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Research Portfolio

UX is about data.

Data can make or break a product or experience. Understanding how your users think and what your users need is key to creating a good design.

Good data starts with good research.

When you need to know what your users are thinking, what problems they are having, or how your product could be better, research is where you go to find the answer. Research tells you how your users move through the product, where the trouble spots are, what they love, what they hate, and anything else you want to know. With research, you can find out just about anything. Without it, you are working blind.

Good methods are good research

Good methodology is the key to good research. I’m an academic by training. I received a Bachelor’s of Science degree in Psychology, then went on to New York University where I got my Master’s of Science in the Games for Learning program. Despite the name, it was the most rigorous program I’ve ever been through. We studied and practiced all the standard methodologies: card sorting, user personas, eye tracking, user interviews, field studies, usability testing, and survey construction, just to name a few.

Users are Key

Before I got into UX, I paid my rent by doing tech support. I have worked in every possible role one can be at a help desk. I’ve done it over the phone and in person. I’ve been level 1, level 2, and level 3. I’ve been a front-line grunt, a manager, and I’ve even been hired to set one up from scratch. I’ve worked help desk for education end-users, factory end-users, retail end-users, corporate end-users. You name it, I’ve done it. What this means is that I’ve talked to pretty much every kind of user there is. And more than that, I’ve talked to them when they have had problems. I’ve heard just about every story there is to hear.

I’m not just a designer sitting in a room writing up a persona for someone I’ve never met. Even before I started in UX, I’ve been in the trenches, working with the products and the users, seeing what they see, and hearing about the trouble spots. I understand how to talk to users and get answers. I’ve been doing it for 20+ years.

Humans are Humans, Behavior is Data

In addition to my tech support experience, I am also trained in psychology. I have a very good understanding of human behavior. Humans are humans no matter where they are. Psychology and behavioral science is the meat and bread of a researcher’s job, and it’s what I’m trained to do. Whether it’s a therapist understanding a patient’s quirks or a researcher analyzing data on users, the same skills apply.

Products and Projects


$O$ is a tech-based non-profit that I co-founded in 2018 with Alan Underwood intended to help unhoused individuals find support. I have acted in several roles at this company. As well as being Co-founder, I am the Head of Research and Development and the current Executive Director.


Anna-bot is a chatbot designed to gather information from unhoused individuals. She consists of several bots working together in a proprietary emergent AI system. Anna-bot is run on Discord using individual bots designed in Botkit.

Anna is designed to mimic human speech patterns as closely as possible. This involved several rounds of testing both in-house and with individuals recruited from Facebook. I designed the survey we used to gather information from the users, recruited the users, analyzed the data, and helped tune Anna through multiple rounds of testing.


Slotbot is one of the sub-bots designed to support Anna-bot. Slotbot’s purpose is to pick the next question that Anna asks users. Slotbot uses a sophisticated algorithm that takes into account the user’s speed of answer, the valence of the questions and answers that the user has given thus far, and the value of the questions that the user has answered thus far. Based on these inputs, the algorithm generates the next question Anna should ask.

Slotbot was tuned through multiple rounds of surveys, which I also designed, recruited users for, and analyzed the data.

Unhoused Support Survey

The purpose of Anna-bot and Slotbot was to ask questions from the Unhoused Support Survey. The Unhoused Support Survey is a survey developed to find support for unhoused individuals. $O$ gathered the questions needed to qualify for as many agencies that supported unhoused individuals as we could find.

Next, we collated the questions. Each question was then given a value based on how many times it showed up in the paperwork. “What is your name” had very high value because it showed up everywhere. “Were you injured in the Vietnam War” was a low-value question because only a few forms asked it.

Those questions are then fed into Slotbot and Anna-bot. Anna-bot interfaces with end-users through text messages. Anna-bot asks the questions. Based on the data received, Slotbot feeds Anna as many high-value questions as possible (based on its algorithm) before the user disengages with the process. We then provide the user with the names of the agencies where they might qualify for aid and provide links to paperwork that was already filled out as much as we could.

The “secret sauce” is in the algorithms that determine how Anna speaks to the users and what questions Slotbot feeds her. Both of these things are designed (through thorough and still-ongoing research) to be as human-like (in Anna’s case) and efficient at getting high-value questions answered (in Slotbot’s case) as possible.

The Unhoused Data Project

Getting accurate data about unhoused individuals is a hard problem by its very nature. Aside from providing aid to these individuals, the other reason $O$ exists is to get accurate information about unhoused individuals.

$O$ is two ongoing longitudinal studies designed to gather as much data as possible about unhoused individuals. Part of my job is creating a survey (which is constantly being tuned) to efficiently gather data. We want to know the stories of the unhoused individuals we help. One study is purely quantitative. How many people are out there? Where are they? How long have they been unhoused? etc. The hard numbers that are so hard to gather.

The other is qualitative. Every person we help is a case study. We get their stories. We go beyond the numbers. While my job as Head Researcher at $O$ is about making our products better, it’s also about getting to know every person we talk to. My job is to listen. To hear what people say. And I do it every time someone knocks on our door. Sometimes that’s all it takes to change a life.

That’s what I do as a researcher. Whether it’s for product design or charity. I listen and hear and try to understand. Because the next life I change might be my own.

Game-Based Stealth Therapy for Depression

My Master’s thesis was designing a prototype for a game-based therapy for depression. With my background in psychology and specialization in game design, I decided to follow up on a project that I started in a Positive Psychology class during my undergraduate studies. I wanted to create a form of therapy for individuals who were therapy-averse.

A problem faced by sufferers of depression is that they are often reluctant, or outright hostile to attempts to help them. Often these issues occur at times when they need help the most, such as after a failed suicide attempt. I wanted to create a form of therapy that would be able to reach this audience: the ones that were least likely to get help on their own, yet needed it the most.

To do so, I integrated techniques from positive psychology and operant conditioning with game design to create a form of therapy that looks, acts, and plays like a game. It uses operant conditioning to reinforce eight key behaviors that depressed individuals often avoid while using the principles of altruism from positive psychology in the plot and theme to help create a positive mood. While working on this, I used a persona to help me target my audience, did surveys and storyboards during iterative prototyping, and user testing when I was nearing the end of the process.

In the end, I was only able to create an interactive story using Twine to demonstrate the principles of my theory. I am not a programmer and the technical challenges of creating something more were too great to overcome. I was also unable to get approval to do testing with an at-risk audience due to the ethics and risks involved, but I did write up several preliminary experimental designs to test the therapy’s effectiveness in the paper that accompanied the prototype.

MAGNET Lab Game Night

My studies at NYU happened at the MAGNET Lab. One of the regular occurrences during my time there was the Thursday night Game Night. The MAGNET Lab is shared by NYU Steinhardt, the Tandon School of Engineering, and the Tisch School for the Arts. The Tisch students were all majoring or minoring in game design, so almost always had new games or projects that they needed testing. So every Thursday night the lab would buy pizza and throw a party where students could show off and test their games.

I was a regular at Game Night. I enjoyed not just playing the games that other students came up with but also used Game Night as a way to practice my research and development skills. I started carrying a notebook with me so I could take notes during each playtest and would often spend more time giving feedback to the developers than I did playing the game. Being a regular participant in many different stages of testing is something I see as an advantage. It’s nice to know what it feels like to be on both sides of the clipboard.

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