Talks and presentations

Bridging Islands in Chemical Space: Evaluating and Enhancing ML Generalization for Drug Discovery

September 15, 2025

Conference talk, EUROPIN Summer School, Vienna, Austria

Oral presentation at the EUROPIN Summer School in Drug Design, summarizing our work on understanding and improving the generalization of machine learning models in chemical space — covering how out-of-distribution data should be defined for molecular property prediction, the limits of in-distribution-based model selection, and practical recommendations for bioactivity and ADMET tasks.

Assessing the Role of Machine Learning-Based Pose Sampling in Virtual Screening

June 02, 2025

Conference poster, 13th International Conference on Chemical Structures (ICCS), Noordwijkerhout, The Netherlands

Poster presentation at the 13th International Conference on Chemical Structures (ICCS) on integrating machine learning-based pose sampling with established scoring functions for virtual screening, exploring how ML-driven pose generation complements physics-based docking. See the associated publication.

Evaluating Machine Learning Models for Molecular Property Prediction: Performance and Robustness on Out-of-Distribution Data

June 02, 2025

Conference poster, 13th International Conference on Chemical Structures (ICCS), Noordwijkerhout, The Netherlands

Poster presentation at the 13th International Conference on Chemical Structures (ICCS) summarizing our systematic evaluation of 14 machine learning models across eight datasets and ten splitting strategies, examining how the choice of OOD generation procedure shapes both absolute performance and the strength of the ID–OOD correlation. See the associated publication.

Development of Machine Learning Models for Domain Generalization in the Chemical Space

September 15, 2024

Invited talk, Boehringer Ingelheim, Vienna, Austria

Invited talk at Boehringer Ingelheim presenting an overview of my PhD research on machine learning for domain generalization in chemical space, including task-hardness quantification for transfer learning and robust evaluation of molecular property prediction models on out-of-distribution data.

Quantifying the Hardness of Bioactivity Prediction Tasks for Transfer Learning

June 26, 2024

Conference poster, Chemoinformatics Strasbourg Summer School (CS3-2024), Strasbourg, France

Poster presentation at the Chemoinformatics Strasbourg Summer School (CS3-2024) introducing a framework to quantify how “hard” a bioactivity prediction task is, with the goal of guiding source-task selection for transfer learning under chronic data scarcity in drug discovery. See the associated publication.

Machine Learning for Molecular Activity Prediction: Quantification of Task Relations and Hardness for Small Organic Molecules and Proteins

September 15, 2023

Conference poster, EUROPIN Summer School, Vienna, Austria

Poster presentation at the EUROPIN Summer School in Drug Design on quantifying the relationships and hardness between molecular activity prediction tasks for small organic molecules and proteins, motivating principled task selection in transfer learning settings.

Causal learning and inference

November 10, 2019

Talk, Sharif University of Technology, Department of Computer Engineering, Tehran

Causal learning and discovery have received significant attention in the machine learning community. Causal learning allows us to answer the question about intervention (i.e., tinkering in the world) and imagination. This framework can mitigate the problem of robustness, adaptation, and explanation that is present in current machine learning practice.

System biology and waddington landscape

October 01, 2019

Talk, Sharif University of Technology, Department of Computer Engineering, Tehran

In this talk, I presented our work on pattern formation during human embryonic stem cell development. We proposed a multicellular mathematical model for pattern formation during the in-vitro gastrulation of human ESCs. This model enhances the basic principles of the Waddington epigenetic landscape with cell-cell communication, which enables us to describe how the pattern and tissue formation occurs in the course of development.