Kwame Porter Robinson is a tireless, creative problem solver with a variety research interests, including human interaction with artificial intelligence (HAII), applied HAII generative economics and applied machine learning. He has a background in electrical engineering, machine learning, and architecting computational systems.
As a researcher, he studies collaboratively designed AI systems at the intersection of community, economics and empowerment. This intersection creates natural sites for quasi-experiments, simulation, and systems building. His primary investigative techniques are quasi-experimental, experimental and simulation based. He largely uses quantitative and mixed-methods designs along with novel system development. Before academia he worked for several years in industry as CEO, research engineer, data scientist and machine learning engineer for both government and private corporations. Kwame is a Ph.D. student at the University of Michigan in the School of Information. He is advised by Dr. Robert and Dr. Eglash.
Robinson, K.P., Robert, L.P. Eglash, R. (2021). “Extrapolating significance of text-based autonomous vehicle scenarios to multimedia scenarios and implications for user-centered design”, RO-MAN 2021, 10.7302/1691
Robinson, K.P., Eglash, R., Bennett, A., Nandakumar, S. and Robert, L.P. (2020). “Authente-Kente: Enabling Authentication for Artisanal Economies with Deep Learning”, AI & Society, 10.13140/RG.2.2.27020.95362/2
Ron Eglash, Lionel P. Robert, Audrey Bennett, Kwame Porter Robinson, Michael L Lachney, William Babbitt. “Automation for the artisanal economy: enhancing the economic and environmental sustainability of crafting professions with human–machine collaboration”. September 2019. AI & Society. DOI: 10.1007/s00146-019-00915-w.
Ron Eglash, Lionel P. Robert, Audrey Bennett, Kwame Porter Robinson, Michael L Lachney, William Babbitt. “AI for a Generative Economy: The Role of Intelligent Systems in Sustaining Unalienated Labor, Environment, and Society”. August 2019. Conference: AAAI Fall 2019 Symposium on AI and Work At: Arlington, Virginia USA
Kwame Tacumah Porter-Robinson (Kwame Porter Robinson). “An Energy Efficient Rate Adaptive Distributed Source Coding Algorithm: RSWITCH”. December 2012. unpublished.
- RO-MAN 2021 -
Extrapolating significance of text-based autonomous vehicle scenarios to multimedia scenarios and implications for user-centered design(8/10/2021)
- Rackham Merit Fellowship
- Rackham Graduate Student Research Grant, 2020 ($925)
- Rackham Conference Travel Grant, 2019 ($800)
- AWS Cloud Credits for Research, 2019 (~$1,000)
Prior to becoming a Ph.D. student at the University of Michigan, Kwame was lead data scientist at BrightHive where he designed scalable natural language processing systems and algorithms for workforce artificial intelligence applications, including unstructured taxonomy matching and multi-level semantic similarity. In 2015 Kwame created and led a data science consultancy that served a variety of private and public organizations, including the WKKF Foundation and the World Bank. Additionally, Kwame has worked on classified projects spanning data science, cyber security and telecommunications research for the Department of Defense.
Kwame holds a master’s degree in Computer Science (University of Maryland, Baltimore County), with a thesis on Slepian-Wolf probabilistic source code correlation, a Bachelor’s degree in Electrical Engineering (New Mexico State University), with a specialization in control systems and a Bachelor’s of Fine Art (Boston University). Kwame is a Ph.D. student at the University of Michigan.
- IEEE Transactions on Technology and Society
- AIS Transactions on Human-Computer Interaction
- New Media and Society Journal
- CHI’20, April 25–30, 2020
Graduate Student Instructor Courses
- SI 671/721: Data Mining: Methods and Applications - (98 students) Automatic, robust, and intelligent data mining techniques have become essential tools to handle heterogeneous, noisy, nontraditional, and large-scale data sets. This is a doctoral seminar course of advanced topics in data mining. The course provides an overview of recent research topics in the field of data mining, the state-of-the-art methods to analyze different genres of information, and the applications to many real world problems.
- SI 699: Big Data Analytics - (34 students) The big data analytics mastery course will require students to demonstrate mastery of data collection,processing, analysis, visualization, and prediction. To develop these skills students will work onsemester-long projects that deal with large or industry-scale data sets, and solve real-world problems.Aligned with best industry practices, students will be expected to work in a fast-paced, collaborativeenvironment, while demonstrating independence and leadership. Students must be able to create and usetools to handle very large transactional, text, network, behavioral, and/or multimedia data sets.
Feel free to reach out to Kwame at firstname.lastname@example.org and he welcomes focused collaboration across a variety of disciplines.