Chihua Ma


My name is Chihua Ma (马驰华), meaning a little horse running around China.

I am currently Director of Visual Analytics, Decision Sciences at Epsilon.

I received my PhD degree in Computer Science from UIC in 2018. My PhD research focused on visual analysis for dynamic, multi-scale and multi-run biological networks with multivariate features. My advisors were Dr. Robert Kenyon and Dr. G. Elisabeta (Liz) Marai.

My CV can be found here

San Francisco Muni Tracker
A web-based interface to show the real-time positions of San Francisco's buses and trains (SF Muni) using JavaScript (d3.js) and React.
SMARTer Therapy Explorer
A web-based visual therapy explorer that enables the systematic similarity-based exploration and analysis of individual factors in the patient repository to guide precision therapy.
Chihua Ma, Andrew Burks, Filippo Pellolio, and G. Elisabeta Marai
We present the design and evaluation of an integrated problem solving environment for cancer therapy analysis. The environment intertwines a statistical martingale model and a K Nearest Neighbor approach with visual encodings, including novel interactive nomograms, in order to compute and explain a patient's probability of survival as a function of similar patient results. A coordinated views paradigm enables exploration of the multivariate, heterogeneous and few-valued data from a large head and neck cancer repository. A visual scaffolding approach further enables users to build from familiar representations to unfamiliar ones.
A web-based visual analytics tool to explore and compare multiple dynamic brain activity networks with multivariate features
Chihua Ma, Filippo Pellolio, Robert Kenyon, and G. Elisabeta Marai
We introduce a web-based visual comparison approach for the systematic exploration of dynamic activation networks across biological datasets. Understanding the dynamics of such networks in the context of demographic factors like age is a fundamental problem in computational systems biology and neuroscience. We design visual encodings for the dynamic and community characteristics of these temporal networks. Our multi-scale approach blends nested mosaic matrices that capture temporal characteristics of the data, spatial views of the network data, Kiviat diagrams and mirror glyphs that detail the temporal behavior and community assignment of specific nodes. A top design specifically targeted at pairwise visual comparison further supports the comparative analysis of multiple dataset activations.
A web-based visualization to explore and compare probability spatio-temporal landscapes from multiple simulations
Chihua Ma, Andrew Burks, and G. Elisabeta Marai
We present a web-based visual analysis tool for the exploration of peak distributions over state space and simulation time in such stochastic networks, and the comparison of peak distributions between multiple simulations. Our approach combines multiple linked views to capture ensemble time-evolving probability landscapes. A peak trajectory cube provides users an overview of peak spatiotemporal distributions between six simulations. A peak projection map shows the exact peak locations of multiple simulations at the user selected time. At a more detailed level, users can inspect a particular state in the peak projection map to view for each simulation both the probability values over time, and the local probability landscape shapes. This information is displayed in a small multiple using two glyphs: profile glyphs and arrow glyphs. The arrow glyph indicates that a state is a peak when all the glyph eight arrows point towards the glyph center.
A web-based visualization of probability distributions in both time and state space of the stochastic gene regulatory networks
Chihua Ma, Timothy Liciani, and G. Elisabeta Marai
Visualizing the complex probability landscape of stochastic gene regulatory networks can further biologists' understanding of phenotypic behavior associated with specific genes. We present PRODIGEN (PRObability DIstribution of GEne Networks), a web-based visual analysis tool for the systematic exploration of probability distributions over simulation time and state space in such networks. PRODIGEN was designed in collaboration with bioinformaticians who research stochastic gene networks. The analysis tool combines in a novel way existing, expanded, and new visual encodings to capture the time-varying characteristics of probability distributions: spaghetti plots over one dimensional projection, heatmaps of distributions over 2D projections, enhanced with overlaid time curves to display temporal changes, and novel individual glyphs of state information corresponding to particular peaks.
Multi-scale Voronoi-based ACT Assessment
IEEE VGTC VPG International Data-Visualization Contest 2017 (Honorable Mention)
