Logan Cadman

Graduate Systems Administrator and Researcher @ Colorado State University. MS in Computer Science Student. Automotive enthusiast and mountain lover.

GitHub lcadman LinkedIn lcadman Discord packetsurge

About Me

Hi, I’m Logan Cadman, a graduate student in Computer Science at Colorado State University. My journey has been anything but traditional. I started out as an automotive technician, where I learned to solve real world problems with my hands. Over time, I discovered how technology can make a lasting impact on people’s lives.

At CSU, I’ve worn many hats, from leading student orientation programs during COVID, an experience that sharpened my leadership skills, to managing Linux systems and networks as a Systems Administrator in the Computer Science department. Those varied roles have given me a unique blend of hands on know how and a big picture approach to problem solving.

An internship at VMware deepened my fascination with cybersecurity and networking, especially how virtualization can address big challenges like reducing environmental impact. Ultimately, I believe technology isn’t just about writing code or deploying systems, it’s about helping people. I’m excited to keep learning, building, and finding new ways to make a positive difference.

Highlights

Graduate Research

Investigating internet transmission errors through the HIPFT project, focusing on error detection and data analysis. Made custom Linux kernel modifications and automated log connection pipelines. Maintained server infrastructure for data collection and analysis.

Systems Administration

Managing over 600 Linux servers and workstations, implementing OpenLDAP migration, GPU monitoring for efficient resource management, system updates, and user support.

Leadership & Collaboration

Supervised orientation programs at CSU, managing a team of 26 orientation leaders, facilitating large-scale events, and training on communication, diversity, and team building.

Education

M.S. Computer Science @ Colorado State University

Colorado State University 2023 - 2025, Fort Collins, CO. Focused on systems, networking, machine learning, and software engineering.

B.S. Computer Science @ Colorado State University

Colorado State University 2019 - 2023, Fort Collins, CO. Officer in the Computer Information Systems Club, member of the Augmented Reality/Virtual Reality Club, Orientation Leader, Undergraduate Systems Administrator.

Experience

+ Graduate Research Assistant @ Colorado State University (2023–Present)
  • Investigated internet transmission errors through HIPFT, simulating real-world issues with custom Linux kernel modifications.
  • Developed automated pipelines to collect and analyze over 10,000 logs for error detection and benchmarking.
  • Applied time series analysis and machine learning to detect patterns and mitigate DDoS attacks.
+ Systems Administrator @ Colorado State University (2020–Present)
  • Managed and maintained 600+ Linux systems, ensuring smooth operations for students, faculty, and researchers.
  • Designed and implemented the migration from NIS to OpenLDAP, enhancing system security and user management.
  • Automated GPU monitoring and enforced resource limits for efficient lab usage.
+ Technical Support Engineer Intern @ VMware (2022)
  • Collaborated with systems administrators to resolve complex VMware vCenter Server networking issues.
  • Completed training toward VMware Certified Professional Certification.
  • Supported enterprise-level clients in troubleshooting cloud and virtualization solutions.
+ Orientation Team Leader @ Colorado State University (2020–2021)
  • Supervised 26 orientation leaders to welcome 5,296 students, fostering community through large-scale events.
  • Conducted interviews for over 200 candidates and led training on communication and teamwork.
  • Redesigned orientation programs during COVID-19 to adapt to virtual and in-person formats.

Projects & Papers

Adaptive Honeypots: Optimizing Dwell Time with Reinforcement Learning

This project explores how we can make honeypots smarter by using reinforcement learning to adapt their lifespan based on real-time network activity. Inspired by DSCOPE, a system that used fixed 10-minute honeypot sessions, I built a dynamic alternative that learns when to keep honeypots running longer to catch delayed or low-frequency attacks. The system runs on Google Cloud, spinning up honeypots, capturing TCP SYN traffic with tcpdump, and feeding contextual data (like connection rate, unique IPs, and traffic trends) into a tabular Q-learning agent. The agent learns to balance the value of captured data against resource usage, gradually favoring longer dwell times (like 45 minutes) in richer threat environments. Key takeaway: fixed honeypot durations can miss out on valuable attack data. By adapting in real time, this system captures more meaningful behavior from slow or stealthy adversaries.

Learn More

Looking for Errors TCP Misses

Inspired by prior work suggesting undetected errors were becoming a problem on the Internet, we set out to create a measurement system to detect errors that the TCP checksum missed. We designed a client-server framework in which the servers sent known files to clients. We then compared the received data with the original file to identify undetected errors introduced by the network. We deployed this measurement framework on various public testbeds. Over the course of 9 months, we transferred a total of 26 petabytes of data. Scaling the measurement framework to capture a large number of errors proved to be a challenge. This paper focuses on the challenges encountered during the deployment of the measurement system. We also present the interim results, which suggest that the error problems seen in prior works may be caused by two distinct processes: (1) errors that slip past TCP and (2) file system failures. The interim results also suggest that the measurement system needs to be adjusted to collect exabytes of measurement data, rather than the petabytes that prior studies predicted.

Learn More

Learning 2048 with Reinforcement Learning

Project for CS545 at Colorado State University Fall 2023, by Chad Schwenke and Logan Cadman. This project utilizes AI to play the 2048 game.

