Jeremy is available for hire
Hire JeremyJeremy Jacobson
Verified Expert in Engineering
Data Scientist and Developer
Atlanta, GA, United States
Toptal member since January 20, 2022
Bio
Jeremy, lead developer and solutions architect for AI within Ricoh USA’s Intelligent Business Platform (IBP) services, is a driving force behind cutting-edge AI solutions for organizations. With research experience at renowned institutions such as Emory University and the Fields Institute, he leverages a deep well of expertise to deliver impactful, innovative results.
Portfolio
Ricoh Americas
Amazon Web Services (AWS), Amazon Textract, Amazon Bedrock...
Emory University (Dep. of Quantitative Theory & Methods)
Python, Bash, Jupyter, NumPy, PyTorch, SQL, Pandas, Scikit-learn, R...
Emory University (Dep. of Quantitative Theory & Methods)
PyTorch, TensorFlow, SQL, NumPy, Pandas, Scikit-learn, R, Google Cloud, Jupyter...
Experience
- Mathematics - 9 years
- SQL - 6 years
- Google Cloud - 6 years
- Data Science - 6 years
- Statistics - 6 years
- Machine Learning - 6 years
- Python - 6 years
- Amazon SageMaker - 4 years
Availability
Part-time
Preferred Environment
Visual Studio
The most amazing...
...thing I've worked on was the integration of multimodal LLM models into OCR capture technology, enabling a new standard for data capture from unstructured data.
Work Experience
AI Architect
2024 - PRESENT
Ricoh Americas
- Developed new document extraction technologies for our largest customer, which enabled a reduction of human review for data capture from 87% to 20% of documents and improved delivery time from five days to the same day.
- Integrated multimodal large language models (LLMs) into our OCR capture technology, enabling a uniform and higher standard for data capture from unstructured data sources within our Intelligent Delivery Services application.
- Led a team of three developers, one PhD, and two AI engineers to scale out this solution to other applications in the portfolio.
Technologies: Amazon Web Services (AWS), Amazon Textract, Amazon Bedrock, Optical Character Recognition (OCR)
Director of Technology
2021 - 2023
Emory University (Dep. of Quantitative Theory & Methods)
- Advanced to director of technology after building a data science environment on AWS for two faculty and five researchers running large-scale linear job-matching models ordered by CareerBuilder that wanted a transparent job-matching model.
- Mentored four student researchers and two faculty advisors in partnership with UPS on using the Google Cloud Platform (GCP) for modeling and data engineering. The project led to UPS cutting costs and saving on labor.
- Held three workshops and provided one-on-one training to department faculty on GPU computing. Performed the admin role on the department's GPU server.
- Built a hosted notebook solution supporting machine learning (ML) workflows for our faculty.
Technologies: Python, Bash, Jupyter, NumPy, PyTorch, SQL, Pandas, Scikit-learn, R, Deep Neural Networks (DNNs), Linux, Google Cloud, Machine Learning, Statistics, Data Science, Amazon SageMaker, Amazon Web Services (AWS), Algorithms, Artificial Intelligence (AI)
Lecturer
2017 - 2023
Emory University (Dep. of Quantitative Theory & Methods)
- Recognized in 2020 by Google Education as one of 34 Google Cloud faculty experts. Selected in 2019 as one of 18 AWS Academy Cloud Council Faculty, including MIT, Harvard, and Georgia Tech members.
- Supervised research, including an honors thesis that trains generative adversarial networks (GAN) models on a novel dataset. Unlike typical GAN applications, this model generates mathematical objects.
- Developed new techniques to overcome GAN problems such as mode collapse.
- Designed highly lauded data science classes, QTM 250 and 350. Led over 500 students in building ML models on AWS and Google Cloud Platform (GCP) from conception through deployment.
Technologies: PyTorch, TensorFlow, SQL, NumPy, Pandas, Scikit-learn, R, Google Cloud, Jupyter, Bash, Python, Deep Neural Networks (DNNs), Linux, Machine Learning, Statistics, Data Science, Amazon SageMaker, Amazon Web Services (AWS), Data Visualization, Data Modeling, Data Engineering, Algorithms, Time Series, Time Series Analysis, Financial Modeling, Amazon DynamoDB, Artificial Intelligence (AI)
ML Engineer (SageMaker) for Established AI Company
2022 - 2022
Toptal Client
- Implemented a neural network-based time series model and trained it using custom Docker containers and AWS SageMaker training jobs. Decreased model building compute costs by a factor of 10 while enabling scalability to orders of magnitude more data.
