Malden, Massachusetts, United States
I am leading research and product development efforts at DIRT Research Technologies. In my current endeavors, I am leveraging my scientific, technical, and computational experiences to build a suite of AI-powered consumer insights tools that capture both human reasoning and emotional capacity, providing our clients with deep understanding of their users’ current and future engagement and purchasing behaviors. More about me: I am a cognitive neuroscientist by training, with over 16 years of experience as a cognitive/behavioral scientist and over 4 years as a product manager. As a behavioral scientist, I am experienced with mixed methods research, neuromarketing methodologies (i.e., eye tracking, EEG, biometrics), advanced statistical analysis, and computational modeling techniques. I am also adept at client interactions and technical writing, with several peer-reviewed publications. As a product manager, I am experienced with market analysis, agile and waterfall project management, user experience design, cross-functional team leadership, data-driven decision-making, and executing go-to-market strategies.
As DIRT's first hire, I am responsible for defining DIRT’s product roadmap, including product visions, key milestones, and feature prioritization. I led the development, design, and release of DIRT's comprehensive market research platform, a 0-to-1 product. To facilitate remote multimodal testing at DIRT, I worked with our hardware vendor to develop a custom firmware of their wearable technology to integrate with our platform in order to provide biophysiological data at scale. I am currently leading initial explorations of adapting existing vision-language models to leverage DIRT’s rich primary data sources to better predict consumer behavior in the mobile gaming sector.
DIRT's scientific consultancy specializes in consumer insights research in the mobile gaming and social media sectors. As Senior Behavioral Scientist, I lead all research efforts at DIRT, including design, implementation, and analysis. Our work has been featured by clients like TikTok where we helped them develop a better understanding of media & brand consumption by their users (https://www.tiktok.com/business/en-US/blog/brand-love-on-tiktok). In addition to the professional services work, I also facilitate academic partnerships, as well as lead our internal ML/AI R&D efforts.
iMotions is a research software company providing researchers in corporate and academic sectors worldwide a simpler solution to conducting in-person multimodal research. As the firm's sole Technical Client Consultant, I delivered both scientific and product consultancy services to key accounts in corporate and academic sectors worldwide, often resulting in renewal of old contracts and facilitating new ones. I also helped managed custom technology and web integration requests from key clients. During COVID, I had the opportunity to work in a more product-adjacent role where I guided the development and launch of a new online research product to enable our clients to continue their research work remotely. To bring this product to market, I collaborated cross-functionally with the executive team and our development team to formulate and execute go-to-market strategies, achieving a 100% conversion rate in MVP testing with pilot clients. I had also crafted and implemented standard operating procedures for this new product to streamline the onboarding process for new clients.
PhD in Psychology, Cognition and Cognitive Neuroscience Track NSF Graduate Research Fellow Dissertation: Evaluating the role of attention in decision making
A month-long internship where I worked extensively with iMotions' API and R Notebook analysis features to conduct big data research. Specifically, I led the analysis effort using Python and R for a large, cross-media data set (N >3000) to understand the predictive power of biophysiological measures (skin conductance, heart rate, eye tracking, facial coding) on market performance. Due to the extensive nature of this data set, I developed image and audio analysis algorithms in Python to automatically detect and isolate key events from prior recordings of consumer interaction with media campaigns in order to expedite analytical efforts for the team and to ensure future replication efforts for the client. In addition to this project, I also developed web scrapers using Python to scrape objective, in-market performance measures for different media campaigns.
A month-long exchange through the Duke and Duke-NUS Medical School Singapore Collaborative Research Initiative, where I worked in Dr. Michael Chee's Cognitive Neuroscience Lab under the guidance of Dr. Stijn Massar. Initiated and completed a project that focused on characterizing different quantitative approaches of understanding effort-based decision making (i.e., effort discounting).