Join our research group

Apply to ForeML

We welcome motivated students and researchers interested in statistical learning, forecasting, health AI and dynamical systems.

Research Areas Meet the Team

Research Areas

We are particularly interested in candidates whose interests align with the lab's research themes.

Global climate map with warm and cool spatial anomalies and a diverging color legend.

Spatiotemporal Forecasting

Forecasting complex spatial and temporal processes in climate, epidemiology, and environmental systems.

Digital healthcare interface beside a person undergoing clinical exercise monitoring.

Health AI

Statistical and AI methods for digital health, epidemiology, and healthcare decision-making.

Three-dimensional statistical response surface with scattered observation points.

Statistical Machine Learning

Statistically principled learning methods for inference, prediction, and uncertainty quantification.

Blue digital human head representing generative artificial intelligence.

Generative AI

Generative models and modern AI methods for scientific and application-driven problems.

Recommended Preparation

The following books and resources provide a useful mathematical and practical foundation for our research areas.

Statistical Learning

Core concepts in statistics, machine learning and data analysis.

Mathematics for Machine Learning Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong An Introduction to Statistical Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani The Elements of Statistical Learning Trevor Hastie, Robert Tibshirani, Jerome Friedman

Deep Learning

Foundations and modern methods in deep learning.

Deep Learning Ian Goodfellow, Yoshua Bengio, Aaron Courville Dive into Deep Learning Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola

Time Series Forecasting

Methods and applications for temporal data and forecasting.

Time Series Analysis and Its Applications Robert H. Shumway, David S. Stoffer Forecasting: Principles and Practice Rob J Hyndman, George Athanasopoulos

Before Contacting Us

Please make sure you meet the following guidelines before reaching out.

A prospective student is expected to have a good understanding of mathematics, probability, and statistics, at least to bachelor's level, including the topics listed under the lab's research areas.

The student is expected to have studied, and practiced the code in R or Python for, the books listed above on statistical learning, forecasting, and deep learning.

It is desirable to have read at least one research paper before applying.

Knowing at least one of RStudio, Python, or Matlab is essential. LaTeX is not mandatory.

How to Apply

If you are interested in joining ForeML, please send the relevant application material to ctanujit@gmail.com.

CV

Your current CV.

Transcript

Your academic transcript(s).

Research Statement

A short statement describing your research interests and relevant background.

A publication in a journal or conference is not required; it is an advantage.

Contact ForeML Meet the Team