dynamic partnerships between people and intelligent machines. This intricate collaboration helps Amazon fulfill orders with unmatched accuracy. Working in symphony with our robotic technology, you’ll have the opportunity to extend your technical capabilities by working alongside some of the industry’s most advanced technologies. This includes our fleet of autonomous mobile robots, sophisticated control software, and technologies like language perception, machine learning, object recognition, and semantic understanding of commands. These technologies help us deliver an ever-improving customer and employee experience, as well as improve the safety of our facilities. Amazon Robotics Amazon Robotics is at the epicenter of robotics innovation. We design, test, and build mobile robots and mechatronic systems. Our offices, labs, test floors, and factories are close together, which means we can design the hardware, write the code, test, build, and watch it all come to life. Our areas of focus include advancements in autonomous mobility, AI, robotic management software, and much more. Fauna Fauna is focused on building capable, safe, and genuinely delightful robots for everyday life. Fauna’s goal is simple: make robots people actually want to live and interact with everyday. Fauna develops tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Fauna’s work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but overall experience. Amazon Fulfillment Technologies Amazon Fulfillment Technologies develops tech solutions that create network optimizations for all Amazon sites worldwide. We own network infrastructure and devices in all buildings and labor planning and scheduling technology for fulfillment centers in North America and Europe.
Principal Applied Scientist, FAIM (Full Funnel Agentic Intelligence and Models)
US, NY, New York
The FAIM (Full Funnel Agentic Intelligence and Models) builds the agentic AI systems behind next-generation advertising. The team develops multimodal foundation models and LLM-driven agents that power shopper understanding, audience-aware creative generation, and agentic advertising workflows. As a Principal Applied Scientist for the FAIM team, you will invent the models that help advertisers achieve desired outcomes. Today's state of the art models can setup ad campaigns to a specification, but do not understand advertising strategy well enough to help with abstract goals that real advertisers ask about like "how can I make more money." This is a rare charter to build foundational optimization science where little exists today, spanning campaign recommendation, cross-product budget allocation, incrementality measurement, and long-term-sales modeling. You will set technical direction for a growing team, partner with engineering and product to take models from research to production at Amazon scale, and directly move advertiser ROAS and new-to-brand growth. In this role you will - Own the science vision for developing evaluations of the Ads Agent - Partner with scientists from across Ads to develop best practices for evaluating agent performance both online and offline - Raise the science bar for all agent evaluations developed in Ads. - Own establishing a data driven foundation for making decisions about when new features are ready to launch in Ads Agent. Key job responsibilities - Define the long-term scientific vision for agent evaluations in Ads - Translate ambiguous advertiser needs and competing objectives into a concrete science roadmap. - Invent, prototype, and productionize machine learning and optimization solutions for joint budget allocation across sponsored ad products, spanning the shopper journey from awareness to purchase. - Develop rigorous approaches to incrementality measurement and long-term-sales modeling that ground optimization in true advertiser value. - Design and lead large-scale experiments and analyses to validate hypotheses and guide product direction. - Partner closely with engineering and product to define technical contracts, data schemas, and serving systems that carry models into production. - Raise the technical bar across science and engineering through mentorship, design reviews, and hands-on collaboration. - Grow scientific talent and publish impactful research internally and at top-tier venues.
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