研究方向
软物质与生物物理，机器学习
研究兴趣
1. 从基因表达产物的时间序列反推其背后的调控网络；
2. 构建多细胞动力学的理论模型，从细胞间的时空关系探索细胞间物理及化学层面的相互作用；
3. 专注于果蝇和线虫等模式生物，在其早期胚胎发育阶段，研究细胞形态变化和功能分化的普遍规律。
代表性成果
1. M. Han, M. Fruchart, C. Scheibner, S. Vaikuntanathan, J. J. de Pablo, & V. Vitelli. “Fluctuating hydrodynamics of chiral active fluids” Nature Phys. 17, 1260–1269 (2021)
2. J. Colen=, M. Han=, R. Zhang, S. A. Redford, L. M. Lemma, L. Morgan, P. V. Ruijgrok, R. Adkins, Z. Bryant, Z. Dogic, M. L Gardel, J. J. de Pablo, & V. Vitelli. “Machine learning active-nematic hydrodynamics.” Proc. Natl. Acad. Sci. U.S.A. 118, 10 (2021)
3. M. Han=, Jing Yan=, S. Granick, & E. Luijten. “Effective temperature concept evaluated in an
active colloid mixture.” Proc. Natl. Acad. Sci. U.S.A. 114, 7513–7518 (2017)
4. Jing Yan=, M. Han=, J. Zhang, C. Xu, E. Luijten, & S. Granick. “Reconfiguring active particles by electrostatic imbalance.” Nature Mater. 15, 1095–1099 (2016)
5. R. Freeman, M. Han, Z. Alvarez, J. A. Lewis, J. R. Wester, N. Stephanopoulos, M. T. McClendon, C. Lynsky, J. M. Godbe, H. Sangji, E. Luijten, & S. I. Stupp. “Reversible self-assembly of superstructured networks.” Science 362, 808–813 (2018)
