Associate Professor Sonika Tyagi has a Ph.D. in Computational Biology and over 17 y of work experience in academia & industry. Throughout her postgraduate studies in India, she was awarded a merit-based or competitive research fellowship. In 2004 she visited the University of Queensland, Australia on a research fellowship. After 3y of postdoctoral research, she worked in research industry leadership roles for 7y. In 2018 she joined a teaching and research position at Monash University to establish her research program. She is currently an Associate Professor at RMIT University and Machine Learning co-investigator in the SuperbugAI flagship project at Monash University.

 

 

Her expertise is in developing new machine learning tools and pipelines, and apply these methods to solve biological research questions. Tyagi has developed novel probabilistic models based on natural language processing to predict non-coding RNA structures ab-Initio and has implemented deep learning approaches to predict functional motifs on these structures. She applied these methods to successfully predict novel miRNAs and detected DNA variants causing melanoma. All of these methods are openly accessible to the community via GitHub/GitLab.

 

Tyagi was a chief investigator on an NHMRC funded project to computationally study human pregnancy (2017). In 2020 she was also awarded an Early Mid Career Research fellowship by the Australian Academy of Science to develop AI models for the diagnosis of preterm birth. This was followed by an NHMRC success in 2021 for Targeted Delivery of Nucleic Acid Therapeutics for Preventing Preterm Birth (2022-25).

 

She has received industry funding (2018), and University grants (2029-20) for developing AI resources to study diseases.

 

Sonika was one of the finalists for Women in AI (WAI) awards - Australia/Newzealand 2022 in the "AI in Health" category.