Shehryar Khan, a graduate student at Virginia Tech, is making strides in the field of applied machine learning as he works toward his Master of Engineering in computer science and applications. After completing his undergraduate degree at Lahore University of Management Sciences in Pakistan, Khan chose Virginia Tech for its reputation as a Tier 1 university and the strength of its academic culture.
Khan’s research focuses on various facets of machine learning, including optimization, post-training evaluation building, and machine learning security. His objective is to enhance the organization, accessibility, and accuracy of research information derived from complex data sources. “I like to say I do ‘research in research,’” Khan explained, highlighting the innovative nature of his work.
In his second semester, Khan secured a graduate assistantship with the University Libraries’ research impact and intelligence team. Under the guidance of Assistant Director for Research Intelligence Sarah Over, he is delving into how machine learning can be applied to patent data, an area gaining significance as the volume of granted patents has more than doubled over the past 15 years.
“Patents, like other forms of publications, are getting harder and harder for even experts to keep up with,” noted Over. “The work Khan proposed and has been working on with me has the potential to tell if a new idea is innovative enough for a new patent.” Khan’s project aims to unlock insights from patent submissions using machine learning methods, a pursuit that intertwines intellectual property with cutting-edge technology.
“We are researching how to push models like ChatGPT to produce new ideas,” Khan said. “To attempt this, we aim to ask these models to generate patents for us but avoid ideas that already exist.” He views this as an exciting frontier in machine learning, asserting that current models often merely predict based on existing knowledge. Khan believes tackling originality in patent generation represents a significant step forward in the field.
Over emphasized the broader implications of Khan’s work: “Virginia Tech needs to keep advancing within fields like machine learning and AI. Even if it is just one project, this ties into other work the University Libraries is doing.” By focusing on patents, the team is filling a gap in AI-related research and contributing unique analyses to the academic community.
Khan’s efforts in researching novelty in patent submissions are expected to culminate in a publication where he will be the first author. “Khan has already contributed to a conference paper that is in peer review and many projects as part of our department, Research Impact and Intelligence,” Over stated, underscoring the significance of his contributions.
With aspirations of pursuing a career in industry as a machine learning engineer or researcher, Khan credits Virginia Tech and his opportunity with University Libraries for bridging the gap between theoretical knowledge and practical application. As he continues to explore the intersection of machine learning and intellectual property, his work could pave the way for new methodologies in evaluating technological innovation.
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