ELI T. BROWN
College of Computing and Digital Media
Eli T. Brown is an Assistant Professor in the College of Computing and Digital Media (CDM) at DePaul University. He earned his B.A. from Cornell University in Computer Science and Math, and his Ph.D. and M.S. in Computer Science from Tufts University. His teaching is focused in the Data Science Program, where he teaches data visualization and machine learning. His research revolves around integrating the two disciplines together for more effective data analytics. He directs the Laboratory for Interactive Human-Computer Analytics (LIHCA; lihca.io), which develops new interactive machine learning technology to address the needs of collaborators in a variety of fields including biomedical, biotechnology and journalism.
My research is interdisciplinary, combining visualization and machine learning to create new ways for humans to use computers for analytics. Rather than create interfaces for directly controlling machine learning tools, I focus on implicit model-steering technology. We create an interface designed to support the human’sinteractive data needs, then use machine learning behind the scenes to support them. We aim to amplify the human effort, using their insight to drive the analytics process without requiring they engage with algorithms and parameter tuning. Note that in my research area, professors are expected to mentor students to build and evaluate techniques and systems, as well as write papers (as opposed to working directly on the software and writing). This type of work requires multiple skillsets: conceiving of a mechanism that connects human and machine, designing the interactions (possibly with a collaborator), figuring out how to adapt machine learning to address the user need, implementing the machine learning, running machine learning experiments to test
validity, running a server and implementing a back-end to run machine learning behind the scenes, and building the front-end interface itself with custom visual components. Students who can do this are difficult to find, however, I have published consistently and push for high quality venues. I am having an impact in my field, demonstrated by my over 600 citations and my current position on the Organizing Committee for the top conference in my field, IEEE VIS (CORE A).
PUBLICATIONS BY YEAR
Jiahao Deng, Eli T. Brown. RISSAD: Rule-based
Interactive Semi-Supervised Anomaly Detection. Eurovis 2021 Short Papers.
Fang Cao, Eli T. Brown. DRIL: Descriptive Rules through Interactive Learning. IEEE VIS 2020 Short Paper.
Priya Deshpande, Alex Rasin, J Son, E Brown, J Furst and DS Raicu. Ontology-based radiology teaching file summarization, coverage, and integration. Journal of digital imaging 33 (3), 797-813.
Priya Deshpande, Alexander Rasin, Fang Cao, Sriram Yarlagadda, Eli T. Brown, Jacob D. Furst, Daniela Stan Raicu. Multimodal Ranked Search over Integrated Repository of Radiology Data Sources. Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K). Volume 1, pages 372-383. 2019.
Xiaoying Pu, Matthew Kay, Michael Correll, Eli Brown. Unbiasing Visual Data Exploration in the Garden of Forking Paths. CHI 2019 Workshop on Human-Centered Study of Data Science Work Practices, 2019.
Fang Cao, David Scroggins, Lebna Thomas, Eli T. Brown. A Human-in-the-Loop Software Platform. Machine Learning from User Interaction for Visualization and Analytics, Workshop at IEEE VIS 2018, published by IEEE.
Eli T. Brown, Sriram Yarlagadda, Kristin Cook, Remco Chang, Alex Endert. ModelSpace: Visualizing the Trails of Data Models in Visual Analytics Systems. Machine Learning from User
Interaction for Visualization and Analytics, Workshop at IEEE VIS 2018, published by IEEE.
Priya Deshpande, Alexander Rasin, Eli T. Brown, Jacob Furst, Steven Montner, Samuel G. Armato III, Daniela Stan Raicu. Augmenting Medical Decision Making with Context Aware
Text Based Search of Radiology Teaching Files and Medical Ontologies. International Journal of Knowledge Discovery in Bioinformatics (IJKDB), 8(2), 18-43. 2018.
Priya Deshpande, Alexander Rasin, Eli T. Brown, Jacob Furst, Daniela S. Raicu, Steven M. Montner, and Samuel G. Armato III. Big Data Integration Case Study for Radiology Data Sources. In 2018 IEEE Life Sciences Conference (LSC), pp. 195-198. IEEE, 2018.
Emily Wall, Subhajit Das, Ravish Chawla, Bharath Kalidindi, Eli T. Brown and Alex Endert. Podium: Ranking Data Using Mixed-Initiative Visual Analytics. IEEE Transactions on Visualization and Computer Graphics, 24(1) 2018.
Priya Deshpande, Alexander Rasin, Eli T Brown, Jacob Furst, Daniela Stan Raicu. Augmenting Medical Decision Making With Context Aware Text Based Image Search: Integrated radiological Image Search (IRIS). Abstract in proceedings, RSNA 2017.
Priya Deshpande, Alexander Rasin, Eli T. Brown, Jacob Furst, Steven Montner, Samuel G. Armato III, Daniela S. Raicu. An Integrated Database and Smart Search Tool for Medical Knowledge Extraction from Radiology Teaching Files. The 1st Workshop on Medical Informatics and Healthcare (MIH) in the Proceedings of ACM KDD, 2017.
Bahador Saket, Hannah Kim, Eli T. Brown, Alex Endert. Visualization by Demonstration: An Interaction Paradigm for Visual Data Exploration. IEEE Transactions on Visualization and Computer Graphics, 23(1), 331-340. 2016.
Eli T. Brown, Remco Chang and Alex Endert. Human-Machine-Learner Interaction: The Best of Both Worlds. Human Centered Machine Learning Workshop at ACM CHI 2016.
Wang, Helen J., et al. The activity platform. 15th Workshop on Hot Topics in Operating Systems (HotOS XV). 2015.
Eli T. Brown, Alvitta Ottley, Jieqiong Zhao, Quan Lin, Richard Souvenir, Alex Endert and Remco Chang. Finding Waldo: Learning about Users from their Interactions. IEEE Transactions on Visualization and Computer Graphics, 20(12):1663-1672. 2014. CORE A. (Presented at VAST 2014).
Daniel Afergan, Evan M. Peck, Erin T. Solovey, Andrew Jenkins, Samuel W. Hincks, Eli T. Brown, Remco Chang, Robert J.K. Jacob. Dynamic Difficulty Using Brain Metrics of Workload. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), pages 3797-3806, ACM, 2014.
Eli T. Brown and Remco Chang. EigenSense: Saving User Effort with Active Metric Learning. KDD 2014 Workshop on Interactive Data Exploration and Analytics (IDEA), 2014.
Eli T. Brown, Jingjing Liu, Carla E. Brodley and Remco Chang. Dis-Function: Learning Distance Functions Interactively. In Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST), pages 83-92, IEEE, 2012.
Jingjing Liu, Eli T. Brown, Remco Chang. Find Distance Function, Hide Model Inference. Poster in Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST), pages 289-290, IEEE, 2011.