I am Anish Ambreth, a Master’s student in Machine Learning at MBZUAI and a graduate of the Indian Institute of Technology (IIT) Hyderabad in Mathematics & Computing and Engineering Sciences. My background sits at the intersection of mathematics, probability, statistics, and machine learning, with experience spanning robust ML research, predictive modeling, and data-driven decision systems.
My long-term goal is to work in quantitative research. I am especially interested in forecasting, time-series modeling, signal extraction, market modeling, and building disciplined research pipelines that turn noisy data into useful decisions. While my current academic publications are primarily in federated learning, adversarial robustness, and trustworthy ML, that work has trained me to think carefully about uncertainty, robustness, model behavior, and real-world constraints, which is exactly the mindset I want to bring into the quant domain.
Alongside research, I have worked on quantitative ML problems in industry, including alpha factor mining for predictive trading models, options pricing, FX dynamics, and time-series forecasting. I enjoy combining mathematical intuition with practical machine learning to build models that are rigorous, interpretable, and useful in production. I am also interested in agent-based systems that can automate parts of the research workflow.
Current Interests:
Beyond Work: