Murat Yildirim, Ph.D - US grants

1 One of the goals of systems neuroscience is to understand how sensory information is transformed into goal- 2 directed behavior via diverse brain regions and circuits. To achieve this aim, it is critical to elucidate computations 3 performed within specific layers of the cortex by specific cell classes and the communication dynamics between 4 multiple brain regions. Two-photon microscopy has been used successfully to perform functional brain imaging 5 at the single-cell level mice, but its penetration is limited by tissue scattering to the top layers of the cortex. I have 6 developed a 3-photon microscope to overcome this challenge. Today, the main drawback of 3-photon 7 microscope is its relatively modest speed, limiting its use for multi-site imaging. Optimizing instrument design 8 and imaging protocol to overcome this limitation is required for broad end-user acceptance. In this proposal, I 9 will construct and optimize a combined 2-photon and 3-photon microscope for multi-site, superficial and deep 10 brain imaging at single-cell resolution. Specifically, I have first developed a custom-made 3-photon microscope 11 with optimized laser and microscope parameters (Aim 1a). Optimizing these parameters can improve imaging 12 speed and imaging depth while lowering the average laser power to avoid damage in the live mouse brain. The 13 microscope performance improvement has been validated by performing functional imaging in the primary visual 14 cortex of GCaMP6 mice to characterize visual responses of each cortical layer and subplate. In addition, I will 15 characterize the effective attenuation lengths (EAL) of higher visual areas in awake mice with label-free imaging 16 and laser-ablation methods. Then, I will demonstrate the microscope?s performance by examining cell-specific 17 differences within a layer 6 (L6) of V1. Since neuronal responses to visual stimuli are modulated by the cortical 18 state such as arousal, or reward expectation, I will image adjacent sets of neurons with distinct projections to the 19 lateral geniculate nucleus (LGN) and lateral posterior (LP) regions (e.g., cortico-cortical [CC] and cortico-thalamic 20 [CT] neurons in L6) in primary and higher visual areas to reveal circuit-based response types within a single 21 cortical layer using retrobead-based tracing methods (Aim 1b). Next, I have developed custom-made 2-photon 22 wide-field microscope to perform neuronal recordings and manipulations in the primary visual cortex and higher 23 visual areas (Aim 2a). I have improved imaging speed and field of view by implementing multifocal multiphoton 24 microscopy (MMM). Multiple foci two-photon excitation efficiency will be optimized by coupling a diffractive 25 element (DOE) with customized intermediate optics. High sensitivity single-photon counting detection will be 26 achieved using a novel avalanche photodiode array detector. To demonstrate microscope performance and 27 which brain regions are necessary for a well-established goal-directed behavioral paradigm, I will perform SLM- 28 based two-photon optogenetics while imaging expert animals (Aim 2b). In addition to imaging and stimulating 29 neuronal activity across superficial depths at single regions and at multiple regions, it is necessary to image and 30 optogenetically manipulate neuronal activity at multiple depths, at targeted locations, and for identified neurons, 31 in order to determine the causality of neuronal subpopulations in behavior. Here, I will design and implement 32 two- and three-photon MMM systems to extend the depth performance of MMM for multi-site neuronal recording 33 across multiple regions and multiple layers and integrate this system with the 2-photon optogenetics system 34 implemented in Aim 2a (Aim 3a). I will use this technology for modulating specific components of the cortico- 35 cortical and cortico-thalamo-cortical projections of V1-V2-PPC-MC circuit (Aim 3b).

Modern vehicle fleets are equipped with increasing levels of sensor instrumentation that generate large quantities of data on asset conditions and operational awareness. In recent years, there has been a growing literature on methods that harness this data to provide predictive insights for operations. Taken individually, these methods provide limited improvements to fleet-level decision making. There are significant and dynamic interdependencies in large-scale vehicle fleets that include (i) continuous asset-to-asset interactions in degradation and failure risks, (ii) interactions across vehicles related to operational coordination and use of shared resources (e.g. mission requirements and spare part resources), and (iii) interactions between spare part logistics, maintenance and operations. Additional layers of challenges are introduced through stringent requirements for data residency, privacy, and computational scalability. This NSF project provides a unified predictive-prescriptive framework for vehicle fleet management that integrates (i) sensor-driven predictions on dynamically evolving asset failure probabilities and operational risks, with (ii) adaptive robust optimization models for fleet-level operations, maintenance and respond logistics. Intellectual merits of the project include formulation of sensor-driven risks within decentralized and differentially private mixed integer optimization models; and a parallel development of tailored solution methods. Broader impacts of the project include dissemination of research findings through publications, coursework, conferences and workshops. The project will support summer internships and undergraduate research opportunities, specifically for students from underrepresented communities, to educate the next-generation of engineers for vehicle fleet management.

Harnessing the true value of sensor data in a fleet management application, requires an integrated and detailed modeling of fleet level interactions, along with a seamless integration of sensor-driven sensing, and decision-making capabilities. To address this challenge, this proposal aims to develop a decentralized and differentially private framework for sensor-driven fleet management. In particular, the proposed project (i) integrates sensor-driven asset remaining life distributions within a joint decision optimization model to identify optimal operations, maintenance and spare part logistics schedule, (ii) dynamically models the perceived asset remaining life, failure risks and other operational uncertainties within an adaptive robust reformulation of the fleet management model, and (iii) reformulates the decision model within a decentralized and differentially private coordination mechanism. Significant computational challenges will be addressed through decentralized solution algorithms that leverage on the structure of the proposed decision optimization models.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The rapid increase in scale and sophistication of wind farms poses a critical challenge relating to the cost-effective management of wind energy assets. A defining characteristic of this challenge is the economic trade-off between two concomitant processes: electricity generation (the primary driver of short-term revenues) and asset degradation (the major determinant of long-term expenses). This NSF project aims to formulate a decision-theoretic approach to jointly optimize generation and maintenance in wind farms. The project will bring transformative change into the status-quo of asset management in the wind industry which, to-date, relies on single-faceted strategies that largely overlook the dependencies between the generation and degradation in wind turbine assets. The intellectual merits of the project include the formulation of novel data and decision science models, blended within a digitization platform, to predict and co-optimize operations and maintenance requirements. The broader impacts of the project include disseminating research findings via coursework, publications, data/software, and industry-academia workshops. A set of use case demonstrations, co-developed with industrial partners, will accelerate the translation of scientific knowledge into tangible industrial impact, in a step towards meeting the 35%-by-2050 U.S. wind energy target. Summer internships and undergraduate researchers, especially from underrepresented groups, will contribute towards educating the next-generation workforce in data, decision, and energy sciences.

Without formally considering the intrinsic dependencies between electricity generation and asset degradation, wind farm operators reap sub-optimal benefits from their operations and maintenance policies. This project aims to formulate a decision-theoretic framework which seeks an optimal balance of how wind loads are leveraged to harness short-term generation revenues, versus alleviated to hedge against longer-term maintenance expenses. The framework comprises decision-aware predictive models for power and asset health degradation forecasting, integrated within mixed integer programs with decision-dependent uncertainty. New reformulations and constraints will ensure an effective predictive-prescriptive coupling, thereby enabling the optimization to search within the prediction space for an optimal prediction-decision pair. An end-to-end digital twin of the wind farm will bind the proposed predictive-prescriptive methodologies within an integrative asset management solution. Engagement of industrial partners and a national laboratory will ensure a sensible impact on asset management in the wind industry.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.