QuakerBot

The University of Pennsylvania team consists of talents with a variety of expertise. We propose to design QuakerBot that can guide users through tasks in a wide variety of domains, like those in WikiHow or Youtube tutorial videos.

University of Pennsylvania - Team Quakerbot.jpg
Location: Philadelphia, PA, USA
Faculty advisor: Chris Callison-Burch

The University of Pennsylvania team consists of talents with a variety of expertise. We propose to design QuakerBot that leverages cutting-edge natural language processing techniques to guide users through a variety of tasks like cooking or home improvement.

The University of Pennsylvania QuakerBot team designs a flexible, mighty, and robust dialog system that assists users with working on household tasks or following recipes. We power its components with state-of-the-art technology in natural language processing and reasoning. Not only reliable in common conversations, the QuakerBot can adapt to unseen domains and scenarios with extremely little human supervision

Li Z. - Team leader

A 3rd year PhD student at the University of Pennsylvania working with Prof. Chris Callison-Burch, focusing on natural language processing (NLP). Previously, I did undergraduate NLP research at the University of Michigan working with Prof. Rada Mihalcea. I served as a Session Chair for the AACL 2020 conference, and a reviewer for many venues such as ACL, COLING, LREC and the Computer Speech and Language journal. I have more than 10 publications, which have been cited more than 100 times. I did internship at IBM Research twice. My research interests lie in reasoning about activities and events

Weiqiu Y.

A second-year PhD student studying computer and information science at Penn advised by Professor Mark Yatskar. My research focuses on the intersection between vision and language, and zero-shot learning. I’m also interested in natural language generation and machine translation.

Dimitri C.

A master’s student in the Computer & Information Technology program at Penn. My professional experience is primarily concentrated in electrical & computer engineering and stems from various R&D programs that I’ve been a part of as engineer at Lockheed Martin. My research interests include sensor fusion and broadband RF sensing.

Lyiang Z.

A senior student at University of Pennsylvania studying Computer Science and Cognitive Science. My primary interest is in NLP and its applications, e.g., math word problem solver, dialogue system. My ideal WLB for the future is to be able to work on NLP projects/research during the week and ski on the weekends.

Manni A.

A final year MS in CIS student at Penn. My research interests lie in NLP and am currently an RA with the Penn NLP lab. I’ve previously worked with Microsoft as a full stack developer on the developer productivity platform for Microsoft Office.

Run S.

A senior at Penn studying computer science and business. I enjoy learning and working with innovative software technology. In my free time I like to play the flute, piano, and tennis.

Yue Y.

I am a second year PhD student studies Computer Science and interested in the field of multimodality and how vision can assist language tasks.

Zhaoyi H.

Currently a first-year graduate student in the UPenn Data Science program. My research interests include natural language understanding, reasoning and cognitions, and machine learning, Aside from CS, my favorite things are cats, traveling and cooking.

Yuxuan W.

A0 first-year Master student majoring in Data Science. My interest lies in the intersection between NLP and traditional statistical learning.

Alyssa H.

A second-year PhD student in the Department of Computer and Information Science at the University of Pennsylvania, where I work with Professors Chris Callison-Burch and Andrew Head on the intersection of NLP and HCI. I am specifically interested in language and interaction aspects of voice agents, such as how virtual assistants like Alexa can give better audio instructions, answer subjective questions, and support complex tasks without visual assistance. I am supported by the NSF Graduate Research Fellowship Program. Before coming to Penn, I received my BS in Computer Science from Columbia University, where I worked with Kathy McKeown on NLU for slang and African American Vernacular English.

Artemis P.

I completed my undergraduate degrees in Computer and Cognitive Science and Philosophy at the University of Pennsylvania, where I moved on to also get a Master’s in Computer and Information Science. During my undergraduate and graduate time at Penn I was working at GRASP lab on the estimation of optical flow. I am currently continuing my education as a PhD student at the University of Pennsylvania with a focus on the intersection of Computer Vision and Natural Language Processing.

Chris Callison-Burch - Faculty Advisor

I am an associate professor of Computer and Information Science at the University of Pennsylvania. Before joining Penn, I was a research faculty member at the Center for Language and Speech Processing at Johns Hopkins University for 6 years. I served as the General Chair of the ACL 2017 conference, and the Program Co-Chair for the EMNLP 2015 conference. I was the Chair of the Executive Board of NAACL from 2011-2013, and the Secretary-Treasurer for SIGDAT from 2015-2017. I have served on the editorial boards of the journals Transactions of the ACL (TACL) and Computational Linguistics. I have more than 100 publications, which have been cited over 20,000 times. I am a Sloan Research Fellow, and I have received faculty research awards from Google, Microsoft, Amazon and Facebook in addition to funding from DARPA and the NSF. My research interests include natural language processing and crowdsourcing.