Timothy Luciani, Juan Trelles, Chihua Ma, Andrew Burks, Manu Mathew Thomas, Krishna Bharadwaj, Shiwangi Singh, Peter Hanula, Lei Li, and G. Elisabeta Marai
Our contest submission aimed to develop a static visual representation that shows how geographic and seasonal changes in the availability of the ACT test affects nearby or adjacent testing sites, by moving students or assessments, changing dates, or some other strategy. The Voronoi visualization (Top Middle) encodes test center distribution at regional level (Illinois) by partitioning each region based on distances to test centers. The Voronoi cell intensity is mapped to Assigned/Capacity; the darker the cell, the higher demand in that region.
Spatial Analysis of Employee Safety Using Organizable Event Quiltmaps
Temporal & Sequential Event Analysis
Dennis McNamara, Jacqueline Tapia, Chihua Ma, Timothy Luciani, Andrew Burks, Juan Trelles, and G. Elisabeta Marai
We present a web-based visualization for the analysis of event-based movement of individuals within a building, as well as for the observation of groups, patterns, and outliers in a population of employees. The visualization comprises a novel organizable event quiltmap, and a 3D spatial heatmap. The quiltmap can be used to cluster similar event trajectories based on a spatial metric. The 3D view reveals employee movements throughout a day in the context of building sensor data. A small multiple display supports the browsing of multiple intervals, and individual event sequences can also be compared for the same employee across multiple days through individual quiltmaps.
An interactive multi-view visualization to explore neuron behaviors within dynamic brain activity networks
Chihua Ma, Angus Forbus, and Robert Kenyon
Study of the behavior of individual members in communities of dynamic networks can help neuroscientists to understand how interactions between neurons in brain networks change over time. Visualization of those temporal features is challenging, especially for networks embedded within spatial structures, such as brain networks. We present the design of SwordPlots, an interactive multi-view visualization system to assist neuroscientists in their exploration of dynamic brain networks from multiple perspectives. Our visualization helps neuroscientists to understand how the functional behavior of the brain changes over time, how such behaviors are related to the spatial structure of the brain, and how communities of neurons with similar functionality evolve over time.
An interactive multi-view visualization tool using Processing & Java to explore the effects of GC on latency and variability in financial trades with an exchange
Chihua Ma, Stanislav Liberman, and Haifeng Zheng
We proposes a method that creates a multi-view interactive visualization that allows users to explore connections between garbage collection (GC) generated by Java Virtual Machine (JVM) and latency in applications used in financial transactions. With this tool users can explore large collections of GC and latency events, easily identify important events, and subsequently focus on the relationships and details of such events without losing the “big picture” perspective on the events as a whole. We discuss the impact of this tool on controlling the effects of GC on latency and variability in financial trades with an exchange.
Weather Station
Class project
Chihua Ma
The goal is to design and implement a weather station, connected to the Internet, wall mounted in a public space.
Titan Design
Class project
Victor Mateevitsi, Thomas Marrinan, Chihua Ma, and BalaPrasath Rajan
The goal is to design an interface to large central display in the kitchen, that a family commonly use everyday. It acts as a central point to various activities: kitchen related (cooking, groceries, ...), day planner, commuting (public transport, traffic, ...), news, etc.
Electric Avenue
Class project
Chihua Ma
This visualization is used to investigate how the global electric data change over time, see how a particular country or region of the world contributes to those totals, and look at differences among countries and regions of the world.
Objects in the Rear View Mirror
Class project
Luca Cioria, Giorgio Cavaggion, and Chihua Ma
The goal of the project is to produce an application that lest the user easily investigate this dataset to look for patterns, trends, and interesting features. What are the most important factors that affect highway fatalities? Are things getting better or worse and why? Where are the most dangerous places, driving conditions, driver impairments?
When the Wind Blows
Class project
Daniel Stack, Chihua Ma, Bhavana Singh, and Sidd Sathyam
The visualization is used to look at the characterization of an epidemic spread. The application provides evidences for what happened in the city of Vastopolis. It provides an answer as to how the epidemic started, progressed and what are the possible causes if any.

