uci-zotbot.jpg
Location: Irvine, CA, USA
Faculty advisor: Sameer Singh

ZotBot

Our team envisions creating a socialbot that will be able to engage in memorable interactions, and can tailor the conversation to reflect the interests of each unique individual.

Our team envisions creating a socialbot that will be able to engage in memorable interactions, and can tailor the conversation to reflect the interests of each unique individual.

William S. - Team leader

William is currently a fifth year undergraduate double majoring in Computer Science and Computational Physics, with a minor in mathematics. His primary focus is information extraction and computational modeling.

Claire U.

Claire is an undergraduate mathematics major at UC Irvine. Her previous experience in programming focused on robotics projects and website design, mainly using Python. Claire is excited for this new experience as a project manager for ZotBot. She has gained valuable communication, organizational, and time management skills through her previous work experiences, as an intern at a nonprofit and a peer academic advisor at UCI. Currently, Claire manages projects that require correspondence among coworkers and is responsible for ensuring the projects are kept to their deadlines.

Yoshitomo M.

Yoshi is a Ph.D. candidate in Computer Science at UCI, working on Machine Learning and its application with Profs. Sameer Singh and Marco Levorato. Before UCI, he obtained his master's and bachelor's degrees from the University of Hyogo and National Institute of Technology, Akashi College, respectively. His master's and bachelor's thesis topics were on behavioral biometrics such as keystroke dynamics and flick authentication. In industry, his projects were on recommender systems for image and online advertisement, and ML model interpretability in NLP tasks.

Dheeru D.

Dheeru is a second year Ph.D. student working under Prof. Sameer Singh. She completed her master’s from LTI at Carnegie Mellon University before joining the Ph.D. program at UCI. Dheeru is interested in complex question and context understanding techniques applied to reading comprehension. Her current work is focused towards inducing natural language questions as programs that can be executed on unstructured context. Conversational agents add a layer of compositional complexity to questions which can be very challenging for machines. This confluence of user and context is exciting for me.

Yao D.

Yao is a Ph.D. candidate in Informatics at the University of California, Irvine (advisor: Katie Salen Tekinbas). Her research examines best practices in interaction design for children with disabilities, service design for speech language pathologists, and game design for therapy activities through touch-based and voice-based interfaces. Using both quantitative (e.g., experiment, survey) and qualitative (e.g., interviews, content analysis) techniques, Yao's empirical projects aim to transform clinical knowledge to inform technical design and development of educational, assistive, and health technology.

Xuan L.

Xuan is a first-year master's student majoring in statistics at the University of California, Irvine. She is a motivated learner with a strong foundation in mathematical and statistical theory. Her research interests lie in data science and machine learning. She is passionate about NLP and can’t wait to explore it!

Moeez Q.

Moeez is a 3rd year Informatics major at UCI and he loves technology.

Lyuyang H.

Lyuyang is a third-year computer science and engineering major at UC-Irvine. Having worked on two research projects where he built control systems for an autonomous sailboat and a linear generator, he has gained much experience in prototyping, research, and software engineering. Last Summer, Lyuyang joined MData (Shanghai) as a Data Engineer Intern and worked on REST APIs, search engines and databases. His interests in technology have also brought him to photography and filmmaking. His current project includes 3D reconstruction with video and image/sound generation with Generative Adversarial Networks.

Arsenii M.

Arseny is a second year Cognitive Science Ph.D. student in the University of California, Irvine. He has a diverse background: Psyhology, Neuroscience, and Machine Learning. In his work, Arseny combines his knowledge and skills of these disciplines in order to contribute to both AI and Cognitive Science research. To learn more, check out his website: r-seny.com.

Michelle L.

Michelle is a second year undergraduate student double majoring in Data Science and Business Economics. Her interests lie in machine learning and artificial intelligence.

Ilene D.

Ilene is an undergraduate Computer Science student at the University of California, Irvine specializing in algorithms with strong interest in blockchain and artificial intelligence. Ilene has experience in cloud computing and programming in object-oriented languages and AI languages. As a Software Developer Summer Intern at Stanford University, they built a REST API on the AWS platform. Ilene's hobbies include cooking, collecting vinyls, and investing in cryptocurrency. Ilene is excited to apply their knowledge and learn more skills while participating in the Alexa Prize.

Daniel A.

Daniel is a Ph.D. student in applied mathamatics, whose research focuses on using kernel based methods to solve partial differential equations. He brings a strong background in probabiliy, statistics, and numerical analysis to UCI's Alexa team.

Sameer Singh - Faculty advisor

Sameer Singh is an Assistant Professor of Computer Science at the University of California, Irvine. He is working on machine learning applied to natural language processing, in particular on explanations for black-box models, adversarial examples for NLP, information extraction, and question answering. Sameer was a postdoctoral researcher at the University of Washington and received his Ph.D. from the University of Massachusetts, Amherst, during which he also worked at Microsoft, Google, and Yahoo!. His group has received funding from Allen Institute for AI, NSF, DARPA, Adobe, and FICO.

Latest news

The latest updates, stories, and more about Alexa Prize.
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US, MA, Boston
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IN, KA, Bengaluru
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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.