GOLBE TITUS

Data Science & AI/Machine Learning Engineer

Overview

Work

Education

Publications

Languages

Certificates

Skills

Interests

Projects

References

Work

Mar 2021 → Jun 2024 ·

Innovate AI Solutions

Senior AI/Machine Learning Engineer

Jul 2018 → Feb 2021 ·

DataGenius Corp

Machine Learning Engineer

Education

Sep 2013 → Jun 2018 ·

Computer Science (Specialization in Artificial Intelligence)

University of Central

Sep 2009 → May 2013 ·

Computer Science

State University

Publications

Nov 2018·

Attention-Based Neural Networks for Cross-Lingual Sentiment Analysis

Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)

Jul 2017·

Transfer Learning for Domain Adaptation in Text Classification

Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL)

Languages

English

Mandarin Chinese

Certificates

Oct 2022·

AWS Certified Machine Learning – Specialty

Amazon Web Services (AWS)

Mar 2019·

Deep Learning Specialization

Coursera (deeplearning.ai)

Skills

Programming Languages

Python, SQL, Java, R, Bash

Machine Learning Frameworks

TensorFlow, Keras, PyTorch, Scikit-learn, XGBoost, LightGBM, Hugging Face Transformers

Interests

Technology

Quantum Computing, Robotics, Generative AI

Hobbies

Hiking, Photography, Chess

Projects

Jan 2023 → Aug 2023 ·

Healthcare Diagnostic Assistant (Personal Project)

Developed an AI-powered diagnostic assistant to aid medical professionals in preliminary disease identification from patient data, leveraging deep learning for image and text analysis.

May 2022 → Dec 2022 ·

Financial Market Prediction Model (Personal Project)

Created a machine learning model to predict stock market trends using historical data, news sentiment, and macroeconomic indicators.

References

Innovate AI Solutions

Senior AI/Machine Learning Engineer

San Francisco, CA, US

Mar 2021

→

Jun 2024

Led end-to-end development and deployment of scalable AI solutions, optimizing model performance and integrating MLOps practices to drive significant business value.

Architected and deployed a real-time fraud detection system using Graph Neural Networks, reducing fraudulent transactions by 18% and saving the client over $2.5M annually.

Developed and productionized a recommendation engine for an e-commerce platform, increasing user engagement by 22% and boosting conversion rates by 15% within six months.

Optimized existing deep learning models for inference speed, achieving a 30% reduction in latency and cutting cloud infrastructure costs by 10% through efficient resource utilization.

Mentored a team of 3 junior ML engineers, establishing best practices for model development, code reviews, and MLOps pipelines using Kubeflow and Azure ML.

Designed and implemented A/B testing frameworks for ML models, ensuring robust evaluation and iterative improvement of deployed solutions based on key business metrics.

DataGenius Corp

Machine Learning Engineer

Seattle, WA, US

Jul 2018

→

Feb 2021

Designed, developed, and maintained machine learning models and data pipelines to support predictive analytics and automation initiatives across various departments.

Developed a predictive maintenance model for industrial machinery using time-series data, forecasting equipment failures with 92% accuracy and reducing unplanned downtime by 25%.

Implemented robust ETL pipelines using Apache Spark and Airflow to process terabytes of raw data, improving data availability for ML training by 40% and reducing processing time by 3 hours daily.

Collaborated with product teams to integrate ML-powered features into core applications, enhancing user experience and contributing to a 10% increase in product adoption.

Researched and experimented with various machine learning algorithms (e.g., XGBoost, Random Forest, SVM) to identify optimal solutions for classification and regression tasks, improving model precision by up to 5%.

Containerized ML models using Docker and deployed them onto AWS SageMaker, streamlining the deployment process and enabling rapid iteration of model versions.

University of Central Research Lab

Research Assistant (Machine Learning)

Central City, IL, US

Sep 2016

→

Jun 2018

Conducted research on novel deep learning architectures for natural language processing, contributing to academic publications and advancing state-of-the-art methods.

Developed a novel neural network architecture for sentiment analysis on social media data, achieving a 3% improvement in F1-score over baseline models.

Authored and co-authored 2 peer-reviewed publications in top-tier AI conferences (e.g., ACL, EMNLP), presenting findings to the academic community.

Implemented and evaluated various natural language processing techniques, including word embeddings, recurrent neural networks, and attention mechanisms, for text classification tasks.

Managed and preprocessed large-scale textual datasets, ensuring data quality and readiness for model training and evaluation.

University of Central

Central City, IL, United States of America

Sep 2013

→

Jun 2018

State University

Stateville, NY, United States of America

Sep 2009

→

May 2013

Attention-Based Neural Networks for Cross-Lingual Sentiment Analysis

Nov 2018

Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)

Presented a novel attention mechanism for improving sentiment analysis performance in low-resource languages.

Transfer Learning for Domain Adaptation in Text Classification

Jul 2017

Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL)

Explored effective transfer learning strategies to enhance text classification models across different domains.

AWS Certified Machine Learning – Specialty

Oct 2022

Amazon Web Services (AWS)

Deep Learning Specialization

Mar 2019

Coursera (deeplearning.ai)

Programming Languages

Python, SQL, Java, R, Bash.

Machine Learning Frameworks

TensorFlow, Keras, PyTorch, Scikit-learn, XGBoost, LightGBM, Hugging Face Transformers.

Deep Learning

CNNs, RNNs, LSTMs, Transformers, GANs, Reinforcement Learning, NLP, Computer Vision.

Cloud Platforms

AWS (SageMaker, EC2, S3, Lambda, EKS), Azure (Azure ML, AKS, Data Lake), GCP (AI Platform, BigQuery, GKE).

MLOps & Deployment

Docker, Kubernetes, MLflow, Airflow, CI/CD, FastAPI, Streamlit, API Development.

Data Engineering & Databases

Spark, Pandas, NumPy, SQL, NoSQL, PostgreSQL, MongoDB, Data Warehousing.

Tools & Methodologies

Git, Jupyter, VS Code, Agile, Scrum, Experiment Tracking, Model Monitoring.

Data Science

Statistical Analysis, Predictive Modeling, Feature Engineering, A/B Testing, Data Visualization, Exploratory Data Analysis (EDA).

Healthcare Diagnostic Assistant (Personal Project)

Jan 2023

→

Aug 2023

Developed an AI-powered diagnostic assistant to aid medical professionals in preliminary disease identification from patient data, leveraging deep learning for image and text analysis.

Financial Market Prediction Model (Personal Project)

May 2022

→

Dec 2022

Created a machine learning model to predict stock market trends using historical data, news sentiment, and macroeconomic indicators.