IWomen Shaping AI at Amazon AWS

Women scientists at Amazon shaping the future of AI

To commemorate International Women’s Day (IWD) 2020, we spoke to women scientists across a variety of Amazon research areas.

To commemorate International Women’s Day (IWD) 2020, we asked women 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

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Yoelle Maarek, vice president

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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US, WA, Seattle
Are you passionate about the use of Machine Learning to improve the experience for Alexa and Smart Home customers? We have a team that is making revolutionary leaps forward in this space and are in need of a Machine Learning Scientist. You will have an enormous opportunity to impact the customer experience, design, architecture, and implementation of a new machine-learning driven product that will impact the lives of people you know every day.Great candidates for this position will have a passion for machine learning and signal processing and will have hands-on experience with product development. You will have the deep expertise to drive the ML vision for our products and technical breadth to make the right decisions about technology, models and methodology choices.As a Machine Learning Scientist at Amazon, you will connect with world leaders in your field working on similar problems. On this team you will analyze and model sensor data, smart home signals, and contextual data to create new experiences for customers. Meeting business requirements will involve combining several different machine learning algorithms with domain knowledge into sophisticated ML workflows. You will work with large distributed systems of data and will tackle Machine Learning challenges in Supervised, Unsupervised, and One-shot Learning, utilizing modern methods such as Deep Neural Networks and others. MLS’s have contributed to and are aware of the state-of-the-art in their respective field of expertise and are constantly focused on advancing the state-of-the-art for improving Amazon’s products and services.KEY RESPONSIBILITIES· Analyze and extract relevant information from large amounts of data to support new experiences for customers· Create novel ML approaches and apply them to achieve project goals· Build ML software and algorithms that cost-effectively scale to millions of customers· Work closely with other teams across Amazon to deliver platform features that require cross-team leadership· Ensure that the quality and timeliness of ML deliverables
IN, KA, Bangalore
What would you do if you had access to the world’s largest product catalog with billions of products, offers, images, reviews, searches, and much more? Amazon Selection and Catalog Systems is looking for an innovative and customer-focused applied scientist to improve the data quality of the world’s biggest product catalog, utilizing state-of-the-art machine learning techniques.An information-rich and accurate product catalog is a critical strategic asset for Amazon. It powers unrivaled product discovery, informs customers’ buying decisions, offers a large selection and positions Amazon as the first stop for our customers. Maintaining and improving the accuracy of product catalog is challenging due to sheer scale (billions of products in the catalog), diversity (products ranging from electronics to groceries to instant video across multiple languages) and multitude of input sources (millions of sellers contributing product data with different quality).You will conceive innovative solutions to measure and improve the quality of various aspects of our product catalog and influence the way millions of our customers discover and buy our products worldwide. The opportunity (puzzle to solve) is that there is no single solution as the problem scope is varied and diverse. The solutions you build will vary from simple rule based systems to machine learning, semantic analysis and text processing. You will have the opportunity to design new data analytical workflows at a scale rarely available elsewhere, utilizing state-of-the-art data science and machine learning tools such as Spark, Python, and Theano and Amazon’s cloud computing technologies such as Elastic Map Reduce (EMR), Kinesis, and Redshift. You will apply your knowledge in data science by creating algorithmic solutions that combine techniques such as clustering, pattern mining, predictive modeling, deep learning, statistical testing, information retrieval, and natural language processing and apply them to the voluminous data describing the products in the catalog and the customer interactions. You will evaluate with scientific rigor and provide inputs to business strategy and technical direction. You will collaborate with software engineering teams to integrate your algorithmic solutions into large-scale highly complex Amazon production systems.Responsibilities include:· Map business requirements and customer needs to a scientific problem.· Align the research direction to business requirements and make the right judgments on research/development schedule and prioritization.· Research, design, implement and deploy scalable machine learned models, including the application of state-of-art deep learning, to solve problems that matter to our customers in an iterative fashion.· Mentor and develop junior applied scientists and developers who work on data science problems in the same organization.· Stay informed on the latest machine learning, natural language and/or artificial intelligence trends and make presentations to the larger engineering and applied science communities.
IN, KA, Bangalore
