um-audrey.jpg
Location: Ann Arbor, MI, USA
Faculty advisor: David Jurgens

Audrey

Each with our own experiences and unique value, we plan to tackle the challenge of creating an active listening, heartwarming, and understanding social bot.

Audrey is a conversational agent that engages users through active listening and mutual storytelling. We are a tightknit team of 7 master’s students and 5 undergraduate students from the University of Michigan with expertise in natural language processing, machine learning, software engineering, and human-computer interaction. We are working with Professor David Jurgens in the School of Information and Nikola Banovic from Computer Science. Each with our own experiences and unique value, we plan to tackle the challenge of creating an active listening, heartwarming, and understanding social bot.

Chung Hoon H. - Team leader

Chung Hoon is a second year data science master’s student at the University of Michigan. His research has been broadly in areas of machine learning and deep learning with particular focus on applications such as image caption generation, solar flare prediction, and neural decoding of myoelectric signals. Chung Hoon interned at LLamasoft in 2019. For the Alexa Prize Competition, he will focus on leading the team and natural language generation using deep learning.

Arushi J.

Arushi is a Master's student in Information in the Data Science track. Arushi's previous NLP experience include story generation via the GTP-2 model, Airbnb Reviews text mining, and LSTM text generation. She will focus on natural language generation. She has been fortunate enough to work with many Universities as a Research Collaborator like Rutgers University, Virginia Tech and UCLA in the fields of Social Computing, and Machine Learning.

Yuan L.

Yuan is a masters student in Data Science studying at the University of Michigan. Passionate about machine learning and deep learning, and with a strong background in terms of data structure and algorithms their studies mainly focus on an interest in natural language processing. Previously, they have joined projects about commonsense inference, image captioning, etc. Yuan used to work as an algorithm intern focusing on implmenting and deploying many NLP models and algorithms on the cloud platform at Tencent, where they got a deeper understanding on several amazing deep learning frameworks.

Junjie X.

Junjie is a master student at the University of Michigan, majoring in Computer Science and Engineering, working with Prof. H.V. Jagadish at UM DBGroup. Generally speaking, Junjie's research interest includes Data Management, Natural Language Processing, Data Mining, Knowledge Representation, etc.

Ryan D.

Ryan is an undergraduate Compuer Science major and will focus on software engineering. His expereince primarily comes through Ai competitions, such as Halite and Terminal, and robotics, such as FRC and Robonation's Robooat competition.

Vihang A.

Vihang is a masters student with reserach interests in the field of Natural Language Processing, Reinforcement Learning and Computer Vision. Projects include developing a virtual pet-sitter which localizes pets and recognizes activity through security cameras, investigating exploration strategies for reinforcement learning and caption generation for images. Vihang's interests in the Alexa Prize involve working on the dialogue policy to develop chatbots that can actively listen.

William C.

William is a sophomore computer science student at Michigan. With a passion for computer vision and natural language processing, William has worked on previous projects with fellow team member Zhizhou to create products such as trAnSLate, an American Sign Language translator. He is currently interning at Taiwan Artificial Intelligence Labs, working on testing and improving a series of medical imaging models that seek to identify diseased areas from medical images. William is extremely excited about the competition and the advances that it will bring to the artificial intelligence realm.

Sagnik R.

Sagnik is a former software engineer pursuing a masters in machine learning with a penchant for natural language processing and computer vision.

Yujian L.

Yujian is a senior student majoring in Computer Science at the University of Michigan with academic interests broadly in AI and ML, particularly in RL and decision making. On team Audrey, Yujian is on the sub-team of ML and mainly responsible for the team's personal understanding model.

Yucen S.

Yucen is an undergraduate student in Computer Science whose research interests are closely related with smart agents: human-computer interaction with machine learning and modelling human behaviour. Yucen focuses on dialog system architecture and integration, especially the cold-start problem, as well as user experience design on team Audrey.

Zhizhuo Z.

Zhizhuo is a maker. He is interested in creating new and interesting things with the latest technology, whether that'd be for something fun or useful. His latest project is an American Sign Language translator that translates hand gestures from a smartphone camera to words via a deployed convolutional neural network.

David Jurgens - Faculty advisor

David is an assistant professor in the School of Information at the University of Michigan. He holds a Ph.D. in Computer Science from UCLA and B.A. in Philosophy from Washington University in St. Louis. His research combines NLP, sociolinguistics, and data mining to discover, explain and predict human behavior in large social systems. His research has been published in top venues such as PNAS, WWW, ACL, ICWSM, EMNLP, and won awards such as the Cozzarelli Prize, Cialdini Prize, best paper at ICWSM, and best paper nomination at ACL.

Latest news

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To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. We are looking for PhD students excited about working on Automated Reasoning or Storage System problems at the intersection of theory and practice to drive innovation and provide value for our customers. AWS Automated Reasoning teams deliver tools that are called billions of times daily. Amazon development teams are integrating automated-reasoning tools such as Dafny, P, and SAW into their development processes, raising the bar on the security, durability, availability, and quality of our products. AWS Automated Reasoning teams are changing how computer systems built on top of the cloud are developed and operated. AWS Automated Reasoning teams work in areas including: Distributed proof search, SAT and SMT solvers, Reasoning about distributed systems, Automating regulatory compliance, Program analysis and synthesis, Security and privacy, Cryptography, Static analysis, Property-based testing, Model-checking, Deductive verification, compilation into mainstream programming languages, Automatic test generation, and Static and dynamic methods for concurrent systems. AWS Storage Systems teams manage trillions of objects in storage, retrieving them with predictable low latency, building software that deploys to thousands of hosts, achieving 99.999999999% (you didn’t read that wrong, that’s 11 nines!) durability. AWS storage services grapple with exciting problems at enormous scale. Amazon S3 powers businesses across the globe that make the lives of customers better every day, and forms the backbone for applications at all scales and in all industries ranging from multimedia to genomics. This scale and data diversity requires constant innovation in algorithms, systems and modeling. AWS Storage Systems teams work in areas including: Error-correcting coding and durability modeling, system and distributed system performance optimization and modeling, designing and implementing distributed, multi-tenant systems, formal verification and strong, practical assurances of correctness, bits-IOPS-Watts: the interplay between computation, performance, and energy, data compression - both general-purpose and domain specific, research challenges with storage media, both existing and emerging, and exploring the intersection between storage and quantum technologies. As an Applied Science Intern, you will work closely with Amazon scientists and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment who is comfortable with ambiguity. Amazon believes that scientific innovation is essential to being the world’s most customer-centric company. Our ability to have impact at scale allows us to attract some of the brightest minds in Automated Reasoning and related fields. Our scientists work backwards to produce innovative solutions that delight our customers. Please visit https://www.amazon.science (https://www.amazon.science/) for more information.
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To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. Help us develop the algorithms and models that power computer vision services at Amazon, such as Amazon Rekognition, Amazon Go, Visual Search, and more! We are combining computer vision, mobile robots, advanced end-of-arm tooling and high-degree of freedom movement to solve real-world problems at huge scale. As an intern, you will help build solutions where visual input helps the customers shop, anticipate technological advances, work with leading edge technology, focus on highly targeted customer use-cases, and launch products that solve problems for Amazon customers. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. You will own the design and development of end-to-end systems and have the opportunity to write technical white papers, create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Amazon Scientist use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science