Pushpak Pati

Overview

Pushpak Pati

Pronouns

He/Him/His

Title

Postdoctoral Researcher

Location

IBM Research Europe - Zurich Zurich, Switzerland

Bio

Pushpak Pati is a Postdoctoral researcher at IBM Research Europe in Zurich in AI for single-cell research. He received his M.Sc. degree in Electrical Engineering specializing in computer vision and machine learning from ETH Zurich, Switzerland, in 2017. His Ph.D. research was carried out working jointly in the Computer Vision Lab at ETH Zurich and IBM Research Europe.

Pushpak's research focuses on modeling spatial tissue microenvironments in terms of biologically comprehensible elements across different biological stains, and understanding how it affects cancer examination, response to treatment, and biomarker identification. To achieve that, he combines deep learning and computer vision approaches to develop computational methods able to extract biologically meaningful patterns from large-scale, heterogeneous, and noisy tissue imaging data. His research also addresses well-known limitations in deep learning related to annotation scarcity, scalability to large image dimensions, and interpretability.

Pushpak has also been the main developer of HistoCartography, that facilitates the development of graph-based computational pathology pipelines, and has been contributing to ATHENA, that facilitates the visualization, processing, and analysis of (spatial) heterogeneity from spatial omics data.

Publications

Patents

Projects

AI methods for precision therapies.png

AI methods for precision therapies

Developing AI methods for heterogeneity-aware precision therapies.
  • Healthcare
  • Accelerated Discovery
  • Machine Learning
CellCycleTRACER.png

CellCycleTRACER

A novel computational method to quantify cell cycle and cell volume variability.
  • Healthcare
Quantifying biological heterogeneity from single-cell data.png

Quantifying biological heterogeneity from single-cell data

Understanding, modeling and quantifying different sources of heterogeneity from single-cell measurements.
  • Healthcare
  • Accelerated Discovery
  • Machine Learning
AI methods for precision therapies

AI for single-cell research

Understanding spatiotemporal heterogeneity across different scales of biological organization.
  • Healthcare
  • Accelerated Discovery
  • Machine Learning
Modeling the 4D genome.png

Modeling the 4D genome

Modeling the 4D genome with deep learning and stochastic simulations.
  • Healthcare
  • Accelerated Discovery
  • Machine Learning
Modeling the Spatial Heterogeneity of the Tumor Microenvironment.png

Modeling spatial heterogeneity of the tumor microenvironment

Combining spatial single-cell omics with AI to model the complexity of the tumor micorenvironment and enable novel spatial biomarker discovery.
  • Healthcare
  • Accelerated Discovery
  • Machine Learning

Top collaborators

MG
Maria Gabrani

Maria Gabrani

Research Staff Member, Technical Assistant to Alessandro Curioni