Portrait of Ian Arawjo

Ian Arawjo

Associate Academic Member
Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research


Ian Arawjo is an assistant professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal. He holds a PhD in information science from Cornell University, where he was advised by Tapan Parikh. His dissertation work spanned the intersection of computer programming and culture, investigating programming as a social and cultural practice. Arawjo has experience applying a range of human-computer interaction (HCI) methods, from ethnographic fieldwork, to archival research, to developing novel systems (used by thousands of people) and running usability studies.

Currently, he works on projects at the intersection of programming, AI and HCI, including how new AI capabilities can help us reimagine the practice of programming. He also works on large language model (LLM) evaluation, through high-visibility open-source projects such as ChainForge. His first-authored papers have won awards at top HCI conferences, including the Conference on Human Factors in Computing Systems (CHI), the Computer-Supported Cooperative Work and Social Computing Conference (CSCW) and the User Interface Software and Technology Symposium (UIST).


An AI-Resilient Text Rendering Technique for Reading and Skimming Documents
Ziwei Gu
Kenneth Li
Jonathan K. Kummerfeld
Elena L. Glassman
ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testing
Chelse Swoopes
Priyan Vaithilingam
Martin Wattenberg
Elena L. Glassman
Evaluating outputs of large language models (LLMs) is challenging, requiring making -- and making sense of -- many responses. Yet tools that… (see more) go beyond basic prompting tend to require knowledge of programming APIs, focus on narrow domains, or are closed-source. We present ChainForge, an open-source visual toolkit for prompt engineering and on-demand hypothesis testing of text generation LLMs. ChainForge provides a graphical interface for comparison of responses across models and prompt variations. Our system was designed to support three tasks: model selection, prompt template design, and hypothesis testing (e.g., auditing). We released ChainForge early in its development and iterated on its design with academics and online users. Through in-lab and interview studies, we find that a range of people could use ChainForge to investigate hypotheses that matter to them, including in real-world settings. We identify three modes of prompt engineering and LLM hypothesis testing: opportunistic exploration, limited evaluation, and iterative refinement.
Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences
Shreya Shankar
J.D. Zamfirescu-Pereira
Bjorn Hartmann
Aditya G Parameswaran
Antagonistic AI
Alice Cai
Elena L. Glassman
Imagining a Future of Designing with AI: Dynamic Grounding, Constructive Negotiation, and Sustainable Motivation
Priyan Vaithilingam
Elena L. Glassman
ChainForge: An open-source visual programming environment for prompt engineering
Priyan Vaithilingam
Martin Wattenberg
Elena Glassman
Notational Programming for Notebook Environments: A Case Study with Quantum Circuits
Anthony DeArmas
Michael Roberts
Shrutarshi Basu
Tapan Parikh
We articulate a vision for computer programming that includes pen-based computing, a paradigm we term notational programming. Notational pro… (see more)gramming blurs contexts: certain typewritten variables can be referenced in handwritten notation and vice-versa. To illustrate this paradigm, we developed an extension, Notate, to computational notebooks which allows users to open drawing canvases within lines of code. As a case study, we explore quantum programming and designed a notation, Qaw, that extends quantum circuit notation with abstraction features, such as variable-sized wire bundles and recursion. Results from a usability study with novices suggest that users find our core interaction of implicit cross-context references intuitive, but suggests further improvements to debugging infrastructure, interface design, and recognition rates. Throughout, we discuss questions raised by the notational paradigm, including a shift from ‘recognition’ of notations to ‘reconfiguration’ of practices and values around programming, and from ‘sketching’ to writing and drawing, or what we call ‘notating.’
To Write Code: The Cultural Fabrication of Programming Notation and Practice
To Write Code: The Cultural Fabrication of Programming Notation and Practice
Writing and its means have become detached. Unlike written and drawn practices developed prior to the 20th century, notation for programming… (see more) computers developed in concert and conflict with discretizing infrastructure such as the shift-key typewriter and data processing pipelines. In this paper, I recall the emergence of high-level notation for representing computation. I show how the earliest inventors of programming notations borrowed from various written cultural practices, some of which came into conflict with the constraints of digitizing machines, most prominently the typewriter. As such, I trace how practices of "writing code" were fabricated along social, cultural, and material lines at the time of their emergence. By juxtaposing early visions with the modern status quo, I question long-standing terminology, dichotomies, and epistemological tendencies in the field of computer programming. Finally, I argue that translation work is a fundamental property of the practice of writing code by advancing an intercultural lens on programming practice rooted in history.