Christopher Homan
Associate Professor
Department of Computer Science
Golisano College of Computing and Information Sciences
5857196835
Office Hours
Tuesday 3:15--15pm Thursday 11am--12pm
Office Location
Christopher Homan
Associate Professor
Department of Computer Science
Golisano College of Computing and Information Sciences
Education
AB in Mathematics, Cornell University; MS, Ph.D. in Computer Science, University of Rochester
Bio
Christopher M. Homan's research centers around novel machine learning algorithms to enable qualitative methods such as grounded theory to study human behavior on scales that were previously unimaginable, through (1) close, participatory collaboration, with public and community health experts on issues such as intimate partner violence, suicide and depression, and labor, with a special focus on under-resourced and hidden communities; he is particularly interested in computer-based linguistic and network analysis, and has published in top conferences devoted to both modalities; (2) investigating basic questions in machine learning and data science raised by his interdisciplinary activities.
5857196835
Areas of Expertise
Artificial Intelligence
Natural language processing
Data science
Machine learning
Medical informatics
Theory
Select Scholarship
Published Conference Proceedings
Aroyo, Lora, et al. "Diversity in Conversational AI Evaluation for Safety." Proceedings of the Neurips. Ed. Alice Oh, Tristan Naumann. New Orleans, LA: n.p., 2024. Web.
Spann, James, Pratik Sanjay Bongale, and Christopher M Homan. "(Un) certainty selection methods for Active Learning on Label Distributions." Proceedings of the OPT 2023: Optimization for Machine Learning. Ed. Cristóbal Guzmán. New Orleans, LA: n.p., 2024. Web.
Weerasooriya, Tharindu Cyril, et al. "Subjective Crowd Disagreements for Subjective Data: Uncovering Meaningful CrowdOpinion with Population-level Learning." Proceedings of the ACL. Ed. Yang Liu. Toronto, CA: CA, 2023. Web.
Wein, Shira, et al. "Follow the leader (board) with confidence: Estimating p-values from a single test set with item and response variance." Proceedings of the ACL Findings. Ed. Yang Liu. Toronto, CA: n.p., 2023. Web.
White, Ann Marie, et al. "Does Reciprocal Gratefulness in Twitter Predict Neighborhood Safety?: Comparing 911 Calls Where Users Reside or Use Social Media." Proceedings of the International AAAI Conference on Web and Social Media (ICWSM 2018) Palo Alto, CA, June 25-28. Ed. N/A. Palo Alto, CA: n.p., Print.
Tornblad, McKenna, et al. "Sensing and Learning Human Annotators Engaged in Narrative Sensemaking." Proceedings of the Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, New Orleans, LA, June 1-June 6. Ed. N/A. N/A, N/A: Association for Computational Linguistics, 2018. Web.
Terkik, A, et al. "Analyzing Gender Bias in Student Evaluations." Proceedings of the Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics. Ed. COLING. Osaka, Japan: The COLING 2016 Organizing Committee, 2016. Web.
Liu, Tong, et al. "Understanding Discourse on Work and Job-Related Well-Being in Public Social." Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics Berlin, Germany. Ed. ACL. Berlin, Germany: n.p., 2016. Web.
Cheng, Qijin, Tong Liu, and Christopher M. Homan. "Changes in Online Discourse of Suicide and Prevention After Robin Williams’ Death." Proceedings of the Asia Pacific Regional Conference of the International Association for Suicide Prevention. Ed. Unknown. Tokyo, Japan: n.p., Web.
Lee, Sang Won, et al. "Creating Interactive Behaviors in Early Sketch by Recording and Remixing Demonstrations." Proceedings of the Human Computation. Ed. Unknown. Québec City, Canada: HCOMP, Web.
Oak, Mayuresh, et al. "Generating Clinically Relevant Texts: A Case Study on Life-Changing Events." Proceedings of the Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Denver, Colorado: NAACL HLT, 2015. Web.
Schrading, Nicolas, et al. "#WhyIStayed, #WhyILeft: Microblogging to Make Sense of Domestic Abuse." Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT). Ed. Rada Mihalcea, Joyce Yue Chai, and Anoop Sarkar. Denver, CO: n.p., Web.
Morabito, Patrick N., et al. "Agent-Based Models of Copycat Suicide." Proceedings of the Social Computing, Behavioral-Cultural Modeling, and Prediction. Ed. Nitin Agarwal, Kevin Xu, and Nathaniel Osgood. Washington, DC: Springer, Web.
