Doctoral Consortium Panellists / Speakers
Knowing Yourself and Learning from Others: Transforming from A Student to A Faculty Member in Universities
Abstract:
Being a faculty member is one of the most important aims for researchers. This is not an easy task, especially for researchers who just graduated from universities. The main difficulties include 1) time management among supervision, securing research funds, teaching, and publications; 2) mindset change from students to faculties; 3) general work-life balance. In this talk, I will list some points regarding the above difficulties based on my experience and hope it will be useful for more junior researchers.
Short Biography:
Dr Feng Liu is a machine learning researcher with research interests in hypothesis testing and trustworthy machine learning. Currently, he is a Lecturer (in Machine Learning) at The University of Melbourne, Australia, and a Visiting Scientist at RIKEN-AIP, Japan. He has served as an Area Chair for ICML, NeurIPS, ICLR. He also serves as an Editor for ACM Transactions on Probabilistic Machine Learning, Associate Editor for the International Journal of Machine Learning and Cybernetics, and Action Editor for Neural Networks. He has received the ARC Discovery Early Career Researcher Award, the Outstanding Paper Award of NeurIPS (2022), the Outstanding Reviewer Award of NeurIPS (2021), and the Outstanding Reviewer Award of ICLR (2021).
Women in Data Computer Science
Abstract:
In this talk, I will delve into the multifaceted journey of women in computer science, with a particular focus on the current situation of female academics in Australia. Despite the significant strides made in gender equality, women continue to face unique challenges in the academic sphere. Drawing from recent data and studies, I will provide an overview of the representation, achievements, and hurdles encountered by female academics in Australia today. Building on this context, I will share my personal experiences and strategies that have enabled me to overcome the obstacles faced by women in this field. From navigating gender biases to balancing professional and personal responsibilities, I will discuss practical approaches that have proven effective in my career. Finally, I will offer actionable suggestions and considerations for female academics in computer science. These insights aim to empower women to thrive in their careers, foster supportive networks, and advocate for systemic changes within their institutions. Join me as we explore the current landscape, share personal narratives, and envision a more inclusive future for women in computer science.
Short Biography:
I serve as a Lecturer at the University of Adelaide. I received my PhD in Computer Application Technology from Jilin University, China, and was a joint PhD student at Data Science group, University of Queensland, Australia. My primary research interest lies in Sequential Data Analysis and its Applications (Medical Data Analytics, EEG Data Analytics, Brain-computer Interface, Social Media Data Analytics, Sentiment Analysis). I have authored 40+ peer-reviewed papers published in prestigious journals and top-tier international conferences. My work has garnered 1,097 citations, with an h-index of 12 and an i10-index of 16 (Google Scholar), featuring 20+ publications in journals and conferences ranked CORE A*/A or Q1. My contributions to the field have been acknowledged with multiple awards, including Best Papers and Best Student Paper. In the realm of professional service, I have been actively involved as a Program Committee member for several esteemed conferences, including AAAI, IJCAI, CIKM, SDM, PAKDD, PRICAI, IJCNN, etc. I served as Proceedings Chair for ADMA, APWeb-WAIM, and AJCAI, as well as an Area Chair and Meta Reviewer for ADMA.
Empowering the Future: Beyond Academia
Abstract:
My journey into the realm of data has been driven by an intrinsic curiosity and a deep-seated passion for understanding complex information. The transition from academia to the professional sphere marked the beginning of a series of enriching experiences and challenges that have significantly shaped my career. This transition not only honed my problem-solving abilities but also underscored the importance of resilience and ongoing learning. Working in the industry has afforded me invaluable opportunities to tackle real-world challenges, bridging the gap between theoretical knowledge and practical application. These experiences have profoundly enhanced my problem-solving skills and allowed me to address issues that often serve as catalysts for research and innovation. Unlike the more static nature of academia, the dynamic environment of the industry encourages a balance between in-depth research and broader exploration, offering a more immediate impact on societal needs. As a woman navigating a predominantly male-dominated field, I have observed firsthand how diversity can act as a powerful driver of innovation. Women in technology bring unique perspectives that not only contribute to more inclusive and effective solutions but also lead transformative initiatives with far-reaching benefits for society. This diversity of thought is essential for fostering creativity and advancing technological progress.
Short Biography:
Dr.Tahereh Pourhabibi is a distinguished leader in data science and technology, bringing a wealth of expertise honed through a journey of academic excellence and practical application. With a foundation laid in rigorous academic pursuits, Dr. Pourhabibi earned her Ph.D. in Business Information Systems from RMIT University, where she delved deeply into anomaly detection, fraud detection and natural language processing. Armed with her doctoral research on graph-based anomaly detection, Dr. Pourhabibi transitioned seamlessly into the industry, driven by a passion to translate theoretical knowledge into impactful real-world solutions. She quickly established herself as a professional data scientist and started her career at National Australia Bank (NAB), leveraging her academic insights to lead the development of an innovative data-driven risk analytics framework, using data engineering and natural language processing to transform complex risk information into actionable insights. After her impactful tenure at NAB, Dr. Pourhabibi transitioned to Intelia, an Australian-owned company known for its cutting-edge projects in data and AI. Throughout her career, she has been a passionate advocate for women in IT, emphasizing the importance of diversity and inclusion to drive creative problem-solving and develop inclusive products.