United States
Neuroscience, robotics, and electrical engineering. Founder of the first Neurotech Society in the UK, at Imperial College. Moved to the US as a Thouron Scholar, and founded Penn Neurotech Society. Met awesome people and mentors through UPenn's Engineering Entrepreneurship Fellows program. Started Phyzarre, a project that aims to bring science concepts to secondary school students through creative writing and interviews with leaders in fields ranging from astrophysics to neuroscience to sustainability. Outside academia, I have worked on short films: starting from horror and shifting to dark surrealism and satire. Sports-wise, I was lucky enough to run for London in national cross-countries before covid and was a regional-level butterfly swimmer. Now I enjoy improv comedy, open-water swimming and hip-hop dance, especially popping & locking. Love talking to people with similar passions!
Developing a low-latency, resource-efficient flow-detection algorithm for onboard FPGA-based drone control (ICRA ’26).
Pre-clinical Testing Team - Designed a high-throughput, mixed-signal PCB to reduce neural device characterization time. - Designed a circuit-level emulator reproducing neuronal spiking patterns for integration with thermal/mechanical testing. - Developed a firmware-controlled evoked-response recording setup for time-critical testing. - Worked on system-level debugging and test strategy, following strict SOPs and ISO standards.
Robotics team, Data Science Centre in the Technology Innovation Division. In collaboration with Medicaroid and the University of Tokyo. - Prototyped robot hardware components (Fusion360) and redesigned end-effector actuation mechanism. - Built a python-based robot controller pipeline, integrated with RoboDK, to optimize the kinematics of the system. - Designed an ergonomic controller interface and implemented the MCU firmware using a state-machine architecture for button handling. - Onsite visits to collaborators and different arms of the company.
Competitive program worth 5,000 CHF to study biotechnology, specifically neuroinformatics and biomedical engineering. Project involved: - An algorithm to convert EMG muscle signals to real forces applied to fingers on a robotic hand. This is implemented on a neuromorphic chip, which emulates signals in neurons and provides a highly efficient alternative to standard (Von-Neumann) computing systems. Skills: neuromorphic computing, Python programming, chip-in-the-loop learning algorithms, spiking neural networks (SNNs). - Creating a 3D-printed robotic glove to measure ground-truth forces applied by each finger. This acts as the labels when training the chip that will then control the robotic hand. Skills: programming in C, Arduino, electronic circuit design, CAD (SolidWorks), CAM, clean room.