stanford-chirpy.jpg
Location: Stanford, CA, USA
Faculty advisor: Christopher Manning

Chirpy Cardinal (2019)

We aim to have a holistic approach towards achieving a multi-turn, on-topic and engaging conversation by designing our systems based on the principles of mixed-initiative.

We are Chirpy Cardinal, a reference to the chirpy colourful bird that shares its name with the ofiicial color and also the names of sports teams at Stanford. Our vision is to create a responsive, empathetic and informative socialbot. We aim to have a holistic approach towards achieving a multi-turn, on-topic and engaging conversation by designing our systems based on the principles of mixed-initiative. Stanford NLP has a strong track record of participating in competitions and shared-tasks, pushing the boundraies of what is considered possible. We are excited to be part of Alexa Prize and bring to bear novel research towards open-domain dialog with real people.

Inside Stanford NLP’s Alexa Prize chatbot: Chirpy Cardinal

Ashwin P. - Team leader

Ashwin is currently a third year Ph.D. student advised by Prof. Chris Manning in the broad area of NLP and Deep Learning. His research focus has been about incorporating structure into language, specifically language models. Going forward, he would like to explore new research questions about conversational AI. Prior to Ph.D., Ashwin did his masters at Stanford working with Prof. Jure Leskovec on data mining, link prediction and graph algorithms research and hid undergrad at IIT Bombay.

Abigail S.

Abi, co-team leader, is a Ph.D. student in the Stanford Natural Language Processing group, where she works on understanding and improving Deep Learning methods for Natural Language Generation. She has interned at Google and Facebook AI Research, where she worked on summarization and chitchat dialogue. With her advisor Chris Manning, she is the co-instructor of CS224n, Stanford's NLP and Deep Learning course. She grew up in the UK and studied Mathematics at Cambridge University.

Peng Q.

Peng is a Ph.D. student at Stanford University studying Natural Language Processing. He is enthusiastic about building NLP systems that help us better understand the knowledge hidden in large amounts of text, as well as building these systems to be explainable and scalable. He is also interested in multilingual and interactive NLP systems that make efficient use of annotated data, by making use of priors such as linguistic knowledge.

Kathleen K.

Kathleen is a Master’s student studying Computer Science (with a focus on Artificial Intelligence) at Stanford University. She has her B.S. in Computer Science from Stanford, where she also minored in Theatre.

Kaushik Ram S.

Kaushik grew up with a fascination for numbers which led him to solve Math problems in high-school which then translated to solving problems using artificial intelligence in grad-school. Kaushik hails from the southern part of India and follows cricket actively. He likes hitting the gym, playing badminton and swimming.

Haojun L.

Haojun is a MSCS student at Stanford University. He did his undergrad at UC Berkeley and was a UGSI for 2 years (CS61A woohoo!). Then Haojun worked at AppDynamics for a year before coming back to school. He has filed 2 patents but is now focusing on NLP research and teaching. In his spare time Haojun likes to cycle, sail, and camp. You can find him either on the road, above the sea (mostly), or in the mountains!

Dilara S.

Dilara is broadly interested in Human Centered AI, fusing design thinking principles with the recent advances in AI to create products that put people in the center. She is specifically interested in virtual assistants. She is currently studying Computer Science at Stanford University, where she was a course assistant in CS224n, NLP and Deep Learning course at Stanford.

Amelia H.

Amelia is a Computer Science Master’s student at Stanford University, specializing in artificial intelligence. As an undergraduate at Stanford, she studied the Computer Science theory track. Her research interests include machine vision, neural verification, and natural language processing.

Minh Phu N.

Minh is a computer science major at Stanford University, with research experience in Artificial Intelligence and industry experience in Product Development. Minh loves to work on cool, useful products and solve challenging problems.

Christopher Manning - Faculty advisor

Christopher Manning is a professor of computer science and linguistics at Stanford University and Director of the Stanford AI Lab. He is a leader in applying deep neural networks to Natural Language Processing, including work on tree recursive models, sentiment analysis, neural machine translation and parsing, and the GloVe word vectors. He founded the Stanford NLP group (@stanfordnlp), developed Stanford Dependencies and Universal Dependencies, and manages development of the Stanford CoreNLP software. Manning is an ACM, AAAI, and ACL Fellow, and a Past President of ACL.

Latest news

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US, TX, Austin
Applied Scientists in AWS Automated Reasoning are dedicated to making AWS the best computing service in the world for customers who require advanced and rigorous solutions for automated reasoning, privacy, and sovereignty. Key job responsibilities The successful candidate will: - Solve large or significantly complex problems that require deep knowledge and understanding of your domain and scientific innovation. - Own strategic problem solving, and take the lead on the design, implementation, and delivery for solutions that have a long-term quantifiable impact. - Provide cross-organizational technical influence, increasing productivity and effectiveness by sharing your deep knowledge and experience. - Develop strategic plans to identify fundamentally new solutions for business problems. - Assist in the career development of others, actively mentoring individuals and the community on advanced technical issues. A day in the life This is a unique and rare opportunity to get in early on a fast-growing segment of AWS and help shape the technology, product and the business. You will have a chance to utilize your deep technical experience within a fast moving, start-up environment and make a large business and customer impact. About the team Diverse Experiences Amazon Automated Reasoning 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 Automated Reasoning? At Amazon, automated reasoning is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for automated reasoning across all of Amazon's products and services. We offer talented automated reasoning 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 Automated Reasoning, 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 automated reasoning 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.
US, MA, Boston
Sr. Applied Scientists in AWS Automated Reasoning are dedicated to making AWS the best computing service in the world for customers who require advanced and rigorous solutions for automated reasoning, privacy, and sovereignty. Key job responsibilities The successful candidate will: - Solve large or significantly complex problems that require deep knowledge and understanding of your domain and scientific innovation. - Own strategic problem solving, and take the lead on the design, implementation, and delivery for solutions that have a long-term quantifiable impact. - Provide cross-organizational technical influence, increasing productivity and effectiveness by sharing your deep knowledge and experience. - Develop strategic plans to identify fundamentally new solutions for business problems. - Assist in the career development of others, actively mentoring individuals and the community on advanced technical issues. A day in the life This is a unique and rare opportunity to get in early on a fast-growing segment of AWS and help shape the technology, product and the business. You will have a chance to utilize your deep technical experience within a fast moving, start-up environment and make a large business and customer impact. About the team Diverse Experiences Amazon Automated Reasoning 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 Automated Reasoning? At Amazon, automated reasoning is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for automated reasoning across all of Amazon's products and services. We offer talented automated reasoning 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 Automated Reasoning, 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 automated reasoning 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.