(GENOME + CELL) Reprogramming Lab
University of Michigan
Direct Cellular Reprogramming
Direct Cellular Reprogramming
Our lab focuses on understanding the dynamics of genome organization in human cells, and how these dynamics shape cell fate in differentiation, cellular reprogramming, and cancer. We have a dual approach of generating time series data in-house using human cells and by using mathematics to identify patterns in data and fill in major unknowns.
Our lab focuses on understanding the dynamics of genome organization in human cells, and how these dynamics shape cell fate in differentiation, cellular reprogramming, and cancer. We have a dual approach of generating time series data in-house using human cells and by using mathematics to identify patterns in data and fill in major unknowns.
Immune System Inspired Learning
Immune System Inspired Learning
We will use immune system architecture as a basis for generating novel machine learning algorithms. The immune system quickly adapts to a new threat with multi-threaded exploration/testing, selectively rewarding better threads for a threat-specific solution and efficiently coded memory. Advantages of immune system architecture:
We will use immune system architecture as a basis for generating novel machine learning algorithms. The immune system quickly adapts to a new threat with multi-threaded exploration/testing, selectively rewarding better threads for a threat-specific solution and efficiently coded memory. Advantages of immune system architecture:
- Able to find solutions to novel problems, mounting a defense while it learns
- Able to find solutions to novel problems, mounting a defense while it learns
- Uses a highly evolved memory management system to avoid adversarial inputs
- Uses a highly evolved memory management system to avoid adversarial inputs
- Provides timely solutions, carefully balancing randomized exploration with a survival of the fittest strategy.
- Provides timely solutions, carefully balancing randomized exploration with a survival of the fittest strategy.
For more detail: A Mathematical Theory of Learning Guided by the Immune System
For more detail: A Mathematical Theory of Learning Guided by the Immune System
Towards a Definition of Emergence
Towards a Definition of Emergence
Various examples in the real world illustrate the emergence of structure over time. As these examples are quite diverse, e.g. structural assembly of a virus and muscle formation, identifying unifying principles is difficult. The purpose of this work is to investigate the common features of such systems with the goal of developing a systematic classification of properties associated with emergence.
Various examples in the real world illustrate the emergence of structure over time. As these examples are quite diverse, e.g. structural assembly of a virus and muscle formation, identifying unifying principles is difficult. The purpose of this work is to investigate the common features of such systems with the goal of developing a systematic classification of properties associated with emergence.
Department of Computational Medicine and Bioinformatics
Department of Computational Medicine and Bioinformatics
Department of Mathematics
Department of Mathematics
Biomedical Engineering
Biomedical Engineering
UNIVERSITY OF MICHIGAN
UNIVERSITY OF MICHIGAN
