davemilller / notification-service Goto Github PK

• Dave Miller
• Bozeman, MT
• [email protected]

Software developer extraordinaire with a computer science degree from Montana State University. Currently working at HappyDoc as a full stack engineer.

I am interested / have experience in:

• Machine learning / AI applications, including but not limited to:
  • NLP
  • Computer vision
  • Neural networks
  • Bayesian networks
  • Reinforcement learning
  • Genetic / swarm algorithms
  • LLMs
• Data science / data mining
  • Data visualization
  • Working with large data sets
  • Graphs / networks
  • Clustering
• Other
  • Genomics
  • Neuroscience
  • Photonics
  • Mapping / geospacial data
  • Signal processing
  • Self-driving
  • Cybersecurity
  • Quantum Computing

For any job or collaboration opportunities, or to just ask a question, please contact me at the email listed above.

Note that some of these repositories are private. Some of these are done for college classes, and the professors have asked to keep them private to prevent cheating, and others are personal projects that I would not like public. If you would care to take a look at any of these, please send me an email.

• Implementing various ML algorithms from scratch using different data sets
  • K-nearest neighbor
  • K-means & K-medoids clustering
  • Feed forward neural network, trained with:
    • Backpropagation
    • Genetic algorithm
    • Differential evolution
    • Particle swarm optimization

• Implementing various AI algorithms from scratch
  • Constraint satisfaction, & sudoku - solving sudoku puzzles using:
    • Backtracking
    • Backtracking w/ forward checking
    • Backtracking w/ arc consistancy
    • Local seach using genetic algorithm
    • Local search using simulated annealing
  • First-order logic inference - solving Wumpus World problems using unification and resolution on clauses
  • Bayesian networks & inference - inferring marginal probability distributions on various Bayes nets using:
    • Variable elimination
    • Gibbs sampling
  • Reinforcement learning
    • Teaching a car to drive a number of different tracks

• Exploring various data mining methods through projects
  • P1 - writing functions to compute mean, covariance, standard deviation, correlation, range normalize, z-score normalize, and label encode for use on a dataset
  • P2 - writing functions to compute cluster coefficient, betweeness centrality, and average shortest path length for use on a graph dataset
  • P3 - writing k-means and DBSCAN for clustering of a dataset
  • Final - Exploring models for predicting stock market data

• Making a walmart RC car self-driving
  • Raspberry Pi
  • Circuits, motor drivers, wires, batteries, cameras
  • OpenCV

• Helping a friend with a research project pertaining to attention / reaction and subliminal cues
  • Pygame
  • SMTP for data collection
  • Executables

• Various networking applications exploring the network stack
  • Rock, paper, scissors client-server application over HTTP
  • Reliable data transmission on the transmission layer using ACKs, NACKs, and re-transmissions
  • Data plane at the network layer - packet segmentation and forwarding through routing tables
  • Control plane at the network layer - design a control packet, implement distance-vector routing, control routing using link costs

• Explored various applications of cybersecurity, including:
  • Environment variables & set-UID programs
  • Shellshock attack
  • Buffer overflow
  • SQL injection
  • Cross site scripting
  • Secret key (symmetric) encryption
  • MD5 collision attack
  • Public key (asymmetric) encryption & signatures

• Various operating system functions, with a focus on Linux, including:
  • Makefiles
  • Containers and VMs
  • Locking, pthreads and mutex locks
  • IO, processes, threads
  • Scheduling

• Implemeted a compiler for a small programming language CatScript, including:
  • Tokenizer
  • Parser
  • Bytecode generator
  • Evaluation on JVM

• Implemented a web application against a SQL database
  • Create, read, update, delete (CRUD) operations
  • Navigating core entries in the DB
  • Track search
  • Paging
  • Group by reports page
  • Redis caching