Hui Han: Tiny Machine Learning at Luleå University of Technology

Hui Han, works as a Senior Lecturer for the Machine Learning Group, with the WASP (Wallenberg AI, Autonomous Systems, and Software Program) professorship at Luleå University of Technology (LTU). Her work bridges cutting-edge machine learning research with practical applications, focussing on innovative solutions that make tangible impacts on everyday life.

Currently, Hui Han’s research focuses on Tiny Machine Learning (TinyML), a ground-breaking field that develops compressed machine learning models to run on small, resource-constrained devices like microcontrollers. TinyML enables these tiny devices to make smart decisions without relying on the Internet or cloud services, which brings several advantages, such as enhanced privacy, reduced latency, and energy efficiency. These capabilities are crucial for many real-life applications, like healthcare (e.g., tracking vital health signs with wearables), agriculture (e.g., smart sensors for monitoring soil conditions), industry (e.g., anomaly detection), retail (e.g., smart shelves), transportation (e.g., vehicle sensors) and smart home devices, where require low energy consumption and real-time decision-making.

“TinyML allows us to bring intelligence directly to the edge, where data is collected. By enabling smart, energy-efficient solutions, we can address many real-life challenges, from improving healthcare accessibility to optimizing resource use in agriculture”, Hui says.

Before joining LTU, Hui Han developed expertise in multiple cutting-edge domains. Her research on Edge AI and TinyML laid the groundwork for enabling compressed machine learning models on resource-constrained devices. In parallel, she focused on creating sustainable models for supply chain management, with an emphasis on reverse logistics and the circular economy. Hui also contributed to research in social commerce, investigating how social media could enhance e-commerce, and delved into Industry 4.0 technologies like IoT, blockchain, and big data analytics. This diverse research portfolio reflects her dedication to addressing both technological challenges and real-world applications in emerging fields.

“I’ve always been driven to solve real-world problems by combining technological innovation with sustainability and practicality. It’s about making an impact beyond academic theory”, Hui says.

Bridging research and real-world impact

Hui has high praise for the Machine Learning Group at LTU, describing it as a dynamic and collaborative team dedicated to pushing the boundaries of innovation. She admires the group’s multidisciplinary approach, which fosters creativity and bridges foundational research with real-world applications. This environment has not only provided opportunities for fruitful collaborations but also allowed her to address complex problems with a broader perspective.

Beyond work, Hui has embraced the northern charm of Luleå, enjoying its scenic landscapes and unique cultural experiences. From hiking in nature to experiencing the snowy winters, she finds a refreshing balance between her professional and personal life.

“The support and camaraderie I’ve found at LTU, combined with the natural beauty of Luleå, have made this journey incredibly fulfilling,” says Hui. “It’s inspiring to be part of a community that values both academic excellence and personal growth.”