CV
You can download my current CV as a PDF and view a short summary here.
Current Position
PhD student, Carnegie Mellon University
School of Computer Science, Computational Biology Department
Advisor: Dr. Jose Lugo-Martinez
Research Focus
My primary research focus is agentic scientific discovery, autonomous laboratories, and laboratory automation. I develop AI agents, reinforcement-learning methods, generative models, and ML systems for reliable scientific decision-making.
Selected Research Experience
- Model Hardware Standard research preview (2026): Led CMU’s work using MHS; built drivers and orchestration for an AI agent to coordinate a liquid handler, plate reader, robotic arm, and cameras in closed-loop dose-response experiments. Case study
- simPCL: Developing a simulator and benchmark generator for planning and fault recovery in programmable cloud laboratories.
- AI Scientist Summer Workshop (2026): Contributed talk on agentic fault recovery for autonomous orchestration of programmable cloud laboratories.
Education
- PhD, Computational Biology — Carnegie Mellon University (2024–2029)
- MSc, Computer Engineering — Bilkent University (2021–2024, GPA: 3.9/4.0)
- BSc, Computer Engineering — Shiraz University (2016–2021, GPA: 3.5/4.0)
Selected Publications
- Ozden, F., Barazandeh, S., et al. (2026). RNAGEN: A generative adversarial network-based model to generate synthetic RNA sequences to target proteins. ChemBioChem, 27(13): e202500445.
- Barazandeh, S., et al. (2025). UTRGAN: Learning to generate 5’ UTR sequences for optimized translation efficiency and gene expression. Bioinformatics Advances, 5(1): vbaf134.
- Tabrizi, S.S., Barazandeh, S., et al. (2025). RNAtranslator: Modeling protein-conditional RNA design as sequence-to-sequence natural language translation. PLOS Computational Biology, 21(10): e1013541.
Seeking
Summer 2027 research and applied-science internships on teams building and deploying ML systems, primarily for agentic scientific discovery, autonomous laboratories, lab automation, and AI for science, and also in generative AI, reinforcement learning, and ML systems.