Field of Study USA: Computer Science

Tanushree Mitra

Dr. Tanushree Mitra is an associate professor at the University of Washington’s Information School, where she leads the Social Computing and Algorithmic Experiences (SCALE) lab group. Her research focus is on human-centered AI and responsible AI wherein she combines computational techniques, AI, natural language processing (NLP), and social science principles to study the complex social processes underlying human–human and human–computer interactions in large-scale online systems. As an interdisciplinary scholar, she draws from a range of methods from the fields of human–computer interactions, large-scale data analytics, machine learning, and AI. Her work consistently centers human impact, interpretability, and the translation of research into real-world governance and safety solutions. Dr. Mitra’s research has been recognized through multiple honors, including an NSF-CAREER, an NSF-CRII, an early career ONR-YIP, and an Adamic-Glance Distinguished Young Researcher awards, along with several best paper awards. She received her PhD in computer science from Georgia Tech’s School of Interactive Computing.

Dr. Mitra’s Fulbright-Nehru project is examining how generative AI systems, such as large language and text-to-image models, reproduce sociocultural biases and misalignments, especially in high-stakes domains in Global South contexts. In partnership with the host institution, the project is developing methodologies to audit AI systems for cultural risks, and is building mixed-initiative workflows by combining machine intelligence, crowd workers, and experts to evaluate those risks at scale. The expected outcomes include new sociocultural AI audit frameworks, datasets, and evaluation metrics, culturally grounded AI capabilities, scalable annotation systems, peer-reviewed research, and strengthened international collaborations focused on responsible, equitable, and globally inclusive AI development.

Tanvi Banerjee

Dr. Tanvi Banerjee is an associate professor in the Department of Computer Science and Engineering at Wright State University, where she also serves as the codirector of the Data Science for Healthcare Lab. She holds a PhD and an MS in electrical and computer engineering from the University of Missouri.

Dr. Banerjee’s core research interest lies at the intersection of computing and medicine, where she focuses on healthcare applications that utilize wearable and non-wearable sensors for chronic disease management, including measuring stress in caregivers of dementia patients. Her work extensively employs machine learning techniques, data fusion, and big data analytics to classify complex sensor data. With an h-index of 27, her publications cover topics such as multimodal data analysis, physiological responses, and predictive modeling for conditions like sickle cell disease, dementia, and chronic pain. Throughout her career, she has secured substantial research funding, with her personal share of grants totaling approximately USD 2.5 million from prestigious institutions such as the National Institutes of Health (NIH), the Department of Energy, and the Air Force Research Laboratory (AFRL). An accomplished educator, she was honored with Wright State’s College of Engineering and Computer Science’s Teaching Award for the 2024–2025 academic year. Her leadership role in the scientific community is reflected in her position as an associate editor for IEEE Transactions on Artificial Intelligence and as local chair and program committee member for several international conferences.

An estimated 8.8 million Indians above the age of 60 live with dementia, yet most remain undiagnosed and lack access to essential resources. To bridge this gap, Dr. Banerjee’s Fulbright-Nehru research project is proposing a culturally nuanced, region-specific Dementia Assessment tool tailored for rural communities. This will enable patients to securely share high-frequency cognitive assessments with their care providers. By deploying AI models to forecast symptom progression, the tool can empower clinicians to deliver proactive interventions and personalized treatment courses, thereby ultimately transforming rural dementia care in India.

Meera Sitharam

Dr. Meera Sitharam is professor of computer science and affiliate professor of mathematics at the University of Florida in Gainesville. After her BTech from the Indian Institute of Technology Madras, she completed her doctoral studies at the University of Wisconsin–Madison in computer science and held positions at Kent State University and Purdue University. She was an Alexander von Humboldt Postdoctoral Fellow at the University of Bonn, a Fields Institute Fellow, and an ICERM Fellow. Her research and over 100 peer-reviewed publications range from pure mathematics (discrete geometry) and theoretical computer science (algorithmic foundations and complexity theory) to the development of open source mathematical software (computational geometry) and geometric modeling in the natural and social sciences and engineering (soft-matter and biophysical modeling, algorithmic game theory, computer-aided mechanical and microstructural design). Her research group’s alumni include at least 15 doctorate holders now in academia, industry and entrepreneurial positions. As a vocal advocate for public higher education, Dr. Sitharam is currently chapter president and chief negotiator for the United Faculty of Florida at the University of Florida. Her academic outreach activities include: faculty advisorship of the Asha for Education chapter, where she works closely with grassroots partners in Tamil Nadu working toward education access and quality; and founding STEM women researchers’ development (Steward@IITM) to mentor and address the barriers faced by women researchers.

She is a graded All India Radio veena artist and engages with a broad range of music.

Dr. Sitharam’s Fulbright-Nehru project, “Exploring Connections: Rigidity, Flexibility, Complexity, and Applications of Geometry Constraints”, aims to leverage the host institution’s unique combination of expertise – on parameterized complexity and derandomization of algorithms, configuration space topology, and soft-matter modeling – to conduct research on Geometric Constraint Systems (GCS), a vibrant, intuitively accessible area that bridges mathematical communities. The GCS lens also intends to spur progress on fundamental theoretical science at the host institution. The expected project outcomes include several peer-reviewed articles, an international workshop, grant proposals for joint US–India programs, seminar series at the host institution, and addressing of research underrepresentation.

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.