In Octuber last year, Amazon and Virginia Tech announced Enaugural Class of Fellowship and Faculty Award as part of the Amazon – Virginia Tech Initiative for Effective and Robust Machine Learning.
The initiative launched in March 2022 is focused on research on effective and robust machine learning. It supports research efforts led by Virginia Tech Faculty members and provides an opportunity for doctoral students at the College of Engineering Artificial-Intelligence Conducting (AI) and Machine Learning (ML) research to apply for Amazon fellowships.
Amazon and Virginia Tech announced today 2023–2024 the class of Academic Scholarships and Faculty Research Award as part of the Joint Initiative.
“Our sincere appreciation of the Virginia Tech team for their unwavering dedication to expertise in both research and education, as reflected in the effective research and significant progress made in the first year of our partnership as well as the high quality proposals and community applications we have years,” said Reza Ghanadan, a senior research researcher in Ai, who leads the in-AMAZON. “I look forward to continuing our collaboration with the reputed faculty and students at Virginia Tech to promote our common goal of transferring the robustness of machine learning systems while creating effective AI applications across different domains that enrich our society.
“We are very pleased to continue our partnership with Amazon to encourage and support our faculty and student scientists who focus on finding solutions to important and worldwide industry -focused problems across a variety of machine learning applications,” said Naren Ramakrishnan, Thomas L. Phillips Professor of Technology and Technology and Technology and Director of Amazon – Virginia Tech Initiative. “As we move into our second year, we are expanded to additional areas of machine learning, such as robust Largage model implementation, which combines large language models with reasoning functions and multimodal interfaces.
The two fellows and five faculty members elected for this year will each receive funding to carry out research projects at Virginia Tech across several disciplines. The following are the recipients and their research areas.
Academic fellows
MINSU KIM Studies under Walid Saad, professor of electric and computer technology, and pursues a PhD. in electric and computer technology. KIM’s current research focus is to build green, sustainable and robust federated learning solutions with concrete benefits for all AI incidental products that use federal learning and wireless communication. KIM’s work requires a more holistic overview of the life cycle for federal learning algorithms, included data collection, algorithm and model design, training and inference/retinaling.
Ying shen Feeling a Ph.D. In computer science and study under Lifu Huang and Ismini Lourentzou, both lecturers in the Department of Computer Science. Shen’s research interests lie in natural language for treatment (NLP) and multimodal messages. Shen is especially excited to build more human-like interactive agents who better understand, interpret and sensibly about the world around us.
Holders of Faculty Research Award
Lififi HuangDeputy, Department of Computer Science, “Semi-parametric open domain conversation generation and evaluation with multidimenalal assessments from instruction mood”
“The goal of this project is twice. First, it will develop an innovative, semi-parametric conversation framework that increases a large parametric conversation production model with a large collection of information, so that the desired nowledge is dynamically picked up and integrated into the generative model, adaptability, and the scale of the conversion to open domain themes. Provide for association with conversation. ”
Ruoxi JiaDeputy, Department of Electric and Computer Engineering, “Cutting to Hunting: Strategic Data Collection and Pream for Effective and Robust Machine Learning”
“This project focuses on developing strategic data collection and pruning techniques that improve training efficiency while addressing robustness against sub -optimal data quality by creating targeted data collection strategies that optimize the collection of the most valuable and informative data for a specific task;
Ming JinProfessor Assistant, “Safe reinforcement learning for interactive systems with stakeholders’ adaptation”
“This project aims to use a unique approach to tackling the challenges of designing secure and adjusted interactive systems. The research aims to develop a new framework for stakeholder decoration of reinforcement reinforcement and game theory, and its results will have important consequences for a number of uses removing systems.”
Ismini LourentzouDeputy, Department of Computer Science, “Diffusion-based stage graph enabled bodily AIA agents”
“The purpose of this research is to design bodily AI agents capable of tracking long-term changes in the environment, modeling how physical characteristics of several objects are transformed as responsibility for agents’ actions.
Xuan WangDeputy, Department of Computer Science, ”FACTS CHECK IN DIFFECTION OF OPENING DOMANCE BY Self-Counting”
“There is a growing concern about accuracy and truth in information provided by open domain dialogue generation system, such as chatbots and virtual assistants-particular in healthcare and funding, where incorrect information can have serious consequences. This project proposals for a new fact-controlling approval language-model-based self-count that automatically appreciate the generated resorts and provides evidence.”