What would you do if you had access to the world’s largest product catalog with billions of products, offers, images, reviews, searches, and much more? Amazon Selection and Catalog Systems is looking for an innovative and customer-focused applied scientist to improve the data quality of the world’s biggest product catalog, utilizing state-of-the-art machine learning techniques.An information-rich and accurate product catalog is a critical strategic asset for Amazon. It powers unrivaled product discovery, informs customers’ buying decisions, offers a large selection and positions Amazon as the first stop for our customers. Maintaining and improving the accuracy of product catalog is challenging due to sheer scale (billions of products in the catalog), diversity (products ranging from electronics to groceries to instant video across multiple languages) and multitude of input sources (millions of sellers contributing product data with different quality).You will conceive innovative solutions to measure and improve the quality of various aspects of our product catalog and influence the way millions of our customers discover and buy our products worldwide. The opportunity (puzzle to solve) is that there is no single solution as the problem scope is varied and diverse. The solutions you build will vary from simple rule based systems to machine learning, semantic analysis and text processing. You will have the opportunity to design new data analytical workflows at a scale rarely available elsewhere, utilizing state-of-the-art data science and machine learning tools such as Spark, Python, and Theano and Amazon’s cloud computing technologies such as Elastic Map Reduce (EMR), Kinesis, and Redshift. You will apply your knowledge in data science by creating algorithmic solutions that combine techniques such as clustering, pattern mining, predictive modeling, deep learning, statistical testing, information retrieval, and natural language processing and apply them to the voluminous data describing the products in the catalog and the customer interactions. You will evaluate with scientific rigor and provide inputs to business strategy and technical direction. You will collaborate with software engineering teams to integrate your algorithmic solutions into large-scale highly complex Amazon production systems.Responsibilities include:· Map business requirements and customer needs to a scientific problem.· Align the research direction to business requirements and make the right judgments on research/development schedule and prioritization.· Research, design, implement and deploy scalable machine learned models, including the application of state-of-art deep learning, to solve problems that matter to our customers in an iterative fashion.· Mentor and develop junior applied scientists and developers who work on data science problems in the same organization.· Stay informed on the latest machine learning, natural language and/or artificial intelligence trends and make presentations to the larger engineering and applied science communities.
US, WA, Seattle
The Central AWS Econ team is dedicated to bringing the most trustworthy evidence-based analysis to the most strategic decisions for AWS leadership.Our studies impact strategic investments, service business model, resource allocation, product priorities and pricing models, go-to-market motions and more.This Senior economist role partners with AWS business leaders across the organization to define and deliver on economic questions that guide their most strategic decisions. The successful candidate will be a problem solver who enjoys diving into data, is excited by difficult modeling challenges and ambiguous starting points, and possesses strong communication skills to effectively interface and collaborate with product, finance, planning and business teams.Specific questions include developing supporting economics for new business model, evaluating the relationship between short and long term growth, mapping and affecting the customer journey through different AWS products and cloud technologies.The Central AWS Econ team is dedicated to answering these (and many more) questions using quantitative, economic and statistical methods.Key Responsibilities:· Identify and propose impactful economic studies based on business meetings· Lead / conduct economic studies, including developing and communicating practical implications to senior leadership· Mentor and develop junior economists and data scientists· Develop new repeatable data analysis pipelines to be used by non-economists
US, CA, Cupertino
Are you a biochemistry research scientist? At Amazon, we are constantly inventing and re-inventing to be the most customer-centric company in the world. To get there, we need exceptionally talented, bright, and driven people. We are a smart team of doers that work passionately to apply cutting-edge advances in technology and to solve real-world problems that will transform our customers’ experiences in ways we can’t even imagine yet.As a Research Scientist, you will be working with a unique and gifted team that is developing exciting products and collaborating with cross-functional teams.Responsibilities:· Collaborate to define product specifications and protocols· Iterate through experimentation to identify optimal product parameters· Identify and qualify new materials· Ensure manufacturability across the design process· Contribute to design control and regulated protocols· Collaborate with engineering teams to design, implement, and harmonize solutions
US, WA, Seattle
