Feedback
Follow Us
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.

By Emory University
Share

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.

About the Author
Emory University

Latest news

The latest updates, stories, and more about Alexa Prize.
Load more

Work with us

See more jobs See more jobs
Applied Scientist, People eXperience and Technology Central Science Team (PXTCS)
CA, ON, Toronto
Are you motivated to explore research in ambiguous spaces? Are you interested in conducting research that will improve associate, employee and manager experiences at Amazon? Do you want to work on an interdisciplinary team of scientists that collaborate rather than compete? Join us at PXT Central Science! The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. Key job responsibilities As an Applied Scientist for People Experience and Technology (PXT) Central Science, you will be working with our science and engineering teams, specifically on re-imagining Generative AI Applications and Generative AI Infrastructure for HR. Applying Generative AI to HR has unique challenges such as privacy, fairness, and seamlessly integrating Enterprise Knowledge and World Knowledge and knowing which to use when. In addition, the team works on some of Amazon’s most strategic technical investments in the people space and support Amazon’s efforts to be Earth’s Best Employer. In this role you will have a significant impact on 1.5 million Amazonians and the communities Amazon serves and ample scope to demonstrate scientific thought leadership and scientific impact in addition to business impact. You will also play a critical role in the organization's business planning, work closely with senior leaders to develop goals and resource requirements, influence our long-term technical and business strategy, and help hire and develop science and engineering talent. You will also provide support to business partners, helping them use the best scientific methods and science-driven tools to solve current and upcoming challenges and deliver efficiency gains in a changing marke About the team The AI/ML team in PXTCS is working on building Generative AI solutions to reimagine Corp employee and Ops associate experience. Examples of state-of-the-art solutions are Coaching for Amazon employees (available on AZA) and reinventing Employee Recruiting and Employee Listening.
Applied Scientist, International Machine Learning
IN, KA, Bengaluru
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to create state-of-the-art solutions for providing better value to Amazon’s customers? Do you want to build and deploy advanced algorithmic systems that help optimize millions of transactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Machine Learning and Data Sciences team for India Consumer Businesses. If you have an entrepreneurial spirit, know how to deliver, love to work with data, are deeply technical, highly innovative and long for the opportunity to build solutions to challenging problems that directly impact the company's bottom-line, we want to talk to you. Major responsibilities - Use machine learning and analytical techniques to create scalable solutions for business problems - Analyze and extract relevant information from large amounts of Amazon’s historical business data to help automate and optimize key processes - Design, development, evaluate and deploy innovative and highly scalable models for predictive learning - Research and implement novel machine learning and statistical approaches - Work closely with software engineering teams to drive real-time model implementations and new feature creations - Work closely with business owners and operations staff to optimize various business operations - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation - Mentor other scientists and engineers in the use of ML techniques
Applied Scientist, Artificial General Intelligence
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading Generative Artificial Intelligence (GenAI) technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Applied Scientist with the AGI team, you will work with talented peers to lead the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in spoken language understanding. About the team The AGI team has a mission to push the envelope in GenAI with LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
Economist, Amazon
US, WA, Seattle
The Global Cross-Channel and Cross- Category Marketing (XCM) org are seeking an experienced Economist to join our team. XCM’s mission is to be the most measurably effective and creatively breakthrough marketing organization in the world in order to strengthen the brand, grow the business, and reduce cost for Amazon overall. We achieve this through scaled campaigning in support of brands, categories, and audiences which aim to create the maximum incremental impact for Amazon as a whole by driving the Amazon flywheel. This is a high impact role with the opportunities to lead the development of state-of-the-art, scalable models to measure the efficacy and effectiveness of a new marketing channel. In this critical role, you will leverage your deep expertise in causal inference to design and implement robust measurement frameworks that provide actionable insights to drive strategic business decisions. Key Responsibilities: Develop advanced econometric and statistical models to rigorously evaluate the causal incremental impact of marketing campaigns on customer perception and customer behaviors. Collaborate cross-functionally with marketing, product, data science and engineering teams to define the measurement strategy and ensure alignment on objectives. Leverage large, complex datasets to uncover hidden patterns and trends, extracting meaningful insights that inform marketing optimization and investment decisions. Work with engineers, applied scientists and product managers to automate the model in production environment. Stay up-to-date with the latest research and methodological advancements in causal inference, causal ML and experiment design to continuously enhance the team's capabilities. Effectively communicate analysis findings, recommendations, and their business implications to key stakeholders, including senior leadership. Mentor and guide junior economists, fostering a culture of analytical excellence and innovation.
Applied Scientist, Amazon
US, WA, Seattle
The XCM (Cross Channel Cross-Category Marketing) team seeks an Applied Scientist to revolutionize our marketing strategies. XCM's mission is to build the most measurably effective, creatively impactful, and cross-channel campaigning capabilities possible, with the aim of growing "big-bet" programs, strengthening positive brand perceptions, and increasing long-term free cash flow. As a science team, we're tackling complex challenges in marketing incrementality measurement, optimization and audience segmentation. In this role, you'll collaborate with a diverse team of scientists and economists to build and enhance causal measurement, optimization and prediction models for Amazon's global multi-billion dollar fixed marketing budget. You'll also work closely with various teams to develop scientific roadmaps, drive innovation, and influence key resource allocation decisions. Key job responsibilities 1) Innovating scalable marketing methodologies using causal inference and machine learning. 2) Developing interpretable models that provide actionable business insights. 3) Collaborating with engineers to automate and scale scientific solutions. 4) Engaging with stakeholders to ensure effective adoption of scientific products. 5) Presenting findings to the Amazon Science community to promote excellence and knowledge-sharing.
Applied Scientist I, BRP (Buyer Risk Prevention)
US, WA, Seattle
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches
Applied Scientist I, BRP (Buyer Risk Prevention)
US, CA, San Diego
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches
Applied Scientist, Conversational AI ModEling and Learning
CA, ON, Toronto
Conversational AI ModEling and Learning (CAMEL) team is part of Amazon Devices organization where our mission is to build a best-in-class Conversational AI that is intuitive, intelligent, and responsive, by developing superior Large Language Models (LLM) solutions and services which increase the capabilities built into the model and which enable utilizing thousands of APIs and external knowledge sources to provide the best experience for each request across millions of customers and endpoints. We are looking for a passionate, talented, and resourceful Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), Recommender Systems and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. A successful candidate will have strong machine learning background and a desire to push the envelope in one or more of the above areas. The ideal candidate would also have hands-on experiences in building Generative AI solutions with LLMs, enjoy operating in dynamic environments, be self-motivated to take on challenging problems to deliver big customer impact, moving fast to ship solutions and then iterating on user feedback and interactions. Key job responsibilities As an Applied Scientist, you will leverage your technical expertise and experience to collaborate with other talented applied scientists and engineers to research and develop novel algorithms and modeling techniques to reduce friction and enable natural and contextual conversations. You will analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence. You will work on core LLM technologies, including Prompt Engineering and Optimization, Supervised Fine-Tuning, Learning from Human Feedback, Evaluation, Self-Learning, etc. Your work will directly impact our customers in the form of novel products and services.
Applied Scientist, Conversational AI ModEling and Learning
CA, ON, Toronto
Conversational AI ModEling and Learning (CAMEL) team is part of Amazon Devices organization where our mission is to build a best-in-class Conversational AI that is intuitive, intelligent, and responsive, by developing superior Large Language Models (LLM) solutions and services which increase the capabilities built into the model and which enable utilizing thousands of APIs and external knowledge sources to provide the best experience for each request across millions of customers and endpoints. We are looking for a passionate, talented, and resourceful Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), Recommender Systems and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. A successful candidate will have strong machine learning background and a desire to push the envelope in one or more of the above areas. The ideal candidate would also have hands-on experiences in building Generative AI solutions with LLMs, enjoy operating in dynamic environments, be self-motivated to take on challenging problems to deliver big customer impact, moving fast to ship solutions and then iterating on user feedback and interactions. Key job responsibilities As an Applied Scientist, you will leverage your technical expertise and experience to collaborate with other talented applied scientists and engineers to research and develop novel algorithms and modeling techniques to reduce friction and enable natural and contextual conversations. You will analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence. You will work on core LLM technologies, including Prompt Engineering and Optimization, Supervised Fine-Tuning, Learning from Human Feedback, Evaluation, Self-Learning, etc. Your work will directly impact our customers in the form of novel products and services.
Data Scientist, EPP Analytics
US, WA, Seattle
We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA Do you love using data to solve complex problems? Are you interested in innovating and developing world-class big data solutions? We have the career for you! EPP Analytics team is seeking an exceptional Data Scientist to recommend, design and deliver new advanced analytics and science innovations end-to-end partnering closely with our security/software engineers, and response investigators. Your work enables faster data-driven decision making for Preventive and Response teams by providing them with data management tools, actionable insights, and an easy-to-use reporting experience. The ideal candidate will be passionate about working with big data sets and have the expertise to utilize these data sets to derive insights, drive science roadmap and foster growth. Key job responsibilities - As a Data Scientist (DS) in EPP Analytics, you will do causal data science, build predictive models, conduct simulations, create visualizations, and influence data science practice across the organization. - Provide insights by analyzing historical data - Create experiments and prototype implementations of new learning algorithms and prediction techniques. - Research and build machine learning algorithms that improve Insider Threat risk A day in the life No two days are the same in Insider Risk teams - the nature of the work we do and constantly shifting threat landscape means sometimes you'll be working with an internal service team to find anomalous use of their data, other days you'll be working with IT teams to build improved controls. Some days you'll be busy writing detections, or mentoring or running design review meetings. The EPP Analytics team is made up of SDEs and Security Engineers who partner with Data Scientists to create big data solutions and continue to raise the bar for the EPP organization. As a member of the team you will have the opportunity to work on challenging data modeling solutions, new and innovative Quicksight based reporting, and data pipeline and process improvement projects. About the team Diverse Experiences Amazon Security 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 Amazon Security? At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & 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, training, 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 flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.
View from space of a connected network around planet Earth representing the Internet of Things.
Have feedback?
Let us know by taking this short survey
Get started

Amazon.com | Conditions of Use | Privacy | © 1996-2024 Amazon.com, Inc. or its affiliates

Follow Us