Wolfgang Losert

Interim Associate Dean for Research
MPower Professor, Physics and IPST
Computer, Mathematical, & Natural Sciences
3400 A.V. Williams Building
Physics
1147 Physical Sciences Complex

 

 

 

Wolfgang Losert leads the Dynamics of Living Systems Laboratory, where his group works at the convergence of physics, biology, and artificial intelligence and analyzes the biological role of multiscale dynamics in cells and tissues. His interdisciplinary research team studies applications ranging from cancer cell metastasis to neural network communication. Dr. Losert has previously led an AFOSR MURI and co-led a NIH U19, and he is currently funded by AFOSR and Lockheed Martin. The focus of current research is the role of non-electrical dynamics in living neural networks and AI algorithms inspired by these unique signals. In the JRL project, Dr. Losert is leading an interdisciplinary team to deploy a living neural network platform, which integrates wetware (live cells), hardware, and software to enhance real-time analysis for defense applications.

Dr. Losert was honored in 2023 as an MPower Professor at the University of Maryland (UMD) for his demonstrated commitment to collaboration, innovation, and discovery. Indeed, he has brought in over $1.5M of funding per year to UMD for the past seven years, with major grants from AFOSR, NIH, and NSF. Of particular importance is the work he led in the AFOSR MURI, which transformed our understanding of how cells sense their physical environment. Dr. Losert is also a Fellow of the American Physical Society and of the American Association for the Advancement of Science.

Dr. Losert currently serves as Co-Director of the National Cancer Institute-UMD Partnership for Integrative Cancer Research and on the Editorial Board of the Biophysicist Journal. He has mentored over 70 undergraduate students, over 30 PhD students, and over 10 postdoctoral researchers.

Under the leadership of Dr. Losert and the Neural Dynamic Platform, the project is focused on designing, building, and harnessing the unique capabilities of a living neural network to engineer hybrid AI architectures. These architectures aim to achieve the efficiency, adaptability, and resilience found in biological systems.