Abay Bektursun, Developer in Austin, TX, United States
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Abay Bektursun

Verified Expert  in Engineering

Artificial Intelligence Engineer and Developer

Location
Austin, TX, United States
Toptal Member Since
July 27, 2022

Abay is an engineer with a relaxed yet determined approach, specializing in computer vision and end-to-end models. He led the creation of a large computer vision product for Apple and built products for governments and global corporations. He also leads a community of over 3,000 AI enthusiasts. Abay's expertise in research and engineering keeps him at the forefront of the latest advancements while remaining grounded in his passion for helping others.

Portfolio

AbstractAI
AI Design, Generative Pre-trained Transformers (GPT), Stable Diffusion...
Eagle Eye Networks
C, Machine Learning, Deep Learning, Computer Vision, Linux, Python 3...
Apple
TensorFlow, Deep Learning, Computer Vision, System Design, Python 3...

Experience

Availability

Part-time

Preferred Environment

Linux, Deep Learning, Artificial Intelligence (AI), PyTorch, Python 3, Hugging Face

The most amazing...

...project I have worked on is leading the development of an AI product at Apple.

Work Experience

Autonomous AI Expert

2022 - PRESENT
AbstractAI
  • Helped found a finance AI startup that raised over a million dollars.
  • Helped a startup build a computer vision product that they were able to sell to the Japanese government.
  • Built an end-to-end vision system for a fashion startup.
Technologies: AI Design, Generative Pre-trained Transformers (GPT), Stable Diffusion, Fine-tuning, LoRa, OpenAI GPT-4 API, Generative Pre-trained Transformer 3 (GPT-3), Workshop Facilitation, OpenAI GPT-3 API, Fairseq, ChatGPT, PyTorch Lightning, Data Scraping, Statistical Methods, Statistical Data Analysis, Statistical Analysis, Language Models, CSS, HTML, Distributed Computing, OpenAI, Llama 2, Falcon, PEFT, BERT, LSTM, Software Architecture, Chatbots, Dashboards, Speech to Text, Google Speech-to-Text API

Computer Vision Engineer

2020 - 2022
Eagle Eye Networks
  • Developed embedded vision features deployed to tens of thousands of cameras worldwide.
  • Prototyped state-of-the-art deep learning methods for surveillance computer vision by harnessing large amounts of surveillance video.
  • Created prototypes with various edge accelerators for computer vision.
Technologies: C, Machine Learning, Deep Learning, Computer Vision, Linux, Python 3, TensorFlow, C++, Leadership, Project Leadership, Team Leadership, Python, NumPy, JSON, CSV, Artificial Intelligence (AI), Machine Learning Operations (MLOps), Image Processing, Neural Networks, Cloud, Machine Vision, Data Engineering, Data Reporting, Data Analytics, Artificial Neural Networks (ANN), Scripting, Deep Neural Networks, PyTorch, Software Engineering, Cloud Services, DevOps, Pandas, SQL, Linear Regression, Clustering, Visualization Tools, Docker, Google Cloud Platform (GCP), Modeling, Data Mining, Back-end, Distributed Systems, GitHub, Back-end Development, Facial Recognition, Google Cloud, REST APIs, Scikit-learn, Keras, Large Language Models (LLMs), Generative Research, AI Design, Internet of Things (IoT), Analytics, Computer Vision Algorithms, OpenCV, Data Analysis, Jupyter, Git, CCTV, Real-time Data, Hardware, Architecture, Hugging Face, Fine-tuning, Statistical Data Analysis, Statistical Analysis, CSS, HTML, LSTM, Software Architecture, Dashboards

Computer Vision Engineer

2019 - 2020
Apple
  • Developed the vision system that detects people's presence in Apple stores.
  • Led the team that developed a computer vision system for Apple store analytics.
  • Applied ideas from an academic research paper to a real-world product.
Technologies: TensorFlow, Deep Learning, Computer Vision, System Design, Python 3, Object Detection, Object Tracking, Machine Learning, Leadership, Project Leadership, Team Leadership, Python, NumPy, JSON, CSV, Artificial Intelligence (AI), Data Modeling, Machine Learning Operations (MLOps), Image Processing, Neural Networks, Cloud, Machine Vision, Data Engineering, Data Reporting, Data Analytics, Artificial Neural Networks (ANN), Scripting, Deep Neural Networks, PyTorch, Software Engineering, Cloud Services, DevOps, Generative Adversarial Networks (GANs), Pandas, SQL, Pytest, Linear Regression, Clustering, Visualization Tools, Docker, Modeling, Data Mining, Back-end, Distributed Systems, GitHub, Java, Back-end Development, Facial Recognition, Data Pipelines, TypeScript, Google Cloud, REST APIs, Scikit-learn, Keras, Generative Research, AI Design, Analytics, eCommerce, Signal Processing, Computer Vision Algorithms, OpenCV, Data Analysis, Jupyter, Git, CCTV, Real-time Data, Hardware, Architecture, Hugging Face, Fine-tuning, Statistical Data Analysis, Statistical Analysis, CSS, HTML, BERT, LSTM, Software Architecture, Dashboards

