Machine Learning, Data Analysis, Python, C++, Stats

J. Yin **********@*****.*** • 585-***-****

Education

HARVARD UNIVERSITY CAMBRIDGE, MA

Doctor of Philosophy, Physics May 2016 – May 2021(expected) Master of Physics May 2019

UNIVERSITY OF ROCHESTER ROCHESTER, NY

Bachelor of Science (Magna Cum Laude), Physics May 2016 Bachelor of Science (Magna Cum Laude), Applied Mathematics May 2016 Relevant Technical Skills:

• Extensive experience with programming in C++, Python, MatLab, Mathematica, and Igor Pro.

• Extensive experience with supervised and unsupervised machine learning techniques (CNNs, RNNs, VAEs, GANs, Reinforcement learning, etc.)

• Extensive experience with computer vision tasks.

• Extensive experience with data analysis of large dataset.

• Extensive experience with statistical modeling.

• Extensive experience with High Performance Computing (HPC). Other Technical Skills:

• Working knowledge of computer networking (including routers, switches, and firewalls).

• Experience with installation and administration of LINUX.

• Experience with analog and digital signal processing and signal transmission. Honor Societies and Awards

• Recipient of a Joseph P. O’Hern Scholarship for Travel and Study in Europe.

• Elected to Phi Beta Kappa, America’s most prestigious academic honor society.

• Inducted into Sigma Pi Sigma, the Honor Society of the Society of Physics Students.

• Member of the Golden Key International Honor Society for overall academic achievements. Publications

(see also http://arxiv.org/a/yin_j_2.html and http://orcid.org/0000-0002-2047-206X):

• A Conditional Autoencoder for galaxy deblending, J. Yin et. al, in preparation. (2021)

• Active Optical Control with Machine Learning: A Proof of Concept for the Vera C. Rubin Observatory, J. Yin et. al, accepted by Astronomical Journal. (2021)

• A Conditional Autoencoder for Galaxy Photometric Parameter Estimation, J. Yin et. al, submitted to Astronomical Journal. (2021)

• Projected WIMP sensitivity of the LUX-ZEPLIN (LZ) dark matter experiment, D. S. Akerib et. al., Phys. Rev. D 101, 052002, https://arxiv.org/abs/1802.06039, (2020).

• A Closer Look at Disentangling in β-VAE, Harshvardhan Sikka, Weishun Zhong, Jun Yin, and Cengiz Pehlevan, Presented at the 53rd Asilomar Conference on Signals, Systems, and Computers, https://arxiv.org/abs/1912.05127, (2019).

• The LUX-ZEPLIN (LZ) experiment, D. S. Akerib et. al., Nucl. Instr. and Methods 953 (2020) 11, https://arxiv.org/abs/1910.09124.

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J. Yin **********@*****.*** • 585-***-****

• Identification of Radiopure Titanium for the LZ Dark Matter Experiment and Future Rare Event Searches, D. S. Akerib et. al., Astroparticle Physic 96 (2017) 1–10, http://arxiv.org/abs/ 1702.02646.

• LUX-ZEPLIN (LZ) Technical Design Report, B. J. Mount et. al., http://arxiv.org/abs/1703.09144

(2017).

• The Dynamic Range of LZ, J. Yin for the LZ Collaboration, Proceedings of the Light Detection in Noble Elements (LIDINE 2015) conference, Journal of Instrumentation, 11, C02054, http:// arxiv.org/abs/1511.00260 (2016).

• The Data Acquisition System for LZ, E. Druszkiewicz for the LZ Collaboration, Proceedings of the Light Detection in Noble Elements (LIDINE 2015) conference, Journal of Instrumentation, 11, C02072, http://arxiv.org/abs/1511.08385 (2016).

• Signal Processing and Electronic Noise in LZ, D. Khaitan for the LZ Collaboration, Proceedings of the Light Detection in Noble Elements (LIDINE 2015) conference, Journal of Instrumentation, 11, C03029, http://arxiv.org/abs/1511.07752 (2016).

• FPGA-based Trigger System for the LUX Dark Matter Experiment, D. S. Akerib et. al., Nuclear Instruments and Methods A818 (2016) 57–67, http://arxiv.org/abs/1511.03541.

• LUX-ZEPLIN (LZ) Conceptual Design Report, D. S. Akerib et. al., http://arxiv.org/abs/ 1509.02910 (2015).

• Expected Background in the LZ Experiment, Vitaly A. Kudryavtsev for the LZ Collaboration, AIP Conf. Proc. 1672 (2015) 060003, http://dx.doi.org/10.1063/1.4927991.

• Digital Electronics for Nuclear Physics Experiments, W. Skulski, D. Hunter, E. Druszkiewicz, D. Khaitan, J. Yin, F. Wolfs Abstract submitted to Digital Processing mini-symposium at the Fall meeting of the Division of Nuclear Physics, Santa Fe, NM (October 2015).

• High Performance Data Acquisition System for Underground Dark Matter Searches, W. Skulski, D. Hunter, E. Druszkiewicz, D. Khaitan, J. Yin, F. Wolfs, Abstract submitted to FRIB DAQ Working group workshop at Argonne National Laboratory (July 2015).

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Presentations:

• Active Optical Control with Machine Learning: A Proof of Concept for the Vera C. Rubin Observatory, Presentation at the 2020 NeurIPS Workshop on Machine Learning and the Physical Sciences. December 2020.

• Machine Leaning Determination of Wavefront Perturbations for LSST, Presentation at the 235th Annual Meeting of the American Astronomical Society. January 2020.

• Machine Leaning Determination of Wavefront Perturbations for LSST, Presentation at the 9th LSST Project and Community Workshop, August 2019.

• Machine Leaning Determination of Wavefront Perturbations for LSST, Presentation at the LSST@Asia Meeting, May 2019.

• The Dynamic Range of LZ, Presentation at the Fall Meeting of the Division of Nuclear Physics of the American Physical Society, October 2015.

• The Dynamic Range of LZ, Presentation at Light Detection in Noble Elements (LIDINE 2015), August 2015.

• Recovering from Saturation, Presentation at the Rochester Symposium for Physics Students at SUNY Oswego (RSPS 2015), April 2015.

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