We advance Artificial intelligence (AI) and Computational Biology research by designing and developing robust, explainable and accessible Machine Learning (ML) algorithms for solving complex Molecular Biology problems.
We seek the truth and knowledge of what is encoded in the language of life through computational modeling and data-driven discovery.
Our vision is to build a world where AI-driven molecular insights will enable a deep understanding of biology, accurate disease prediction, rapid personalized drug design for effective and timely treatments for each patient.
We relentlessly aim to achieve the following goals:
The fusion of computer science and molecular biology has opened new avenues for decoding the complex language of life. AI-algorithms can analyze vast omics data, distill deeper knowledge, detect patterns and simulate molecular interactions that once required decades of laboratory work. We believe future AI-driven insights will advance our understanding of fundamental biological mechanisms and pave the way for novel treatments.
Where: ENB313 (Engineering Building II, Room 313).
When: Every Friday, 3-4PM.
Feel free to join this lively discussion.
Published in PNAS: 'Predicting epistasis across proteins by structural logic'
16 January, 2026Accepted in BICOB2026: 'Predicting Obstetric and Non-obstetric Diagnoses Co-occurrences during Pregnancy'