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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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. 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.As a Manager, Applied Science, you will wear many different hats and work on multiple components of the entire system, in a highly collaborative environment that’s more startup than big company. You will tackle problems that span a variety of domains: computer vision, image recognition, machine learning, real-time and distributed systems.You will also help in defining the creative vision, product requirements, and user experience for this robotics program. Working with a cross-disciplinary team of analysts, designers, hardware engineers, and researchers, you will define and drive the product from concept all the way through to production.You should be a self-starter with a bias towards independent problem solving. Clear communication and prioritization will be important as you plan, design, and deliver the best experience for millions of customers. Your passion for the potential of using technology to improve people’s lives, and your experience leading complex technology projects will help you make strong business judgments.If you’re entrepreneurial and want to build and own transformative technology-driven products, join us in making history.As a part of this role, you will:· Lead the design and implementation of a cutting-edge technology in the Robotics space, focusing on SLAM.· Build and manage a team of Scientists.· Foster career growth and a strong team culture.· Recruit, hire, mentor, and coach technical staff.· Interface with our internal customers to understand requirements, set priorities and communicate direction and progress.· Own all operational metrics and support for your team.· Manage the agile development process and methodology to deliver incremental value to customers.· Help develop long-term roadmaps and business technology strategies.
US, WA, Seattle
Ever wonder how you can keep the world’s largest selection also the world’s safest and legally compliant selection? Then come join a team with the charter to monitor and classify the billions of items in the Amazon catalog to ensure compliance with various legal regulations.Our mission is to enable business users to self-serve build, deploy, maintain and improve prod cut classifiers at Amazon catalog scale. To enable this mission, one needs an ecosystem that proactively interacts and assists its users in delivering a robust ML solution. A self-serve product classification system that proactively engages with its user in a near real-time fashion by providing intuitive “insights and biases” about a model’s learning and seeks user feedback enabling continuous re-learning and correction.In this role, you will have an opportunity to lead state of the art machine learning algorithms on large datasets. You will need to lead & build Amazon scale applications running on Amazon Cloud that both leverage and create new technologies to process large volumes of data that derive patterns and conclusions from the data.We are seeking an Applied Science Manager who has a solid background in applied Machine Learning and AI, deep passion for building data-driven products; ability to communicate data insights and scientific vision, and has a proven track record of leading both applied scientists and software engineers to execute complex projects and deliver business impacts.In this role, you will:· Lead a group applied scientists (predominantly) and software engineers to deliver machine-learning and AI solutions to production.· Advance team's engineering craftsmanship and drive continued scientific innovation as a thought leader and practitioner.· Develop science and engineering roadmap, run Sprint/quarter and annual planning, and foster cross-team collaboration to execute complex projects.· Perform hands-on data analysis, build machine-learning models, run regular A/B tests, and communicate the impact to senior management.· Hire and develop top talents, provide technical and career development guidance to both scientists and engineers in the organization.
US, WA, Seattle
How many FireTV devices should Amazon build?If you're interested in using science to answer critical business questions like this, Amazon Devices Demand Planning is the place to be. We develop scalable and robust state-of-the-art forecasting solutions for the entire portfolio of Amazon devices. As a scientist on the team, you will have an opportunity to both develop advanced scientific solutions and drive critical customer and business impacts. You will play a key role to drive end-to-end solutions from understanding our business requirements, exploring a large amount of historical data, building prototypes and exploring conceptually new solutions. You will collaborate closely with peers in engineering as well as business stakeholders. You will be at the heart of a growing and exciting focus area for Amazon Devices.Key responsibilities:· Research and develop new methodologies for demand forecasting.· Improve upon existing methodologies by adding new data sources and implementing model enhancements.· Drive scalable solutions.· Create and track accuracy and performance metrics (both technical and business metrics).· Create, enhance, and maintain technical documentation, and present to other scientists, engineers and business leaders.· Drive best practices on the team; mentor and guide junior members to achieve their career growth potential.
