I gave a presentation for the St. Louis Unix Users Group (SLUUG) about Large Language Models (LLMs) on Linux. The talk was titled “Demystifying Large Language Models (LLMs) on Linux: From Theory to Application”.

I presented my master’s project in October 2023. It was titled ‘Reliability Estimation in Series Systems: Maximum Likelihood Techniques for Right-Censored and Masked Failure Data’.

The basic theory behind an entropy map is to map values in the domain to values in the codomain by hashing to a prefix-free code in the …

This blog post introduces the Bernoulli Model, a framework for understanding probabilistic data structures and incorporating uncertainty into data types, particularly Boolean values. It highlights the model’s utility in optimizing space and accuracy in data representation.

I’m been thinking about the power and limitations of abstractions in our understanding of the world. This blog post is from a chat I had with a ChatGPT, which can be found here and here.

Introduction Accelerated Critical Sections (ACS) Trade-offs Trade-off #1 Trade-off #2 Trade-off #3 Trade-off #4 Comparison: ACS vs SCMP Conclusion Introduction In the paper, “Accelerating Critical Section Execution with Asymmetric Multi-Core Architectures,” the authors, Suleman, Mutlu, Qureshi, and Patt, essentially concern themselves with the problem popularly revealed in Amdahl’s law, \[ \operatorname{speedup}(N) = \frac{1}{1 - P + \frac{P}{N}}, \] where \(\operatorname{speedup}\) is the total execution time of a single-threaded version of a program divided by the total execution time an \(N\)-threaded implementation of the same program.

In [1], the authors present a method for constructing a symbolic (nominal) representation for real-valued time series data. A symbolic representation is desirable because then it becomes possible to use many of the effective algorithms that require symbolic representation, like hashing and Markov models.