Hi, I am Sneha Gathani

I am a 2nd year PhD student advised by Prof. Zhicheng Liu at the University of Maryland, College Park (UMD). My research interests lie in Visual and Interactive Analytic System Building.
I graduated with a Master's in Computer Science from UMD in May 2020. During Master's, I worked with Prof. Leilani Battle in the BAttle Data Lab (BAD Lab) in the intersection of Data Visualization, Databases and HCI. Over Masters and PhD, I have also gathered experience in the areas of database manaagement systems, computer graphics, computer vision and machine learning. Prior to grad school, I completed my Bachelors in Computer Engineering from University of Pune, India with a specialization in Computer Science.


August 2021 I am continuing my summer intern at Sigma Computing Inc. with Çağatay Demiralp through the Fall semester
August 2021 Summer work at Sigma submitted to CIDR 2022


Debugging Database Queries: A Survey of Tools, Techniques, and Users

Sneha Gathani, Peter Lim, Leilani Battle
CHI 2020: SIGCHI Conference on Human Factors in Computer Systems | Acceptance Rate: 23.8%


My research interest broadly lies in the intersection of multiple areas: Data Visualization, Databases and HCI. The multi-disciplinary nature and application-oriented aspect of these areas inspires me. I am interested in understanding, developing and evaluating interactive visual systems across domains.

Augmenting Decision Making via Interactive What-If Analysis

Sneha Gathani, Madelon Hulsebos, James Gale, Peter J. Haas, Çağatay Demiralp

The fundamental goal of business data analysis is to improve business decisions by understanding the relationship between data and objectives. Business users such as sales, marketing, product, or operations managers often make decisions to achieve key performance indicator (KPI) goals such as increasing customer retention, decreasing investments, increasing sales, etc. To discover the relationship between data and their KPI of interest, business users perform data exploration by analyzing multiple slices of the dataset mentally. For example, analyzing customer retention across quarters of the year or suggesting optimal media channels across strata of customers. However, the increasing complexity of datasets combined with the cognitive limitations of humans makes it challenging to carry over multiple hypotheses, even for simple datasets. Therefore performing such analyses is hard mentally. Existing commercial tools provide partial solutions whose effectiveness remains unclear. They are also often developed for data scientists, not business users. Here we argue for four functionalities that we believe are necessary to enable business users to reason with insights, learn the relationships between data and KPIs, and facilitate data-driven decisions. We implement these functionalities in SigmaDecision, an interactive visual data analysis system enabling business users to experiment with the data by asking what-if questions. We evaluate the system through three business use cases: marketing mix modeling analysis, customer retention analysis, and deal closing analysis, and report on feedback from multiple business users. Overall, business users find SigmaDecision intuitive and useful for quick testing and validation of their hypotheses around interested KPI as well as in making effective and fast data-driven decisions.

A Programmatic Approach to Evaluating Visualization Taxonomies in Log Analysis Contexts

Sneha Gathani, Alvitta Ottley, Leilani Battle

The visualization community has created many different taxonomies to guide the design and development of visualization systems. However, it is unclear how to evaluate a taxonomy, i.e. it is hard to understand which taxonomy is best for a given interaction log dataset, or even how to apply the taxonomy to a set of interaction log datasets. In this paper, we present a two-stage approach to assess whether existing taxonomies are generalizable enough to automate the way we analyze real-world interaction log datasets. First, we leverage Gotz and Zhou’s multi-tier characterization of user’s analytic activities to create a general-purpose framework that clusters 30 different visualization taxonomies by the kinds of interaction log analyses they can support. Our framework has four levels: interaction level, sequence level, task level and reason level. Second, we present a novel process for programmatically mapping different taxonomies to interaction log datasets. Specifically, we develop programmable templates that can label interaction logs with their corresponding categories from a given taxonomy. We refer to these templates as embeddings. Our embeddings enable easy translation from one taxonomy to another and ease hand-offs between adjacent levels of our framework, such as passing the output of an interaction level taxonomy as input to a sequence level taxonomy. We create seven embeddings for taxonomies from the first two levels of our framework (interaction and sequence), allowing us to quantitatively measure the applicability of these taxonomies to three real-world visualization interaction log datasets. However, we find the applicability of these taxonomies to be severely limited at both levels. Our findings suggest that existing taxonomies are not well-suited to support a wide range of user interactions across visualization systems. Based on our findings, we make recommendations on how existing taxonomies could be augmented, or new taxonomies could be developed, to better support and guide user interactions.

A Model and Application for Cross-Domain Visualization System Design

Sneha Gathani, Daniel Votipka, Kristopher Micinski, Jeffrey Foster, Michelle Mazurek, Leilani Battle

Visualization design studies are notoriously difficult to design effectively. Though existing models highlight the major pitfalls, their guidance is not as user-friendly for individuals new to design studies. We present anupdated design study model providing step-by-step guidelines, concrete examples, and discussion of differences and similarities between design studies in eight different domains. To demonstrate the value of our model and guidelines, we apply them in the security domain to help fledgling analysts reverse engineer (RE) Android applications (apps) for potential security and privacy vulnerabilities. Through our design study, we develop TraceInspector, an interactive visualization tool that integrates both static and dynamic Android app data, connects relevant temporal event sequencesand method dependencies, and executes app code in a single visualization interface. Finally, we evaluate TraceInspector with nine RE users and find that the tool eases the learning of RE tasks for novice RE users, validating our synthesized design study guidance.

Sneha Gathani, Peter Lim, Leilani Battle

Database management systems (or DBMSs) have been around for decades, and yet are still difficult to use, particularly when trying to identify and fix errors in user programs (or queries). We seek to understand what methods have been proposed to help people debug database queries, and whether these techniques have ultimately been adopted by DBMSs (and users). We conducted an interdisciplinary review of 112 papers and tools from the database, visualization and HCI communities. To better understand whether academic and industry approaches are meeting the needs of users, we interviewed 20 database users (and some designers), and found surprising results. In particular, there seems to be a wide gulf between users’ debugging strategies and the functionality implemented in existing DBMSs, as well as proposed in the literature. In response, we propose new design guidelines to help system designers to build features that more closely match users debugging strategies.

CHI 2020: SIGCHI Conference on Human Factors in Computer Systems | Acceptance Rate: 23.8%

Selected Course Projects


Apart from a growing and budding researcher, I am an absorbing painter and an inquisitive dissectologist. I enjoy most of my weekends painting. Over vacations, when I have more free time on hand, I am caught up making jigsaw puzzles.

I also enjoy exploring nature trails, sight-seeing and reading about histories of places.



2105, Brendan Iribe Center,
University of Maryland,
College Park, MD - 20740