Dadetyn Wood

About

Software developer and machine learning engineer with two years of work experience in research, back-end development, and machine learning pipelines. Recently, I graduated from Rochester Institute of Technology with a B.S. and M.S. in Computer Science with a focus in intelligent systems.

I have a passion for artificial intelligence and building robust, efficient systems that help solve real-world problems. Check out some of my projects below.

When I'm not programming, you can find me reading, playing games, or exploring other hobbies such as learning German or playing the piano.

Experience

June — Aug 2023
ControlExpert

Research & Development Co-op

Developed machine learning models for detecting image manipulations on images of cars for auto insurance purposes.
- Python
- PyTorch
- Computer Vision
Jan 2022 — Jan 2023
Thread

Machine Learning Engineer

Collaborated to develop a machine learning pipeline and corresponding API for processing data, training models, and performing inference. Created methods for analyzing objects detected in images. Designed comprehensive unit tests and automated documentation for the machine learning pipeline.
- Python
- PyTorch
- PyTest
- Flask
- RabbitMQ
- Celery
- Computer Vision
June — Aug 2021
DePaul University

Undergraduate Researcher

Designed and developed an end-to-end machine learning architecture combining the benefits of a convolutional neural network and the k-nearest neighbors algorithm.
- Python
- TensorFlow
- Research
- Multivar Calc
- Backtracking
Feb — May 2021
Carrier

Software Engineer Co-op

Developed a microservice application for generating XML product reports. Created a console application for automating testing processes.
- C#
- Agile
- Micro-services
- Excel

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Projects

GraphGrad

Custom C++ tensor library with pybinding support that allows many standard tensor operations.
- C++
- Python
- CUDA
- OpenMP
- Pybind
GraphGrad

Github Repo

Description

A custom-built C++ tensor library to perform matrix and tensor operations efficiently with automatic differentiation support for machine learning algorithms (a simplified version of PyTorch/TensorFlow). The library allows for multiple operations such as add, subtract, matmul, etc. and supports Python usage through pybindings. Multiple optimization techniques were implemented such as Lazy Evaluation, OpenMP CPU optimizations, SIMD computation, Common Subexpression Elimination, and GPU kernels. For more information and documentation for the library, check out the GitHub repository linked above.

Why?

This was a semester group project for a course on Machine Learning System Implementations and our group wanted to better understand what it took to implement the machine learning libraries we've so often used.

My Contributions

We collaborated to come up with the overall architecture design and I implemented many of the standard operations, some GPU kernels, OpenMP optimizations, a couple of the backpropogation functions, the majority of the pytests, and initial benchmark comparisons to evaluate our library's efficiency.
Text Recovery from Palimpsests

Spatial revising variational autoencoder for the purposes of recovering text of from Palimpsests, or old manuscripts which were chemically altered to be reused, using multi-spectral images.
- Python
- PyTorch
- Computer Vision
Text Recovery from Palimpsests

Github Repo

Description

A PyTorch implementation of the Spatial Revising Variational Autoencoder from Yu et al. for extracting useful features from hyperspectral image data. After implementation, this model was then used to extract features from multi-spectral images of palimpsests in order to perform segmentation on images and recover hidden or damaged text.

Why?

This was a class project for a Neural Networks and Machine Learning course done in a group. For our project, we chose our topic to be text recovery from palimpsests as one of the group members was an image science major working on a Capstone project in the same topic and had access to multi-spectral image data for training and inference. While searching for potential model architectures that would work well, we came across a Spatial Revising Variational Autoencoder used for segmentation of hyperspectral images. Since this was exactly what we wanted to do, we began our project as an application study to apply this model to our topic.

My Contributions

I implemented the CNN module, parts of the LSTM module, modified the loss function, built most of the encoder and decoder, and trained and evaluated the model on our data.
Speaker Diarization in Expressive Dialogues

Master's Capstone Project

Joint, end-to-end, speaker diarization and automatic speech recognition system trained and evaluated on expressive dialogues and compared to traditional speaker diarization methods.
- Python
- PyTorch
- Speech Processing
- Research
Speaker Diarization in Expressive Dialogues

Description

An end-to-end, joint Speaker Diarization and Automatic Speech Recognition model implementation for recognizing the words spoken in audio recordings as well as who spoke them. This was done as my Master's capstone project with the main goal to evaluate how a newer end-to-end system performed in comparison to traditional methods on more difficult dialogue containing expressive, or emotional, speech.

Why?

While attending Rochester Institute of Technology, I was exposed to a lot of deaf or hard of hearing students as it was also the home of the National Technical Institute of the Deaf, so topics like automatic speech recognition came up often. From one of my peers, I also learned more about speaker diarization and how it could be used to automate transcriptions and provide more rich ASR output. Combining these two pieces with my interest for natural language processing and speech processing, I took this as an opportunity to learn more about how to create these types of systems.

My Contributions

I performed all of the work on this project (model implementation, training, and evaluation, technical report, and poster) with the help of my Capstone advisor, Cecilia Alm.
Web Checkers

A web application for users to play checkers against one another or against a simple AI.
- Java
- Agile
- Spark
- JavaScript
- Maven
Web Checkers

Description

A web application users can sign onto and play checkers against one another or a simple artificial intelligence bot. Able to handle multiple simultaneous games on the server and allowed users to select who they wished to play against.

Why?

This was a semester long group project for a Software Engineering course learning about the Agile development process and developing a full "product" (the checkers app) for our "client" (the professor). It involved daily stand ups, weekly sprints, demos and presentations, etc.

My Contributions

I worked on a lot of the backend logic for the checkers application, helped to work out a simple AI bot, and helped design the API and communication between the server and clients.

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About

Experience

ControlExpert

Research & Development Co-op

Thread

Machine Learning Engineer

DePaul University

Undergraduate Researcher

Carrier

Software Engineer Co-op

Projects

GraphGrad

GraphGrad

Description

Why?

My Contributions

Text Recovery from Palimpsests

Text Recovery from Palimpsests

Description

Why?

My Contributions

Speaker Diarization in Expressive Dialogues

Master's Capstone Project

Speaker Diarization in Expressive Dialogues

Description

Why?

My Contributions

Web Checkers

Web Checkers

Description

Why?

My Contributions