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Catalysts are essential in increasing reaction rates of chemical reactions. They have not only shaped our modern world but are also used by nature in many biochemical reactions. Understanding catalytic mechanisms and developing new catalysts holds promise to e.g. solve energy challenges of our society.
Before this background, I am developing new methodologies based on magnetic resonance to unravel processes in catalysis and work towards nano-materials in which nuclear spin states can be controlled
during reactions. Thereby, I am making use of the technique of parahydrogen induced polarization, which is an enhancement technology in NMR, boosting signals by four orders of magnitude. This approach uses parahydrogen, a spin isomer of normal hydrogen gas that interacts with a catalyst and undergoes a chemical reaction. During this process, the spin order of parahydrogen is converted into largely enhanced magnetic resonance signals and acts as a spy molecule for the catalytic process.
In recent years my group has pioneered the use of parahydrogen to study metalloenzymes and more in specific hydrogenases. Hydrogenases are considered nature's blueprint for efficient hydrogen activation catalysts. Although they represent an important class of enzymes, the catalytic mechanisms leading to hydrogen activation are not fully understood. My developed tools allowed for new insights that no other analytical technology could provide and thereby refined details of the catalytic mechanisms.
Additionally, my group has been researching the development of nano-catalysts that can allow for maintaining the para-hydrogen spin order on surfaces. This promises on one side to develop new enhancement strategies in particular to boost the signal of mobile protons that can e.g. exchange with proteins or small molecules leading to their further enhancements in solution. On the other side, a precise control of nuclear spin states during chemical reactions in solution can allow for the future production of
large quantities of spin-controlled chemicals such as para-water or formaldehyde in the para-state. These are chemicals found in e.g. interstellar clouds showing different ratios between ortho (triplet) and para (singlet) states as compared to earth and are thought to display different reactivities. Understanding the effect of nuclear spin states on reactions could lead to new application in chemical reactions and catalysis
in the future.

The Amazon Quantum Solutions Lab works closely with enterprise customers to identify use cases where quantum technologies might have impact in the fault-tolerant future, but also to develop creative ways to solve complex business challenges at scale today. In this presentation, I will showcase selected customer use cases and discuss where and when quantum machines can have an impact.

Early warning systems are a class of risk prediction tools that have recently become part of the de facto approach towards improving high school graduation rates in the United States. These systems aim to help schools efficiently target resources to students by predicting which individuals are least likely to graduate, and hence need the most help.

In this talk, I will present the results of a collaboration with the Wisconsin Department of Public Instruction in which we conducted the first large-scale evaluation of the long-term impacts of early warning systems on high school graduation rates. Using a decade's worth of data and models, we find that risk assessments made by the system have been highly accurate at predicting student dropout, yet ineffective in improving outcomes. We will see how both of these findings can be simultaneously explained by the influence of structural, social factors. We will close with broader discussion regarding the broader policy implications of our work.

:The non-linearity and variability in individual mood responses pose multiple analytic and experimental challenges. These challenges limit our understanding of mental health disorders with aberrant mood dynamics such as depression, and the development of more effective treatments. Computational approaches can help overcome some of these challenges by creating and modeling individual mood transitions. I will describe a study where closed-loop control approach was used to generate individual mood transitions and then a computational modeling approach was used to characterize the temporal effects on these mood changes. This study showed that early events exert a stronger influence on reported mood compared to recent events (a primacy weighting), in contrary to previous theoretical accounts which assumed that recent events are most influential on mood. This Primacy model accounted better for mood reports compared to a range of alternative temporal representations, in random, consistent, or dynamic reward environments, across different age groups, and in both healthy and depressed participants. Moreover, I will show how this temporal relation between early experiences and mood is mediated by specific neural signals. Interestingly, in repetitive reward environments or resting-state conditions, we found that mood reports consistently decline over time, stressing the importance of accounting for temporal effects in mood responses. These findings hold implications for the timing of events when addressing mood and behavior in experimental and in clinical settings.