Bismark is an associate professor in operational research in the School of Mathematical Sciences at the University of Southampton. He began at Southampton in 2022 as an assistant professor (senior lecturer) in 2022. He received a habilitation in mathematics from the Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany in 2023. He received his PhD and MSc degrees in 2016 and 2013, respectively, in operations research with a focus on mathematical optimization at The University of Texas at Austin (USA). He received his B.Tech. degree in chemical engineering from the Indian Institute of Technology Delhi in 2011. Between 2016 and 2019 he held positions at Sandia National Laboratories in the Discrete Math & Optimization group. In 2017, he also worked at the Karlsruhe Institute of Technology as a postdoc and at the Stockholm Environment Institute as a senior expert.

Data-driven decision-making under uncertainty (particularly using discrete stochastic optimization models) – formulations, algorithms, and applications – is Bismark’s key area of interest. His research has been successfully funded by agencies including the Deutsche Forschungsgemeinschaft, the Horizon 2020 program, the Bavarian State Ministry for Science and Art, and the US Department of Energy. His research has been published in several multidisciplinary journals encompassing a range of disciplines transcending mathematics: including computer science, operations research, and data science and also spanning learning, epidemiology, and public policy. He is a Senior Member of IEEE, a Fellow of Institute of Mathematics and its Applications, and an Associate Fellow of The OR Society. He is the Winner of the 2023 Mathematics Young Investigator Award. In 2024, he is a Distinguished Research Fellow at TU Dresden.

Abstract of the talk// Practically any real-world system requires decision-making under uncertainty. Stochastic programming is a field of mathematical optimization that is especially equipped to quantifiably model risk for such decision-making. Here, reliable decisions must be taken before the uncertainty is revealed. However, such systems are data-intensive and require tailored algorithmic approaches for their solution. Bismark will begin with a vision of the future of interdisciplinary science and the vital role mathematical optimization will play in it. He will demonstrate his claim via three examples. First, by describing his long-standing collaboration with the State of Texas to guide their public health policy for both the 2009 H1N1 and COVID-19 pandemics.  Second, by describing how such decision-making is employed for designing reliable energy management systems particularly under unreliable, but clean, power sources such as wind and photovoltaics. Third, he will present some recent work conducted in Bavaria and Southampton on closing recycling centers without compromising national sustainability goals.