Research-oriented software engineer with a strong academic foundation in computer science, mathematics, and physics, and multiple peer-reviewed IEEE publications.
I hold a Master’s degree in Computer Applications (Data Science) from RV College of Engineering, where my research focused on distributed systems, cryptography, computer vision, and data-driven optimization. I have authored multiple IEEE conference papers and have hands-on experience building scalable, fault-tolerant systems in both academic and industry settings. I am currently preparing for PhD applications with research interests in data-centric AI and distributed systems.
BSc (CS, Math, Phy)
St. Joseph's College
MCA (Data Science)
RV College of Engineering
I am interested in building theoretically grounded and system-aware AI methods that scale to real-world data, with a focus on learning under distribution shift, system constraints, and imperfect data.
Learning systems that prioritize data quality, structure, and provenance over model complexity. My interests include dataset curation, noise-robust learning, weak supervision, and evaluation under real-world data imperfections.
I am particularly motivated by settings where data is distributed, partially observed, or evolves over time.
Designing reliable, fault-tolerant, and low-latency systems for large-scale learning and data processing. I am interested in distributed training, data pipelines, consistency models, and system-level tradeoffs between performance, correctness, and scalability.
This includes bridging systems abstractions with learning objectives.
Developing AI systems that are interpretable, secure, and resilient to failures and adversarial conditions. My interests include robustness, privacy-preserving learning, decentralized architectures, and trustworthy deployment of AI in safety-critical settings.
I am drawn to problems at the intersection of theory, systems, and deployment.
A detailed overview of my academic background, research experience, publications, and engineering work. This resume is tailored for research-oriented and PhD applications in Computer Science.
Peer-reviewed research work published in IEEE conferences, focusing on scalable systems, security, computer vision, and data-driven optimization.
End-to-end software and systems projects translating research concepts into scalable, real-world applications and infrastructure.
Select professional and academic certifications reflecting formal training in computer science, data systems, and applied engineering practices.
Open to research collaborations, PhD opportunities, and academic discussions in computer science and related areas.
+91 8217497190
leandraandersona@gmail.com
linkedin.com/in/leandra-anderson/
