I am an Applied Scientist at Amazon. I work on conversational AI that helps customers with returns, and I lead its global expansion. My work focuses on making these systems helpful and reliable across languages. I am also building AI tools that help teams improve prompts and manage data annotation.
My academic research focuses on how language models and graph-based learning can support better decisions in business and finance, from understanding financial news to forecasting sales and building investment portfolios. My work has been published in leading venues, including Production and Operations Management (UTD24, FT50, ABS4) and the International Conference on Information Systems (ICIS).
I earned my Ph.D. in Data Science from Stevens Institute of Technology and previously worked at AllianceBernstein and Jefferies. I also enjoy teaching and have taught master’s-level courses in business analytics.
Latest news
I successfully defended my Ph.D. dissertation and earned my Ph.D. degree!
Our paper, “To Automate or Not? How Open Collaboration Communities Decide on Bot Adoption,” was accepted for oral presentation at ICIS 2024 in Bangkok, Thailand.
I will join Amazon Science as an Applied Scientist Intern.
I was invited to give a talk on “Incorporating LLMs and Graphs for Portfolio Construction” at the Q-Group Investment Webinar at AllianceBernstein L.P., New York.
I was invited to give a talk on “From Text to Treasure: Leveraging LLMs and Graphs for Stock Movement Predictions” at the NLP and Machine Learning in Investment Management Conference, New York.
Our LLM paper, “Deciphering Corporate Online Reputation through Employee Reviews,” was invited for presentation at the 2024 POMS Annual Meeting.
I successfully passed my Ph.D. proposal defense. My final dissertation defense is planned for next year.
Our paper, “Predict Misinformation Spread with StanceAware GNN,” was accepted for oral presentation at ICIS 2023 in Hyderabad, India.
I was invited to give a talk on “How ChatGPT and LLMs Can Enhance Stock Market Prediction” at the U.S. Bank AI Research Seminar.
Our paper, “Modeling Inverse Demand Function with Dual Neural Networks,” was accepted for oral presentation at ICAIF 2023 in New York.
Our paper, “ChatGPT+GNN for Stock Prediction,” was accepted for oral presentation at the Workshop on Robust NLP for Finance at KDD 2023, California.
We will present our work in the “Generative AI for Business Analytics” session at the 2023 INFORMS Annual Meeting in Phoenix, Arizona.
Our FinTech paper integrating ChatGPT and GNNs for stock movement prediction is available on arXiv and SSRN.
We were invited to present our work in the “Making Sense of AI” session at the 2022 INFORMS Annual Meeting in Indianapolis, Indiana.
I was awarded the Data Science Fellowship from Jefferies Group LLC. I will join Jefferies as a Software Developer Intern this summer.
Our paper was accepted and nominated for the Best Paper Award at AIS SIGDSA 2021. It will be presented at the symposium.