Machine Learning Developer

2016 - 2019
Hewlett Packard Enterprise
  • Joined the company as an intern and was recognized as one of the top three interns.
  • Led a development team for an entirely automated financial department. Reported to the CEO and saved the company $3 million.
  • Took leadership roles outside everyday work. Led employee volunteering programs, organized hackathons, and taught technical classes on Linux and machine learning.
  • Participated in NLP projects, summarizing and classifying company reviews to improve branding and analyzing employee survey text to improve the company culture.
Technologies: Hadoop, Python 3, Machine Learning, Data Science, Data Visualization, Tableau, TensorFlow, Deep Learning, C++, Leadership, Project Leadership, Team Leadership, Python, NumPy, JSON, CSV, Word2Vec, Artificial Intelligence (AI), Data Modeling, Machine Learning Operations (MLOps), Robot Operating System (ROS), Image Processing, Forecasting, Neural Networks, Cloud, Machine Vision, Data Engineering, Data Reporting, Data Analytics, Artificial Neural Networks (ANN), Scripting, Automation, Automated Data Flows, Deep Neural Networks, PyTorch, Software Engineering, Cloud Services, DevOps, Text Mining, Pandas, SQL, ETL, Linear Regression, Clustering, Visualization Tools, Amazon Web Services (AWS), Docker, Google Cloud Platform (GCP), Modeling, Predictive Modeling, Predictive Analytics, Data Mining, Back-end, Distributed Systems, GitHub, Java, Back-end Development, Facial Recognition, Web Scraping, Data Pipelines, Google Cloud, REST APIs, Big Data, Scikit-learn, Keras, AI Design, Internet of Things (IoT), Analytics, Computer Vision Algorithms, OpenCV, Amazon S3 (AWS S3), Data Analysis, Jupyter, Git, Reinforcement Learning, Real-time Data, Hardware, Architecture, Workshop Facilitation, Data Scraping, Statistical Data Analysis, Statistical Analysis, Language Models, CSS, HTML, Distributed Computing, Financial Modeling, BERT, LSTM, Software Architecture, Chatbots, Dashboards

Software Engineer Intern

2015 - 2016
Centene
  • Developed and maintained a documentation website, both its front-end and back-end work. Wrote scripts to process and parse EDI files.
  • Ran routine jobs and processed health insurance claims. Automated manually run jobs and reports.
  • Produced ad-hoc and scheduled reports for different departments. Helped vendors resolve issues and support third-party software.
Technologies: Python 3, Oracle, Databases, MongoDB, Electronic Data Interchange (EDI), Neural Networks, Cloud, Machine Vision, Data Engineering, Data Reporting, Data Analytics, Artificial Neural Networks (ANN), Scripting, Automation, Automated Data Flows, Deep Neural Networks, PyTorch, Software Engineering, Cloud Services, DevOps, Django, Pandas, SQL, ETL, Linear Regression, Clustering, Visualization Tools, Modeling, Predictive Modeling, Predictive Analytics, Data Mining, Back-end, GitHub, Back-end Development, Web Scraping, REST APIs, Scikit-learn, Keras, Internet of Things (IoT), Analytics, OpenCV, Data Analysis, Jupyter, Git, Statistical Data Analysis, Statistical Analysis, CSS, HTML, Software Architecture, Dashboards

Why Does Batch Normalization Work?

https://abay.tech/blog/2018/07/01/why-does-batch-normalization-work/
A theoretical and experimental exposition on Batch normalization that explains the real reason why it works so well. The ML community believes Batch Norm improves optimization by reducing internal covariate shift (ICS). As I show, ICS has little to no effect on optimization.

Built a Community of Three Thousand People

https://www.meetup.com/Austin-Deep-Learning/
Austin Deep Learning is the largest deep learning community in Texas. We invite talks from machine learners and data scientists applying deep learning to solve problems, with tutorials and lessons learned. Talks are open to all deep learning frameworks, such as TensorFlow, Keras, PyTorch, and others.

Robotic Surgery

I built my surgery robot to insert electrodes into insects to record the spiking activation of neurons. I built decoding modeling using the spike data. For example, I used a cricket as a proximity sensor by reading its mind.

HumanText

https://humantext.site/
As LLMs become increasingly ubiquitous, many have turned to them for automated writing. But in doing so, the human touch is lost, and the art of genuine writing is left to wither away. That's why I created an app designed to verify the authenticity of the text, ensuring that every word was crafted by a human hand. Say goodbye to soulless, automated writing and hello to the genuine artistry of the written word.

