Cohort 2 (2025 Entry)

I believe that achieving truly equitable data privacy solutions requires developing frameworks that are context-sensitive and capable of addressing systemic inequities.

I completed my undergraduate Bsc(Hons) degree in Computer Science at Cardiff University and went on to do a master’s (MS) at Columbia University.

At Columbia University, my research focused on the application of differential privacy to sensitive datasets, investigating how we can design privacy frameworks that offer stronger guarantees, particularly for marginalized communities.

I have a particular interest in leveraging continual and federated learning for urban forecasting systems. 

While my research is primarily grounded in time-series, the methods I explore aim to inform broader seq2seq foundation model research.

I pursued my Master of Science in Computer Software and Theory at the School of Computer Science of Peking University in China, under the supervision of Prof. Zhi Jin. I worked primarily on a heterogeneous mixture-of-experts approach to robust time-series forecasting, which formed the basis of my master’s thesis as well as presenting this as a guest speaker at Microsoft Research Asia. My undergraduate degree is in Mathematics, Operational Research, Statistics, and Economics (MORSE) from Lancaster University. Outside of academics I am interested in Eastern and Western philosophy, Mandarin, and tea culture. 
 

My core goal is improving performance for specific workloads. 

I did my undergrad and masters in electronics at the International Institute of Information Technology, Hyderabad, India. Most of my research work then was primarily in the domain of embedded systems for multiple uses, which introduced me to computer science. I worked on porting a Cortex M4 processor in a proprietary Texas Instruments SBC to the Zephyr RTOS as part of an internship I undertook. I also worked extensively on performance characterisation for serverless applications as a visiting research student at the University of Edinburgh. 

For my PhD, I'd like to improve small LM inference on mobile devices for better efficiency and hardware utilisation, with deliberate emphasis to make the optimisations general and scalable, to allow for better performance at the datacenter scale as well.

I've recently completed my BA in Computer Science at the University of Cambridge. 

For my dissertation I built my own compiler that compiled functional programs into Interaction Nets and evaluated them with multiple threads. 

My PhD research focuses on compilers and how programs can be made to run efficiently on a variety of architectures.

My research focuses on Network Digital Twinning and energy efficiency in networked systems, utilizing data-driven approaches to understand and optimize these systems. 

I work with real data and simulations to reduce energy use and carbon emissions. 

I’m particularly interested in how Network Digital Twin technology can make large-scale systems, such as mobile networks and data centers, more efficient and sustainable.

Aside from research, I also enjoy teaching, and I'm learning about quantum computing for fun.

Working on another bunch of letters to put with BSc and MPhil. Actually likes working with lambda calculi. She/her 🏳️‍⚧️ 

I studied at Durham for my undergraduate and Cambridge for masters, both in computer science, and developed an interest in the theory of computation and PL theory which led to my current research interests. Currently, I'm working on program synthesis techniques that involve restricting the syntactic variation allowed in a language in order to enumerate semantically distinct programs more quickly.