Dr David Khachaturov

Supervisions

I have supervised students from a number of different colleges over the years :- Churchill, Magdalene, Homerton, Hughes Hall, Lucy Cavendish, St Catharine's, Sidney Sussex, Newnham, Downing, St Edmund's, Murray Edwards, Trinity, Trinity Hall, Pembroke, Clare, Gonville and Caius, and King's. That is, 17 out of the 29 colleges that admit undergraduates.

I supervise the following undergraduate Computer Science courses:

• Part IA: Machine Learning and Real-world Data [32]; Software and Security Engineering [61]; Introduction to Probability [6]
• Part IB: Data Science [82]; Economics, Law and Ethics [71]; Security [82]; Computer Networking [26]
• Part II: Bioinformatics [27]; Information Theory [24]

The respective number of students I taught for each course is shown in square brackets next to the course name. This brings the total number of groups I supervised to 160 over 15 terms, resulting in 529 hours of teaching so far (not counting revision and dissertation supervisions). Those groups were made up of 410 students in total if we're double-counting, and 214 if we're only talking about unique individuals -- assuming a cohort size of 125 a year, over 5 years, this is 34.2% of all CS Tripos students! Numbers are accurate as of Easter Term 2026.

I have supervised the following students for their Part II projects, in no particular order:

• 2025/26
  • Pelumi Babalola [HOM]: Fluxx in Reinforcement Learning
  • James Leung [R]: Towards Automated Mechanistic Interpretability; best-scoring dissertation in the year at 90%
  • Eric Li [T]: Point-in-Time Discrete Event Simulation for Causal Financial Market Research
  • Neel Dasgupta [CL]: Using Bayesian Inference to Deviate from Equilibrium Strategies in Imperfect Information Poker Games
  • Ben Yang [SID]: Stable Probabilistic Numeric Solvers for Training Neural ODEs
  • Harry Chen [HOM]: An Implementation and Evaluation of the TLS Poisoning Attack 
• 2024/25
  • Navaneeth Madhavan [M]: Detecting Hype in Reddit Stock Posts Using Sentiment and Market Data
  • David McIntosh [CHU]: Using Bayesian Networks to Simulate Bias in Algorithmic Recruiting; high-scoring at 87.5% 
  • Matthew Gregson [HOM]: Solving Reversi using Reinforcement Learning
  • Joseph de Souza [K]: Building a Lie Detector for LLMs
  • Yun Shao Kee [ED]: Evaluation of LLM Cybersecurity Capabilities
• 2023/24
  • Dylan Mankin [CC]: Machine Learning for Financial Portfolio Management
  • Ria Mundhra [DOW]: Developing a Graphical User Interface for Econometric Analysis
  • Kai Tanna-Shah [CHU]: Creating an Interpretable Chess Engine
  • Tayo Oluranti-Ahmed [DOW]: Forecasting Implied Volatility with Machine Learning Techniques
  • Peter Isaksen [CHU]: Implementing and Evaluating an Adversarial Attack on an Aligned Large Language Model

I have also supervised the following external students, all of whom managed to publish their research work:

• Florian Hoppe [DAR] (2025/26) was a Visiting Research student from TUM working on his Master's dissertation with me. The research focused on mechanistic interpretability in LLMs - Controllable and explainable personality sliders for LLMs at inference time
• Isha Gupta [N] (Mich 2024) was a Visiting Research Scholar from ETH Zurich during her Master's year. I supervised her together with Prof Robert Mullins for a research paper -  "I am bad": Interpreting Stealthy, Universal and Robust Audio Jailbreaks in Audio-Language Models
• Micha Nowak (2023/24) as an academic advisor for his Master's Thesis in Artificial Intelligence at the University of Wuerzburg in Germany. This was done under the supervision of Professor Frank Puppe, and further advised by Dr. Adrian Krenzer. The research focused on adversarial attacks on federated learning - QBI: Quantile-based bias initialization for efficient private data reconstruction in federated learning