Journal Publications

G. E. Marai, C. Ma, A. Burks, F. Pellolio, G. Canahuate, D. Vock, A.S.R. Mohamed, and C.D. Fuller. Precision Risk Analysis of Cancer Therapy with Interactive Nomograms and Survival Plots. IEEE Transactions on Visualization and Computer Graphics, vol. 14, pp. 1-11, March 2018.

C. Ma, F. Pellolio, D.A. Llano, R.V. Kenyon, and G. E. Marai. RemBrain: Exploring Dynamic Biospatial Networks with Mosaic-Matrices and Mirror Glyphs. Journal of Imaging Science and Technology, Volume 61, Number 6, November 2017, pp. 60404-1-60404-13(13).

C. Ma, T. Luciani, A. Terebus, J. Liang, and G. E. Marai. PRODIGEN: Visualizing the Probability Landscape of Stochastic Gene Regulatory Networks in State and Time Space. BMC Bioinformatics, 18, no. 2 (2017): 24. PDF

C. Ma, A. Forbes, D.A. Llano, T. Berger-Wolf, and R.V. Kenyon. SwordPlots: Exploring Neuron Behavior within Dynamic Communities of Brain Networks. Journal of Imaging Science and Technology, Volume 60, Number 1, January 2016, pp. 10405-1-10405-13(13). Charles E. Ives Journal Award. PDF

Peer-reviewed Conference Papers

D. Kirilov, I. Lindmae, A. Burks, C. Ma, and G. E. Marai. "MC1: A Bespoke Analysis Tool for Spatio-temporal Park Traffic Data" IEEE Visual Analytics Science and Technology (VAST) Challenge 2017 Proceedings, pp. 1-2, 2017.

D. McNamara, J. Tapia, C. Ma, T. Luciani, A. Burks, J. Trelles, and G. E. Marai. Spatial Analysis of Employee Safety Using Organizable Event Quiltmaps. In Proceedings of the IEEE VIS 2016 Workshop on Temporal & Sequential Event Analysis, Baltimore, MD, USA, October 2016. PDF

C. Ma, R.V. Kenyon, A. Forbes, T. Berger-Wolf, B.J. Slater, and D.A. Llano. Visualizing Dynamic Brain Networks Using an Animated Dual-Representation. In Proceedings of the Eurographics Conference on Visualization (EuroVis’15 Short papers), pp. 73-77, Cagliari, Italy, May 2015. PDF

C. Ma, S. Liberman, and H. Zheng. GCLViz: Garbage Collection vs. Latency Visualization. In Proceedings of the 5th International Conference on Information Visualization Theory and Applications (IVAPP), pp. 292-299, Lisbon, Portugal, 5-8 January, 2014. PDF

Poster/Abstract Presentations

C. Ma, A. Burks, T. Luciani, A. Terebus, J. Liang, and G. E. Marai. Visualizing ensemble time-evolving probability landscapes of stochastic networks. ISMB/ECCB 2017, pp. 1-2, BioVis’17, Jul 2017. PDF

T. Luciani, J. Trelles, C. Ma, A. Burks, M. M. Thomas, K. Bharadwaj, S. Singh, P. Hanula, L. Di and G. E. Marai. Multi-scale Voronoi-based ACT Assessment. IEEE VGTC VPG International Data-Visualization Contest, Baltimore, MD, USA, October 2016. Honorable Mention. Link

T. Luciani, C. Ma, J. Trelles, and G. E. Marai. Developing a Data-Driven Wiki of Spatial-Nonspatial Integration Tools. In Proceedings of the IEEE VIS 2016 Workshop on Creation, Curation, Critique and Conditioning of Principles and Guidelines in Visualization (C4PGV), Baltimore, MD, USA, October 2016. PDF

C. Ma, R.V. Kenyon, T. Berger-Wolf, and D.A. Llano. Visualizing Communities in Dynamic Mouse Brain Networks. In Proceedings of the IEEE Information Visualization Conference (InfoVis’14), Paris, France, 9-14 November, 2014. PDF




Contact Me

Github: chihuama

LinkedIn: Chihua Ma