- Decomposed monolithic time series model code into processing, training, and deployment steps implemented in an AWS SageMaker Pipeline. Decreased lines of code by a factor of 10.
- Mentored a team of three data scientists on using AWS SageMaker tools for MLOPs. Presented to directors in biweekly meetings.
- Impressed the client (a large US rail company) with the project so much that they plan to use it as a building block for new work by their data science team.
Technologies: Amazon SageMaker, Amazon Web Services (AWS), Deep Neural Networks (DNNs), Data Science, Data Visualization, Data Modeling, Data Engineering, Algorithms, Time Series, Time Series Analysis, Supply Chain Optimization, Financial Modeling, Supply Chain, Supply Chain Management (SCM), Artificial Intelligence (AI)
Visiting Assistant Professor
2013 - 2017
Emory University (Dep. of Math. & Computer Science)
- Developed and managed multiple research projects, two of which delivered publications in high-ranking journals.
- Lectured as an invited speaker at scientific conferences in England, Germany, and the USA.
- Investigated the use of TensorFlow and deep learning on a classification problem in algebraic geometry. Ported code to TensorFlow, benchmarked models and presented this work at the Meeting on Applied Algebraic Geometry, GaTech, 2018.
- Led a team of three postdoctoral researchers in teaching Linear Algebra and Multivariable Calculus.
- Managed curriculum development and evaluation and was recognized for teaching excellence, progressing to Lecturer.
Technologies: TensorFlow, Deep Neural Networks (DNNs), Linux, Mathematics, Jupyter, Bash, Python, NumPy, Data Science, Algorithms, Artificial Intelligence (AI)
Experience
Counting Real Roots Using Neural Networks
https://github.com/jeremyallenjacobson/RealRootsReproductionIn a .ipynb notebook file in the GitHub repository, I explain how to reproduce the results using Google Cloud, demonstrating how to use neural networks to count the number of real solutions to polynomial systems.
For details, see the slides available at http://slides.com/jeremyjacobson/deep-learning. They are from an informal talk I gave as a part of the QTM chalk talk series.
For details, see the slides available at http://slides.com/jeremyjacobson/deep-learning. They are from an informal talk I gave as a part of the QTM chalk talk series.
Roman Urdu NLP
https://github.com/jeremyallenjacobson/roman-urdu-nlpA sentiment classifier that maximizes accuracy to provide a clear window into the social media conversations of the corporation's customer base. See the notebook (.ipynb file) in the included repo for details.
Model Creation and Evaluation for Sentiment Analysis
https://github.com/jeremyallenjacobson/roman-urdu-nlp/blob/master/roman-urdu-nlp.ipynbModel creation and evaluation of machine learning models which automatically identify the sentiment of the conversations of a corporation's customer base on social media. We constructed a flexible Roman Urdu sentiment classifier suitable for multiclass or binary classification by composing existing ML models. Despite its simplicity, we have shown that its accuracy is competitive with state-of-the-art models from current research.
Education
2006 - 2012
PhD in Mathematics and Computer Science
Louisiana State University - Baton Rouge, LA, USA
2001 - 2005
Bachelor's Degree in Mathematics and Computer Science
University of Wisconsin – Madison - Madison, WI, USA
Certifications
FEBRUARY 2020 - DECEMBER 2023
Google Cloud Faculty Expert
Google Cloud
AUGUST 2019 - AUGUST 2022
AWS Certified Cloud Practitioner
Amazon Web Services
MARCH 2019 - DECEMBER 2023
AWS Academy Accredited Educator
Amazon Web Services
Skills
Libraries/APIs
NumPy, PyTorch, TensorFlow, Pandas, Scikit-learn
Tools
Amazon SageMaker, Jupyter, Amazon Textract, Visual Studio
Languages
Python, Bash, SQL, R
Platforms
Amazon Web Services (AWS), Linux, Jupyter Notebook
Storage
Google Cloud, Amazon DynamoDB
Other
Mathematics, Data Science, Statistics, Machine Learning, Data Modeling, Algorithms, Time Series, Time Series Analysis, Artificial Intelligence (AI), Deep Neural Networks (DNNs), Data Visualization, Data Engineering, Supply Chain Optimization, Financial Modeling, Supply Chain, Supply Chain Management (SCM), Amazon Bedrock, Optical Character Recognition (OCR)
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