Shivangi Gupta

The University of Alabama in Huntsville (UAH), a part of the University of Alabama System, houses exceptional researchers like Shivangi Gupta, a determined and accomplished 4th-year Ph.D. student in the Department of Computer Science. Her research centers on drug-target interactions using cutting-edge Artificial Intelligence (AI) and Machine Learning (ML) techniques.

Encouraged by her uncle, a mathematician, and professor who collaborated with UAH's Earth System Science Lab in the 90s, she chose UAH. Gupta's academic journey began in 2017 when she pursued her master's degree at UAH. Her love for math, fostered by her mother's tutoring, naturally drew her to computer science and data analysis.

A turning point came when Dr. Vineetha Menon inspired Gupta with a practical and applied approach to big data. Recognizing her potential, Dr. Menon encouraged Gupta to pursue a Ph.D. in this field, and became her advisor, driving her to dedicate her career to research on ML and AI for healthcare.

In the realm of drug discovery, the process of bringing a new drug to the market can be a laborious and expensive journey. However, Gupta's groundbreaking research seeks to transform this process by employing cutting-edge ML methods to identify and align proteins with target proteins. Through this innovative approach, there is the potential to dramatically reduce the timeline for drug development, leading to considerable time and resource savings while simultaneously boosting the success rate of drug candidates.

Gupta is interning with the Predictive Biology group at the Lawrence Livermore National Laboratory in California. Her work revolves around utilizing deep learning models to annotate functions in protein sequences, with the ultimate goal of enhancing the protein design process through the application of machine learning techniques.

In her project, Gupta focuses on the computational design of overlapping genes using different DNA reading frames to preserve genetic information; this involves fusing two genes, where the first gene is essential for cell survival, and the second gene encodes a product of interest. The resulting overlapping gene relies on the first gene for survival, ensuring the product of interest is produced only when the first gene is present.

In her role, Gupta analyzes synthetic protein sequences using deep learning models to predict their expected Pfam label. Pfam is a widely used protein family database containing computational annotations describing protein domain functions.

By leveraging machine learning models, Gupta's work contributes to the understanding of improving the protein design processes, with potential implications in various fields of biotechnology and synthetic biology.

Gupta's academic journey is marked by her involvement in leadership roles, such as volunteering at prestigious conferences like SC22 and IEEE BIBM. She is preparing to take on the Lead Student Volunteer for Inclusivity role at the upcoming SC23 conference, highlighting her commitment to making a meaningful impact in her field.

A core motivation for Gupta lies in her desire to make a positive impact in the world, particularly as a woman in STEM. She aspires to become an authority in AI and ML, harnessing their power to address pressing global challenges across diverse fields. Additionally, she is devoted to increasing the representation and support of women in STEM degrees and careers.

Gupta advises fellow students to network with individuals from various fields, volunteer, and attend conferences to explore multiple opportunities and broaden their perspectives.

Her journey exemplifies the transformative potential of AI and ML in drug discovery and healthcare. This College of Science student's dedication, leadership, and commitment to empowering women in STEM make her a promising force in computational biology and beyond.