
Ganesh Sistu completed our 2-year online National MSc in Artificial Intelligence, delivered with University of Limerick, in 2020. He now uses his role as Principal Artificial Intelligence Architect at Valeo to help mentor AI research scientists as part of the work placement projects. We spoke to Ganesh about mentoring on the Centre for Research Training in Foundations of Data Science (CRT).
The CRT, funded by Science Foundation Ireland and Skillnet Ireland and supported and facilitated by Technology Ireland ICT Skillnet, is a large-scale collaborative initiative between the University of Limerick, University College Dublin, and Maynooth University to train a cohort of PhD students with world-class foundational understanding in the horizontal themes of Applied Mathematics, Statistics, and Machine Learning.
You previously studied with University of Limerick and Technology Ireland ICT Skillnet on Ireland’s first ever Masters level course in Artificial Intelligence. How did this upskilling journey shape your career and prepare you for the work involved at Valeo?
I was already working in the AI domain but the course helped me strengthen my theoretical knowledge and moreover opportunities to meet UL academic experts and widen my network.
What were your key responsibilities as a mentor in the CRT for Foundations of Data Science, and how did you support the mentee in developing essential skills and knowledge throughout their in-company placement?
Since 2019, I have mentored over 14 CRT PhD data science students, focusing primarily on advanced AI topics, particularly in autonomous driving and perception. My mentorship approach was designed to align with both the students’ skill levels and their research goals.
Before the 12-week internship began, I conducted preliminary discussions with the students to assess their expertise in deep learning, Python, and PyTorch. For those with limited exposure to Computer Vision, PyTorch, or Python, I allocated the initial two weeks of their internship for skill-building and onboarding.
The subsequent eight weeks were dedicated to the research topics assigned, ensuring students had adequate time to dive deep into experimental and research-focused challenges. During the final week, the focus shifted to documentation and presentation preparation. On the last day of the internship, the students presented their findings and outcomes to a broader audience of researchers at Valeo Ireland, providing a platform to showcase their work and gain valuable feedback.

What has the mentoring experience meant to you, both in terms of your professional growth and personal development?
Mentoring has been an invaluable experience, allowing me to interact with students from diverse academic and professional backgrounds who frequently present innovative, out-of-the-box ideas. Through these engagements, I have gained the confidence to lead applied research teams working on complex, open-ended problem statements.
The projects assigned to PhD students are often experimental and research-intensive, providing the organization with valuable insights to help determine strategic research directions. This experience has significantly enhanced my ability to define and refine research goals aligned with the organization’s long-term vision.
Why is collaboration between academic programmes and industry, such as the partnership seen with the CRT in Foundations of Data Science, crucial for driving innovation in artificial intelligence and data science?
Our extensive experience with clients across the EU, US, and Asia provides us with a unique perspective on emerging trends and demands within the AI sector, particularly in the automotive industry. Collaborating with the CRT in Foundations of Data Science has been especially impactful, as the program actively involves industry experts like us in shaping its scope and long-term vision.
This partnership creates a healthy balance between meeting real-world industry needs and fostering academic research into new AI advancements. In my view, this collaborative approach has been a key driver of the program’s overall success and the growth of its innovation ecosystem.
Looking to the near future, what specific areas of AI and data do you see as the most critical, or exciting, for technology companies?
Current trends in AI are highly specialized, with critical technologies emerging based on their application areas. In the context of autonomous driving, the most promising and impactful areas include:
- Large Language Models (LLMs): Advancing the development of unified neural network architectures for end-to-end autonomous driving systems, with a strong focus on explainability and transparency.
- Neural Radiance Fields (NeRF) and Gaussian Splatting: These technologies facilitate the creation of digital twins and generate realistic synthetic datasets, crucial for both training and validation processes.
- Reinforcement Learning and Imitation Learning: These approaches enable the creation of fully data-driven autonomous driving systems, enhancing adaptability and performance in real-world conditions.
These advancements are shaping the future of AI across various industries, particularly in autonomous systems and data-driven innovation.
Creating a pipeline of highly skilled graduates to engage with industry and drive innovation in the Irish economy.
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