Guest Speaker: Dr. Shiree Hughes

Date: Monday, February 9, 2026 Time: 8:30 AM - 9:00 AM EST Format: In-class discussion

Dr. Shiree Hughes

About Dr. Hughes

Dr. Shiree Hughes is a Software Engineer at General Motors on the Big Data team, where she designs and implements data pipelines using Hadoop, Spark, Kafka, and other distributed technologies. Her work supports automotive applications that process massive volumes of data to improve vehicle performance, safety, and customer experience.

Dr. Hughes brings a unique perspective to data engineering, having transitioned from embedded systems and mobile development into large-scale distributed computing. This background gives her deep insight into the full stack of modern data systems, from the sensors and devices that generate data to the cloud infrastructure that processes it.

Beyond her technical contributions, Dr. Hughes is dedicated to increasing diversity in STEM fields. She served as President of ACM-Women from 2015 to 2017 and continues to mentor students and early-career engineers.

Education

Ph.D., Computer Science - Florida Atlantic University Dissertation: “Changing Consumer Behavior through Ambient Displays in Smart Cafeterias and Detecting Anomalous Reporting Behavior in Wireless Sensors”

Her doctoral research explored two interconnected themes: using technology to influence sustainable behavior and building reliable sensor networks. At the Institute for Sensing and Embedded Systems Networks Engineering (I-SENSE), she led an interdisciplinary team developing the W.A.S.T.E. R.E.D.U.C.E. system (Waste Auditing Sensor Technology to Enhance the Reduction of Edible Discards in University Cafeterias & Eateries), which used sensor networks and ambient displays to reduce food waste on college campuses through social norming techniques.

M.S., Computer Engineering - University of Florida, CISE (2012) Dr. Hughes is a proud Gator who earned her Master’s degree from UF’s Department of Computer & Information Science & Engineering.

B.S., Mathematical Science - Clemson University (2010) Her undergraduate foundation in mathematics provides the analytical rigor that underpins her work in data engineering and distributed systems.

Professional Experience

General Motors - Software Engineer, Big Data Team (Current) At GM, Dr. Hughes works on the data infrastructure that powers modern automotive systems. Her responsibilities include:

Designing and implementing data pipelines using Apache Hadoop, Spark, and Kafka
Building systems that process streaming data from connected vehicles
Developing solutions for data quality, reliability, and scalability
Collaborating with data scientists and analysts to enable analytics at scale

Motorola Solutions - Embedded Systems Engineer, Android Team Before transitioning to big data, Dr. Hughes worked on mobile platforms at Motorola Solutions:

AOSP (Android Open Source Project) development for Hardware Abstraction Layer (HAL) and Linux Kernel
Device driver development and system-level debugging
Defect repair and unit testing in an Agile/Scrum environment
Experience that provides deep understanding of how data originates at the device level

Technical Expertise

Category	Technologies
Big Data	Apache Hadoop, Apache Spark, Apache Kafka, HDFS, YARN
Programming	Java, C/C++, Python, Scala, MATLAB
Embedded	Linux Kernel, Device Drivers, Atmel AVR/SAM4L
Cloud	Microsoft Azure (Certified Fundamentals)
Methodologies	SAFe 5 Practitioner, DevOps Practitioner

Research Publications

Dr. Hughes has published research on sensor networks, sustainability technology, and anomaly detection:

W.A.S.T.E. R.E.D.U.C.E. - Waste Auditing Sensor Technology to Enhance the Reduction of Edible Discards in University Cafeterias & Eateries
Research on automated driver generation and sensor network monitoring
Work on detecting anomalous reporting behavior in wireless sensor networks

Leadership & Community

President, ACM-Women (2015-2017) - Led initiatives to support women in computing
Board Member, Clemson University Alumni Association - Contributes to university advancement
STEM Advocacy - Passionate about increasing diversity in technology fields and mentoring the next generation of engineers

Pre-Session Preparation

Please review these resources before the session to make our discussion more productive:

Required Reading

Resource	Description	Time
Apache Kafka Introduction	Official introduction to Kafka’s distributed streaming platform	15 min
Apache Spark Quick Start	Official Spark getting started guide	20 min
Hadoop Tutorial (GeeksforGeeks)	Overview of HDFS, MapReduce, and YARN	20 min

Optional Deep Dives

Resource	Description
Apache Kafka for Beginners (DataCamp)	Comprehensive beginner guide to Kafka concepts
Spark By Examples	Hands-on Spark tutorials with code examples
Building Blocks of Hadoop (Pluralsight)	Deep dive into Hadoop architecture

Video Resources

Resource	Description	Time
Big Data Explained	Overview of big data concepts	10 min

Come Prepared With Questions

Think about technical questions you’d like to ask Dr. Hughes:

Data Pipeline Architecture

How do you decide when to use Kafka vs. Spark Streaming vs. batch processing?
What does a typical data pipeline look like for processing vehicle telemetry?
How do you handle schema evolution when data formats change?

Scaling Challenges

How does GM handle real-time data from millions of connected vehicles?
What strategies do you use for partitioning data across a Hadoop cluster?
How do you manage backpressure when data ingestion exceeds processing capacity?

Data Quality & Reliability

How do you detect and handle anomalies in sensor data from vehicles?
What monitoring and alerting do you have for pipeline failures?
How do you ensure exactly-once semantics in distributed systems?

Technology Trade-offs

What are the pros and cons of HDFS vs. cloud object storage (S3/GCS)?
When would you choose Spark over Flink for stream processing?
How do you balance latency vs. throughput in your pipelines?

Connect

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