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<div class="ContentPasted0 elementToProof">This is an announcement of Jacob Williams's MS Presentation</div>
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<div class="ContentPasted0">Candidate: Jacob Williams</div>
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<div class="ContentPasted0">Date: Friday, February 03, 2023</div>
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<div class="ContentPasted0">Time: 3 pm CST</div>
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<div class="ContentPasted0">Remote Location: <a href="https://urldefense.com/v3/__https://uchicago.zoom.us/j/98622767991?pwd=eDBmRTdQd0N3ZWVsbjJ5T1hWdmk2UT09__;!!BpyFHLRN4TMTrA!_km8JSORgHOxp1N3qL3S8OfE8B7HpndyiShEfComfW9vAA33TYCHNzr7nvSWp-N2ZAaawTOOXGdkZHqO7P9bfEqSyCsqN6d7zo97$" class="">https://uchicago.zoom.us/j/98622767991?pwd=eDBmRTdQd0N3ZWVsbjJ5T1hWdmk2UT09</a> Meeting
ID: 986 2276 7991 Passcode: 287940</div>
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<div class="ContentPasted0">Location: JCL 298</div>
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<div class="ContentPasted0">M.S. Paper Title: Rapid Prediction of Full Spin Systems using Uncertainty-Aware Machine Learning</div>
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<div class="ContentPasted0">Abstract: Accurate simulation of solution NMR spectra requires knowledge of all chemical shift and scalar coupling parameters, traditionally accomplished by heuristic-based techniques or ab initio computational chemistry methods.
Here we present a novel machine learning technique which combines uncertainty-aware deep learning with rapid estimates of conformational geometries to generate Full Spin System Predictions with UnCertainty (FullSSPrUCe). We improve on previous state of the
art in accuracy on chemical shift values and are able to predict all scalar coupling values, unlike previous GNN models. Our uncertainty quantification shows a strong, useful correlation with accuracy, with the most confident predictions having significantly
reduced error. We can also correctly handle stereoisomerism and intelligently augment experimental data with ab initio data through disagreement regularization to account for deficiencies in training data.</div>
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<div class="ContentPasted0">Advisors: Eric Jonas</div>
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<div class="ContentPasted0">Committee Members: Ian Foster, Eric Jonas, and Rebecca Willett</div>
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