dhillon_inderjit_0.jpg
Amazon Fellow Inderjit Dhillon
Credit: University of Texas at Austin

How Amazon Fellow Inderjit Dhillon is improving shopping discovery on Amazon

Developing machine learning frameworks that can enhance context-aware AI has been an area of focus for Dhillon’s entire career.

There are millions of items in the Amazon catalog. To ensure a great customer experience along a customer’s shopping journey, it is important to surface the right products or information at the right time.

Suppose you type the query “digital cameras” on Amazon.com. The system ought to recognize this as the beginning of your shopping journey. A variety of options to help you conduct your research might be better than just showing the top-selling products in the camera category. The results could include a list of editorial recommendations that call out the “must-have” features at the most affordable prices. As another example, consider two consecutive queries made by a customer: “shorts for boys” followed by “red adidas shorts for toddler boy”.

These queries have age, gender, color, brand and a product type specified, but the information is added progressively as the customer moves through their shopping mission. To ensure a good customer experience, the shopping engine should be able to display progressively more relevant results that are closely aligned with the customer intent, that is likely to evolve as the customer interacts with the store.

Enter Amazon Fellow Inderjit Dhillon, who is using his expertise in machine learning to invent and deploy new AI methods to help customers along their shopping journey.

Context-aware artificial intelligence (AI) – where agents make decisions based on a broader awareness of the customer’s actions – is one of the most challenging problems in machine learning. Developing machine learning frameworks that can enhance context-aware AI has been an area of focus for Dhillon’s entire career.

“I have been passionate about, and involved in, machine learning research for over 20 years both in academia and in the industry,” says Dhillon. “After getting my PhD from UC Berkeley, I worked at IBM Research, where I developed machine learning methods to enhance user facing systems – in particular, call center and search systems. This customer-centric work helped drive new technical research, namely, the development of new document and graph clustering and co-clustering algorithms that are effective and efficient in high-dimensional spaces.”

Dhillon’s interest in machine learning motivated him to join academia.

“After I graduated, I felt the incessant need to read up on the existing machine learning literature. I figured that the best way to stay up to date with the latest in machine learning was to teach it. So I became a professor at UT Austin, and have taught machine learning to bright-eyed and incredibly smart and motivated students since then.”

Dhillon joined Amazon as an Amazon Fellow in 2017. Amazon Fellows are a select group of scientists from academia that are working at the company to solve hard science problems that can deliver a broad impact.

“Throughout my research career, I have been passionate about developing impactful open-source software. As part of my graduate studies, I wrote mathematical software based on my PhD dissertation that became part of the ubiquitous open-source LAPACK package. My software is now used by millions of users of the popular R software package when they need to perform principal components analysis or do other eigenvalue computations. My group's open-source software for recommender systems, high-dimensional document clustering and co-clustering, graph clustering and inverse covariance estimation has also been widely adopted.”

Dhillon was impressed by Amazon’s approach to customer-obsessed science.

“While my software had been used in fields as diverse as healthcare, computational chemistry, social-network analysis and recommender systems, what was missing was direct interaction with the end customer. So Amazon was a natural destination.”

One of the innovations that Dhillon is driving is the Prediction for Enormous and Correlated Output Spaces (PECOS) project. The PECOS machine learning framework aims to find relevant results from an enormous output space of potential candidates.

There are a large number of outputs for a typical shopping query. Many of the results displayed after a customer’s search are related to infrequent ‘tail’ items. Surfacing ‘tail’ items is tricky when you consider that training data is available primarily for the most common ‘head’ outputs. Given the inherent paucity of training data for most of the items, developing machine learning models that perform well for spaces of this size is challenging.

Dhillon’s team is tackling the problem in three phases, each of which select the most-relevant items to a shopper from Amazon’s vast catalog.

“In the semantic indexing phase, we form a data structure based on training data to organize the enormous but correlated output space. In the next matching phase, we use this index to efficiently reduce the output space to a much smaller candidate set for a given input. Finally, a more computationally expensive ranking step identifies the best outputs from the smaller match set. For each of these phases, we are developing new state-of-the-art machine learning approaches that are able to effectively transfer data that is observed for the head items to the tail items, thereby returning more relevant results. Moreover, the inference phase in PECOS needs to have incredibly low latency in order to be responsive to the evolving customer intent. Accomplishing all the above is not straightforward; indeed our work has brought together scientists and engineers working in machine learning, computer science, statistics and high-performance computing.”

Dhillon says that he is looking to solve similar problems that have large-scale impact by developing further collaborations between Amazon and academia. In doing so, he wants to build on his track record of open-sourcing general purpose software that helps Amazon’s customers, while at the same time, advancing the state-of-the-art in machine learning.