Learn More

Analyzing the Communication Patterns of Different Teammate Types in a Software Engineering Course Project

Effective communication is vital for the success of professional software engineering (SE) teams. As SE courses teach essential industry skills like teamwork and collaboration, ensuring effective communication becomes important in student projects. However, poor engagement from team members can lead to conflicts, uneven workloads, and diminished learning experiences. Teammate types such as Couch Potatoes, who contribute minimally, and ā€œHitchhikersā€, who rely on others while taking credit, exacerbate these issues. In contrast, ā€œLone Wolvesā€ work independently, potentially isolating themselves, while ā€œGood Teammatesā€ actively collaborate and contribute somewhat, driving team success. In this study, we aimed to investigate the communication patterns of teammate types such as Couch Potato, Hitchhiker, Lone Wolf, or Good Teammate during a SE testing course. We applied Ordered Network Analysis (ONA) to the conversational data of the teams to examine the distinct communication patterns of students whose contributions were either perceived positively (e.g., Good Teammates) or negatively (e.g., Couch Potato, Hitchhiker, Lone Wolf) by their peers. The findings reveal distinct communication behaviors across teammate types. While Good Teammates and Couch Potatoes discussed similar content, Good Teammates communicated more frequently and consistently throughout the project. Lone Wolves seldom engaged in pleasantries, reflecting a task-focused approach, whereas Hitchhikers rarely contributed substantively to technical discussions, such as pull requests, but often interacted through pleasantries. These patterns emphasize the need for early interventions and communication planning to promote accountability balance and effective student collaboration during class projects.

Learn More

Courses

CS-540

Artificial Intelligence

Level: Graduate

About the Course

Covers topics from various subareas of AI, including learning, planning, and reasoning.

CS-542

Natural Language Processing

Level: Graduate

About the Course

Covers advanced NLP techniques such as parsing, language models, and applying machine learning to text analysis.

CS-580B2

Human/Social Factors in Software Engineering

Level: Graduate

About the Course

Examines the impact of social and human dynamics on software teams, focusing on communication and collaboration.

CS-501

Introduction to Research in Computer Science

Level: Graduate

About the Course

Introduces research methods, literature reviews, and effective academic communication in computer science.

CS-510

Image Computation

Level: Graduate

About the Course

Explores computational techniques for image processing, focusing on algorithms and real-world applications.

CS-535

Big Data

Level: Graduate

About the Course

Focuses on large-scale data processing, including Hadoop, Spark, and distributed systems for analytics.

CS-514

Software Product and Process Evaluation

Level: Graduate

About the Course

Teaches evaluation techniques for software products, processes, and methodologies for quality assurance.

CS-545

Machine Learning

Level: Graduate

About the Course

Covers machine learning fundamentals, focusing on algorithms, classification, regression, and neural networks.

CS-415

Software Testing

Level: Undergraduate

About the Course

Introduces methods for software testing, including test design, debugging, and quality assurance practices.

CS-458

Blockchain Principles and Applications

Level: Undergraduate

About the Course

Explores blockchain concepts, including cryptocurrency, consensus mechanisms, and decentralized applications.

CS-464

Principles of Human-Computer Interaction

Level: Undergraduate

About the Course

Examines the design, implementation, and evaluation of user interfaces for improved user experiences.

CS-456

Modern CyberSecurity

Level: Undergraduate

About the Course

Covers principles and practices of cybersecurity, including cryptography, network security, and threat mitigation.

CS-457

Computer Networks and the Internet

Level: Undergraduate

About the Course

Introduces networking principles, including protocols, architecture, and the Internet's technical foundations.

CS-462

Engaging in Virtual Worlds

Level: Undergraduate

About the Course

Focuses on virtual and augmented reality concepts, including interface design and immersive experiences.

CS-320

Algorithms - Theory and Practice

Level: Undergraduate

About the Course

Covers algorithm design, analysis, and implementation, focusing on efficiency and computational complexity.

CS-356

Systems Security

Level: Undergraduate

About the Course

Focuses on system vulnerabilities, security threats, cryptography, and secure software development practices.

CS-370

Operating Systems

Level: Undergraduate

About the Course

Covers operating system concepts such as process management, memory management, file systems, and concurrency.

CS-314

Software Engineering

Level: Undergraduate

About the Course

Introduces software development lifecycle, project management, and team-based software design methodologies.

CS-270

Computer Organization

Level: Undergraduate

About the Course

Explores computer architecture, digital logic, assembly language, and the relationship between hardware and software.

CS-253

Software Development with C++

Level: Undergraduate

About the Course

Teaches object-oriented programming, memory management, and software design principles using C++.

CS-220

Discrete Structures and their Applications

Level: Undergraduate

About the Course

Covers mathematical concepts such as logic, set theory, combinatorics, and graph theory for computer science.

CS-165

CS2--Data Structures

Level: Undergraduate

About the Course

Focuses on fundamental data structures such as linked lists, trees, stacks, and queues, along with algorithms.

CS-163

CS1---No Prior Programming Experience

Level: Undergraduate

About the Course

Introduces programming fundamentals, including variables, loops, functions, and basic problem-solving techniques.

CS-150

Introduction to Programming (CS0)

Level: Undergraduate

About the Course

Provides an overview of programming concepts and problem-solving, designed for students with no programming background.

CIS-355

Business Database Systems

Level: Undergraduate

About the Course

Explores database design, normalization, SQL, and data management systems for business applications.

CIS-320

Project Management for Information Systems

Level: Undergraduate

About the Course

Teaches project management methodologies, risk analysis, and resource planning for IT projects.

CIS-240

Application Design and Development

Level: Undergraduate

About the Course

Focuses on designing, developing, and deploying applications with an emphasis on user experience and functionality.

Resume

View my complete professional history and skills below.

Download My Resume

Contact

Use the contact button below to send me an email, send me a message on LinkedIn, or find me on Discord (@packetsurge) šŸŽ‰.

Email Me