Best Bioengineering MSc Graduate of the Year: Bianca-Maria Cosma!
Since 2020, Delft Bioengineering Institute (BEI) organizes a cross-campus competition for MSc students who performed remarkably well at their graduation projects in bioengineering. This year, ten very impressive theses were submitted. After a strenuous review and discussion, the jury agreed that Bianca-Maria Cosma (MSc Computer Science – Bioinformatics), Lohit Gandham (MSc Computer and Embedded Systems Engineering) and Laura Hoebus (MSc Aerospace Engineering – Aerospace Structures & Materials) have delivered the most innovative, interdisciplinary bioengineering projects of 2024. On top of eternal fame, they will receive personal cash prizes of €1000, €500 and €250.
# | MSc Graduate | Thesis title | MSc | Supervising BEI PI(s) |
1 | Bianca-Maria Cosma | The artificially generated microbiome – A study on the generation and potential use cases of predicted meta-omics data | Computer Science – Bioinformatics | Thomas Abeel (EWI) |
2 | Lohit Gandham | Neuromorphic Compression and Distributed Computing for On-Implant Neural Signal Processing in Brain-Computer Interfaces | Computer and Embedded Systems Engineering | Rajendra Bishnoi (EWI), Nergis Tömen (EWI) |
3 | Laura Hoebus | Development of passive anti-icing surfaces by incorporating ice-binding proteins | Aerospace Engineering | Santiago Garcia (LR), Baris Kumru (LR) |
Bianca-Maria Cosma
For her MSc thesis, The artificially generated microbiome: A study on the generation and potential use cases of predicted meta-omics data, Bianca was awarded the highest possible grade of 10, earning her the distinction cum laude. Her work focused on investigating whether machine learning could bridge the gaps that arise when certain molecular data types are unavailable. Achieving this through computational methods would represent a significant advancement in many life science research domains that critically depend on diverse data modalities. For example, metagenomics data is cheap to produce but hard to interpret, while metabolomics data is easier to link to diseases but much harder and more expensive to acquire. Her research demonstrated that affordable metagenomics data can indeed be used to computationally derive the more useful metabolomics data. Additionally, she also investigated the integration of multiple data modalities to increase predictive performance.
Lohit Gandham
In his MSc thesis, entitled Neuromorphic Compression and Distributed Computing for On-Implant Neural Signal Processing in Brain-Computer Interfaces, Lohit proposed a novel neuromorphic compression system designed to overcome the challenges of neural signal processing in Brain-Computer Interfaces (BCIs). The system combines delta-modulated analog-to-digital converters (Δ-ADC) with Spiking Neural Networks (SNNs) to improve data transmission, power efficiency, and signal quality. With compression rates up to five times higher than state-of-the-art methods, Lohit’s proposed approach offers transformative solutions for neural prosthetics and implantable medical devices. This work advances the field of BCIs, a vital area where biological and technological systems intersect, contributing to innovations in health and neural rehabilitation. By efficiently compressing and processing neural signals, Lohit’s thesis work bridges the gap between technology and biology.
Laura Hoebus
For her MSc thesis entitled Development of passive anti-icing surfaces by incorporating ice-binding proteins, Laura was awarded a 9 out of 10. In her research, Laura delved into the world of protein chemistry and studied the effect on ice propagation and growth of anti-freeze (AFP) and ice-nucleating proteins (INP) from two different biological sources grafted onto metal surfaces and into polymer hydrogel networks. As a result of a combination of a range of techniques and smart experiments, Laura’s research allowed for rationalizing the effect of the degrees of freedom of proteins as a key factor contributing to low or high surface icing (i.e. restricting protein mobility converts ice suppressor “AFPs” into ice-promotor). The outcome of her research has been discussed with other experts working on anti-ice proteins and anti-icing surfaces as it unveils new insights for the community.
