Amazon at ICML: Industry and academia meet at Expo Day

Expo cochair and Amazon scientist Alice Zheng on the respective strengths of industry and academic machine learning research.

At this year’s International Conference on Machine Learning (ICML), Alice Zheng, a senior manager of applied science in Amazon’s display advertising organization, was one of the cochairs of Expo Day, a chance for the conference’s corporate sponsors to report their latest research and demonstrate their latest technologies.

Alice Zheng photo.png
Alice Zheng, a senior manager of applied science in Amazon’s display advertising organization.

“ICML Expo talks are pretty much like research talks that you would see in the main conference, and here is where academic conferences differ a little bit from industry conferences, based on my observation.” Zheng says. “I used to be part of a small local startup, doing machine learning. And we attended a lot of industry vendor conferences, for marketing. I had come from an academic background, and I walk into this giant pavilion with row upon row upon row of vendors with booths that have people dancing aerobics to disco tunes every afternoon from 3:00 to 4:00 to draw a crowd. I was like, ‘What is happening here? This is such a circus.’”

At academic conferences like ICML, Zheng says, sponsor presentations have a much different tone. “In academic conferences, sponsors are generally companies trying to hire,” Zheng says. “That changes how they present themselves and what they talk about. The Expo chairs are responsible for reading proposals and making accept or reject decisions. We want to uphold a certain level of quality: They should be research talks. They should be informative. They should be relevant to the topics of the conference.”

ICML’s Expo Day, which took place on Sunday, was organized into three tracks, Zheng explains. One track consisted of hour-long research talks and demos. The other two consisted of four-hour workshops in which attendees could gain first-hand experience working with new technologies developed by conference sponsors.

But while Zheng and her cochair, Hsuan-Tien Lin of National Taiwan University, upheld rigorous scientific standards in their evaluation of submitted talks, there are, she says, differences in emphasis between industry and academic research.

Amazon at ICML 2021

Learn more about Amazon's presence at ICML 2021, including research papers, workshop participation, tutorials, and more.

“One of the disconnects that I see between industry and academia is that academia focuses way more on modeling and math,” she says. “The full cycle for machine learning development and operations starts with ideation: you come up with an idea, or there's a problem that you want to solve. You formulate the problem, you propose a solution, you test it, both offline and online. You iterate on the process, and you eventually end up with something that works well. You work with engineering to get a robust implementation — and that can take a while — you deploy it, and you monitor it, to make sure that it's continually working as it should.

“That's the full cycle. In academia, I would say that oftentimes even the ideation is fairly rote — people work on pre-established problems. The focus is on the proposal of new methods, new solutions, followed by very light testing. It's like one out of five steps of the entire cycle.

“Several years ago, I created a talk about operating machine learning models, and I highlighted the importance of evaluation and metrics. It’s tempting to say ‘Oh, I'll just measure the AUC [area under the curve].’ But in reality, it's more complicated than that, because in many, many application areas, it's not one homogeneous set of data, one monolithic model. The data can be subdivided, and you should look at each slice separately. How do you slice the data? How do you create metrics that are stable enough to operate on and still sensitive enough that if something were to go wrong, it would appear abnormal?

“So I created this talk, and I gave it at our intern symposium, and people loved it. Students were raising their hands and saying, ‘How can we learn more? Where can we go to do more of this?’ This is the kind of thing that industry research excels at. Academia does not have the same amount or variety of application data.”

Explainable AI

Of course, industry researchers stay abreast of the latest academic research, and from her vantage as ICML Expo chair, Zheng can see which academic trends have recently begun to take hold in industry, as well. One of these, she says, is model explainability.

Model explainability is a question of fundamental scientific interest: neural networks are black boxes, and it’s natural to want to understand how they do the remarkable things they do. But, Zheng explains, it’s also a question with immediate business implications.

“Sometimes the business needs insights, in which case an explainable model is the thing that they're looking for,” Zheng says. “Oftentimes you need some way to examine if something is going wrong. It gets back to model operations.”

The need for model explainability, Zheng says, frequently arises in the context of her work at Amazon, which focuses on the design of algorithms for automatically bidding on advertising space on other sites and responding to bids for space on Amazon sites.

