University: University of Technology Sydney
Academic supervisors:
Distinguished Professor Fang Chen
Industry partner: Nasdaq
Academic Background and Work Experience
Kaveesha has completed her undergraduate studies in Computer Engineering from University of Peradeniya, Sri Lanka. She is a PhD student at UTS. She collaborates with Nasdaq, Sydney in reseacrh related to cross-market manipulation. Her research involves applying deep learning methods in the detection of cross- market manipulations. Her research interests include Graph based deep learning, neural network, and time- series models.
Thesis Topic
Anomaly Detection in Stock Market data using Deep Learning Models
Kaveesha’s thesis topic is Anomaly detection in stock market data using deep learning models. It deals with various anomalies in the stock market data, specifically insider trading. Insider trading is crucial to maintain the market integrity and fairness of trading to all. Detecting this has been done in isolation considering the securities as independent units with no correlation. Kaveesha’s research seeks to improve the performance of the existing methods by taking into consideration the correlation of data.
About Nasdaq
Nasdaq is a global technology company that delivers world-leading platforms that improve the integrity, transparency and liquidity of the global economy.
What part of the business is the researcher involved in?
Anti-Financial Crime Technology
What questions/problems is the researcher helping to answer/solve?
Improve the performance of the existing system to detect insider trading.