“Developing truly impactful solutions necessitates collaboration with the wider research community. To this end, we’re working with the Berkeley Artificial Intelligence Research Lab at UC Berkeley on open research projects.”

Making an impact at scale has been important to me throughout my career. It has been a personal barometer of satisfaction, and I am delighted that I am able to achieve these goals at Amazon.
Inderjit Dhillon, Amazon Fellow

Amazon is a part of the BAIR Open Research Commons’ Joint Collaboration Tier. Other industry sponsors of BAIR include Google, Facebook, and Samsung. BAIR brings together UC Berkeley researchers across the areas of machine learning, natural language processing, reinforcement learning, computer vision, and robotics. These include 30 faculty members (such as Michael I. Jordan, Amazon Scholar and professor at U.C. Berkeley), and more than 200 graduate students and postdocs pursuing research in cross-cutting themes including multi-modal deep learning, human-compatible artificial intelligence (AI), and connecting AI with other scientific disciplines and the humanities.

“Making an impact at scale has been important to me throughout my career,” says Dhillon. “It has been a personal barometer of satisfaction, and I am delighted that I am able to achieve these goals at Amazon.”

Related content

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, Bellevue
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, (Bayesian) time series, macroeconomic, as well as basic familiarity with Matlab, R, or Python is necessary, and experience with SQL would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
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
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, CA, Cupertino
We're looking for an Applied Scientist to help us secure Amazon's most critical data. In this role, you'll work closely with internal security teams to design and build AR-powered systems that protect our customers' data. You will build on top of existing formal verification tools developed by AWS and develop new methods to apply those tools at scale. You will need to be innovative, entrepreneurial, and adaptable. We move fast, experiment, iterate and then scale quickly, thoughtfully balancing speed and quality. 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. 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. Key job responsibilities Deeply understand AR techniques for analyzing programs and other systems, and keep up with emerging ideas from the research community. Engage with our customers to develop understanding of their needs. Propose and develop solutions that leverage symbolic reasoning services and concepts from programming languages, theorem proving, formal verification and constraint solving. Implement these solutions as services and work with others to deploy them at scale across Payments and Healthcare. Author papers and present your work internally and externally. Train new teammates, mentor others, participate in recruiting and interviewing, and participate in our tactical and strategic planning. About the team Our small team of applied scientists works within a larger security group, supporting thousands of engineers who are developing Amazon's payments and healthcare services. Security is a rich area for automated reasoning. Most other approaches are quite ad-hoc and take a lot of human effort. AR can help us to reason deliberately and systematically, and the dream of provable security is incredibly compelling. We are working to make this happen at scale. We partner closely with our larger security group and with other automated reasoning teams in AWS that develop core reasoning services.
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 and recent PhD graduates 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.
US, NY, New York
Search Thematic Ad Experience (STAX) team within Sponsored Products is looking for a leader to lead a team of talented applied scientists working on cutting-edge science to innovate on ad experiences for Amazon shoppers!. You will manage a team of scientists, engineers, and PMs to innovate new widgets on Amazon Search page to improve shopper experience using state-of-the-art NLP and computer vision models. You will be leading some industry first experiences that has the potential to revolutionize how shopping looks and feels like on Amazon, and e-commerce marketplaces in general. You will have the opportunity to design the vision on how ad experiences look on Amazon search page, and use the combination of advanced techniques and continuous experimentation to realize this vision. Your work will be core to Amazon’s advertising business. You will be a significant contributor in building the future of sponsored advertising, directly impacting the shopper experience for our hundreds of millions of shoppers worldwide, while delivering significant value for hundreds of thousands of advertisers across the purchase journey with ads on Amazon. Key job responsibilities * Be the technical leader in Machine Learning; lead efforts within the team, and collaborate and influence across the organization. * Be a critic, visionary, and execution leader. Invent and test new product ideas that are powered by science that addresses key product gaps or shopper needs. * Set, plan, and execute on a roadmap that strikes the optimal balance between short term delivery and long term exploration. You will influence what we invest in today and tomorrow. * Evangelize the team’s science innovation within the organization, company, and in key conferences (internal and external). * Be ruthless with prioritization. You will be managing a team which is highly sought after. But not all can be done. Have a deep understanding of the tradeoffs involved and be fierce in prioritizing. * Bring clarity, direction, and guidance to help teams navigate through unsolved problems with the goal to elevate the shopper experience. We work on ambiguous problems and the right approach is often unknown. You will bring your rich experience to help guide the team through these ambiguities, while working with product and engineering in crisply