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My research focuses on the intersection of discrete mathematics and theoretical computer science, including algorithms, combinatorics and related fields. I have published influential results in the field of cryptography, specifically in searchable encryption. More recently, I’ve spent many summers, as well as a 2022-23 sabbatical, working at the Center for Communications Research - La Jolla, a federally-funded research and development center that performs vital research for the National Security Agency. At Rose-Hulman, I advise theses and independent studies that involve algorithm design, cryptographic protocols, and mathematical analysis or proofs in the service of computer science theory. 

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Recommended Courses for Research: CSSE230, MA276, MA/CSSE473 (strongly recommended for algorithms), MA/CSSE479 (optional for cryptography), MA/CSSE474 (optional, for computability), MA371, MA374, MA378, MA477 (optional, for various discrete math topics) 

My research builds on concepts that students see in CSSE 304 Programming Language Concepts and CSSE 376 Software Quality Assurance. As in 304, I write code that reads other code and does interesting things with it. The three areas I research are: static code analysis, formal verification, and mutation testing.  

Static code analysis: I write code that reads other code and judges its quality, by some measures that I invent and research. 

Formal verification: I write code that reads other code and creates a mathematical model of the code. I use this model to prove or disprove interesting properties of the code, like, "Does this code contain a SQL Injection Attack vector?" 

Mutation testing: I write code that reads other code and modifies it in buggy ways. Then I run the code's test cases on the buggy code to see how good the test cases, themselves, are. 

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Recommended Courses for Research: CSSE304 Programming Languages Concepts, CSSE376 Software Quality Assurance, CSSE490 Static Code Analysis (independent study by request) 

I have a background in computer science and software engineering. My research focuses on trying to understand obstacles faced by CS students when learning new CS topics, primarily in (but not limited to) the early courses, e.g., CSSE 132, CSSE220, and CSSE230. I have worked with several Rose CSSE students on various projects aimed at gaining insights to these obstacles. Some of this work has led to papers co-written by CSSE students and published at CS education conferences. This research often involves brainstorming ideas, developing support software, doing literature research to see what has already been done, setting up and executing experiments, collecting and analyzing data, presenting preliminary results, and writing summaries of our findings. All of these steps we work through together during periodic (e.g., weekly) meetings. At a minimum, if you have made it through CSSE220, we will find some research avenues for you.

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I conduct or advise research around human-computer interaction (HCI), especially health HCI or HCI education. HCI is a broad interdisciplinary field that focuses less on building cool technology and more on building cool technology that people want to use. You can design great technology, but if people don’t want to use it and it isn’t solving an actual problem for them, it’s useless. 

My background is in healthcare, so I have mentored Rose students on health-focused projects, such as “Improving Telehealth for Older Adults Post-COVID.” I’m also interested in teaching HCI better to undergraduate students. CSSE, BME or Engineering Design students would likely be most interested in these areas, but any major is welcome. You will need to be comfortable talking to people to conduct HCI research. 

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Recommended Courses for Research: CSSE371, ENGD240 and ENGD250 

My research interests lie at the intersection of computer science and psychology, and include machine learning, individual differences, well-being, and computer science education. For example, used open-vocab text mining to predict and analyze work-personal conflict (related to work-life balance) using social media data. I am currently working with several Rose-Hulman students to investigate the sense of belonging in group projects. I encourage students to reach out if you are interested in any of these research areas. 

My research interests include algorithmic game theory, graph algorithms, and combinatorial optimization. I work on problems with real-world applications, such as political redistricting and other geospatial partitioning problems. Most projects include both theoretical results (proofs) and computational experiments. 

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Recommended Courses for Research: CSSE 230, MA 276 (essential), CSSE/MA 473, CSSE/MA 474, MA 374, MA 381, MA 444, MA 445 (helpful, but not essential when starting research)  

My research interests are in database systems and identifying techniques for engineering and processing data at scale. As the amount of data gathered by organizations increases rapidly, it is important to identify efficient techniques to build pipelines needed to move, model and transform the data to be conducive for determining actionable intelligence. 