** LOCATION CAN BE EITHER TEMPE, SEATTLE OR NASHVILLE**Amazon Transportation Services (ATS) Line Haul is looking for a talented Data Scientist who will own analytics and develop solutions to drive insights and optimization for Network Planning and Forecasting. As a member of this team, you will have an opportunity to be an innovator in Amazon Logistics and work with a group of talented program managers, product managers, research scientists, software developers, and business stakeholders to design the Amazon network of the future.This position requires innovative thinking, ability to quickly approach large ambiguous problems, technical and engineering expertise to rapidly research, validate, visualize, prototype and deliver solutions. This position also requires significant cross functional work and integration with transportation, tech, operations and finance. Successful candidates will thrive in a fast-paced environment.As you further your career as a Data Scientist at Amazon, you will focus on improving corporate reporting frameworks and data visualization. You will examine performance data, discover and solve real world problems related to forecasting, and build critical metrics and business cases. We are focused on your success and want to build and support strong pioneers within Amazon Transportation Services. You can expect to leverage your problem solving skills and have full ownership of the projects you work on.Responsibilities:· Perform complex data research to identify opportunities to reduce fulfillment costs as well as improve efficiencies and customer experience· Design, develop and establish KPIs to provide strategic insights to drive growth and performance· Ability to perform/own reoccurring and ad-hoc business intelligence projects, including the development of advanced statistical models that improve the forecasting capabilities of the Amazon transportation network· Develop standardized metrics to evaluate and benchmark pertaining to short and long term network planning and forecasting· Communicate complex insights to stakeholders, both verbally and in writing
US, NY, New York
Amazon Web Services is looking for world class scientists to join the research team within AWS Security Services. You would be entrusted with researching and developing core data mining and machine learning algorithms for various AWS security services like GuardDuty (https://aws.amazon.com/guardduty/) and Macie (https://aws.amazon.com/macie/). On this team, you will invent and implement innovative solutions for never-before-solved problems. If you have passion for security and experience with large scale machine learning problems, this will be an exciting opportunity.The AWS Security Services team builds technologies that help customers strengthen their security posture and better meet security requirements in the AWS Cloud. The team interacts with security researchers to codify our own learnings and best practices and make them available for customers. We are building massively scalable and globally distributed security systems to power next generation services.Key Responsibilities:· Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative and business judgment· Collaborate with software engineering teams to integrate successful experiments into large scale, highly complex production services.· Report results in a scientifically rigorous way· Interact with security engineers and related domain experts to dive deep into the types of challenges that we need innovative solutions for
US, WA, Seattle
Amazon delights millions of customers around the world. Meet PI-Squared, the behind-the-scenes team, that enables our HR and Operations Leaders to make informed decisions and improve the overall experience of a million frontline employees and leaders throughout their journey at Amazon. Our diverse team of statisticians, machine learning experts, and social scientists strive to make Amazon HR the most scientific HR organization in the world. We form hypotheses about the best talent acquisition, talent retention, and talent development techniques, and then set out to prove or disprove them with experiments and careful data collection.The ambition of Amazon HR is to be the most scientific organization in the world. We bring data and machine learning into management science to deliver workforce, associate experience, and leadership insights so Amazon leaders can focus their efforts in ways that will engage, retain and grow their talents. You will have the opportunity to work with operation leaders across different business lines to gain deep insights into Amazons’ daily operation and directly impact productivity, quality, and safety of hundreds of thousands of employees’ everyday life.Roles and Responsibilities:(1) Undertake econometric / statistical analysis to measure impact of various initiatives in the HR space.(2) Design and measure experiments(3) Undertake qualitative analysis to augment the findings from quantitative studies(4) Build scalable analytic solutions using state of the art tools based on large datasetsThis role requires an individual with strong quantitative modeling skills and the ability to apply statistical/machine learning, econometric, and experimental design methods. Preference will be given to candidates with additional experience in qualitative analysis in a variety of settings such as focus groups, field studies, surveys, and observational studies.
US, WA, Seattle
Try Before You Buy (TBYB) team at Amazon Fashion is looking for an Applied Scientist to join us to build our next-generation personalized recommendation systems for Personal Shopper and Prime Wardrobe. In this role, you will be responsible for researching, developing, and deploying machine learning, computer vision, and NLP models to make customers' fashion shopping experience at Amazon engaging and joyful.The primary responsibilities of this role include:· · Build ETL pipelines to collect and process data· · Frame and transform ambiguous business challenges into science hypotheses. Design and implement offline and online experiments to evaluate them· · Develop prototypes to test new concepts/proposals for models and algorithms· · Design and build automated, scalable pipelines to train and deploy ML models