The Emory University team, seen here, and its Emora socialbot are the winners of the 2020 Alexa Prize.
The Emory University team and its Emora socialbot are the winners of the 2020 Alexa Prize. Editor's Note: This photo was taken in October 2019, prior to the COVID-19 outbreak. Thus, team members aren't wearing masks.

Emora (2019)

We expect our socialbot, Emora, to be your companion who will care about you, learn from you, share thoughts and feelings with you, and most importantly, always be there for you.

Team Emora is a diverse group of graduate and undergraduate students from the NLP and IR labs at Emory University who strive to bring the true meaning of “socialbot"" to conversational AI.

We expect our socialbot, Emora, to be your companion who will care about you, learn from you, share thoughts and feelings with you, and most importantly, always be there for you. Emora is a self-evolving socialbot whose best intention is to adjust herself for you to have more satisfying conversations so you will feel the contentment of talking to your close friend, rather than an information desk.

Sarah F. - Team leader

Sarah is a second-year Ph.D. student in Computer Science in the Intelligent Information Access Lab at Emory University. Her research interests are in natural language processing, especially in the context of conversational systems, information retrieval, and information extraction. During her first year at Emory, Sarah worked on improving the capabilities of conversational search systems through the incorporation of engagement detection. Before coming to Emory, her research efforts focused on developing automated dialogue systems and investigating the sharing of personal information between humans in chat-oriented dialogues.

Harshita S.

Harshita is a third-year computer science Ph.D. student at Emory University, where he is part of the Information Retrieval lab. Harshita's research interests include conversational search, personalized recommender systems, and knowledge graphs.

James F.

James is a first year Ph.D. student and is passionate about natural language processing and, more broadly, artificial intelligence as a whole. James has three years of experience in task-oriented human-robot dialogue research, and hopes to further explore techniques for human-computer dialogue in more open-domain, chat oriented dialogues. His current research also includes a project focusing on information extraction from unstructured text in the news domain.

Zihao W.

Zihao is a thrid year Ph.D student in Computer Science at Emory University. His research interests lie in Conversational AI, especially in response ranking and selection in dialogue systems. Zihao participated in the Alexa Prize competition in 2017 and 2018 which were great experiences for him. Zihao has had internships in IBM Research China and Uber AI, in which he worked on different scopes of interesting projects.

Ali A.

Ali is a third year Ph.D. student in Computer Science and obtained their Master's degree in Artificial Intelligence. Ali was a team member in the 2017 and 2018 Alexa Prize competition, and the team achieved 5th and 4th in the final ranking in 2017 and 2018, respectively. Ali mainly worked on the design of the multi-level intent and topic classifier, the proactive recommendation mechanism, and several other information retrieval components such as News and Movies. Furthermore, Ali has been working on procedural question-answering by experimenting with different information retrieval, and deep learning methods.

Jason C.

Jason is a graduate student at Emory University specializing in open-domain spoken conversational systems, mainly in ML & NLU & IR aspects. He is also a returning member from the 2017 & 2018 Alexa Prize. Jason's research focus will be on dialogue management that integrates conversational satisfaction prediction, neural response generation, failure detection & recovery and phonetic language representations. With these signals, Jason looks forward to improving the team's existing system to maintain a more natural conversational flow in a more intelligent way.

Sonny X.

Sonny is a rising junior student at Emory University and majoring in Computer Science and Applied Math. Sonny was a team member in the 2018 ACM International Collegiate Programming Contest that won 1st Place out of 82 teams in the Southeast Region Division II. Sonny is also interested in software development and his team won 2nd Place overall, out of 44 teams, and 1st Place in Social Innovation bracket in 2018 at HackATL, by designing the website Asaga.

Bill Q.

Bill is a rising senior at Emory University, majoring in computer science and applied mathematics. He is currently an Emory NLP lab member. Bill has also worked for several semesters in "ELITE" lab where he conducted research on computer science education. Bill is interested in machine learning. He really enjoyed hisgraduate-level machine learning course and plans to pursue a Ph.D. degree in machine learning.

Han H.

Androids Do Dream of Electric Sheep

Liyan X.

Liyan is a Ph.D. student at Emory University and likes cats!

Zihan W.

Zihan is a rising senior majoring in CS. She has been in multiple Hackathons and competitions and created several interesting apps, but the Alexa Prize is the most challenging but interesting one. Zihan enjoys coding and solving problems but her favorite thing to do is still eating hot-pot with friends.

Xiangjue D.

Xiangjue is a master's student majoring in computer science at Emory University since August 2019. Her previous research and project experience were in information retrieval, computer vision, and deep learning area. She completed several projects, like Human Activity Detection, Forum Post Tagging, New Features Development for Role-based Healthcare Web Application iTrust and CUDA Parallel Programming for Neural-Network Framework MXNet. Now she is interested in conversational AI and trying to do relevant research.

