Amazon Research Award recipient Shrikanth Narayanan is on a mission to make inclusive human-AI conversational experiences.
Amazon Research Award recipient Shrikanth Narayanan, university professor and Niki & C. L. Max Nikias Chair in Engineering at the University of Southern California, is on a mission to make inclusive human-AI conversational experiences.
USC

“Who we are shapes what we say and how we say it”

Amazon Research Award recipient Shrikanth Narayanan is on a mission to make inclusive human-AI conversational experiences.

To hear Shrikanth Narayanan describe it, every single human conversation is a feat of engineering — a complex system for creating and interpreting a dizzying array of signals.

“When I'm speaking, I'm producing this audio signal, which you're able to make sense out of by processing it in your auditory system and neural systems,” Narayanan says. “Meanwhile, you’re decoding my intent and emotions. I've always been fascinated by that.”

Narayanan uses signal processing and machine learning to better understand this sort of real-world information transfer as university professor and Niki & C. L. Max Nikias Chair in Engineering at the University of Southern California (USC).

In 2020, his lab earned an Amazon Research Award for work on creating “inclusive human-AI conversational experiences for children." Today, he continues to collaborate with Amazon researchers through The Center for Secure and Trusted Machine Learning at the USC Viterbi School of Engineering. He’s also gained a reputation for training future Amazon scientists, with dozens of his former students now working full time for the company.

They’re finding new approaches to machine learning privacy, security, and trustworthiness that are helping to shape a future that Narayanan hopes will be more equitable, more secure, and more empathetic.

A signal with ‘complex underpinnings’

Narayanan recalls being fascinated by the scientific side of the human experience as early as high school. At the time, he says, he was mainly interested in our physiology. But in retrospect, he says, his curiosity had the tenor of a tinkering engineer.

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“I was always interested in how it all worked,” he says. “I wanted to know how the heart worked, what happened in the brain, how it worked together. I was looking at humans through this lens of systems — the information flow that happens within individuals and between individuals.”

It was in the early ‘90s, while he was pursuing a PhD in electrical engineering at the University of California, Los Angeles, that he managed to combine his diverse interests.
“I was training in electrical engineering, but I really wanted the chance to look at something more directly connected to those human systems,” he says. He got the chance to intern at AT&T Bell Laboratories and realized human language held all the sorts of mysteries he’d been hoping to help solve.

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“Human speech is a signal that has these complex underpinnings,” he says. “There’s a cognitive aspect, the mind, and motoric aspects. We use the vocal instrument to create the signal, which in turn gets processed by people.”

Narayanan was fascinated by all the data involved in helping a conversation go right — and how easily conversations can go wrong.

He also became interested in the ways developmental disorders and health conditions could change the process of creating and interpreting speech, as well as how the rich diversity of human cultural contexts could impact the efficacy of voice recognition and synthesis.

In 2000, Narayanan founded USC’s Signal Analysis and Interpretation Laboratory (SAIL) to focus “on human-centered signal and information processing that address key societal needs.”

Over the last two decades, SAIL has enabled advances in audio, speech, language, image, video and bio signal processing, human and environment sensing and imaging, and human-centered machine learning. The lab also applies their findings to create “technologies that are inclusive, and technologies that support inclusion,” Narayanan says.

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By that, he means that in addition to making sure technologies like voice recognition actually work for everyone — some of his earliest work involved helping AI pick up on a speaker’s emotional state regardless of their spoken language — he uses signal analysis and interpretation to help uncover and spotlight inequality.

In 2017, SAIL created algorithms for analyzing movie script dialogue in order to measure representation of BIPOC characters. Another SAIL tool analyzed footage directly to track and tally female screen time and speaking time.

In 2019, the lab reported that an algorithm trained on human speech patterns could predict whether or not couples facing hard times would actually stay together. It did so even better than a trained therapist presented with video recordings of the couples in question. Instead of interpreting the content of the discussions —or any visual cues— the algorithm focused on factors like cadence and pitch. A similar tool predicted changes in mental well-being in psychiatric patients as well as human physicians could.

Building trust in AI

“Even if we speak the same language,” Narayanan says, “who we are shapes what we say and how we say it. And this is particularly fascinating for children, because their speech represents a moving target with ongoing developmental changes.”

Even if we speak the same language, who we are shapes what we say and how we say it. And this is particularly fascinating for children, because their speech represents a moving target with ongoing developmental changes.
Shrikanth Narayanan

It’s not just that a child’s vocal instrument is constantly changing as they grow. They’re also developing cognitively and socially. That can mean rapid shifts in the words they use and how they use them. When you add in other factors that might make those speech shifts different from the already diverse average —cultural contexts, speaking or hearing impairments, cognitive differences, or developmental delays — training a voice assistant to effectively communicate with kids poses a real challenge.

The analysis gets even more complicated when interacting with two humans at once, especially if one is an adult and one is a child. Using Amazon Elastic Compute Cloud (Amazon EC2) to process their data, SAIL made advances in core competences like automatic speech recognition to improve speaker diarization — the process of partitioning audio of human speech to determine which person is speaking when.

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In 2021, SAIL also published a detailed empirical study of children’s speech recognition. They found that the state-of-the-art end-to-end systems setting high benchmarks on adult speech had serious shortcomings when it came to understanding children. The following year, the lab proposed a novel technique for estimating a child’s age based on temporal variability in their speech.

By measuring the same aspects of speech that make children difficult for AI to interact with — like variations in pause length and the time it takes to pronounce certain sounds — his team was able to reliably measure a child’s developmental stage. That could help AI adapt to the needs of users with less sophisticated language skills. Because the analysis relies on signals that can be stripped of other identifying information, the method also has the potential to help protect a child’s privacy.

Narayanan refers to this and similar projects as “trustworthy speech processing,” and says he and collaborators he’s found through Amazon are working to spread interest in the idea across their booming field. In March, the International Speech Communication Association (ISCA) awarded him their ISCA Medal for Scientific Achievement — the group’s most prestigious award — for his sustained and diverse contributions to speech communication science and technology and its application to human-centered engineering systems. He will receive the medal and deliver the opening keynote lecture in August at Interspeech 2023, held in Dublin, Ireland.

Narayanan notes that the last five years have seen radical changes in our ability to gather and analyze information about human behavior.

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“The technology systems have made this sort of engineering leap and allowed applications we hadn’t even imagined yet,” he says. “All these people are interacting with these devices in open, real-world environments, and we have the machine learning and deep learning advances to actually use that audio data.”

The next big challenge, he says, is figuring out how to process that data in a way that not only serves the user, but ensures their trust. In addition to continuing to study how various developmental differences might impact voice recognition—and how AI can learn to adapt to them—Narayanan hopes to find new ways to mask as much user data as possible for privacy while pulling out the signals that voice assistants need.

Ushering in the next generation of researchers

Working with Amazon enables Narayanan’s lab to explore key research themes through a practical lens. He notes that collaborations of this nature provide academics like himself with the time and support to tackle complex, delicate research questions — such as those involving children and other vulnerable populations.

In addition, Naraynan’s graduate students get to work directly with Amazon scientists to understand the potential practical applications of their research.

“This kind of partnership really takes research to the next level,” he says.

The AI revolution that's happening has a very nice connection to what's happening at Amazon, so naturally it was a place where my students found the most exciting challenges and opportunities.
Shrikanth Narayanan

Narayanan has also encouraged dozens of his students to pursue internships at Amazon to explore what industry has to offer. Just as his time at Bell Laboratories helped to crystalize his own interests, he says, he’s watched countless young engineers find exciting new applications for their skills at Amazon.

What started as a gentle nudge to consider Amazon internships and job postings has grown into a steady pipeline of Amazon hires — one that Narayanan says owes entirely to the merits of his lab’s alums.

