I’m a Master’s student in Data Intelligence at San Jose State University with a background in data engineering and distributed systems. I enjoy building scalable systems and experimenting with agentic AI applications. Previously, I worked at Optum and Hindustan Unilever, where I developed data pipelines and analytics solutions. I’m passionate about combining real-world systems engineering with intelligent AI workflows.

Technical architecture, intelligent data systems, scalable data pipelines, machine learning, adaptive AI-driven insights, agentic AI applications, distributed systems, cloud platforms, big data tools, backend systems, AI-enabled workflows.

Currently, I’m working on building intelligent data systems that combine scalable pipelines with machine learning to improve real-world decision making. The goal is to design architectures that can efficiently process large volumes of data while enabling adaptive, AI-driven insights that generalize across diverse and dynamic environments.

Shreya Akotiya is an Applied Data Intelligence graduate student at San Jose State University with a strong focus on machine learning, deep learning, and generative model applications. She builds end-to-end ML solutions using Python and SQL, with hands-on experience in scikit-learn as well as deep learning frameworks like TensorFlow, Keras, and PyTorch. Her work spans predictive modeling and large-scale analytics ranging from credit-risk and fraud identification using data-driven models in banking to a stock price–volume analysis pipeline where she built and optimized a multi-linear regression model to quantify market relationships. She also developed StreamTide, a big-data pipeline processing 660M+ NYC taxi records, applying streaming/probabilistic methods (e.g., Bloom Filters, LSH) and p

I am looking to learn more and connect with people working in applied AI/ML (especially predictive modeling and deep learning), data engineering for ML (Spark/Kafka pipelines, MLOps), and responsible AI (privacy-aware analytics). I’m open to collaborating on real-world projects where we can build end-to-end solutions from data to model to deployment.

We’re currently developing a mirror speech system that aims to reduce the accent gap using neural networks and AI. The goal is to help speech models better understand and adapt to unseen accents by learning accent-invariant representations and dynamically correcting pronunciation differences. By improving generalization across diverse speaking styles, we hope to make voice technology more inclusive, accurate, and accessible globally.

I’m a software developer with a strong background in Java, Python, and full-stack application development, currently pursuing my Master’s in Computer Science at Cleveland State University. I enjoy building scalable systems, APIs, and Android applications, and I have hands-on experience with machine learning, time-series forecasting, and cloud-based development. I’ve worked on both industry and research projects, ranging from POS systems and CI/CD pipelines to smart grid energy forecasting and fraud detection using ML and quantum ML. I’m passionate about solving real-world problems through clean, efficient code and continuously learning new technologies.

I am particularly interested in building scalable backend systems, developing intelligent applications using machine learning, and exploring the intersection of classical and quantum computing for real-world problems. I’m eager to collaborate on projects involving cloud-native development, API design, Android applications, and AI-driven solutions. I also enjoy connecting with others to share knowledge, learn about emerging technologies, and contribute to innovative software and research

Right now, I’m mainly working on time-series forecasting for smart grid energy consumption as part of my research developer role at Cleveland State University. I’m experimenting with models like LSTM, ARIMA, and Facebook Prophet in Python to predict hourly and daily energy demand using historical load data, weather conditions, holidays, and calendar features. I’m focused on feature engineering and model evaluation to improve accuracy and make the predictions more useful for real-world energy

Vince is the founder of Sphere AI - an AI Data marketplace and robotic data supplier. Background in Computer science, finance, accounting and business

AI. AI Data Infrastructure. Full stack software development

Sphere AI - Sphere organizes the world data for AI