Konstantinos Banos

I am a Ph.D. candidate in Statistics at McMaster University, conducting research at the intersection of statistical machine learning, Bayesian nonparametrics, probabilistic modeling, and stochastic processes, with applications in language modeling and quantitative finance. I am fortunate to be under the supervision of Prof. Narayanaswamy Balakrishnan.

My research focuses on developing principled probabilistic methods for complex data. In my master’s thesis, I proposed a novel modeling framework for continuous-time document streams and implemented scalable inference methods on large-scale datasets.

I am broadly interested in the theoretical foundations of machine learning and AI, and in developing methods that combine mathematical rigor with practical relevance. I also have experience with scientific discovery using symbolic regression, academic presentations, and collaborative research environments, including IBM Research, where I developed a strong interest in automated scientific discovery.

Academic Role

At McMaster University, I serve as a Graduate Teaching Assistant in the Department of Mathematics & Statistics, supporting large undergraduate courses in statistics, probability, and financial mathematics.

I have worked with courses including:

• Statistical Methods in Science (STATS 2B03)
• Probability and Statistics (STATS 2D03)
• Financial Mathematics (MATH 2FM3)

I support several hundred students each term through laboratory instruction, office hours, and one‑to‑one mentoring, with a focus on statistical reasoning, applied data analysis, and programming in R.

Background & Achievements

Education & Recognition

• Ph.D. Candidate in Statistics (McMaster University, starting Sep 2026)
  Fully funded doctoral position.
• M.Sc. in Statistics (McMaster University, Sep 2024 – Jan 2026)
  First Class with Honours (GPA: 4.00/4.00); recipient of multiple competitive scholarships for academic excellence and research potential.
• B.Sc. in Statistics and Insurance Science (University of Piraeus, 2019–2023)
  First Class with Honours (GPA: 4.00/4.00; top 1% of cohort); recipient of Academic Excellence Awards from both the University of Piraeus and the Insurance Union of Greece.

Entrepreneurial & Practical Experience

Alongside academic work, I actively engage with real‑world financial markets through independent trading and portfolio management. Since 2020, I have designed and refined discretionary and quantitative trading strategies across equities, combining behavioral market analysis with quantitative evaluation.

My work involves applying technical, fundamental, and order‑flow analysis to identify liquidity dynamics, market structure patterns, and risk asymmetries. I maintain detailed trading journals, conduct systematic performance reviews, and continuously refine strategies with strong emphasis on risk management and disciplined decision‑making. My portfolio has achieved consistent annual performance, including 44% return in 2024 and 35% year‑to‑date performance in 2025.

Research Philosophy

I believe that meaningful progress in AI requires moving beyond surface‑level pattern recognition toward models that capture structure, causality, and uncertainty. While large language models are powerful, I do not believe that scaling them alone will lead to human‑level intelligence, as linguistic fluency does not imply deep understanding of the world.

I am particularly motivated by the need to develop models that better accommodate the structure of natural language data, grounded in rigorous mathematical principles. More broadly, I am interested in approaches that combine mathematical rigor with practical relevance in domains such as language and finance.

I am open to collaboration and ideas that challenge prevailing assumptions, with broad interests in statistical machine learning, Bayesian nonparametrics, probabilistic modeling, and stochastic processes, particularly in applications to language and finance.