Stanford University - 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.

Meet the 2019-2020 Teams

Alexa Prize Socialbot Grand Challenge 3