Dennis Kim, CFA | 김인현 | Colorado State University

About Me

I am a first-year PhD student at Colorado State University, focusing on decision sciences by leveraging techniques at the intersection of computer science, mathematics, operations research, and statistics. Generally speaking, I'm fascinated by how we interact with our environment, each other, and the technologies we create to solve our problems.

I am seeking Summer, or part-time, internships that provide challenging problems and the opportunity to construct impactful strategies based on quantitative and qualitative best practices. Ultimately, I aspire to join an organization that shares my professional interests with a pioneering work culture.

Outside of academics, I enjoy cooking (slow-cooking/neopolitan style pizzas), playing the cello, learning the bass guitar, playing with my dog (Bert), and enjoying a relaxing meal on the patio.

Research Interests

Decision-Sciences and Planning
Reinforcement Learning
Statistical & Causal Inference
Artificial Intelligence
Machine Learning

News

[Aug. 2024] Began my CS PhD journey @ CSU
[May 2023] Completed MS in Mathematical Sciences @ NYU

Coding Skills

Languages: Python, Java, C, R, SQL
Platforms/Tools: AWS, PyCharm, IntelliJ, RStudio, emacs
Libraries: SciPy, NumPy, Pandas, Keras, Scikit-learn, Statsmodels, Selenium, cvxpy
Paradigms: OOP/SOLID

Academics

Coursework:
- Statistical Inference & Regression (including L1 and L2)
- Foundations of Fine-Grained Parallelism
- Embedded Systems and Machine Learning
- Neural Networks
- Big Data Mining
- Linear Algebra
- Real Analysis
- Time Series Analysis & Stochastic Modeling
- Quantitative Trading & Algorithmic Strategies

Projects, Papers, & Videos

Positive Behavioral Canine Training with Neural Networks and Transfer Learning Optimized for Resource Constrained Platforms

Dennis Kim

PDF

Extract-Transform-Load for Revenue Aggregation (UCI Data)

Dennis Kim

Code

Teaching Experience

Courses:
- (Fall 2024) CS 250: Foundations of Computer Systems
  CS250 covers foundational aspects of computer systems encompassing processors, networks, storage, and computing frameworks. A holistic understanding these constructs is key to designing systems (AI, Big Data, clouds, and security) that scale by surfacing problems and solutions from different vantage points. The course includes a discussion of processors, cores, and co-processors (GPUs). Speed differentials across the memory hierarchy and the implications of caching will be covered as well. The discussion of the memory subsystem is complemented with the design of data structures for storage systems. Core foundations of networking including the design of the key elements of the networking stack (IP, TCP, and UDP) will be covered alongside a discussion of DNS and BGP. Current state of the art computing systems are based on parallel and distributed computing frameworks; the course includes an overview of these systems. Finally, the course ends with a future gazing component that explores new, promising systems on the horizon with an emphasis on neuromorphic computing.
Tutoring

Bert

bert