In this research, a deployed model of biped that can be built has been considered, and then its walking performance sensitivity such as efficiency, stability...
Here, we propose a multicellular mathematical model for pattern formation during in vitro gastrulation of human ESCs. This model enhances the basic principle...
Recognizing arrow of time in short stories is a challenging task. i.e., given only two paragraphs, determining which comes first and which comes next is a di...
Proximity-inducing compounds (PICs) are an emergent drug technology through which a protein of interest (POI), often a drug target, is brought into the vicin...
Machine learning models are employed to enhance the speed and provide novel insights in drug discovery due to their demonstrated effectiveness in predicting ...
Today, machine learning methods are widely employed in drug discovery. However, the chronic lack of data continues to hamper their further development, valid...
Physics-based docking methods have long been the cornerstone of structure-based virtual screening (VS). However, the emergence of machine learning (ML)-based...
Today, machine learning models are employed extensively to predict the physicochemical and biological properties of molecules. Their performance is typically...
In this talk, I presented our work on pattern formation during human embryonic stem cell development. We proposed a multicellular mathematical model for pat...
Causal learning and discovery have received significant attention in the machine learning community. Causal learning allows us to answer the question about ...
I worked as the teaching assistant of “Advanced bioinformatics course”. This is a graduate level course which covers topics such as: Microaarray and RNA-Seq ...
I am running a machine learning workshop, which I present diverse topics; from knowledge distillation and compression to causal inference and invariance.
Active learning and Bayesian optimization have become important/prevalent tools in drug discovery and design. They provide a systematic way to select sample...
GNNs For Chemists
Diffusion Models Course: Implementation-First Learning of Score-Based and Diffusion Generative Models