If you are trying to enroll in COGS1001 but the class is full, please read this before emailing me or Dr. Yang!
If you are a COGS major/minor or a student in COGS1001 trying to meet with me, and my office hours below do not work for you (please try these first!), you can find a time to meet with me here.
If you need to correspond with me about COGS adivisng (e.g., majoring/minoring in COGS), please use cogs-ad@sas.upenn.edu rather than my personal or Penn emails.
If you want me to write a (good!) letter of rec for you, we should have had more interaction than simply taking COGS 1001 with me. For example, if you took COGS 1001 with me, and you came to my office hours regularly, and you are my major advisee, then I can potentially write you a letter.
My Zoom room.
Fall 2024 office hours
COGS 1001 In person: Tuesday 9:30am to 11am in Goddard 203. Virtual: Wednesday 10:00am to 11:30am on Zoom. COGS Major Advising In person: Tuesday 11am to 12:30pm in Goddard 203. Virtual: Wednesday 11:30am to 1:00pm on Zoom.
I am the Associate Director of Cognitive Science and MindCORE at the University of Pennsylvania. Previously (2021-2022), I was a a data scientist in the Tsui Lab at the Children's Hospital of Philadelphia. Before that, (2018-2021) I was a post-doc with Sudeep Bhatia in the Computational Behavioral Science Lab of the Department of Psychology at the University of Pennsylvania. In 2017, I received my PhD in Developmental Psychology at the University of Connecticut. There I primarily worked in the Language Creation Lab under the mentorship of Marie Coppola, and in the SoLab under Whit Tabor. I was also a fellow in the Language Plasticity -- Genes, Brain, Cognition, & Computation IGERT (which is now the Neurobiology of Language program), and an affiliate of the CT Institute for the Brain and Cognitive Sciences.
My interests and work have changed a lot over the years, but the common thread is using data science, machine learning, and natural language processing tools for social, cognitive, behavioral, biomedical, and geospatial problems. See my papers below for more!
Here's a link to my vita.
Han, S., Richie, R., Shi, L., Tsui, F.R. (in press). Automated Matchmaking of Researcher Biosketches and Funder Requests for Proposals using Deep Neural Networks. IEEE Access
Richie, R.. (unpublished manuscript). Using aerial parcel imagery to predict single family home sale prices. This is the final paper I wrote for MUSA 6500: Remote Sensing in Spring 2024.
Richie, R., Ajmal, N.*, & Hebart, M. (2024). Unrealized promise of joint modeling of choice and reaction time in improving representation learning. 46th Annual Meeting of the Cognitive Science Society. *Student collaborator
He, L.*, Richie, R.*, & Bhatia, S. (2024). Limitations to optimal search in naturalistic active learning. Journal of Experimental Psychology: General., 153(5), 1165-1188. *Equal contribution
Richie, R.*, Ruiz, V.*, Han, S., Shi, L., Tsui, F.R. (2023).
Extracting social determinants of health events with transformer-based multitask, multilabel named entity recognition.
Journal of the American Medical Informatics Association. *Equal contribution
Han, S., Shi, L., Richie, R., Tsui, F.R. (2022). Building Siamese attention-augmented-recurrent convolutional neural networks for document similarity scoring. Information Sciences, 615, 90-102.
Richie, R., Aka, A., & Bhatia, S. (2022). Free association in bidirectional memory networks. Psychological Review.
Bhatia, S., & Richie, R. (2022). Transformer networks of human concept knowledge. Psychological Review. PsyArXiv preprint.
Richie., R., Grover, S., Tsui, F.R. (2022). Inter-annotator agreement is not the ceiling of machine learning performance: Evidence from a comprehensive set of simulations. In Proceedings of the 21st Workshop on Biomedical Language Processing (pp. 275–284). Association for Computational Linguistics. Preprint
Han, S., Zhang, R.F., Shi, L., Richie, R., Liu, H., Tseng, A., Quan, W., Ryan, N., Brent, D., & Tsui, F.R. (2022). Classifying social determinants of health from unstructured electronic health records using deep learning-based natural language processing. Journal of Biomedical Informatics, 103984.
Singh, M.*, Richie, R.*, & Bhatia, S. (2022). Representing and predicting everyday behavior. Computational Brain & Behavior, 5.(1), 1-21.PsyArXiv preprint. *Equal contribution
Zhao, W.J., Richie, R., & Bhatia, S. (2022). Process and content in decisions from memory. Psychological Review, 129(1), 73–106. PsyArXiv preprint
Richie, R., & Bhatia, S. (2022). Knowledge in everyday judgment and decision making: An introduction to the distributed semantics approach. In R. Boyd & M. Dehghani (Eds.) The Atlas of Language Analysis in Psychology. Guilford Press. PsyArXiv preprint.
Richie, R., & Bhatia, S. (2021). Similarity judgment within and across categories: A comprehensive model comparison. Cognitive Science, 45(8), e13030 PsyArXiv preprint.
Richie, R., & Verheyen, S. (2020). Using cross-validation to select dimensionality in multidimensional scaling. ICCM extended abstract.
Richie, R.*, Hall, M.*, Cho, P.W., & Coppola, M. (2020). Converging evidence: Enhanced conventionalization of gestural referring expressions in richly-connected networks. Language Dynamics and Change, 10(2), 259-290. PsyArXiv preprint. *Equal contribution
Richie, R., White, B., Hout, M.C., & Bhatia, S. (2020). The spatial arrangement method of measuring similarity can capture high-dimensional, semantic structures. Behavior Research Methods, 52, 1906–1928. PsyArXiv preprint.
Giovannone, N., Fitzroy, A. B., Richie, R., Jasińska, K., Wood, S., Landi, N., Coppola, M. & Breen, M. (unpublished). Prosodic phrase boundary processing in native signers of American Sign Language. Slides from ETAP 2019.
Richie, R., Zou, W., & Bhatia, S, (2019). Predicting High-Level Human Judgment Across Diverse Behavioral Domains. Collabra: Psychology, 5(1), 50. PsyArXiv preprint.
Bhatia, S., Richie, R., & Zou, W. (2019). Distributed semantic representations for modeling human judgment. Current Opinion in Behavioral Sciences, 29, 31-36.
Richie, R. (2016). Functionalism in the lexicon: Where is it, and how did it get there? In G. Jarema, G. Libben, & V. Kuperman (Eds.), Thematic Issue of The Mental Lexicon: New Questions for the Next Decade, 11(3), 429–466. Amsterdam: John Benjamins. doi 10.1075/ml.11.3.05ric. PsyArXiv preprint.
Richie, R., Yang, C., & Coppola, M. (2014). Modeling the emergence of natural language lexicons in homesign systems. Topics in Cognitive Science, 6(1), 183-195.
Roseberry, S., Richie, R., Hirsh-Pasek, K., Golinkoff, R.M., Shipley, T. (2011). Babies catch a break: 7- to 9-month-olds track transitional probabilities in continuous dynamic events. Psychological Science, 22(11), 1422-1424.
I am on the steering committee for 5th Square, Philadelphia's urbanist PAC. Check them out if you want to make Philadelphia more sustainable, equitable, and accessible.
I am also on the board of The Robert Schalkenbach Foundation, a foundation dedicated to Georgist economic ideas like land value taxation. Check them out if you are interested in a more just and productive society!
Our cats