Porter Jenkins
Short Bio Timeline News Research CV Contact
Porter Jenkins

AI Scientist

Short Bio

I am an Assistant Professor in the Computer Science department at Brigham Young University. I completed my PhD student at Penn State University studying machine learning and data mining under the supervision of Jessie Li . I graduated from Brigham Young University with a Bachelor's degree in Statistics.
My current research interest lies in real-world artificial intelligence (AI). Specifically, my research studies physical perception [WACV'23], tasks plagued by small data [AAAI'22, IJCAI'21, CIKM'19, GeoSim'21], and problems where the cost of an error made by an AI system is high [AAAI’24]. The latter typically involves uncertainty quantification of some kind. Previously, I have studied important problems in recommender systems [AAAI'21, WWW'20].

News

- [2023-08] I served as a reviewer for WACV'25

- [2023-07] I served as a reviewer for NeurIPS'24

- [2023-12] Excited to announce one paper was accepted to AAAI'24

- [2023-11] I served as PC member for the WebConf'24

- [2023-04] I served as a reviewer for Collaborative Robot (COBOT)

- [2022-10] Excited to announce one paper was accepted to WACV'23

- [2022-04] Selected as PC member for KDD'22

- [2021-12] Served as a reviewer for IEEE Transactions on Pattern Analysis and Machine Intelligence

- [2021-11] Excited to announce one paper was accepted to AAAI'22

- [2021-10] Excited to announce one paper was accepted to SIGSPATIAL GeoSim'21

- [2021-10] Selected as a PC member for SDM'22

- [2021-05] Excited to announce one paper was accepted at IJCAI'21

- [2021-04] I served as a PC member for KDD'21

- [2021-03] I will co-organize a workshop, CityBrain, at KDD'21

- [2021-03] I joined the Computer Science department at BYU

- [2020-12] I served as a reviewer for SDM'21

- [2020-12] Excited to announce one paper was accepted to AAAI'21

Timeline

Education

2016 - 2020
PhD Candidate
School: Penn State University - College of Information Sciences and Technology
Area of Research: Machine Learning and Data Mining
GPA: 3.71
2014
B.S. Statistics
School: Brigham Young University
Courses: Bayesian Statistics, Object-oriented Programming in C++, Nonparametric Statistics, Survival Analysis, Regression, Statistical Computing

Industry Experience

2019-Present
Consulting Research Scientist
Company: Delicious AI
Location: Lehi, UT
Description: Developing mobile-based machine learning algorithms to collect real-time product data (consumer packaged goods) from raw images and infrared sensors. We plan to also develop reinforcement learning algorithms to help retailers optimize product placement decisions.
2019
AI Research Intern
Company: Pinterest Labs
Location: San Francisco, CA
Description: Worked on the Content engineering team, one of the core ML groups at Pinterest. Deployed algorithm to generate natural langauge annotations for Pinterest boards. Performed research on neural language embedding imputation.
2018
Machine Learning Engineer (Intern)
Company: Domo
Location: American Fork, UT
DescriptionDeveloped and shipped Automated Insights, a recommendation system designed to automatically identify relevant statistical insights in tabular data.
2015
Data Scientist
Company: Visible Equity
Location: Salt Lake City, UT
Description: Worked on loan default modeling, customer purchase probability models, and an automobile valuation model.

Research

2023

Porter Jenkins, Kyle Armstrong, Stephen Nelson, Sidd Gotad, J. Stockton Jenkins, Wade Wilkey and Tanner Watts. CountNet3D: A 3D Computer Vision Approach to Infer Counts of Occluded Objects. 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023.

2022

Porter Jenkins, Hua Wei, Stockton Jenkins, Zhenhui Li. 2022. Bayesian Model-based Offline Reinforcement Learning for Product Allocation. Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022 [PDF].

2021

Porter Jenkins, Hua Wei, J. Stockton Jenkins, and Zhenhui Li. 2021. Probabilistic Simulation of Spatial Demand for Intelligent Product Allocation. In Proceedings of 4th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation , Beijing, China, November 2, 2021 (GeoSim’21), 10 pages [PDF].

Guanjie Zheng, Porter Jenkins, Yanyan Xu, and Dongyao Chen. 2021. Overview of the 1st Workshop on City Brain Research. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD '21)

Guanjie Zheng, Chang Li, Hua Wei, Porter Jenkins, Chacha Chen, Tao Wen, Zhenhui Li. Knowledge-based Residual Learning, In Proceedings of 30th International Joint Conference on Artificial Intelligence (IJCAI-21). Montreal, Canada, 2021 [PDF].

Porter Jenkins, J. Stockton Jenkins, Ahmad Farag, Huaxiu Yao, Suhang Wang, Zhenhui Li. Neural Utility Functions, In Proceedings of the Thirty-fifth AAAI Conference (AAAI 2021). Virtual, 2021 [PDF].

2020

Porter Jenkins, Jennifer Zhao, Heath Vinicombe and Anant Subramanian. Natural Language Annotations for Search Enginge Optimization, In Proceedings of the 2020 World Wide Web Conference (WWW 2020). Taipei, Taiwan April 20-24, 2020 [PDF].

Porter Jenkins. Structured Paragraph Embeddings of Financial Earnings Calls. Companion Proceedings of the 2020 World Wide Web Conference (WWW 2020). Taipei, Taiwan April 20-24, 2020 [PDF].

Porter Jenkins, Hua Wei, Stockton Jenkins and Zhenhui Li. A Probabilistic Simulator of Spatial Demand for Product Allocation, In Proceedings of the Thirty-fourth AAAI Conference (AAAI 2020) workshop on Intelligent Process Automation, New York, NY, Feb. 2020. [PDF]

2019

Porter Jenkins, Ahmad Farag, Suhang Wang and Zhenhui Li. Unsupervised Representation Learning of Spatial Data via Multimodal Embedding. In Proceedings of The 28th ACM International Conference on Information and Knowledge Management, Beijing, China, November 3–7, 2019 (CIKM ’19), 10 pages. [PDF]

Porter Jenkins. ClickGraph: Web Page Embedding using Clickstream Data for Multi-task Learning. Companion Proceedings of the 2019 World Wide Web Conference, San Francisco, CA, USA, May 13–17, 2019 (WWW ’19 Companion). [PDF]

Hongjian Wang, Porter Jenkins, Hua Wei, Fei Wu, and Zhenhui Li. 2019. Learning Task-Specific City Region Partition. In Proceedings of the 2019 World Wide Web Conference (WWW ’19), May 13–17, 2019, San Francisco, CA, USA. ACM, New York, NY, USA. [PDF]

Contact

pjenkins@.cs.byu.edu

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