Hey, I'm Adam!
I'm recruiting PhD students with a background in data visualization, visual analytics, HCI, and applied
AI/ML to
join my lab for Fall 2027. I'm looking
for students passionate about building visual analytics tools for:
• human-AI collaboration in text document sensemaking
• interactive, explainable, and trustworthy AI in education
Apply to either the
Computer Science PhD
or Data Science PhD program
at Stevens by January 15th, 2027 and mention my name in your application. Please also email me at
[email protected] with your CV, a few sentences about what
you'd like to work on, and a sample visualization project you've worked on!
I'm a professor in the
School of Computing at
Stevens Institute of Technology
and a member of the
Stevens Institute for Artificial Intelligence. I design and develop interactive systems for data visualization and analysis. I am also the founding
director of the Stevens Visualization Lab.
Read this if you want to join my
group!
I received my PhD from the
School of Interactive Computing at
Georgia Tech, where I worked with Alex Endert in the
Visual Analytics Lab. My dissertation was on
Detecting and Mitigating Pedagogical Risks in Large Language Models with Visual Analytics.
Over the years, I've collaborated with many incredible analysts, educators, engineers, and scientists at
NSF AI Institute for
Adult Learning in Online Education (AI-ALOE),
MIT Lincoln
Laboratory, 
National
Security Agency (NSA),
Laboratory for
Analytic Sciences (LAS),
Adobe Research,
NASA Jet Propulsion
Laboratory (JPL),
ArtCenter
College of Design, and the
Monterey Bay
Aquarium Research Institute (MBARI).
Research Themes
At Stevens, I lead the Stevens Visualization Lab. Our mission is to help people make sense of complex data. We combine methods from AI, data science, and human-computer interaction to develop new interactive systems for data visualization and analysis. We engage in iterative co-design and longitudinal field deployments with domain experts to study how such systems impact data-driven decision-making. Our work contributes open-source software, empirical research, and design guidelines to visualization, HCI, and AI communities. See all of our projects on our lab website. Our research thrusts include:
Traceable and Defensible Human-AI Collaboration in Text Document Sensemaking
How can we verify and steer AI reasoning against underlying data using interactive visualizations? Examples include: goal tracking in LLM conversations (OnGoal) and traceable document analysis with LLMs and knowledge graphs (VisPile).
Making AI Interactive, Explainable, and Trustworthy in Education
How can visualizations help learners, instructors, and engineers to safely integrate AI into the teaching and learning process? Examples include: debugging AI essay scoring (iScore), provenance analytics for intelligent tutors (VisTA), and in-text traces of AI assistance in writing (DraftMarks).
Visual Analytics for Expert-Driven Scientific Discovery
Co-designing interactive systems with scientists to help them analyze complex, multi-dimensional scientific data (DeepSee).
Bias-Aware Visual Analytics Using Interaction Traces
Studying how showing analysts a live record of their own interactions and interactions with AI can make them aware of unconscious bias and help them adjust how they analyze data (Lumos; Left, Right, and Gender).
