Visual Computing

Our research in visual computing lies at the intersection of visualization, computer graphics, and computer vision. It spans a wide range of topics, including bio-medical visualization, image and video analysis, 3D fabrication, and data science.

Our Research

Our goal is to combine interactive computer systems with the perceptual and cognitive power of human observers to solve practical problems in science and engineering. We are providing visual analysis tools and methods to help scientists and researchers better process and understand large, multi-dimensional data sets in various domains such as neuroscience, genomics, systems biology, astronomy, and medicine. And we are developing data-driven approaches for the acquisition, modeling, visualization, and fabrication of complex objects. 


Michaela Kapp
Administrative Manager of Research

33 Oxford Street
Maxwell Dworkin 143
Cambridge, MA 02138
Office Phone: (617) 496-0964

Our Lab

Our group belongs to Harvard's School of Engineering and Applied Sciences and the Center for Brain Science. We are located in the Maxwell Dworkin Building (33 Oxford St.) as well as the Northwest Laboratory (52 Oxford St.) on Harvard's main campus in Cambridge, Massachusetts.

Recent Publications

Data-Driven Guides: Supporting Expressive Design for Information Graphics
Kim NW, Schweickart E, Liu Z, Dontcheva M, Li W, Popovic J, Pfister H. Data-Driven Guides: Supporting Expressive Design for Information Graphics [Internet]. IEEE Transactions on Visualization and Computer Graphics (InfoVis’16) 2017;PP(99):1-1. Publisher's VersionAbstract

In recent years, there is a growing need for communicating complex data in an accessible graphical form. Existing visualization creation tools support automatic visual encoding, but lack flexibility for creating custom design; on the other hand, freeform illustration tools require manual visual encoding, making the design process time-consuming and error-prone. In this paper, we present Data-Driven Guides (DDG), a technique for designing expressive information graphics in a graphic design environment. Instead of being confined by predefined templates or marks, designers can generate guides from data and use the guides to draw, place and measure custom shapes. We provide guides to encode data using three fundamental visual encoding channels: length, area, and position. Users can combine more than one guide to construct complex visual structures and map these structures to data. When underlying data is changed, we use a deformation technique to transform custom shapes using the guides as the backbone of the shapes. Our evaluation shows that data-driven guides allow users to create expressive and more accurate custom data-driven graphics.

Screenit: Visual Analysis of Cellular Screens
Dinkla K, Strobelt H, Genest B, Reiling S, Borowsky M, Pfister H. Screenit: Visual Analysis of Cellular Screens. IEEE Transactions on Visualization and Computer Graphics (InfoVis’16) 2017;PP(99):1-1.Abstract

High-throughput and high-content screening enables large scale, cost-effective experiments in which cell cultures are exposed to a wide spectrum of drugs. The resulting multivariate data sets have a large but shallow hierarchical structure. The deepest level of this structure describes cells in terms of numeric features that are derived from image data. The subsequent level describes enveloping cell cultures in terms of imposed experiment conditions (exposure to drugs). We present Screenit, a visual analysis approach designed in close collaboration with screening experts. Screenit enables the navigation and analysis of multivariate data at multiple hierarchy levels and at multiple levels of detail. Screenit integrates the interactive modeling of cell physical states (phenotypes) and the effects of drugs on cell cultures. In addition, quality control is enabled via the detection of anomalies that indicate low-quality data, while providing an interface that is designed to match workflows of screening experts. We demonstrate analyses for a real-world data set, CellMorph, with 6 million cells across 20,000 cell cultures. An Education System with Hierarchical Concept Maps
Schwab M, Strobelt H, Tompkin J, Fredericks C, Huff C, Higgins D, Strezhnev A, Komisarchik M, King G, Pfister H. An Education System with Hierarchical Concept Maps. IEEE Transactions on Visualization and Computer Graphics (Inf 2017;PP(99):1-1.Abstract

Information hierarchies are difficult to express when real-world space or time constraints force traversing the hierarchy in linear presentations, such as in educational books and classroom courses. We present, which allows linear and non-linear presentation and navigation of educational concepts and material. To support a breadth of material for each concept, is Web based, which allows adding material such as lecture slides, book chapters, videos, and LTIs. A visual interface assists the creation of the needed hierarchical structures. The goals of our system were formed in expert interviews, and we explain how our design meets these goals. We adapt a real-world course into, and perform introductory qualitative evaluation with students.

