Shivam Agarwal

Academic Staff

Shivam Agarwal

S-GW 311
+49 201 183 4654
Institut für Informatik und Wirtschaftsinformatik (ICB)
Universität Duisburg-Essen
Gerlingstraße 16
45127 Essen, Germany


Shivam Agarwal is a doctoral candidate at University of Duisburg-Essen. He completed his Masters degree in Information Technology from International Institure of Information Technology, Bangalore. Additionally, he has experience in academia, working as Research Associate on visualization projects and in software development.

For a full list of publications, check out the Google Scholar  profile.

Fields of Research:

Shivam Agarwal's research interests primarily include two domains of computer science: visualization and artificial intelligence. He is motivated to build and discover research solutions that have applications in diverse areas.

Sets and graphs are two general concepts that have vast and diverse applications. However, analyzing changes over time is challenging. Taking on the challenge, one of Shivam Agarwal's research interests includes finding ways to visualize dynamic sets and graphs.

Additionally, Shivam is interested in analyzing how computer players (trained by artificial intelligence) play games. For instance, to discover the strategies the computer players have learned over the training process and how they can be used to help game designers via playtesting, among others.

Shivam is also interested in pedagogy, where he builds research solutions to enhance the learning of students. These projects include building visualizations to show student engagement, collaboration in small teams (project groups), and finding simplistic visual alternatives to complex concepts in artificial intelligence.

Other interests include text analysis, data comics, etc. For a full list of offered project/thesis topics, check out this page. 

Keywords: visual analytics, dynamic set/graph visualization, artificial intelligence (AI), game analytics, AI playtesting, and learning analytics


  • Agarwal, Shivam; Herrmann, Christian; Wallner, Günter; Beck, Fabian: Visualizing AI Playtesting Data of 2D Side-scrolling Games. In: Proceedings of IEEE Conference on Games (to appear). 2020. PDF Citation Details

    Human playtesting is a useful step in the game development process, but involves high economic costs and is time-consuming. While playtesting through artificial intelligence is gaining attention, it is challenging to analyze the collected data. We address the challenge by proposing visualizations to derive insights about level design in 2D side-scrolling games. To focus on the navigation behavior in the level design, we study the trajectories of computer agents trained using artificial intelligence. We demonstrate the effectiveness of our approach by implementing a web-based prototype and presenting the insights gained from the visualizations for the game Sonic the Hedgehog 2. We highlight lessons learned and directions to customize the approach for other analysis goals of playtesting.

  • Agarwal, Shivam; Beck, Fabian: Set Streams: Visual Exploration of Dynamic Overlapping Sets. In: Computer Graphics Forum, Vol 39 (2020) No 3, p. 383-391. doi:10.1111/cgf.13988 PDF Citation Details

    In many applications, membership of set elements changes over time. Since each element can be present in multiple sets, the sets also overlap. As a result, it becomes challenging to visualize the temporal change in set membership of elements across several timesteps while showing individual set intersections. We propose Set Streams, a visualization technique that represents changing set structures on a timeline as branching and merging streams. The streams encode the changing membership of elements with set intersections. A query-based selection mechanism supports a flexible comparison of selected groups of elements across the temporal evolution. The main timeline view is complemented with additional panels to provide details about the elements. While the proposed visualization is an application-independent visualization technique for dynamic sets, we demonstrate its effectiveness and applicability through three diverse application examples and expert feedback.

  • Agarwal, Shivam; Wallner, Günter; Beck, Fabian: Bombalytics: Visualization of Competition and Collaboration Strategies of Players in a Bomb Laying Game. In: Computer Graphics Forum, Vol 39 (2020) No 3, p. 89-100. doi:10.1111/cgf.13965 PDF Citation Details

    Competition and collaboration form complex interaction patterns between the agents and objects involved. Only by understanding these interaction patterns, we can reveal the strategies the participating parties applied. In this paper, we study such competition and collaboration behavior for a computer game. Serving as a testbed for artificial intelligence, the multiplayer bomb laying game Pommerman provides a rich source of advanced behavior of computer agents. We propose a visualization approach that shows an overview of multiple games, with a detailed timeline-based visualization for exploring the specifics of each game. Since an analyst can only fully understand the data when considering the direct and indirect interactions between agents, we suggest various visual encodings of these interactions. Based on feedback from expert users and an application example, we demonstrate that the approach helps identify central competition strategies and provides insights on collaboration.

  • Agarwal, Shivam; Bernard, Jürgen; Beck, Fabian: Computer-supported Interactive Assignment of Keywords for Literature Collections. In: Proceedings of the 1st International Workshop on Machine Learning from User Interaction for Visualization and Analytics at IEEE VIS. 2018. PDF Citation Details

    A curated literature collection on a specific topic helps researchers to find relevant articles quickly. Assigning multiple keywords to each article is one of the techniques to structure such a collection. But it is challenging to assign all the keywords consistently without any gaps or ambiguities. We propose to support the user with a machine learning technique that suggests keywords for articles in a literature collection browser. We provide visual explanations to make the keyword suggestions transparent. The suggestions are based on previous keyword assignments. The machine learning technique learns on the fly from the interactive assignments of the user. We seamlessly integrate the proposed technique in an existing literature collection browser and investigate various usage scenarios through an early prototype.


Project Group: Compat AI (SoSe 2020)