Angeliki Metallinou, a senior applied science manager for Alexa AI, joined Amazon fulltime in 2014 with Narayanan’s encouragement. Alexa was a top-secret project at the time, so she didn’t know exactly what she’d be working on until she got there. She credits Narayanan with encouraging her to dive in.

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“As a student, I hadn’t realized the extent that Amazon scientists collaborate with academia and are able to publish their work at top tier venues and conferences,” she recalls. “I wasn’t even aware that there was such a strong science community here. But Shri already had a few former PhD students working at Amazon, and he recommended it as a great place for an industry career.”

Rahul Gupta, a senior applied scientist for Amazon Alexa, first connected with Amazon for an internship near the end of his SAIL PhD in 2015. These days, he says, he has one or two SAIL students doing summer internships in his group alone.

“There's really good cultural alignment between SAIL and Amazon,” Gupta says.

Narayanan, who proudly displays photos of all of his lab graduates on the wall of his office, admits he’s lost count of how many have worked at Amazon over the years.

“It's exciting,” he says. “The AI revolution that's happening has a very nice connection to what's happening at Amazon, so naturally it was a place where my students found the most exciting challenges and opportunities. But I’ve also seen many of them progress into leadership positions, which I did my best to set them up for — I always encourage creativity and collaboration, and I don’t micromanage them in my lab.”

Now that his graduates are thriving at Amazon, he says, the internship opportunities for his current students are all the more robust.

“It sustains itself,” he says. “They shine in what they do at Amazon and in the community, and that connects back to the lab. It’s incredibly exciting.”