defining the science scope and opportunities. * Have strong product and business acumen to drive both shopper improvements and business outcomes. A day in the life * Lead a multidisciplinary team that embodies “customer obsessed science”: inventing brand new approaches to solve Amazon’s unique problems, and using those inventions in software that affects hundreds of millions of customers * Dive deep into our metrics, ongoing experiments to understand how and why they are benefitting our shoppers (or not) * Design, prototype and validate new widgets, techniques, and ideas. Take end-to-end ownership of moving from prototype to final implementation. * Be an advocate and expert for STAX science to leaders and stakeholders inside and outside advertising. About the team We are the Search thematic ads experience team within Sponsored products - a fast growing team of customer-obsessed engineers, technologists, product leaders, and scientists. We are focused on continuous exploration of contexts and creatives to drive value for both our customers and advertisers, through continuous innovation. We focus on new ads experiences globally to help shoppers make the most informed purchase decision while helping shortcut the time to discovery that shoppers are highly likely to engage with. We also harvest rich contextual and behavioral signals that are used to optimize our backend models to continually improve the shopper experience. We obsess about our customers and are continuously seeking opportunities to delight them.
US, CA, Palo Alto
Amazon is the 4th most popular site in the US. Our product search engine, one of the most heavily used services in the world, indexes billions of products and serves hundreds of millions of customers world-wide. We are working on a new initiative to transform our search engine into a shopping engine that assists customers with their shopping missions. We look at all aspects of search CX, query understanding, Ranking, Indexing and ask how we can make big step improvements by applying advanced Machine Learning (ML) and Deep Learning (DL) techniques. We’re seeking a thought leader to direct science initiatives for the Search Relevance and Ranking at Amazon. This person will also be a deep learning practitioner/thinker and guide the research in these three areas. They’ll also have the ability to drive cutting edge, product oriented research and should have a notable publication record. This intellectual thought leader will help enhance the science in addition to developing the thinking of our team. This leader will direct and shape the science philosophy, planning and strategy for the team, as we explore multi-modal, multi lingual search through the use of deep learning . We’re seeking an individual that can enhance the science thinking of our team: The org is made of 60+ applied scientists, (2 Principal scientists and 5 Senior ASMs). This person will lead and shape the science philosophy, planning and strategy for the team, as we push into Deep Learning to solve problems like cold start, discovery and personalization in the Search domain. Joining this team, you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon [Earth's most customer-centric internet company]. We provide a highly customer-centric, team-oriented environment in our offices located in Palo Alto, California.
JP, 13, Tokyo
Our mission is to help every vendor drive the most significant impact selling on Amazon. Our team invent, test and launch some of the most innovative services, technology, processes for our global vendors. Our new AVS Professional Services (ProServe) team will go deep with our largest and most sophisticated vendor customers, combining elite client-service skills with cutting edge applied science techniques, backed up by Amazon’s 20+ years of experience in Japan. We start from the customer’s problem and work backwards to apply distinctive results that “only Amazon” can deliver. Amazon is looking for a talented and passionate Applied Science Manager to manage our growing team of Applied Scientists and Business Intelligence Engineers to build world class statistical and machine learning models to be delivered directly to our largest vendors, and working closely with the vendors' senior leaders. The Applied Science Manager will set the strategy for the services to invent, collaborating with the AVS business consultants team to determine customer needs and translating them to a science and development roadmap, and finally coordinating its execution through the team. In this position, you will be part of a larger team touching all areas of science-based development in the vendor domain, not limited to Japan-only products, but collaborating with worldwide science and business leaders. Our current projects touch on the areas of causal inference, reinforcement learning, representation learning, anomaly detection, NLP and forecasting. As the AVS ProServe Applied Science Manager, you will be empowered to expand your scope of influence, and use ProServe as an incubator for products that can be expanded to all Amazon vendors, worldwide. We place strong emphasis on talent growth. As the Applied Science Manager, you will be expected in actively growing future Amazon science leaders, and providing mentoring inside and outside of your team. Key job responsibilities The Applied Science Manager is accountable for: (1) Creating a vision, a strategy, and a roadmap tackling the most challenging business questions from our leading vendors, assess quantitatively their feasibility and entitlement, and scale their scope beyond the ProServe team. (2) Coordinate execution of the roadmap, through direct reports, consisting of scientists and business intelligence engineers. (3) Grow and manage a technical team, actively mentoring, developing, and promoting team members. (4) Work closely with other science managers, program/product managers, and business leadership worldwide to scope new areas of growth, creating new partnerships, and proposing new business initiatives. (5) Act as a technical supervisor, able to assess scientific direction, technical design documents, and steer development efforts to maximize project delivery.