Experiences
Research I'm doing on the side, the internships and roles that filled the gaps in between, and the coursework at McGill underneath it all.
Research
My honours project in Prof. Reddy's group, advised day-to-day by Gaurav. I am looking at whether token-level entropy spikes in an LLM's reasoning trace are a more faithful diagnostic of uncertainty than the confidence score the model says out loud, using PROBCOPA as a stress-test dataset for probabilistic reasoning.
An exploratory stretch of reading and literature review on cultural bias in transformer language models, focused on parliamentary corpora like the Nunavut Hansard against the Canadian Hansard. A lot of this was reading papers and trying to understand what masked language modeling bias evaluation actually looks like in practice.
A semester-long reading program where I studied persistent homology under Julien Cheng. This was the first time I had to sit with graduate-level math for its own sake instead of for a course, and it let me me explore what theoretical math research really is.
My first real research experience, working with Prof. Yao and Qiaoyi on triadic pattern structures for XAI. It became a first-author paper at IJCRS 2025 (Springer).
Internships
Starting this May in Montreal on RBC's Technology & Operations internship program, joining a backend team. I'll be primarily working on backend using Java Spring Boot.
Spent the summer in Regina writing internal WPF apps used by the shipping, training, and finance teams. It was my first time writing C# and shipping something that real coworkers would use every week.
Introduction to Statistical Computing.
Principles of Statistics 1
Courses
NLP and language data science, covering text processing and corpus-level analysis, sequence modeling, language similarity, information retrieval and extraction, question answering, and ethical considerations for language data.
Classical AI fundamentals: search algorithms, logical and probabilistic knowledge representation, planning and decision making under uncertainty, and a first pass at machine learning.
Graduate survey of modern machine learning with a focus on Transformers, alongside neural networks, SVMs, decision trees, and clustering. Covers the practical pieces around them: feature selection, dimensionality reduction, validation, parallelization, and deploying models on real datasets.
Honours-level algorithm and data structure design, with a focus on correctness proofs, asymptotic analysis, and computational complexity across sorting, graphs, and the standard problem families.
Computer systems from the ground up: number representations, digital circuits, MIPS instructions and architecture, datapath and control, caches, virtual memory, interrupts, and pipelining.
Programming language design and paradigms through functional and logic programming in OCaml, with a focus on binding and scoping, lambda abstraction, data abstraction, and type systems.
Automata and formal languages through to Turing machines, models of computation, computability theory, and undecidability via reduction-based proofs.
Advanced algorithm design and analysis: linear programming, complexity classes, NP-completeness, and reduction-based techniques for harder algorithmic problems.
Multivariable and vector calculus: partial derivatives and Jacobians, scalar and vector fields, orthogonal curvilinear coordinates, multiple and line and surface integrals, culminating in Green's, divergence, and Stokes's theorems.
Honours linear algebra: linear equations over a field, vector spaces, linear maps and matrix representations, determinants, canonical forms, duality, bilinear and quadratic forms, and diagonalization of self-adjoint operators on inner product spaces.
Honours real analysis: point-set topology and metric spaces, normed and Banach spaces, compactness and the Heine-Borel theorem, the Banach fixed point theorem, the Riemann-Stieltjes integral, uniform convergence, and infinite series.
Classical statistical inference: sampling distributions, point and interval estimation, hypothesis testing, analysis of variance, contingency tables, nonparametric inference, regression, and a first look at Bayesian inference.
Honours probability: axioms and combinatorial probability, conditional probability and Bayes' Theorem, standard distributions, expectations and moment generating functions, random vectors and multivariate transformations, conditional distributions, and convergence results culminating in the Law of Large Numbers and Central Limit Theorem.