“Digital advertising, or programmatic advertising, operates in a high-volume, high-velocity environment,” Zheng explains. “Websites send bid requests out to ad exchanges, and then the ad exchanges contract with different bidders. And the bidders say, ‘Okay, I'm willing to pay this much for an opportunity to show an ad.’ Our algorithms need to make split-second decisions about where to bid and how much to bid.

“The advertisers want to know, ‘What can I do to improve the performance of my ad?’ They're trying to understand this black-box system. They are always looking for insights in terms of, ‘How can I better set up? What actually drives conversions?’ 

“And then when we operate our model, we are also interested in general model analysis. Say our conversion rate prediction model is giving a lot of weight to ads that are selling consumer products. Because customers may buy toilet paper every month, whereas they buy a camera maybe every two years. Is that helping advertisers reach the right audience? By being able to understand what the model is doing, we can detect problems, and we can improve it.”

Research areas

Related content

US, WA, Seattle
The AWS AI Labs team has a world-leading team of researchers and academics, and we are looking for world-class colleagues to join us and make the AI revolution happen. Our team of scientists have developed the algorithms and models that power AWS computer vision services such as Amazon Rekognition and Amazon Textract. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. AWS is the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems which will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. Our research themes include, but are not limited to: few-shot learning, transfer learning, unsupervised and semi-supervised methods, active learning and semi-automated data annotation, large scale image and video detection and recognition, face detection and recognition, OCR and scene text recognition, document understanding, 3D scene and layout understanding, and geometric computer vision. For this role, we are looking for scientist who have experience working in the intersection of vision and language. We are located in Seattle, Pasadena, Palo Alto (USA) and in Haifa and Tel Aviv (Israel).
US, WA, Seattle
Amazon Prime Video is changing the way millions of customers enjoy digital content. Prime Video delivers premium content to customers through purchase and rental of movies and TV shows, unlimited on-demand streaming through Amazon Prime subscriptions, add-on channels like Showtime and HBO, and live concerts and sporting events like NFL Thursday Night Football. In total, Prime Video offers nearly 200,000 titles and is available across a wide variety of platforms, including PCs and Macs, Android and iOS mobile devices, Fire Tablets and Fire TV, Smart TVs, game consoles, Blu-ray players, set-top-boxes, and video-enabled Alexa devices. Amazon believes so strongly in the future of video that we've launched our own Amazon Studios to produce original movies and TV shows, many of which have already earned critical acclaim and top awards, including Oscars, Emmys and Golden Globes. The Global Consumer Engagement team within Amazon Prime Video builds product and technology solutions that drive customer activation and engagement across all our supported devices and global footprint. We obsess over finding effective, programmatic and scalable ways to reach customers via a broad portfolio of both in-app and out-of-app experiences. We would love to have you join us to build models that can classify and detect content available on Prime Video. We need you to analyze the video, audio and textual signal streams and improve state-of-art solutions while being scalable to Amazon size data. We need to solve problems across many cultures and languages, working alongside an operations team generating labels across many languages to help us achieve these goals. Our team consistently strives to innovate, and holds several novel patents and inventions in the motion picture and television industry. We are highly motivated to extend the state of the art. As a member of our team, you will apply your deep knowledge of Computer Vision and Machine Learning to concrete problems that have broad cross-organizational, global, and technology impact. Your work will focus on addressing fundamental computer vision models like video understanding and video summarization in addition to building appropriate large scale datasets. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with independence and are often assigned to focus on areas with significant impact on audience satisfaction. You must be equally comfortable with digging in to customer requirements as you are drilling into design with development teams and developing production ready learning models. You consistently bring strong, data-driven business and technical judgment to decisions. You will work with internal and external stakeholders, cross-functional partners, and end-users around the world at all levels. Our team makes a big impact because nothing is more important to us than pleasing our customers, continually earning their trust, and thinking long term. You are empowered to bring new technologies and deep learning approaches to your solutions. We embrace the challenges of a fast paced market and evolving technologies, paving the way to universal availability of content. You will be encouraged to see the big picture, be innovative, and positively impact millions of customers. This is a young and evolving business where creativity and drive will have a lasting impact on the way video is enjoyed worldwide.
US, NY, New York
Amazon is looking for an outstanding Data Scientist to help build the next generation of selection systems. On the Specialized Selection team within the Supply Chain Optimization Technologies (SCOT) organization, we own the selection systems that determine which products Amazon offers in our fastest delivery programs. We build state-of-the-art models leveraging tools from machine learning, numerical optimization, natural language processing, and causal inference to automate the management of Amazon's sub-same day (SSD) selection at scale. We sit as a part of one of the largest and most sophisticated supply chains in the world. We operate a highly cross-functional team across software, science, analytics, and product to define and scalably execute the strategic direction of SSD and speed selection more broadly. As a Data Scientist on the team, you will work with scientists, engineers, product managers, and business stakeholders to conduct analyses that reveal key business insights and leverage data science and machine learning techniques to develop new models and solutions to emergent business problems. Key job responsibilities Understanding business problems and translate them to appropriate scientific solutions; Using data to provide new insights and clarity to ambiguous situations; Designing effective, scalable, and achievable solutions to key business problems; Developing the right set of metrics to evaluate efficacy of your models and solutions; Prototyping and analyzing new models and business logic; Communicating, both written and verbally, with both technical and business audiences throughout each project; Contributing to the scientific community across the organization
US, CA, Palo Alto
Join a team working on cutting-edge science to innovate search experiences for Amazon shoppers! Amazon Search helps customers shop with ease, confidence and delight WW. We aim to transform Search from an information retrieval engine to a shopping engine. In this role, you will build models to generate and recommend search queries that can help customers fulfill their shopping missions, reduce search efforts and let them explore and discover new products. You will also build models and applications that will increase customer awareness of related products and product attributes that might be best suited to fulfill the customer needs. Key job responsibilities On a day-to-day basis, you will: Design, develop, and evaluate highly innovative, scalable models and algorithms; Design and execute experiments to determine the impact of your models and algorithms; Work with product and software engineering teams to manage the integration of successful models and algorithms in complex, real-time production systems at very large scale; Share knowledge and research outcomes via internal and external conferences and journal publications; Project manage cross-functional Machine Learning initiatives. About the team The mission of Search Assistance is to improve search feature by reducing customers’ effort to search. We achieve this through three customer-facing features: Autocomplete, Spelling Correction and Related Searches. The core capability behind the three features is backend service Query Recommendation.
US, CA, Palo Alto
Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. The Ad Response Prediction team in Sponsored Products organization build advanced deep-learning models, large-scale machine-learning (ML) pipelines, and real-time serving infra to match shoppers’ intent to relevant ads on all devices, for all contexts and in all marketplaces. Through precise estimation of shoppers’ interaction with ads and their long-term value, we aim to drive optimal ads allocation and pricing, and help to deliver a relevant, engaging and delightful ads experience to Amazon shoppers. As the business and the complexity of various new initiatives we take continues to grow, we are looking for energetic, entrepreneurial, and self-driven science leaders to join the team. Key job responsibilities As a Principal Applied Scientist in the team, you will: Seek to understand in depth the Sponsored Products offering at Amazon and identify areas of opportunities to grow our business via principled ML solutions. Mentor and guide the applied scientists in our organization and hold us to a high standard of technical rigor and excellence in ML. Design and lead organization wide ML roadmaps to help our Amazon shoppers have a delightful shopping experience while creating long term value for our sellers. Work with our engineering partners and draw upon your experience to meet latency and other system constraints. Identify untapped, high-risk technical and scientific directions, and simulate new research directions that you will drive to completion and deliver. Be responsible for communicating our ML innovations to the broader internal & external scientific community.
US, CA, Palo Alto
We’re working to improve shopping on Amazon using the conversational capabilities of large language models, and are searching for pioneers who are passionate about technology, innovation, and customer experience, and are ready to make a lasting impact on the industry. You'll be working with talented scientists, engineers, and technical program managers (TPM) to innovate on behalf of our customers. If you're fired up about being part of a dynamic, driven team, then this is your moment to join us on this exciting journey!"?
US, CA, Santa Clara
AWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on foundation models, large-scale representation learning, and distributed learning methods and systems. At AWS AI/ML you will invent, implement, and deploy state of the art machine learning algorithms and systems. You will build prototypes and innovate on new representation learning solutions. You will interact closely with our customers and with the academic and research communities. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. Large-scale foundation models have been the powerhouse in many of the recent advancements in computer vision, natural language processing, automatic speech recognition, recommendation systems, and time series modeling. Developing such models requires not only skillful modeling in individual modalities, but also understanding of how to synergistically combine them, and how to scale the modeling methods to learn with huge models and on large datasets. Join us to work as an integral part of a team that has diverse experiences in this space. We actively work on these areas: * Hardware-informed efficient model architecture, training objective and curriculum design * Distributed training, accelerated optimization methods * Continual learning, multi-task/meta learning * Reasoning, interactive learning, reinforcement learning * Robustness, privacy, model watermarking * Model compression, distillation, pruning, sparsification, quantization About Us Inclusive Team Culture Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Work/Life Balance Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives. Mentorship & Career Growth Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, WA, Seattle
Do you want to join an innovative team of scientists who use machine learning to help Amazon provide the best experience to our Selling Partners by automatically understanding and addressing their challenges, needs and opportunities? Do you want to build advanced algorithmic systems that are powered by state-of-art ML, such as Natural Language Processing, Large Language Models, Deep Learning, Computer Vision and Causal Modeling, to seamlessly engage with Sellers? Are you excited by the prospect of analyzing and modeling terabytes of data and creating cutting edge algorithms to solve real world problems? Do you like to build end-to-end business solutions and directly impact the profitability of the company and experience of our customers? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Selling Partner Experience Science team. Key job responsibilities Use statistical and machine learning techniques to create the next generation of the tools that empower Amazon's Selling Partners to succeed. Design, develop and deploy highly innovative models to interact with Sellers and delight them with solutions. Work closely with teams of scientists and software engineers to drive real-time model implementations and deliver novel and highly impactful features. Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. Research and implement novel machine learning and statistical approaches. Lead strategic initiatives to employ the most recent advances in ML in a fast-paced, experimental environment. Drive the vision and roadmap for how ML can continually improve Selling Partner experience. About the team Selling Partner Experience Science (SPeXSci) is a growing team of scientists, engineers and product leaders engaged in the research and development of the next generation of ML-driven technology to empower Amazon's Selling Partners to succeed. We draw from many science domains, from Natural Language Processing to Computer Vision to Optimization to Economics, to create solutions that seamlessly and automatically engage with Sellers, solve their problems, and help them grow. Focused on collaboration, innovation and strategic impact, we work closely with other science and technology teams, product and operations organizations, and with senior leadership, to transform the Selling Partner experience.
US, WA, Seattle
The AWS AI Labs team has a world-leading team of researchers and academics, and we are looking for world-class colleagues to join us and make the AI revolution happen. Our team of scientists have developed the algorithms and models that power AWS computer vision services such as Amazon Rekognition and Amazon Textract. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. AWS is the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems which will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. Our research themes include, but are not limited to: few-shot learning, transfer learning, unsupervised and semi-supervised methods, active learning and semi-automated data annotation, large scale image and video detection and recognition, face detection and recognition, OCR and scene text recognition, document understanding, 3D scene and layout understanding, and geometric computer vision. For this role, we are looking for scientist who have experience working in the intersection of vision and language. We are located in Seattle, Pasadena, Palo Alto (USA) and in Haifa and Tel Aviv (Israel).
GB, London
Are you excited about applying economic models and methods using large data sets to solve real world business problems? Then join the Economic Decision Science (EDS) team. EDS is an economic science team based in the EU Stores business. The teams goal is to optimize and automate business decision making in the EU business and beyond. An internship at Amazon is an opportunity to work with leading economic researchers on influencing needle-moving business decisions using incomparable datasets and tools. It is an opportunity for PhD students in Economics or related fields. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL would be a plus. As an Economics Intern, you will be working in a fast-paced, cross-disciplinary team of researchers who are pioneers in the field. You will take on complex problems, and work on solutions that either leverage existing academic and industrial research, or utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you may even need to deliver these to production in customer facing products. Roughly 85% of previous intern cohorts have converted to full time economics employment at Amazon.