Faculty Research
Each faculty member has a specific area of expertise that inspires their work, and are often eager to share these interests with student research assistants.
Sean Barker
- Smart buildings
- data analytics
- Sustainability
- greening homes and IT equipment
- Distributed systems
- Cloud computing
- resource management in big data systems
David Byrd
- Machine Learning in Financial Markets
- Privacy-Preserving Federated Learning
- Securely learning from distributed training data
- Simulation of Complex Systems
- Geographically-distributed mobile devices
- Electronic financial markets
- Responsible Machine Learning (e.g. learning not to spoof a financial market)
Jeová Farias
- Statistical Learning and Spectral Techniques for Image Processing.
- Semi-supervised and Unsupervised Learning
- Discrete Optimization
- New Interpretable Models for Deep Learning
- Applications in Biomedical and SAR imagery.
Cibele Freire
- Resilience problem in databases
- Inconsistent databases
- Computational complexity, with special interest for dichotomy results
Sarah Harmon
- Computational Creativity
- Human-Computer Interaction
- Narrative Intelligence
Mohammad Irfan
- Interdisciplinary areas that combine artificial intelligence with:
- sociology (e.g, influence in social networks)
- economics (e.g., microfinance markets)
- arts (e.g., authentication of Jackson Pollock's paintings)
- Computational geometry and graph drawing
Bobak Kiani
- Quantum computation
- Quantum algorithms
- Machine learning theory
Jeff Knockel
- Security and privacy
- Censorship measurement
- Surveillance measurement
- Internet freedom
Christopher Martin
- Computer Science Education
- Making Computer Science more approachable and explorative
- Developing new mindful curriculum and interactive/personalizable assignments
- Education Technology (EdTech)
- Intelligent Tutoring Systems
- Visualizations and Gamification
- Integrating tools into the classroom
- Machine Learning
- Computer Science Ethics
Laura Toma
- Efficient algorithms and data structures for large data (cache-efficient, resource-efficient, parallel)
- Algorithms for spatial data
- Algorithm engineering