Nachiket Midha

Nachiket Midha is an MSc in Politics candidate at the University of Oxford (St. Antony's College). He recently graduated with distinction from Ashoka University with a PGDipASR (Summa Cum Laude) and BA (Hons.) in Political Science (Magna Cum Laude). His research spans political dynasties, democratisation, and the intersections of gender and governance, with a focus on India and global political trends.

Nachiket has contributed to various think tanks and research organisations, including the National University of Singapore’s Institute of South Asian Studies (NUS-ISAS) and the Observer Research Foundation (ORF), and has published on topics such as elections, nationalism, and law. He has co-authored work (forthcoming) on political dynasties and has experience as a research assistant supporting projects in comparative politics and governance. He is also committed to public problem-solving and was involved in the public health sector during his stint at the International Center for Advanced Studies in New Delhi. He is also working as a pro bono external consultant for a Tibetan early education initiative in India, helping the organisation in its expansion and M&E initiatives.

At Oxford, he remains actively involved in fostering collaborative research, combining academic rigour with policy problems. He is also the co-founder of Oxford Computational Political Science Group (supported by DPIR) to promote work at the intersection of computational methods and political science.

Awards

• Oxford-Cambridge Society of India Award (2024-25)
• Foundation for Universal Responsibility of His Holiness Dalai Lama Fellowship (2024-25)

Teaching

Teaching assistant for four courses at Ashoka University

• History of India-Pakistan Relations (Fall 2022)
• Economy, Politics, Society (Fall 2023)
• Public Opinion, Voting Behaviour (Spring 2024)
• Politics of Peace (Spring 2024)

Assisted Professors in designing the syllabi, collating the readings and managing attendance and participation in the class. Held lab sessions for statistical training for two courses.