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GB, London
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ES, M, Madrid
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Ops Integration: Concessions team is looking for a motivated, creative and customer obsessed Snr. Applied Scientist with a strong machine learning background, to develop advanced analytics models (Computer Vision, LLMs, etc.) that improve customer experiences We are the voice of the customer in Amazon’s operations, and we take that role very seriously. If you join this team, you will be a key contributor to delivering the Factory of the Future: leveraging Internet of Things (IoT) and advanced analytics to drive tangible, operational change on the ground. You will collaborate with a wide range of stakeholders (You will partner with Research and Applied Scientists, SDEs, Technical Program Managers, Product Managers and Business Leaders) across the business to develop and refine new ways of assessing challenges within Amazon operations. This role will combine Amazon’s oldest Leadership Principle, with the latest analytical innovations, to deliver business change at scale and efficiently The ideal candidate will have deep and broad experience with theoretical approaches and practical implementations of vision techniques for task automation. They will be a motivated self-starter who can thrive in a fast-paced environment. They will be passionate about staying current with sensing technologies and algorithms in the broader machine vision industry. They will enjoy working in a multi-disciplinary team of engineers, scientists and business leaders. They will seek to understand processes behind data so their recommendations are grounded. Key job responsibilities Your solutions will drive new system capabilities with global impact. You will design highly scalable, large enterprise software solutions involving computer vision. You will develop complex perception algorithms integrating across multiple sensing devices. You will develop metrics to quantify the benefits of a solution and influence project resources. You will validate system performance and use insights from your live models to drive the next generation of model development. Common tasks include: • Research, design, implement and evaluate complex perception and decision making algorithms integrating across multiple disciplines • Work closely with software engineering teams to drive scalable, real-time implementations • Collaborate closely with team members on developing systems from prototyping to production level • Collaborate with teams spread all over the world • Track general business activity and provide clear, compelling management reports on a regular basis We are open to hiring candidates to work out of one of the following locations: Luxembourg, LUX
GB, London
Ops Integration: Concessions team is looking for a motivated, creative and customer obsessed Snr. Applied Scientist with a strong machine learning background, to develop advanced analytics models (Computer Vision, LLMs, etc.) that improve customer experiences We are the voice of the customer in Amazon’s operations, and we take that role very seriously. If you join this team, you will be a key contributor to delivering the Factory of the Future: leveraging Internet of Things (IoT) and advanced analytics to drive tangible, operational change on the ground. You will collaborate with a wide range of stakeholders (You will partner with Research and Applied Scientists, SDEs, Technical Program Managers, Product Managers and Business Leaders) across the business to develop and refine new ways of assessing challenges within Amazon operations. This role will combine Amazon’s oldest Leadership Principle, with the latest analytical innovations, to deliver business change at scale and efficiently The ideal candidate will have deep and broad experience with theoretical approaches and practical implementations of vision techniques for task automation. They will be a motivated self-starter who can thrive in a fast-paced environment. They will be passionate about staying current with sensing technologies and algorithms in the broader machine vision industry. They will enjoy working in a multi-disciplinary team of engineers, scientists and business leaders. They will seek to understand processes behind data so their recommendations are grounded. Key job responsibilities Your solutions will drive new system capabilities with global impact. You will design highly scalable, large enterprise software solutions involving computer vision. You will develop complex perception algorithms integrating across multiple sensing devices. You will develop metrics to quantify the benefits of a solution and influence project resources. You will validate system performance and use insights from your live models to drive the next generation of model development. Common tasks include: • Research, design, implement and evaluate complex perception and decision making algorithms integrating across multiple disciplines • Work closely with software engineering teams to drive scalable, real-time implementations • Collaborate closely with team members on developing systems from prototyping to production level • Collaborate with teams spread all over the world • Track general business activity and provide clear, compelling management reports on a regular basis Basic Qualifications -Masters in Computer Science, Machine Learning, Robotics or equivalent with a focus on Computer Vision. -2+ years of experience of building machine learning models for business application -Broad knowledge of fundamentals and state of the art in computer vision and machine learning -Strong coding skills in two or more programming languages such as Python or C/C++ -Knowledge of fundamentals in optimization, supervised and reinforcement learning -Excellent problem-solving ability Preferred Qualifications -PhD and 4+ years of industry or academic applied research experience applying Computer Vision techniques and developing Computer vision algorithms -Depth and breadth in state-of-the-art computer vision and machine learning technologies and experience designing and building computer vision solutions -Industry experience in sensor systems and the development of production computer vision and machine learning applications built to use them -Experience developing software interfacing to AWS services -Excellent written and verbal communication skills with the ability to present complex technical information in a clear and concise manner to a variety of audiences -Ability to work on a diverse team or with a diverse range of coworkers -Experience in publishing at major Computer Vision, ML or Robotics conferences or Journals (CVPR, ICCV, ECCV, NeurIPS, ICML, IJCV, ICRA, IROS, RSS,...) We are open to hiring candidates to work out of one of the following locations: London, GBR