Michael A. Alcorn
2024-04-09: "Paved2Paradise: Cost-Effective and Scalable LiDAR Simulation by Factoring the Real World" was accepted to the Synthetic Data for Computer Vision workshop at CVPR 2024!
2023-12-04: Our manuscript "Paved2Paradise: Cost-Effective and Scalable LiDAR Simulation by Factoring the Real World" is now available on arXiv! The accompanying code can be found here.
2023-09-13: My Data Science Society at Auburn University slides can be found here.
2023-01-30: I will be joining Bear Flag Robotics as a Senior CV/ML Engineer!
2023-01-23: My manuscript "AQuaMaM: An Autoregressive, Quaternion Manifold Model for Rapidly Estimating Complex SO(3) Distributions" is now available on arXiv! The accompanying code can be found here.
2022-12-12: My AGU 2022 slides can be found here.
2022-11-14: Daniel Ginn presented our poster "Species-Level Weed Biomass Estimation from Video Imagery Using 3D Point Clouds" at the 2022 ASA, CSSA, SSSA International Annual Meeting!
2022-10-19: My Long-Term Agroecosystem Research Grazinglands Working Group Annual Meeting slides can be found here (not yet publicly available).
2022-10-04: Our abstract "Modeling the Spread of a Livestock Disease With Semi-Supervised Spatiotemporal Deep Neural Networks" was accepted to this year's AGU Fall Meeting!
2022-08-29: The slides for my SCINet Geospatial Workshop lightning talk can be found here (not yet publicly available).
2022-06-08: The slides for my deep learning mini workshop for the Charleston Data Science Meetup can be found here.
2022-02-01: Our poster "A Deep Learning Approach for Estimating the Impact of Cover Crops on Water Availability in Soil" was accepted to Envisioning 2050 in the Southeast: AI-Driven Innovations in Agriculture!
2021-11-09: My SCINet and AI COE Fellows Conference slides can be found here (not yet publicly available).
2021-09-09: The slides for my deep learning mini workshop for the Data Science Society at Auburn University can be found here.
2021-07-15: I successfully defended my dissertation and I now have a Ph.D.! The slides for my defense can be found here.
2021-06-15: Our manuscript "The DEformer: An Order-Agnostic Distribution Estimating Transformer" was accepted to INNF+ 2021! The accompanying code can be found here.
2021-05-07: I'm officially a Ph.D. candidate!
2021-04-26: Our manuscript "baller2vec++: A Look-Ahead Multi-Entity Transformer For Modeling Coordinated Agents" is now available on arXiv! The accompanying code can be found here.
2021-03-11: My PyData Jeddah Meetup slides can be found here.
2021-03-29: My Auburn Research Student Symposium slides can be found here.
2021-03-11: My Statistics and Data Science Seminar slides can be found here.
2021-02-08: Our manuscript "baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotemporal Modeling" is now available on arXiv! The accompanying code can be found here.
2020-12-07: I've officially completed the course requirements for the Graduate Minor in Mathematics—and with a 4.0!
2020-09-16: "A Cost-Effective Method for Improving and Re-Purposing Large, Pre-Trained GANs by Fine-Tuning Their Class-Embeddings" was accepted to ACCV 2020! We were awarded the Huawei Best Application Paper Honorable Mention!
2020-07-07: The slides for my deep learning mini workshop for the Cleveland Indians R&D team can be found here.
2020-04-29: I will be joining the Cleveland Indians as a Deep Learning Fellow this summer!
2020-03-01: Our manuscript "A Cost-Effective Method for Improving and Re-Purposing Large, Pre-Trained GANs by Fine-Tuning Their Class-Embeddings" is now available on arXiv! The accompanying code can be found here.
2019-04-09: My Auburn Research Student Symposium slides can be found here. I received the Second Place Award for Oral Presentation in Science, Technology, Engineering and Mathematics!
2019-03-19: My Finish in 5 slides can be found here.
2019-03-11: "Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects" was accepted to CVPR 2019!
2019-02-11: I will be joining Adobe as a Machine Learning Intern this summer!
2018-11-29: Our manuscript "Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects" is now available on arXiv! Be sure to check out the companion tool, which allows users to generate their own adversarial poses of objects.
2018-07-20: I will be starting a Ph.D. in Machine Learning at Auburn University this fall!
2018-02-10: My paper "(batter|pitcher)2vec: Statistic-Free Talent Modeling With Neural Player Embeddings" was selected as a finalist for the Research Papers Competition at the 2018 MIT Sloan Sports Analytics Conference! The accompanying code can be found here. I made it to the finals and finished in third place! My slides can be found here.
