Starting a new venture is hard work! Have you wondered how AI can help you launch your startup? Modern advances in AI have produced tools and technologies that can make you smarter, faster, and more innovative.
In this talk, UMBC CSEE Professor Tim Oates will explore several ways that Large Language Models (LLMs) can be used as force multipliers in startups, from ideation to marketing to customer acquisition and more. Hear about the lessons Dr. Oates learned while using AI to scale his own data science company, Synaptiq.
Sponsored by the Alex. Brown Center for Entrepreneurship and Innovation.
UMBC’s Dennise Cardona interviewed Sai Manvitha Nadella, M.P.S. ’25, and Sampath Kumar, M.P.S. ’25, co-founders of AIEdumate and graduates of UMBC’s Data Science Program. AI-Edumate is an AI-powered learning platform born from their capstone project.
Their story highlights how hands-on learning, entrepreneurial resources, and a shared vision for personalized education came together at UMBC. You can watch the interview on YouTube, listen to an audio version, or read a lightly edited transcript.
UMBC Professors Lujie Chen (PI) and Shimei Pan (Co-Pi) received an NSF award of over $365,000 from the NSF Innovations in Graduate Education program in collaboration with the University of Central Florida. The project will augment, refine, and pilot Caselet, a scalable case-based practice tool, by leveraging AI, machine learning, and data analytics approaches, including large language models (LLMs).
The new effort will support the development of data science problem-solving skills in cognitive (the knowledge and skills themselves) and metacognitive domains (the skills for learning how to learn). It addresses the rapidly changing landscape of education in computing and data-intensive courses regarding what and how we teach. The project will augment and refine the Caselet practice tool in three dimensions to support scalable deployment and adoption through an iterative design and test framework.
Caselet will be extended with new features and piloted and tested by up to 1000 students drawn from three graduate programs over at UMBC. The work will focus on three tasks to address scale-up challenges. The first task will explore the approach to help scale up the authoring of Caselet using Large Language Models. The second task aims to scale up the cognitive skills assessment in data science problem solving using machine learning models to track students’ skill mastery at a refined level of precision. The third task will focus on the scalable assessment of metacognitive competencies related to data science problem-solving through multichannel multimodal data collection in controlled lab environments and course-based and self-paced settings.