Large Language Models | Alignment Experiment

https://www.linkedin.com/feed/update/urn:li:activity:7072309610354728960/
An experiment on aligning large language models (LLMs) was conducted for research during the OSS LLMs workshop. The main hypothesis, proposed by David Ha, aimed to investigate whether a pronounced moral bias could potentially compromise the core effectiveness of LLMs.

The participants were invited to design three tasks with varying difficulty levels – elementary, intermediate, and advanced – tailored explicitly for LLMs. The models used for this experiment included Vicuna-13B, Vicuna-13B Uncensored, and Vicuna-7B, which served as a baseline for comparison. The participants assessed and rated the performance of each model based on their respective tasks.

The central focus was to examine the impact of intense alignment bias on the overall efficacy of LLMs. The findings of this study provided substantial evidence to support the hypothesis. The Vicuna-13B Uncensored model, trained on an augmented dataset with fewer moral constraints, achieved an average score of 5.75 out of 10, whereas the censored model secured an average of 3.95. This observation could be attributed to the tendency for stronger alignment to encourage deeper mode-seeking within the model distribution.
2014 - 2017

Bachelor's Degree in Computer Science

University of Central Arkansas - Conway, AR, USA

SEPTEMBER 2022 - PRESENT

Inferential Statistical Analysis

University of Michigan, via Coursera

SEPTEMBER 2022 - PRESENT

AWS Machine Learning

AWS, via Coursera

MAY 2018 - PRESENT

Deep Learning Specialization

DeepLearning.AI, via Coursera

NOVEMBER 2017 - PRESENT

Machine Learning Specialization

Stanford University, via Coursera

NOVEMBER 2016 - PRESENT

The Arduino Platform and C Programming

Coursera

Libraries/APIs

NumPy, PyTorch, REST APIs, Keras, OpenCV, LSTM, Google Speech-to-Text API, TensorFlow, Pandas, Scikit-learn, PyTorch Lightning, Node.js

Tools

GitHub, Jupyter, Git, Tableau, Vendor Independent Messaging (VIM), Scikit-image, Pytest, ChatGPT, Google AI Platform

Languages

Python 3, Python, SQL, CSS, HTML, Falcon, JavaScript, Java, TypeScript, C, C++, Rust, Embedded C

Paradigms

Data Science, Automation, ETL, Parallel Programming, DevOps, Web App Design, Distributed Computing

Platforms

Visual Studio Code (VS Code), Linux, Docker, Google Cloud Platform (GCP), MacOS, Oracle, Amazon Web Services (AWS), Arduino

Storage

JSON, Google Cloud, Data Pipelines, Databases, MongoDB, Amazon S3 (AWS S3)

Frameworks

Hadoop, Django, Flask

Other

Machine Learning, Deep Learning, Computer Vision, Computer Vision Algorithms, CSV, Word2Vec, Artificial Intelligence (AI), Data Modeling, Machine Learning Operations (MLOps), Image Processing, Neural Networks, Cloud, Machine Vision, Data Reporting, Data Analytics, Artificial Neural Networks (ANN), Scripting, Deep Neural Networks, Software Engineering, Linear Regression, Clustering, Modeling, Data Mining, Back-end, Back-end Development, AI Design, Analytics, Language Models, Data Analysis, CCTV, Architecture, Fine-tuning, OpenAI GPT-4 API, Workshop Facilitation, Data Scraping, Statistical Methods, Statistical Data Analysis, Statistical Analysis, OpenAI, Llama 2, PEFT, BERT, Software Architecture, Chatbots, Dashboards, Speech to Text, Data Visualization, Natural Language Processing (NLP), Statistics, Probability Theory, Numerical Optimization, Optimization, Leadership, Project Leadership, Team Leadership, Fairseq, Transformers, Forecasting, Data Engineering, Automated Data Flows, Cloud Services, Generative Adversarial Networks (GANs), Text Mining, Visualization Tools, Predictive Modeling, Predictive Analytics, Facial Recognition, Big Data, Large Language Models (LLMs), Signal Processing, Reinforcement Learning, Real-time Data, Hardware, Generative Artificial Intelligence (GenAI), Hugging Face, Generative Pre-trained Transformers (GPT), Generative Pre-trained Transformer 3 (GPT-3), LoRa, OpenAI GPT-3 API, Financial Modeling, System Design, Object Detection, Object Tracking, Science, Calculus, Programming, Computational Neuroscience, Mathematical Analysis, Robot Operating System (ROS), Microcontrollers, Electronic Data Interchange (EDI), Distributed Systems, Web Scraping, Generative Research, Internet of Things (IoT), eCommerce, Recurrent Neural Networks (RNNs), Amazon Machine Learning, Embedded Systems, DALL-E, Stable Diffusion, Models

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