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The Worldwide Defect Elimination (WWDE) Team is seeking a highly skilled economist to estimate the customer impact of each Customer Service action. Your analysis will assist teams across Amazon to prioritize defect elimination efforts and optimize how we respond to customer contacts. You will partner closely with our product, program, and engineering teams to deliver your findings to users via systems and dashboards that guide Customer Service planning and policies. Key job responsibilities - Develop causal, economic, and machine learning models at scale. - Engage in economic analysis; raise the bar for research. - Inform strategic discussions with senior leaders across the company to guide policies. A day in the life We thrive on solving challenging problems to innovate for our customers. By pushing the boundaries of technology, we create unparalleled experiences that enable us to rapidly adapt in a dynamic environment. Our decisions are guided by data, and we collaborate with engineering, science, and product teams to foster an innovative learning environment. If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: * Medical, Dental, and Vision Coverage * Maternity and Parental Leave Options * Paid Time Off (PTO) * 401(k) Plan About the team The WWDE team's mission is to understand and resolve all issues impacting customers and connect all organizations in Amazon to customer experiences. Our vision is to be the ultimate steward of the Voice of the Customer (VoC), empowering CS and Amazon teams to easily measure, listen, and act on customer feedback. The team broadly supports defect detection, root cause identification, and resolution to earn customer trust. The Customer Service Economics & Optimization team is a force multiplier within this group. Through causal analysis, we estimate the effectiveness of our efforts to delight the customer
US, WA, Seattle
We are seeking an experienced and innovative Battery Research Scientist III to lead advanced research initiatives in battery cell technology and safety. This senior-level position will focus on pushing the boundaries of current battery technology while developing cutting-edge solutions for thermal runaway mitigation and protection. Key Responsibilities: 1. Battery Cell Technology Research: -Lead the development of next-generation battery cell architectures, focusing on improving energy density, power output, and cycle life. -Spearhead research into novel electrode materials, electrolytes, and separators. -Design and oversee complex experiments to evaluate battery performance, degradation mechanisms, and failure modes. -Develop predictive models for battery performance and lifespan. 2. Thermal Runaway Mitigation and Protection Research: -Direct research efforts in advanced thermal management solutions for high-capacity lithium-ion batteries. -Pioneer innovative materials and designs to enhance battery safety and prevent thermal runaway events. -Oversee the development and implementation of sophisticated thermal runaway tests and failure mode analyses. -Lead the creation of industry-leading safety protocols for battery testing, manufacturing, and usage. 3. Leadership and Project Management: -Serve as principal investigator on major research projects, managing budgets, timelines, and deliverables. -Mentor junior scientists and provide technical leadership to cross-functional teams. -Collaborate with senior management to align research goals with organizational objectives. -Establish and maintain partnerships with academic institutions and industry leaders. 4. Innovation and Intellectual Property: -Drive the creation of new intellectual property, leading patent application processes. -Represent the organization at high-level conferences and industry events. -Author and co-author publications in top-tier scientific journals. -Identify emerging trends in battery technology and propose strategic research directions. Amazon.com is an Equal Opportunity-Affirmative Action Employer - Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.#0000
US, WA, Bellevue
At AWS, we use Artificial Intelligence to be able to identify every need of a customer across all AWS services before they have to tell us about it and help customers adopt best practices while architecting on the cloud. We are looking for Applied Scientists to drive innovation with Gen AI to bring paradigm shift to how the business operates and build “best in the world” experience that customers will love! Some of the science challenges we work on include fine-tuning Large language models for domain specific use cases, Reinforcement Learning, Auto-generating code from natural language and generating strategic insights and recommendations from very large datasets. You will have an opportunity to lead, invent, and design tech that will directly impact every customer across all AWS services. We are building industry-leading technology that cuts across a wide range of ML techniques from Natural Language Processing to Deep Learning and Generative Artificial Intelligence. You will be a key driver in taking something from an idea to an experiment to a prototype and finally to a live production system. Our team packs a punch with principal level product, science, engineering, and leadership talent. We are a results focused team and you have the opportunity to lead and establish a culture for the big things to come. We combine the culture of a startup, the innovation and creativity of a R&D Lab, the work-life balance of a mature organization, and technical challenges at the scale of AWS. We offer a playground of opportunities for builders to build, have fun, and make history! AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. Key job responsibilities - Deliver real world production systems at AWS scale. - Work closely with the business to understand the problem space, identify the opportunities and formulate the problems. - Use machine learning, data mining, statistical techniques, Generative AI and others to create actionable, meaningful, and scalable solutions for the business problems. - Analyze and extract relevant information from large amounts of data and derive useful insights. - Work with software engineering teams to deliver production systems with your ML models - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation A day in the life Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.
US, CA, Sunnyvale
The Amazon Artificial General Intelligence (AGI) Personalization team is looking for a passionate, highly skilled and inventive Applied Scientist with strong machine learning background to build state-of-the-art ML systems for personalizing large-scale, high-quality conversational assistant systems. As a Applied Scientist, you will play a critical role in driving the development of personalization techniques enabling conversational systems, in particular those based on large language models, information retrieval, recommender systems and knowledge graph, to be tailored to customer needs. You will handle Amazon-scale use cases with significant impact on our customers' experiences. Key job responsibilities - Use deep learning, ML and NLP techniques to create scalable solutions for creation and development of language model centric solutions for building personalized assistant systems based on a rich set of structured and unstructured contextual signals - Innovate new methods for contextual knowledge extraction and information retrieval, using language models in combination with other learning techniques, that allows effective grounding in context providers when considering memory, compute, latency and quality - Research in advanced customer understanding and behavior modeling techniques - Collaborate with cross-functional teams of scientists, engineers, and product managers to identify and solve complex problems in personal knowledge aggregation, processing, modeling, and verification - Design and execute experiments to evaluate the performance of state-of-the-art algorithms and models, and iterate quickly to improve results - Think Big on conversational assistant system personalization over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems - Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports About the team The AGI Personalization org uses various contextual signals to personalize Large Language Model output for our customers while maintaining privacy and security of customer data. We work across multiple Amazon products, including Alexa, to enhance the user experience by bringing more personal context and relevance to customer interactions.