Computer Science Archives

Harshini Venkatachalam

Harshini Venkatachalam has a BA in computer science and visual art from Brown University. For six semesters, she was a teaching assistant in the computer science department at Brown and received a Senior Prize for contributions to the department. Harshini is broadly interested in using computing and technology for social good.

Harshini’s Fulbright-Nehru project is developing technology to help learners develop computational thinking skills. Computational thinking encompasses a range of skills in problem solving and system design, with one key skill being abstraction – the ability to overcome complexity by generalizing solutions. Harshini’s project is motivated by the need to understand how novice programmers learn abstraction within the existing pedagogy and thus develop novel methods to help them learn abstraction. During her study, in the course of development of tools, data is also being collected about participant engagement. The deliverables of the project include a novel tool (a mobile application), a literature review, and a detailed report.

Aditi Anand

Aditi Anand is an undergraduate student majoring in computer engineering at Purdue University. She is also pursuing a minor in biology and a concentration in artificial intelligence (AI). Aditi intends to pursue a career in healthcare and is specifically interested in applications of AI in the field of medicine. Her research has explored creating more brain-like artificial neural networks; improving the robustness of AI models used in medical imaging; and early and low-cost diagnosis of congestive heart failure. Aditi has received the Presidential Scholarship, Paul and Peggy Reising Scholarship, Stimson Family Scholarship, and Charles W. Brown Scholarship, all from Purdue University. She has also received the National Honorable Mention Award for Aspirations in Computing from the National Center for Women & Information Technology and the Sigma Xi Top STEM Talk Award at the Purdue Spring Undergraduate Research Conference. Aditi has served as a crisis intervention specialist for Mental Health America; as an emergency room volunteer at the IU Arnett Hospital, Lafayette; as vice chair of the Engineering in Medicine and Biology Society, Purdue Student Chapter; as vice president of WorldHealth Purdue; and as event coordinator for the Indian Classical Music Association at Purdue. She has also volunteered for Udavum Karangal, Chennai, organizing personal hygiene and health awareness workshops, and for the Ankit Foundation Corp to develop a mobile app for mental health.

In her Fulbright-Nehru program, Aditi is working with the Robert Bosch Center for Data Science and Artificial Intelligence at the Indian Institute of Technology (RBC-DSAI) in Chennai to develop a high-performing AI model that can be deployed in Indian clinical conditions to diagnose breast cancer through low-cost mammograms. The model that she is developing with Dr. Balaraman Ravindran’s team at RBC-DSAI seeks to overcome the challenges that India and other countries face due to lack of resources and access to radiologists.

Ramakanth Kavuluru

Dr. Ramakanth Kavuluru is a professor of biomedical informatics (Department of Internal Medicine) in the College of Medicine at the University of Kentucky (UKY). He also has a joint courtesy appointment in the Department of Computer Science at UKY. He graduated with a PhD in computer science in 2009 from UKY with a focus on the security properties of pseudorandom sequences. Subsequently, he worked in knowledge-based search systems for focused bioscience domains as a postdoctoral scholar at Wright State University. Since 2011, he has been working as a faculty member at UKY focusing on natural language processing methods and their use in biomedicine and healthcare.

High-level applications of Dr. Kavuluru’s research include cohort selection for clinical trials, literature-based knowledge discovery, computer-assisted coding, social media-based surveillance for substance abuse, and clinical-decision support for precision medicine. He employs methods from machine learning (including deep learning) and data mining fields to drive his research agenda. His recent methodological contributions deal with zero-shot and few-shot classification, large language models, transfer learning, domain adaptation, and end-to-end relation extraction. Thus far, in his capacity as primary advisor, he has helped seven doctoral students and 10 master’s students attain their graduation.

Predicting disease onset ahead of time is an important application of artificial intelligence (AI) and this is being actively pursued in the U.S. and other western nations. From a global health perspective, it is not clear if the implications of the findings of U.S. patient-based modeling translate to more populous and diverse areas of the world. Thus, using latest machine learning methods and data sets from Indian healthcare facilities, Dr. Kavuluru’s Fulbright-Nehru project is rigorously assessing how well the promise of AI holds when applied to the Indian patient setting compared to the simpler standard-of-care approaches to risk stratification.