I am a faculty member in the Khoury College of Computer Sciences at Northeastern University, where I direct the Mixed Reality Lab. I am also a Research Scientist in the Computer Science & Artificial Intelligence Laboratory at Massachusetts Institute of Technology, where I work in the Center for Advanced Virtuality and design, develop, and evaluate immersive experiences to raise awareness on racial bias and discrimination. I received my PhD in Human Computer Interaction from Iowa State University (ISU) in 2017. Prior to joining Northeastern in Fall 2019, I was a faculty member in the Department of Computer Science at State University of New York at Oswego (2017-2019), where I directed the Virtual Reality Lab and taught in the HCI graduate program.
My current research is in the areas of HCI and immersive environments, striving to apply HCI principles to the design, development, and evaluation of 3D interfaces to support and improve human interactions with virtual/augmented/mixed reality environments. Currently, my research projects revolve around
I am genuinely intrigued by the idea of contributing to academic development of future CS and HCI professionals, who will be designing, developing, implementing, and evaluating the next generation of information and communication technologies with which people will interact. By disseminating my knowledge and expertise through teaching, I hope to have an impact on the way future technologies are introduced to our lives, and I wish to instill a human-centered mindset in my students so that they are mindful of their target audience throughout their career. In so doing, I think my teaching can cause a ripple effect, and the ripples I may cause through my students are what excites me about teaching HCI!
Following is a list of courses I have been teaching at my current and previous institutions.
This course covers the principles of human-computer interaction and the design and evaluation of user interfaces. Topics include an overview of human information processing subsystems (perception, memory, attention, and problem solving); how the properties of these systems affect the design of user interfaces; the principles, guidelines, and specification languages for designing good user interfaces; and a variety of interface evaluation methodologies that can be used to measure the usability of software.
Data Science is a field of inquiry concerned with the study and application of systematically extracting generalizable knowledge from data and using this knowledge to draw useful and educated conclusions. This course is an introductory DS class focusing on the foundations of DS as an emerging field. The course introduces core modern DS tools and methods that provide a foundation for subsequent DS classes. As a skills-based course, DS 3000 will cover the use of Python for DS and will introduce some of the widely-used essential Python libraries, such as NumPy, pandas, matplotlib, and scikit-learn. More specifically, this class covers working with tensors and applied linear algebra in standard numerical computing libraries (e.g., NumPy); loading, processing, and integrating data from a variety of structured and unstructured sources using Python libraries (e.g., pandas), visualizing data using basic techniques and tools (e.g., matplotlib/seaborn); applying introductory concepts in probability, statistics, and machine learning using Python libraries (e.g., scikit-learn); and using a standard DS tool (e.g., Jupyter Notebook).
This course introduces the different subsystems used to create a 3D game, including rendering, animation, collision, physics, audio, trigger systems, game logic, behavior trees, and simple artificial intelligence. It also offers students an opportunity to learn the inner workings of game engines and how to use multiple libraries such as physics and graphics libraries to develop a game for various platforms. The class will particularly cover game development for Mixed Reality environments, in addition to traditional gaming platforms.
This course is an advanced research methods and statistics class focusing on the application of quantitative research methods and statistical analyses in the design, analysis, and dissemination of HCI experiments. Building on HCI 509, this class provides an in-depth coverage of complex experimental designs and data analysis techniques, varying from factorial ANOVAs to applied machine learning approaches to experimental data analysis.
As a naturally interdisciplinary field of inquiry, Human Computer Interaction (HCI) incorporates multiple disciplines, varying from psychology to computer science. While the beauty of HCI research resides in its interdisciplinary focus, the diversity of research methods employed by HCI researchers and practitioners coming from different backgrounds can sometimes be bewildering, because each field of inquiry has its own standards for measurement, validity, and rigor. This can be even more challenging when it comes to conducting research studies involving human participants, which is ostensibly very common in HCI research. In an attempt to address these challenges, this course is designed to provide an introduction to the various research methods and data analysis techniques commonly utilized in HCI research, with a particular focus on the foundations of behavioral research and experimental studies.
Human Factors (HF) is a truly interdisciplinary discipline that is closely linked to the field of Human Computer Interaction (HCI). Drawing upon the broad scientific knowledgebase in human behavior, capabilities, and limitations, HF professionals seek to apply psychological principles to the design, development, and evaluation of human-computer systems, with the goal of making the interaction between people and technology more effective, more efficient, easier to learn, more intuitive, more enjoyable, etc. This course is designed to help you master these HF principles, guidelines, and practices.
Digital technologies permeate every aspect of our lives. With increasing dependence on technology have arisen concerns over the putative effects of technology on how humans think, feel, and behave. Is Google making us stupid or smarter? Do we remember more or less if we rely too much on our smartphones? Are habitual media multitaskers more susceptible to distractions and mind wandering? In this graduate research seminar, we review, reflect on, and critique empirical studies investigating the influence of technology on human mind and cognition. Example topics include smartphones, attention, and distraction, cognitive offloading, technology use and memory, pathological technology use, and positive, affective technology.
This course provides students with a detailed introduction to the methodologies used in the design and evaluation of user experiences and human computer interfaces as well as research in HCI. These methodologies permit the evaluation of user needs, comparisons of design alternatives, the evaluation of existing products, and basic research in HCI. This course was designed to help students realize that UX engineering is an ongoing process throughout the entire product lifecycle and that developing the human-computer interface is not something to be done at the last minute, when the rest of the system is finished.
Virtual reality (VR) is a cutting-edge technology that has numerous applications in HCI, along with other fields. Given the hype around VR, it is easy to jump on the VR bandwagon for many people without critically thinking about its scientific merit. HCI researchers, on the other hand, need to be knowledgeable about scientific research studies regarding the feasibility, viability, and efficaciousness of VR in various HCI domains. To that end, this course seeks to expose you to the primary concepts, methods, and applications of VR in HCI research. As a graduate seminar, this course will provide you with a strong background in the applications of VR in HCI research through weekly readings, in-class discussions, and a semester-long VR project.
As a naturally interdisciplinary field of inquiry, HCI incorporates multiple disciplines, varying from psychology to computer science. While the beauty of HCI research resides in its interdisciplinary focus, the diversity of research methods employed by HCI researchers and practitioners coming from different backgrounds can sometimes be bewildering, because each field of inquiry has its own standards for measurement, validity, and rigor. This can be even more challenging when it comes to conducting research studies involving human participants, which is ostensibly very common in HCI research. In an attempt to address these challenges, this course is designed to provide an introduction to the various research methods and data analysis techniques commonly utilized in HCI research, with a particular focus on the foundations of behavioral research and experimental studies.
This graduate seminar provides a survey of UX research methods, with a special emphasis on the practical application of various methods.
I am humbled by the scholarly interest in using the Nomophobia Questionnaire (NMP-Q) in research studies. Please feel free to use the NMP-Q in your research studies without seeking permission. You can access the relevant articles under Publications. You can also download the questionnaire, along with the scoring guide, below.