I am Senior Lecturer in Electronics at The Dyson Institute. I previously worked at the University of Sussex for over five years as a Lecturer in Mechatronics and Teaching Fellow in Electrical Engineering. During that time, I designed, developed and implemented two new courses in Robotics at UG and PG level; and I was the lead of those programs for four years. Before becoming a Teaching Fellow, I was an Associate Tutor for five years, where I supported teaching and laboratory delivery of various engineering subjects.
My teaching experience includes Electrical Machines, Power Electronics, Control Systems, Automation, and Robotics; both at UG and PG level. I have supervised over 35 UG final year student projects and 14 MSc student projects, mostly in the subjects of robotics and sensors. As part of my programme design experience I have also designed several individual modules, most of which include designing laboratory and practical activities.
The University of Sussex awarded me the ‘Award for Excellence in Teaching’ in 2017 in the category of Outstanding or Innovative Undergraduate Teaching, for co-designing and implementing an interdisciplinary module where students tackle real-life engineering problems; and students at the same University honoured me with the student-led teaching award for ‘Outstanding or Innovative Undergraduate Teaching’ in 2018 for outstanding teaching delivery. In 2016, I was awarded a government grant to implement and deliver the MSc in Robotics.
As part of my current role I lead the electronics academic team and the Hardware stream in years 3 and 4.
Why you chose to come to The Dyson Institute
I find the Academic-Industry collaboration very exciting, and this is an intrinsic element of The Dyson Institute. I think it has a very positive impact in our graduates that enjoy a great academic programme, work in a fantastic engineering environment, and develop personal and professional skills in various contexts, including amazing projects.
In terms of my professional development I found this a great opportunity to be in an Institution from its early years, having greater impact in shaping it and giving me a better insight into various elements of higher education and its relationship with industry. I also liked the possibility to collaborate with Dyson engineers in the design of modules, activities, and projects that are relevant to industry and academia.
Academic qualifications & professional memberships
PhD, BSc, FHEA, PGCertHE, AMIMechE
Research & academic papers
- Spino, Filippo, Pouryazdan, Arash, Costa, Julio, Ponce Cuspinera, Luis and Munzenrieder, Niko (2019) Directly 3D-printed monolithic soft robotic gripper with liquid metal microchannels for tactile sensing. IOPScience Flexible and Printed Electronics. Vol. 4, No. 3.
- Ciliberto, Mathias, Ponce Cuspinera, Luis and Roggen, Daniel (2019) WLCSSLearn: Learning Algorithm for Template Matching-based Gesture Recognition Systems. International Conference on Activity and Behavior Computing, Washington, USA, May 30 – Jun 2, 2019. Best Paper Award.
- Ciliberto, Mathias, Ponce Cuspinera, Luis and Roggen, Daniel (2017) Complex human gestures encoding from wearable inertial sensors for activity recognition. International Conference on Embedded Wireless Systems and Networks, Madrid, Spain, February 14-16, 2018. Published in: Giustiniano, Domenico and Koutsonikolas, Dimitrios, (eds.) International Conference on Embedded Wireless Systems and Networks. ACM – Best Demo Award.
- Ponce Cuspinera, Luis, Uetsuji, Sakura, Ordonez Morales, Francisco Javier and Roggen, Daniel (2016) Wearable beach volleyball serve type recognition. ISWC 2016: the 20th International Symposium on Wearable Computers, September 12-16, 2016, Heidelberg, Germany.
- Dunne, J F and Ponce Cuspinera, L A (2015) Optimal gear ratio planning for flywheel-based kinetic energy recovery systems in motor vehicles. Journal of Dynamic Systems, Measurement and Control.
- Roggen, Daniel, Ponce Cuspinera, Luis, Pombo, Guilherme, Ali, Falah and Nguyen-Dinh, Long-Van (2014) Limited-memory warping LCSS for real-time low-power pattern recognition in wireless nodes. 12th European Conference on Wireless Sensor Networks. Best Paper Award.