US, WA, Seattle
Are you passionate about conducting measurement research and experiments to assess and evaluate talent? Would you like to see your research in products that will drive key talent management behaviors globally to ensure we are raising the bar on our talent? If so, you should consider joining the Global Talent Management (GTM) Science Team!Amazon GTM Science team is an innovative organization that exists to propel Amazon HR toward being the most scientific HR organization on earth. The GTM Science mission is to use Science to assist and measurably improve every talent decision made at Amazon. GTM Science does this by discovering signals in workforce data, infusing intelligence into Amazon’s talent products, and guiding the broader GTM team to pursue high-impact opportunities with tangible returns. This multi-disciplinary approach spans capabilities, including: data engineering, reporting and analytics, research and behavioral sciences, and applied sciences such as economics and machine learning.We are seeking Assessment and Measurement Scientists with expertise developing assessment and validating measures (assessments, performance evaluations, and surveys) to evaluate talent at Amazon. This person will possess knowledge of different assessment approaches to evaluate performance, a strong psychometrics background, scientific survey methodology, computing various content validity analyses, and experience developing legally defensible talent evaluation programs. In this role you will:· Lead the global research strategy developing performance evaluations both quantitative and qualitative· Execute a scalable global content development and research strategy Amazon-wide· Conduct psychometrics analyses to evaluate integrity and practical application of content· Identify research streams to evaluate how to mitigate or remove sources of measurement error· Partner closely and drive effective collaborations across multi-disciplinary research and product teams· Manage full life cycle of large scale research programs (Develop strategy, gather requirements, execute, and evaluate)
US, WA, Seattle
Do you want to join a brand new team building an AI system that would disrupt the industry? Do you enjoy dealing with ambiguity and working on hard problems in a fast-paced environment?Amazon Connect is a highly disruptive cloud-based contact center that enables businesses to deliver engaging, dynamic, and personal customer service experiences. With Amazon Connect, you can create your own cloud-based contact center and be taking calls in minutes. Amazon Connect leverages the power of Artificial Intelligence and the large ecosystem of AWS services such as Lex, Transcribe, Lambda, S3, and Kinesis to provide a truly frustration free and natural customer experience. With this technology, we are transforming an industry and the way customers interact with businesses and how agents service them.As an Applied Scientist on our team, you will analyze data from huge data sets, create ML forecasting and classification models from conception to deployment, and work closely with other senior technical leaders within the team and across AWS. You will demonstrate your deep Applied Science knowledge and experience at prototyping and building accurate and effective ML models using technology such as AWS Sagemaker, PyTorch, and SparkML. Our team is at an early stage, so you will have significant impact on our ML deliverables with no operational load from existing models/systems.We have a rapidly growing customer base and an exciting charter in front of us that includes solving highly complex engineering and algorithmic problems. We are looking for passionate, talented, and experienced people to join us to innovate on this new service that addresses customer needs to build modern contact centers in the cloud. The position represents a rare opportunity to be a part of a fast-growing business soon after launch, and help shape the technology and product as we grow. You will be playing a crucial role in developing the next generation contact center, and get the opportunity to design and deliver scalable, resilient systems while maintaining a constant customer focus.Learn more about Amazon Connect here:https://aws.amazon.com/connect/
US, WA, Bellevue
Amazon's Customer Delivery Excellence team is looking for a motivated Data Scientist with proven ability to develop, enhance, automate, and manage optimization and cutting edge prediction and learning/Artificial Intelligence models using strong quantitative skills. The successful candidate will have strong data mining, statistical modeling, machine learning skills and is comfortable facilitating ideation and working from concept through to execution.The position will partner with Engineering, Supply Chain teams, Finance and Technology teams to enhance short term and long term volume prediction and optimization models that use a range of data science methodologies to automate data analysis or to solve complex business problems for the NA Transportation network. Responsibilities include building automated tools and support structures needed to analyze data, design metrics for complex systems, dive deep to determine root cause of forecast errors & changes, create statistical definition of the outliers and methodologies to systematically identify and mitigate model variance drivers.A qualified candidate must have demonstrated ability to develop and manage medium to large-scale models and methodologies that are statistically grounded but also functional and practical. Must possess strong written and verbal communication skills, proven ability to engage and collaborate with customers to drive improvements. Possess high intellectual curiosity with ability to quickly learn new concepts/frameworks, algorithms and technology.Additional Responsibilities include:· Research machine learning algorithms and implement by tailoring to particular business needs and test on large datasets.· Manipulating/mining data from databases (Redshift, SQL Server, Oracle DW)· Create automated metrics using complex databases· Providing analytical network support to improve quality and standard work results· Root cause research to identify process breakdowns within departments and providing data through use of various skill sets to find solutions to breakdown· Collaborate with BI/Data Engineer teams and drive the collection of new data and the refinement of existing data sources to continually improve data quality· Foster culture of continuous engineering improvement through mentoring, feedback, and metricsAmazon is an Equal Opportunity-Affirmative Action Employer- Female/Minority/Disability/Vet
UK, Cambridge
We are looking for someone who is excited to apply cutting-edge techniques from deep learning or natural language processing (NLP) to the text-to-speech (TTS) technology behind Alexa and our AWS cloud speech service.As a Machine Learning Scientist you will be responsible for leading the development and launch of core product features. You will have significant influence on our overall strategy by helping define these product features, drive the system architecture, and spearhead the best practices that enable a quality product.We believe that, like a human speaker, a text-to-speech system can produce more natural speech if it has an improved understanding of the meaning and context of the text. If you agree...You will have the opportunity to solve hard problems with voice – we’ve spent years of invention on this. When we started working on this, the technology didn’t even exist – we had to invent it. Join us if you want to apply your deep learning skills in a dynamic field that is revolutionising the way people interact with devices and services.We believe that voice will fundamentally improve the way people interact with technology. It can make the complex simple—it’s the most natural and convenient user interface. Voice is going to be a big part of our future and we are inventing it here.RESPONSIBILITIES· Use your expertise in deep learning to research and implement novel approaches to make improvements to our text-to-speech technology.· Lead and Mentor junior engineers and scientists.· Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for spoken language applications.