Guidelines for Effective Usage of Text Highlighting Techniques
Strobelt H, Oelke D, Kwon BC, Schreck T, Pfister H. Guidelines for Effective Usage of Text Highlighting Techniques. IEEE Transactions on Visualization and Computer Graphics 2016;22(1):489-498.Abstract

Semi-automatic text analysis involves manual inspection of text. Often, different text annotations (like part-of-speech or named entities) are indicated by using distinctive text highlighting techniques. In typesetting there exist well-known formatting conventions, such as bold typeface, italics, or background coloring, that are useful for highlighting certain parts of a given text. Also, many advanced techniques for visualization and highlighting of text exist; yet, standard typesetting is common, and the effects of standard typesetting on the perception of text are not fully understood. As such, we surveyed and tested the effectiveness of common text highlighting techniques, both individually and in combination, to discover how to maximize pop-out effects while minimizing visual interference between techniques. To validate our findings, we conducted a series of crowd-sourced experiments to determine: i) a ranking of nine commonly-used text highlighting techniques; ii) the degree of visual interference between pairs of text highlighting techniques; iii) the effectiveness of techniques for visual conjunctive search. Our results show that increasing font size works best as a single highlighting technique, and that there are significant visual interferences between some pairs of highlighting techniques. We discuss the pros and cons of different combinations as a design guideline to choose text highlighting techniques for text viewers.

Vials: Visualizing Alternative Splicing of Genes
Strobelt H, Alsallakh B, Botros J, Peterson B, Borowsky M, Pfister H, Lex A. Vials: Visualizing Alternative Splicing of Genes. IEEE Transactions on Visualization and Computer Graphics 2016;22(1):399-408.Abstract

Alternative splicing is a process by which the same DNA sequence is used to assemble different proteins, called protein isoforms. Alternative splicing works by selectively omitting some of the coding regions (exons) typically associated with a gene. Detection of alternative splicing is difficult and uses a combination of advanced data acquisition methods and statistical inference. Knowledge about the abundance of isoforms is important for understanding both normal processes and diseases and to eventually improve treatment through targeted therapies. The data, however, is complex and current visualizations for isoforms are neither perceptually efficient nor scalable. To remedy this, we developed Vials, a novel visual analysis tool that enables analysts to explore the various datasets that scientists use to make judgments about isoforms: the abundance of reads associated with the coding regions of the gene, evidence for junctions, i.e., edges connecting the coding regions, and predictions of isoform frequencies. Vials is scalable as it allows for the simultaneous analysis of many samples in multiple groups. Our tool thus enables experts to (a) identify patterns of isoform abundance in groups of samples and (b) evaluate the quality of the data. We demonstrate the value of our tool in case studies using publicly available datasets.

Automatic Neural Reconstruction from Petavoxel of Electron Microscopy Data
Suissa-Peleg A, Haehn D, Knowles-Barley S, Kaynig V, Jones TR, Wilson A, Schalek R, Lichtman JW, Pfister H. Automatic Neural Reconstruction from Petavoxel of Electron Microscopy Data [Internet]. In: Microscopy and Microanalysis. 2016 p. 536-537. Publisher's VersionAbstract

Connectomics is the study of the dense structure of the neurons in the brain and their synapses, providing new insights into the relation between brain’s structure and its function. Recent advances in Electron Microscopy enable high-resolution imaging (4nm per pixel) of neural tissue at a rate of roughly 10 terapixels in a single day, allowing neuroscientists to capture large blocks of neural tissue in a reasonable amount of time. The large amounts of data require novel computer vision based algorithms and scalable software frameworks to process this data. We describe RhoANA, our dense Automatic Neural Annotation framework, which we have developed in order to automatically align, segment and reconstruct a 1mm3 brain tissue (~2 peta-pixels).