Zikang Song
Doctor of Philosophy candidate
His PhD brings together neuroscience, clinical research and artificial intelligence, says Zikang Song who came to AUT as an international student from China.
“My PhD research focuses on machine learning and EEG-based epileptic seizure prediction. I’ve always been interested in how computational methods can help us make sense of complex brain signals, and epilepsy is an area where better prediction tools could make a real difference to people’s lives.
“A lot of research in this area reports very strong results, but my work looks more carefully at whether these methods would still be reliable in more realistic situations, especially when they’re tested on new patients. A PhD gave me the chance to go deeper than just building models. It let me ask a harder question: are these models actually reliable enough to be useful in a clinical setting? This matters because a prediction tool would only be useful if people could trust it and if it didn’t create too many false alarms. I like that the research is technical, but still connected to something that could affect people’s everyday lives.”
Zikang’s PhD research is supervised by Associate Professor Mangor Pedersen and Dr Dion Henare.
Academic freedom encouraged
Zikang has enjoyed the freedom to explore a difficult research question in depth while still feeling supported.
“AUT has given me space to develop my own ideas, test them carefully and have honest conversations with my supervisors when the results are more complicated than expected. I’ve learned how to take a research idea and develop it step by step, from reading and planning through to analysis, writing and presenting the work. I enjoy how interdisciplinary the work is. Some days I’m thinking about EEG and epilepsy, and on other days I’m working through machine learning, statistics or clinical interpretation. That mix has kept the project challenging in a good way.
“My biggest achievement has been developing the confidence to share my research through writing and presentations. My topic can be quite technical, so learning how to explain it clearly to different audiences has been an important part of my PhD journey. Through AUT, I’ve been able to turn my research into manuscripts and presentations, and that process has helped me grow both academically and personally.”
He wouldn’t hesitate to recommend AUT to other students.
“I’d recommend AUT to students who want to do meaningful postgraduate research with a practical focus. The programme has given me strong academic guidance while also encouraging independence and critical thinking. For students interested in neuroscience, health technology or data-driven research, AUT is a good place to learn how to connect technical work with real clinical questions.”
Advice for other students
Zikang, who expects to complete his PhD in 2028, has some great advice for other students considering doctoral study.
“Small habits make a big difference: reading regularly, keeping good notes, sharing drafts and listening to feedback. I’d also encourage students to make use of their supervisors and the wider AUT community. You don’t have to figure everything out alone.”
Uncertainty is normal, he adds.
“My advice is to ask questions early and not to be afraid of uncertainty. In postgraduate study, it’s normal not to have all the answers straight away.”