Schrading, Nicolas, et al. "An Analysis of Domestic Abuse Discourse on Reddit." Proceedings of the 2015 Conference on Empirical Methods for Natural Language Processing. Ed. Daniele Pighin and Yuval Marton. Lisbon, Portugal: n.p., Web.
Cui, Biru, Shanchieh Jay Yang, and Christopher M. Homan. "Non-Independent Cascade Formation: Temporal and Spatial Effects." Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, Shanghai 2014. Ed. Jianzhong Li and X. Sean Wang. New York, New York: ACM, Print.
Lasecki, Walter S, Christopher M. Homan, and Jeffrey Bigham. "Tuning the Diversity of Open-Ended Responses From the Crowd." Proceedings of the Second AAAI Conference on Human Computation and Crowdsourcing. Ed. Jeffrey P. Bigham and David C. Parkes. Pittsburgh, PA: AAAI, Web.
Homan, Christopher M., et al. "Toward Macro-Insights for Suicide Prevention: Analyzing Fine-Grained Distress at Scale." Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. Ed. Philip Resnik, Rebecca Resnik, and Margaret Mitchell. Stroudsburgh, PA: ACL, 2014. Print.
Sadilek, Adam, et al. "Modeling Fine-Grained Dynamics of Mood at Scales." Proceedings of the WSDM 2014 Workshop on Diffusion Networks and Cascade Analytics. Ed. Peng Cui, et al. New York, NY: n.p., Web.
Homan, Christopher M., et al. "Social Structure and Depression in TrevorSpace." Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing. Ed. Susan Fussell and Wayne Lutters. New York, NY: ACM, 2014. Print.
Homan, C., V. Silenzio, and R. Sell. "Respondent-driven Sampling in Online Social Networks." Proceedings of the Social Computing, Behavioral-Cultural Modeling and Prediction. Ed. Ariel M. Greenberg, William G. Kennedy, and Nathan D. Bos. Washington, DC: Springer, 2013. Print.
Journal Paper
Lytle, Megan C., et al. "Suicidal and Help-Seeking Behaviors Among Youth in an Online Lesbian, Gay, Bisexual, Transgender, Queer, and Questioning Social Network." Journal of Homosexuality 65. 13 (2018): 1916– 933. Print.
Homan, Christopher M. and Sven Kosub. "Dichotomy results for fixed point counting in boolean dynamical systems." Theoretical Computer Science 573. (2015): 16—25. Print.
DiFonzo, Nicholas, et al. "Network Structure Moderates Intergroup Differentiation of Stereotyped Rumors." Social Cognition 32. 5 (2014): 409-448. Print.
Lasecki, Walter S., Christopher M. Homan, and Jeffrey P. Bigham. "Architecting Real-Time Crowd-Powered Systems." Human Computation 1. 1 (2014): 67-93. Web.
DiFonzo, N., et al. "Rumor Clustering, Consensus, and Polarization: Dynamic Social Impact and Self-Organization of Hearsay." Journal of Experimental Social Psychology 49. 3 (2013): 378-399. Print.
Invited Keynote/Presentation
Lytle, Megan C., et al. "Suicidal and Help-Seeking Behaviors Among an Online Lesbian, Gay, Bisexual, and Transgender Social Network." 47 Annual Conference of the American Association of Suicidology. American Association of Suicidology. Los Angeles, CA. 9 Apr. 2014. Conference Presentation.
Book Chapter
Homan, C. and V. Silenzio. "Social Networking Services and Analysis." Social Net-working: Recent Trends, Emerging Issues and Future Outlook. Hauppauge, NY: Nova Science Publishers, 2013. 74-100. Print.
Currently Teaching
CSCI-331
Introduction to Artificial Intelligence
3 Credits
An introduction to the theories and algorithms used to create artificial intelligence (AI) systems. Topics include search algorithms, logic, planning, machine learning, and applications from areas such as computer vision, robotics, and natural language processing. Programming assignments are an integral part of the course.
CSCI-464
Xtreme Theory
3 Credits
A fast paced, informal look at current trends in the theory of computing. Each week is dedicated to a different topic and will explore some of the underlying theory as well as the practical applications of the theory. Sample topics may include: quantum cryptography, networks and complex systems, social welfare and game theory, zero knowledge protocols. Students will be evaluated on homework assignments and a final presentation. Offered every other year.