Sailaja Rajanala

Sailaja Rajanala is a Lecturer at the Malaysian School of Information Technology, Monash University. She specializes in interdisciplinary research at the intersection of machine learning, computer vision, and cryptography. Her work focuses on areas such as adversarial machine learning, generative models, and ethical AI applications. Rajanala is a Chief Investigator in the Æinstein project (2024–2026), exploring adversarial AI in materials discovery domains.

Her research interests include facial expression analysis, bias detection in AI systems, and cryptographic applications of neural networks. Notable contributions include techniques for distinguishing synthesized music from human performances and causal analysis of micro-expression recognition systems. She actively publishes in top-tier conferences like IEEE ICASSP, GLOBECOM, and APSIPA ASC.

Rajanala collaborates internationally, with projects addressing topics like unbiased generative editing (GENIE), medical image denoising, and legal text classification using BERT models. Her publications reflect a strong emphasis on practical AI solutions with ethical and security considerations.