Carnegie Mellon University - Team Tartan.jpg
Location: Pittsburgh, PA, USA
Faculty advisor: Alex Rudnicky

Tartan (2020)

Team Tartan is a mix of software engineers, linguistics researchers, product managers and human-computer interaction designers.

Team Tartan from CMU - Carnegie Mellon's students are participating in the Alexa Prize from 2 different continents and multiple time zones. The pandemic has not been able to stop them from putting their best foot forward. Team Tartan is a mix of software engineers, linguistics researchers, product managers and human-computer interaction designers.

Daksh S. - Team leader

Daksh is a Product Manager, and currently a graduate student at Carnegie Mellon University. He is curious about understanding Human Behavior and Technology's influence on it. In addition to building products that people love, he hosts a podcast, Dollar Gujarati, where he interviews immigrant entrepreneurs and learns about how they built their businesses. In his free time, Daksh loves reading science fiction, fantasy and business books.

Feng-Guang S.

I am currently a Master’s student at Carnegie Mellon University, focusing on Natural Language Processing. I have done a lot of projects related to Machine Learning and Multi-modal. For this project, I will be responsible for the models.

Vaishakh K.

I am an engineer passionate about building reliable and scalable intelligent systems that can solve real problems.

I have 4 years of work experience as a Software Engineer. This has left me enriched with knowledge of engineering solutions and their effective implementation to make them end-user ready, while not compromising on engineering and operational excellence. I also have research experience in the area of Natural Language Processing.

Being a believer in continuous learning, I would like to build on the technical and conceptual skills to build systems that solve everyday problems by easing the interaction between man and machines.

Yue F.

I'm a first-year Ph.D. student in the Department of Computer Science at University College London, affiliated with the Web Intelligence Group of the Centre for Artificial Intelligence. I am grateful to be advised by Prof. Emine Yilmaz. I aim to design systems that robustly and efficiently learn to understand human languages and web data to the end of advancing artificial intelligence web service and web information processing. My current research interests lie in natural language processing, information retrieval, machine learning, and data mining.

Chi C.

I’m a UX and product designer currently finishing a Master of HCI at Carnegie Mellon University, School of Computer Science. My prior experiences include art historian, curatorial researcher, and yoga instructor. I’m always passionate about designing solutions at the intersection of art and technology, and believe any meaningful experience starts from where human connection is built.

Li-Wei C.

I’m Li-Wei, a first year Master of Language Technology studying at Carnegie Mellon University. My research interests are in speech processing and natural language processing, especially applying machine learning as a tool.

Clive G.

A graduate student at Carnegie Mellon University pursuing a Masters in Artificial Intelligence and Innovation. Main areas of interest include NLP and Image Processing.

Sujay K.

I am currently in the MIIS program at Language Technologies Institute in CMU. Before this, I was leading the NLP team at an early stage startup based in Silicon Valley (hypersonix.ai) valued at $200 million. We built a natural language interface over Amazon Redshift for our enterprise clients. Previously, I have worked at Citrix and vernacular.ai (another early stage startup working on multi-lingual speech and NLP for Indian languages). I graduated from PESIT, Bangalore in 2017 with an undergrad degree in Computer Science. As you might have already noticed, I have a penchant for early-stage startups, having been employee no. < 5 at 2 seed-stage startups. I am also an avid biker and trekker. Generally an outdoor person.

Dhruv N.

Dhruv is a Master’s student at the Language Technologies Institute, CMU. He aims to build intelligent multimodal systems that can enhance the human experience. Before coming to CMU, he worked as a Machine Learning Engineer, developing Intent and Entity Recognition systems. In his free time, he enjoys gaming and scuba diving.

Karthik G.

I am currently in the MIIS program at Language Technologies Institute in CMU . Before this I was working as a Machine learning Engineer at Vernacular.ai a series A funded conversational AI startup where I built and deployed multi-lingual SLU systems.My research interests include Spoken language understanding,Reading comprehension based QA systems,Emotion recognition and End-to-End SLU systems.

Jiajun B.

Jiajun is a graduate student at Carnegie Mellon University. His interest lies in multilingual natural language processing. He did his undergraduate at the University of Michigan, majoring in computer science. In his spare time, he likes watching football games.

Alex Rudnicky - Faculty advisor

Alexander is a Professor Emeritus at Carnegie Mellon University, in the Language Technologies Institute of the School of Computer Science. Dr. Rudnicky's research has spanned many aspects of spoken language, including knowledge-based recognition systems, language modeling, spoken language system architectures, multi-modal interaction, analysis of conversational structure, and design principles for speech interfaces. He has been active in research into spoken dialog, and has made contributions to dialog management, language generation, confidence metrics for recognition and understanding and human-robot interaction. Dr. Rudnicky is interested in the induction of concepts and task structure from speech, and proactively acquire of knowledge through dialog.

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IN, KA, Bengaluru
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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.
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.