Athena

Athena is named after the Greek goddess of wisdom and strategy and is comprised of PhD and master's students researching conversational AI, natural language processing, and computer vision.

Coming straight from the redwoods of Santa Cruz, the Natural Language and Dialogue Systems Lab is making its fifth appearance in the competition. Our social bot will be able to use commonsense knowledge and external information to generate meaningful responses.

team athena 2023
Team Athena (2023)

Yue Fan - Team leader

Fan is a second year PhD student at UCSC. Fan received a master's robotics from Johns Hopkins University and a bachelor's in automation at Shandong University. Fan's study interests are in NLP, CV, and AI. Fan is focusing on how to improve intelligent embodied agents to perform better and converse with humans in real-world.

Saaket Agashe

Agashe is a master's student in the Computer Science Department at UCSC. Agashe's research interests include natural language processing, conversational AI, and, language-based visual grounding. Agashe previously worked studying predictive text systems through qualitative analysis and is currently working on visual grounding, specifically localization using spatial descriptions and dialogs.

Kevin Bowden

Bowden, a sixth year PhD candidate at UCSC, was a member of team Athena for SGC3 and SGC4 and previously led team SlugBot in SGC1 and SGC2. His primary focus has been on the user experience and the design of call flows to maximize user interest in the conversation. Most recently, his work on Athena has focused on his thesis topic: user modeling and personalization in open-domain conversational AI.

Winson Chen

Chen is a first-year master's student in the Applied Mathematics Department at UCSC. Chen earned a bachelors degree in computer science from UCSC in 2022. Chen has worked as undergraduate research assistant in ERIC Lab under Xin (Eric) Wang. Chen's research interests are natural language processing, computer vision, and machine learning.

Wen Cui

Cui is a PhD candidate specializing in deep learning, natural language processing, and conversational AI. Cui's research focuses on applying deep learning techniques and leveraging large-scale knowledge graphs to improve entity linking in open- domain dialogue systems.

Vrindavan Harrison

Harrison is a fifth year PhD student and a member of the Natural Language and Dialogue Systems lab. His research focuses on dialogue systems and natural language generation. Davan participated in the SBGC 3 and 4 as a member of Team Athena. Davan grew up in Santa Cruz and in his free time surfs and practices jiu jitsu.

Xing Wang - Faculty advisor

Xin (Eric) Wang is an assistant professor of computer science and engineering at UC Santa Cruz. His research interests include NLP, CV, and ML, with an emphasis on building embodied AI agents that can communicate with humans using natural language to perform real-world multimodal tasks. Xin has served as Area Chair for ACL, NAACL, EMNLP, ICLR, etc., and Senior Program Committee (SPC) for AAAI and IJCAI. He organized multiple workshops and tutorials at CVPR, ICCV, ACL, NAACL, AACL, etc. He has received a CVPR Best Student Paper Award (2019), an Amazon Alexa Prize Award (2022-2023), a Google Faculty Research Award (2022), etc.

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US, MA, Boston
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The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
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US, MA, Boston
The Artificial General Intelligence (AGI) team is seeking a dedicated, skilled, and innovative Applied Scientist with a robust background in machine learning, statistics, quality assurance, auditing methodologies, and automated evaluation systems to ensure the highest standards of data quality, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As part of the AGI team, an Applied Scientist will collaborate closely with core scientist team developing Amazon Nova models. They will lead the development of comprehensive quality strategies and auditing frameworks that safeguard the integrity of data collection workflows. This includes designing auditing strategies with detailed SOPs, quality metrics, and sampling methodologies that help Nova improve performances on benchmarks. The Applied Scientist will perform expert-level manual audits, conduct meta-audits to evaluate auditor performance, and provide targeted coaching to uplift overall quality capabilities. A critical aspect of this role involves developing and maintaining LLM-as-a-Judge systems, including designing judge architectures, creating evaluation rubrics, and building machine learning models for automated quality assessment. The Applied Scientist will also set up the configuration of data collection workflows and communicate quality feedback to stakeholders. An Applied Scientist will also have a direct impact on enhancing customer experiences through high-quality training and evaluation data that powers state-of-the-art LLM products and services. A day in the life An Applied Scientist with the AGI team will support quality solution design, conduct root cause analysis on data quality issues, research new auditing methodologies, and find innovative ways of optimizing data quality while setting examples for the team on quality assurance best practices and standards. Besides theoretical analysis and quality framework development, an Applied Scientist will also work closely with talented engineers, domain experts, and vendor teams to put quality strategies and automated judging systems into practice.
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