Jiaying L.

Jiaying is a first year Computer Science Ph.D. student at Emory and a member of Emory NLP Lab. His research interest lies in NLP, especially understanding human generated text with the help of knowledge and reasoning. Before joining Emory, Jiaying has conducted research in vision and language field. He also has two years of industrial experience at Baidu. He obtained his master and bachelor degrees from BUPT, China.

Sergey V.

Sergey recieved his bachelor’s degree in Mathematics from the Higher School of Economics in Moscow, Russia. Currently he is a 2nd year Ph.D. student. Sergey worked on the Alexa Prize with the Emory team last year.

Jinho Choi - Faculty advisor

Jinho Choi is an assistant professor of Computer Science at Emory University, where he leads the NLP research laboratory. Dr. Choi has introduced many state-of-the-art models for core NLP tasks and also presented several open-source NLP frameworks including ClearNLP, NLP4J, and ELIT. He has started a novel machine comprehension project called “Character Mining” that aims to interpret both implicit and explicit contexts in multiparty conversations. Dr. Choi has been active in the NLP community and area chairs for several top conferences including ACL, NAACL, and EMNLP.

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IN, KA, Bengaluru
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US, NY, New York
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US, NY, New York
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US, NY, New York
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ES, B, Barcelona
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US, MA, Boston
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US, CA, San Francisco
Amazon is on a mission to redefine the future of automation — and we're looking for exceptional talent to help lead the way. We are building the next generation of advanced robotic systems that seamlessly blend cutting-edge AI, sophisticated control systems, and novel mechanical design to create adaptable, intelligent automation solutions capable of operating safely alongside humans in dynamic, real-world environments. At Amazon, we leverage the power of machine learning, artificial intelligence, and advanced robotics to solve some of the most complex operational challenges at a scale unlike anywhere else in the world. Our fleet of robots spans hundreds of facilities globally, working in sophisticated coordination to deliver on our promise of customer excellence — and we're just getting started. As a Sr. Scientist in Robot Navigation, you will be at the forefront of this transformation — architecting and delivering navigation systems that are intelligent, safe, and scalable. You will bring deep expertise in learning-based planning and control, a strong understanding of foundation models and their application to embodied agents, and as well as have in-depth understanding of control-theoretic approaches such as model predictive control (MPC)-based trajectory planning. You will develop navigation solutions that seamlessly blend data-driven intelligence with principled control-theoretic guarantees. Our vision is bold: to build navigation systems that allow robots to move fluidly and safely through dynamic environments — understanding context, anticipating change, and adapting in real time. You will lead research that bridges the gap between cutting-edge academic advances and production grade deployment, collaborating with world-class teams pushing the boundaries of robotic autonomy, manipulation, and human-robot interaction. Join us in building the next generation of intelligent navigation systems that will define the future of autonomous robotics at scale. Key job responsibilities - Design, develop, and deploy perception algorithms for robotics systems, including object detection, segmentation, tracking, depth estimation, and scene understanding - Lead research initiatives in computer vision, sensor fusion and 3D perception - Collaborate with cross-functional teams including robotics engineers, software engineers, and product managers to define and deliver perception capabilities - Drive end-to-end ownership of ML models — from data collection and labeling strategy to training, evaluation, and deployment - Mentor junior scientists and engineers; contribute to a culture of technical excellence - Define and track key metrics to measure perception system performance in real-world environments - Publish research findings in top-tier venues (CVPR, ICCV, ECCV, ICRA, NeurIPS, etc.) and contribute to patents A day in the life - Train ML models for deployment in simulation and real-world robots, identify and document their limitations post-deployment - Drive technical discussions within your team and with key stakeholders to develop innovative solutions to address identified limitations - Actively contribute to brainstorming sessions on adjacent topics, bringing fresh perspectives that help peers grow and succeed — and in doing so, build lasting trust across the team - Mentor team members while maintaining significant hands-on contribution to technical solutions About the team Our team is a group is a diverse group of scientists and engineers passionate about building intelligent machines. We value curiosity, rigor, and a bias for action. We believe in learning from failure and iterating quickly toward solutions that matter.
US, NY, New York
The Ads Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) team of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of advertiser's ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We also own the science solutions for AI tools that unlock new insights and automate high-effort customer workflows, such as custom query and report generation based on natural language user requests. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision. As a Senior Applied Scientist on the team, you will be at the forefront of innovation, developing measurement solutions end-to-end from inception to production. You will set the technical vision and innovate on behalf of our customers. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers. You will partner with engineering to deploy these solutions into production. You will work with key stakeholders from various business teams to enable advertisers to act upon those metrics. Key job responsibilities * Lead the development of ad measurement models and solutions that address the full spectrum of an advertiser's investment, focusing on scalable and efficient methodologies. * Collaborate closely with cross-functional teams including engineering, product management, and business teams to define and implement measurement solutions. * Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models that measure the impact of ad spend across multiple platforms and timescales. * Drive experimentation and the continuous improvement of ML models through iterative development, testing, and optimization. * Translate complex scientific challenges into clear and impactful solutions for business stakeholders. * Mentor and guide junior scientists, fostering a collaborative and high-performing team culture. * Foster collaborations between scientists to move faster, with broader impact. * Regularly engage with the broader scientific community with presentations, publications, and patents. A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate business insights and opportunities, design simulations and experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the advertising organization. You will prepare written and verbal presentations to share insights to audiences of varying levels of technical sophistication. Team video https://advertising.amazon.com/help/G4LNN5YWHP6SM9TJ About the team We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.
US, WA, Seattle
At Amazon Selection and Catalog Systems (ASCS), our mission is to power the online buying experience for customers worldwide so they can find, discover, and buy any product they want. We innovate on behalf of our customers to ensure uniqueness and consistency of product identity and to infer relationships between products in Amazon Catalog to drive the selection gateway for the search and browse experiences on the website. We're solving a fundamental AI challenge: establishing product identity and relationships at unprecedented scale. Using Generative AI, Visual Language Models (VLMs), and multimodal reasoning, we determine what makes each product unique and how products relate to one another across Amazon's catalog. The scale is staggering: billions of products, petabytes of multimodal data, millions of sellers, dozens of languages, and infinite product diversity—from electronics to groceries to digital content. The research challenges are immense. GenAI and VLMs hold transformative promise for catalog understanding, but we operate where traditional methods fail: ambiguous problem spaces, incomplete and noisy data, inherent uncertainty, reasoning across both images and textual data, and explaining decisions at scale. Establishing product identities and groupings requires sophisticated models that reason across text, images, and structured data—while maintaining accuracy and trust for high-stakes business decisions affecting millions of customers daily. Amazon's Item and Relationship Platform group is looking for an innovative and customer-focused applied scientist to help us make the world's best product catalog even better. In this role, you will partner with technology and business leaders to build new state-of-the-art algorithms, models, and services to infer product-to-product relationships that matter to our customers. You will pioneer advanced GenAI solutions that power next-generation agentic shopping experiences, working in a collaborative environment where you can experiment with massive data from the world's largest product catalog, tackle problems at the frontier of AI research, rapidly implement and deploy your algorithmic ideas at scale, across millions of customers. Key job responsibilities Key job responsibilities include: * Formulate novel research problems at the intersection of GenAI, multimodal learning, and large-scale information retrieval—translating ambiguous business challenges into tractable scientific frameworks * Design and implement leading models leveraging VLMs, foundation models, and agentic architectures to solve product identity, relationship inference, and catalog understanding at billion-product scale * Pioneer explainable AI methodologies that balance model performance with scalability requirements for production systems impacting millions of daily customer decisions * Own end-to-end ML pipelines from research ideation to production deployment—processing petabytes of multimodal data with rigorous evaluation frameworks * Define research roadmaps aligned with business priorities, balancing foundational research with incremental product improvements * Mentor peer scientists and engineers on advanced ML techniques, experimental design, and scientific rigor—building organizational capability in GenAI and multimodal AI * Represent the team in the broader science community—publishing findings, delivering tech talks, and staying at the forefront of GenAI, VLM, and agentic system research
US, CA, San Francisco
In this role, you will act as the primary specialist for physics engine internals and dynamics, developing high-fidelity, vectorized simulation environments for robotics locomotion, navigation, and interaction/manipulation. You will collaborate with hardware engineers to validate robot models and partner with research scientists to ensure numerical stability and physical accuracy for Sim2Real transfer. Your work focuses on tuning solvers, optimizing collision dynamics, and performing system identification to enable the training of robust robot control policies for complex, physical interactions. Key job responsibilities * Develop and maintain the shared simulation software framework, specifically owning the physics integration, robot state management, and control layers * Develop and optimize parallelized (vectorized) physics environments for high-throughput reinforcement learning (e.g., Isaac Lab, MuJoCo) * Tune physics engine parameters (solvers, friction, restitution) to support complex contact-rich scenarios required for dexterous manipulation and agile locomotion. * Implement and validate complex robot models (URDF/MJCF) involving precise actuator and sensor modeling * Collaborate with robot engineers and scientists to perform System Identification (SysID) to minimize the Sim2Real gap About the team At Frontier AI & Robotics (FAR), we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through frontier foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.