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Akash Awasthi, ECE Ph.D. Student, Conducting Critical Research in AI and Image Analysis
By
Alex Keimig
ECE Ph.D. candidate Akash Awasthi's recent research efforts in artificial intelligence, computer vision and biomedical image analysis have been receiving significant attention from the academic community. 
ECE Ph.D. candidate Akash Awasthi's recent research efforts in artificial intelligence, computer vision and biomedical image analysis have been receiving significant attention from the academic community. 

Electrical and Computer Engineering senior Ph.D. candidate Akash Awasthi is making a name for himself. With a paper nominated for the 2024 IEEE International Symposium on Biomedical Engineering’s Best Poster Award, a second paper selected for oral presentation at the IEEE 26th International Workshop on Multimedia Signal Processing, and a third co-authored paper with Houston Methodist published in Nature Scientific Reports, his recent research efforts in artificial intelligence, computer vision and biomedical image analysis have been receiving significant attention from the academic community.

“Decoding radiologists’ intentions: a novel system for accurate region identification in chest X-ray image analysis”, 2024 IEEE International Symposium on Biomedical Imaging — Best Poster Award

               “This nomination was a very prestigious honor, and my work was mostly about using AI models to assist radiologists in disease diagnosis. We don’t want to develop an AI model as a standalone system, or one which can replace the radiologists or doctors. We want to develop a system that can be a collaborative resource with the radiologist, because medical diagnosis is a critical task — there is a lot of trust involved there, and we cannot just completely hand that over to a machine. We want the doctor to be able to learn from the AI, and for the AI to be able to learn from the doctor in order to help with diagnosis and enhance training programs in medical science.

“Anomaly Detection in Satellite Videos using Diffusion Models”, IEEE 26th International Workshop on Multimedia Signal Processing — Oral Presentation

               “This work was in collaboration with NASA Ames. We were trying to use diffusion models — basically generative AI — in detecting anomalous events in the livestreams of NASA satellite video feeds. NASA collects satellite data from all over the US, and we wanted to use that high-resolution satellite video and some kind of generative AI technique that can detect anomalous events like a fire in the forest, or even fog or a tornado — any kind of anomaly event in the live stream of video. We made the focus a theoretical problem of anomaly detection rather than doing any specialized focus on a specific type of event, and that theoretical framework can ultimately — hopefully — be put anywhere with any problem and solve it in a particular way.”

“Deep learning-derived optimal aviation strategies to control pandemics”, Nature Scientific Reports

“We wanted to develop a specific framework using the edge graph neural network and see how we could use that to understand the non-linear dynamics of pandemics. This work specifically concerned COVID-19, but we developed a very generalized framework which can be applied to any potential future pandemics, or even past pandemics, to understand the factors which really drive pandemic spread. Because my work is more into developing very specialized algorithms for scientific applications, this was really interesting work.”

“My research is mostly about large multimodal models, and how we can use current large language models and large multimodal models for certain scientific applications — specifically medical data and radiology,” Awasthi said. “For example, we’re working on using all of the current AI tools available to develop collaborative systems, for both diagnostic and training purposes, for radiologists.”

In addition to medical imaging, Awasthi also works with the Department of Energy’s Argonne National Laboratory using generative AI to solve problems related to weather and wind data, and has been invited by NASA Ames to present his work on novel approaches to urban mapping and the precise classification of urban elements like trees and buildings with combined LIDAR and aerial imagery.

“AI has a lot of power,” he said, “and I would say it’s as intelligent as it is unintelligent. We have to develop an understanding of how we can really make sense out of these models for useful tasks, because there’s a lot of capability there, but we have to instruct it in the proper direction to really make use of it.”

While Awasthi’s recent above-mention projects appear to span a broad spectrum of topics — enhanced biomedical radiological image analysis, the automatic detection of anomalous events via satellite feed, and a framework for understanding the dynamics of pandemic spread and growth — they all boil down to one essential focus: algorithms.

“My particular area of research is more into algorithmic sciences and even basic algorithms: how can we specialize our foundational models for different tasks? Every application and dataset has a different group of challenges and complexities to address,” Awasthi added. “If we can harness the power of these big models and personalize them for our specific use cases, we will be able to automate many, many tasks and make them more efficient. These models won’t be making big scientific discoveries for us, but they can absolutely be used to automate certain tasks and find patterns in data. AI can run the calculations that allow humans to use reasoning and innovation to move us forward.”

 

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