Recommended Courses for Research: CSSE333 Introduction to Database Systems, CSSE433 Advanced Database Systems 

My research involves ethnographic and humanistic methods of inquiry, primarily using methods such as interviews, observations, grounded theory, and phenomenography. My research mainly interacts with the Association for Computing Machinery’s Special Interest Group on Computer–Human Interaction (SIGCHI) community. My primary focus is on exploring human-computer interaction (HCI) design in livestreaming subcultures. Previous sets of studies examined livestream interaction practices in the video game speedrunning subculture, and I aim to continue applying ethnographic methods to explore live streaming and other technology subcultures. I am also interested in applying qualitative methods to computer science education, interacting with ACM’s SICCSE community. I am currently working with other Rose-Hulman faculty to bridge software requirement engineering with industry expectations and work. I’m also collaborating with other universities on a large-scale study examining student mismatch perception and expectations of undergraduate teaching assistants.  

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Recommended Courses for Research: CSSE371 Software Requirements Engineering and CSSE492/493 Undergraduate Research in Computer Science/Software Engineering. Additionally, any ARTS, ANTH, and HUM courses are optional, but will be greatly useful. 

My research centers around autonomous vehicles, reinforcement learning and robotics. I’m also enthusiastic about applying machine learning methodologies to multidisciplinary research. Before joining Rose-Hulman, my research portfolio included extensive work in wireless networking and mobile computing, with a specific focus on WiFi/LTE network bandwidth measurement and PHY/MAC cross-layer designs. 

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I study socio-technical security and privacy: the use of technology to leverage social expectations to defraud, deceive, manipulate, infer, or steal data from people — and ways to limit this abuse.

Web Tracking: Most websites rely on third parties (other websites) to give them functionality or display advertisements. Usually these relationships are invisible and can lead to an organization you've never heard of learning enough about you to make you uncomfortable. This  shouldn’t exist, and you should be in control of how your information is monetized. Can we put you back in control?

Wireless Signal Privacy: You probably carry a smartphone in your pocket, and possibly a fitbit, smart watch, key locator tag or other devices that emit wireless signals. These signals can be used to track you across time and space. Can we protect your privacy?

Social Media Privacy: There’s a disconnect between what social media platforms like Facebook do with your data and what you might think they do. In addition, these sites have unexpected impacts on how you live your life. Can we make social media work for you? 

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My research is at the intersection of computing and biology, particularly using simulations and genomics methods. Genomics research involves processing and analyzing large amounts of DNA sequences to make inferences about populations. This research involves approaches from classical statistics to machine learning. Simulating populations helps with genomics studies to generate testing data, but simulations can also be used to answer specific questions about things like animal behavior or evolution. 

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I’m trained as an applied mathematician, and my research area is computer vision. I look for problems where I can bring math to bear on a hard computational tasks.

Geometric Computer Vision – multiple photos of the same place often contain enough information to reconstruct the 3D shape of the scene. Here’s the (with interactive demos!) for my favorite paper.

Big Graphs – the problems I work on almost always have underlying graph structure. I have questions about these graphs, such as how graph splits affect their spectral properties.

Machine Learning – most of the field of computer vision has been taken over by machine learning techniques. I’m excitedly expanding into this area. Currently I’m interested in ML forgetting, which has important applications for privacy protection.

Recommended courses: CSSE 132, 286, 335, 415, 416, 461, 463, 473. MA 371 or 373.

I enjoy conducting research with students and am actively engaged in AI education research. I have published 12 articles on this subject, many with students. I’ve been co-chair of the major symposium on AI education and is the AI education column editor for AI Magazine. I recently argued for a sea-change in AI education: basing AI and AI education on pattern matching and neural networks. I also work in the field of human-machine teaming; specifically, in a mutual context that needs to be maintained when teaming. I’ve published several book chapters on this subject together with students. My most recent research involves the power of AI and how to best harness it towards the benefit of humanity.

My research interests lie in the interaction between cognitive science, biology and computer science. Specifically, I am interested in computer models of biological phenomena, especially those applied to imitating intelligent or lifelike behaviors both in the pursuit of knowledge (science) and problem-solving (engineering). In many of these areas of research, evolutionary algorithms (EAs) are utilized, which imitate key elements of the process of biological evolution such as mutation, selection and inheritance across a population of individuals. Artificial Neural Networks (ANNs) are another primary component in many of my research projects. I have ongoing research projects and interests in modeling evolutionary development, evolvable hardware, and developmental neural networks. I also have broader interests in many subdomains of artificial life. 

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Recommended Courses for Research: CSSE220 (opt into the Genetic Algorithm Research Project), CSSE230, CSSE490 (Bio-Inspired Artificial Intelligence)