Organization Team

The following colleagues will serve on the organization committee

Ilknur Icke, PhD, Director of Applied Machine Learning, Bayer

Ilknur is interested in the study of complex systems, at the intersection of sensing and computational modeling for better understanding and interventions. Her prior experience includes active learning based design of real-time fMRI experiments and computational modeling of such experiments to study human brain. Ilknur has been in the pharmaceuticals R&D domain for almost a decade working in highly interdisciplinary teams developing capabilities in such areas as modeling & simulation tools for PK/PD analysis, molecular imaging applications for neuroscience and oncology, as well as analysis of multi-omics data and generative modeling for de-novo compound design. She has run a company-wide Quantum Computing focus group bringing together internal and external colleagues. Ilknur also has been serving as a reviewer for the MICCAI conference.

Matteo Aldeghi, PhD, Director of Machine Learning Research, Bayer

Matteo is broadly interested in the development of computational approaches that can accelerate pharmaceutical R&D, especially the application of machine learning to the design of new therapies, from small-molecule drugs to biological therapies. He is also interested in leveraging large biological datasets, like multi-omics data, to inform early therapeutic design decisions. Matteo has a expertise in biophysical simulations for drug discovery and biomolecular design, structure-based drug design, active learning applied to the chemical design, ML-guided experimental optimization, the development of graph neural networks for molecular property predictions, as well as broad knowledge of computational chemistry and cheminformatics. He was a reviewer for past NeurIPS workshops (e.g. AI for Accelerated Materials Design) and a key/primary organizer of a structure-based drug design workshop attended by approximately 120 academics and pharma industry professionals.

Ming Tommy Tang, PhD, Director of Computational Biology, Immunitas Therapeutics

Ming ‘Tommy’ Tang is the Director of Computational Biology at Immunitas Therapeutics. At Immunitas, they employ a single-cell sequencing platform to dissect the biology of immune cells in human tumors. They use machine learning approaches to study single-cell RNA, single-cell TCR, and spatial transcriptome data for new target discovery. He is a computational biologist with extensive experience in genomics, epigenomics, and (single-cell) transcriptomics data. He has over ten years of computational biology experience and six years of wet lab experience. He uses R primarily for data wrangling and visualization in the tidyverse ecosystem; he uses Python for writing Snakemake workflows and reformatting data; he is a Unix geek learning shell tricks almost every month; he cares about reproducible research and open science. Prior to joining Immunitas, Tommy was at Dana-Farber Cancer Institute and Harvard University, where he led a team to analyze immune-oncology-related single-cell sequencing datasets and spearheaded an NIH-funded project called Cancer Immunological Data Commons (CIDC). More about him can be found at https://divingintogeneticsandgenomics.com/

Program Committee

The following colleagues will be part of the program committee

Ardigen

Agata Miezaniec, Tomasz Jetka

Bayer

Azeez Adebimpe, Adrien Bitton, Andrew Branen, Daniel Gusenleitner, Luis Muniz, Farzaneh Nasirian, Marc Osterland, Sara Rahmati