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Posts by Collection
portfolio
Portfolio item number 2
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publications
Fault Analysis and Predictive Maintenance of Induction Motor using Machine Learning
Published in In Proceedings of the International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques: ICEECCOT 2018, 2018
Context Canvas: Enhancing Text-to-Image Diffusion Models with Knowledge Graph-Based RAG
Published in In arXiv preprint arXiv:2412.09614, 2024
FluxSpace: Disentangled Image Editing in Rectified Flow Models
Published in In arXiv preprint arXiv:2412.09611, 2024
talks
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
Crafting Balance: The Dual-Edged Sword of Open-Source LLMs in the Quest for Responsible AI
Published:
This talk dissected the complex dynamics of open-source Large Language Models, emphasizing their power to democratize AI and drive innovation against the backdrop of ethical challenges. The talk outlined strategies for harnessing the benefits of open-source LLMs while addressing critical concerns like bias and privacy, aiming for a responsible AI future.
teaching
DS 2000 - Programming with Data
Undergraduate Course, Northeastern University, 2021
Introduces programming for data and information science through case studies in business, sports, education, social science, economics, and the natural world. Presents key concepts in programming, data structures, and data analysis through Python. Explains the data analytics pipeline and how to apply programming at each stage. Discusses the programmatic retrieval of data from application programming interfaces (APIs) and from databases. Applies data visualization techniques to summarize and communicate the analysis of data.
CS 4804 - Introduction to AI
Undergraduate course, Virginia Tech, 2024
This course introduces the foundations of modern artificial intelligence (AI) and key ideas and techniques underlying the design of intelligent computer systems. It comprises practice effective methods of reasoning about AI problems, which will generalize beyond the specific topics we study in class. Topics include (but are not limited to) search, game playing, logic, machine learning, deep learning, natural language processing, robotics and image processing.