Kushagri Tandon

About

I am a PhD Research Fellow in the Section of Statistics and Data Science at the Department of Mathematics, University of Oslo. My research sits at the intersection of statistical climatology, Bayesian inference, and generative modelling, with precipitation as the central object of study. I am supervised by Prof. Thordis Linda Thorarinsdottir and by Prof. Geir Olve Storvik.

We build statistical and generative models for precipitation over space and time, using high‑resolution observational data from the Oslofjord region. 

Keywords

Conferences and events

Recent

I participate regularly in conferences and workshops in statistics, AI, and climate applications. Recent and selected events include:

Background

I hold a B.Sc. (Honours) in Mathematics from Lady Shri Ram College for Women, University of Delhi, and an M.Sc. in Mathematics from the Indian Institute of Technology Delhi, where I graduated with the highest CGPA in my cohort and received institute‑level awards for academic excellence.

Before coming to Oslo, I worked on natural language processing (topic modelling, multi‑label classification) with work appearing in workshop proceedings of leading NLP venues. I also taught statistics extensively at undergraduate and graduate levels: statistical inference, probability theory, nonparametric methods, multivariate analysis. That combination of mathematical training, applied machine learning research, and time spent explaining hard ideas to students is something I carry into everything I do now.

Teaching

I have several years of experience as a teaching assistant and lab instructor in statistics and applied machine learning, covering:

• Statistical inference and advanced probability
• Nonparametric statistical methods and multivariate analysis
• Introduction to AI and machine learning with Python

In these roles, I have designed assignments and exams, developed teaching materials, led tutorials and labs, and mentored students in applying statistical thinking to real data.

Roles and representation

Let's collaborate

I am always glad to hear from researchers and practitioners working on problems that overlap with mine, particularly in climate and weather modelling, spatio‑temporal statistics, Bayesian methods, uncertainty quantification, or machine learning for environmental applications. If that sounds like your work, feel free to get in touch.

Writing and public talks

I write and speak publicly about AI, statistics, and what it means to work in these fields, with a particular interest in the questions that get less airtime than capability benchmarks. Here is a list of selected few:

• When Mom called about an AI colleague, Medium, April 2026
  On what AI agents actually are, what they are not, and why the gap between the two matters for how we think about accountability in everyday work.
• Thinking about AI beyond capability: Access, Privacy, and Representation, Integreat Insights, April 2026
  Reflections on AI as societal infrastructure, and why questions of access, privacy, and who belongs in the field deserve as much attention as technical progress.
• Debunking AI Myths: What's Hype, What's Real, and Why It Matters, Integreat Insights, November 2025
  Separating what AI can genuinely do from the narratives, utopian and apocalyptic, that tend to crowd out more useful thinking.
• Women in AI – From Algorithm to Action (NORA, 2026)
  Panelist on AI research, impact, and diversity.

Earlier outreach

• Alumna Retreat: Acing Academia After College (2022): Talk on academic pathways and research careers.
• The Need for an Interdisciplinary Approach for Tackling Crises (2022): Panelist on interdisciplinarity and societal challenges.
• Steer Thy Career (2021): Talk on postgraduate pathways and competitive entrance examinations.
• Volunteer Mentor, Lumiere Education (Dec 2025 – Mar 2026): Mentored an undergraduate student on an independent research project in environmental data analysis.

Supervisors

Research profiles