We do AI for Science, that is, building machine intelligence that works alongside human reasoning to accelerate scientific discovery. Our core conviction is that AI systems must go beyond pattern recognition: they need to reason, explain, and collaborate with humans in the pursuit of scientific knowledge. To that end, we develop methods that combine the scalability of modern deep learning with the interpretability and structure of symbolic reasoning, and apply them to hard open problems in computational biology and drug discovery.

Neurosymbolic AI

Statistical learning and symbolic reasoning have complementary strengths. Neural networks learn well from large, noisy data but are opaque and data-hungry. Symbolic methods — such as Inductive Logic Programming (ILP) — are interpretable and data-efficient but do not scale to raw data easily. We build neurosymbolic methods that integrate both: neural networks guided by symbolic domain knowledge, and symbolic engines that learn from neural representations.

Key contributions include Compositional Relational Machines (CRMs), knowledge-guided graph neural networks (BotGNN, VEGNN), and methods for extracting interpretable rules from trained networks. Applications span drug discovery, genomics, and other scientific domains where both data and prior knowledge are available.

Deep Learning

We work on foundational aspects of deep learning relevant to scientific applications: sequence models and transformers for genomic data, model calibration and uncertainty quantification, knowledge distillation, and explainability. A recurring theme is reliability which enforces that predictions in science must come with well-calibrated confidence and interpretable justification.

We also study large language models (LLMs) for scientific tasks, including using LLMs with logical feedback for molecular design, and building genomic foundation models that capture regulatory and codon-usage patterns across eukaryotes.

Computational Biology and Drug Discovery

Biology is a natural domain for AI for Science: the data are vast, the problems are hard, and mechanistic interpretability is essential. We work on:

  • Gene regulation: predicting gene expression from DNA sequences to understand cis-regulatory logic (e.g. Camformer, Bioinformatics Advances 2025).
  • Genomic foundation models: learning representations of codon usage and transcript stability from large-scale eukaryotic genomic datasets.
  • Multi-omics cancer analysis: Boolean and ML methods for cancer biomarker discovery, macrophage polarisation, and single-cell transcriptomics.
  • Drug discovery: neurosymbolic models for novel lead generation, molecular property prediction via GNNs, and explainable compound screening.

Research in these direction will involve collaborating with scientists from these fields.

Causality

Correlation-based models are brittle: they fail under distribution shift and cannot answer interventional or counterfactual questions that science demands. We are interested in causal machine learning, specifically, in methods that go beyond associational learning to discover and exploit causal structure in data.

This includes learning causal graphs from observational and interventional data, integrating causal priors with deep learning, and using causal reasoning to improve robustness and interpretability of models in biological and clinical settings. A longer-term goal is to build AI systems that can reason about why a biological phenomenon occurs, not just that it occurs, which is a prerequisite for trustworthy AI in science.

Most of our code is publicly available at the MAHI-Group GitHub organisation and Tirtharaj’s personal GitHub.