Paolo Barucca
Associate Professor
Applies statistical physics of complex systems and machine learning to finance and economics.
Paolo Barucca's lab at UCL works on computational social science, learning theory & optimization and reasoning & neuro-symbolic AI. 14 papers since 2023, mostly Journal of Artificial Intelligence Research.
At a glance
- papers, 2023–2026
- 4/ year
- at T1 venues
- 0%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 4 papers2026 · T1 0 · collecting
- 4 papers2025 · T1 0
- 3 papers2024 · T1 0
- 3 papers2023 · T1 0
T1 venuesOtherVenues: Journal of Artificial Intelligence Research
Research focuspaper titles that use each topic's words
- Learning theory & optimization4 papers
- Computational social science0 papers
- Reasoning & neuro-symbolic AI0 papers
- complex systems0 papers
- statistical physics0 papers
Paper list
From the lab homepage.
20264 papers
- PNAS NEXUSHow the interplay between power concentration, competition, and propagation affects the resource efficiency of distributed ledgers
- Journal of Artificial Intelligence ResearchInformation-based calibration of uncertainty quantification in product-of-experts gaussian process models
- PHYSICAL REVIEW EMaximum entropy temporal networks
- PHYSICAL REVIEW EPhysics-informed neural networks for solving derivative-constrained
20254 papers
- Synthese LibraryHow AI Can Reshape Market Efficiency
- Data-Driven ModellingMODELLING EQUITY TRANSACTIONS AS BURSTY PROCESSES
- International Conference on AI in Finance-ICAIFProbabilityDensityConsistent Physics-Informed Neural Networks for Stochastic Local Volatility Model Calibration
- Physical Review E: Statistical, Nonlinear, and Soft Matter PhysicsRandom matrix ensemble for the covariance matrix of Ornstein-Uhlenbeck processes with heterogeneous temperatures (opens in a new tab)
20243 papers
- MABSMulti-agent Financial Systems with RL: A Pension Ecosystem Case
- CIFErWhack-a-mole Learning: Physics-Informed Deep Calibration for Implied Volatility Surface (opens in a new tab)
- ICAIF '24: ACM International Conference on AI in FinanceWhack-a-mole Online Learning: Physics-Informed Neural Network for Intraday Implied Volatility Surface (opens in a new tab)
20233 papers
- The Journal of Financial Data ScienceEnsemble Meta-Labeling (opens in a new tab)
- EntropyHeterogeneous Retirement Savings Strategy Selection with Reinforcement Learning (opens in a new tab)
- Journal of Financial Data ScienceOnline Learning with Radial Basis Function Networks (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to UCLFall 2027
Not announced yet
Computer Science MPhil/PhD
- Round
- Main
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
Similar labsnearest by research summary
- CNPeking
Hong Mei
- Graph learning
- CNSJTU
Shengzhong Liu
- Distributed, cloud & edge systems
- USMIT
Devavrat Shah
- Learning theory & optimization
- USStanford
Christopher Ré
- Generative & foundation models
- USUCLA
Wei Wang
- Graph mining & networks
- GBCambridge
Nicholas Lane
- Efficient training & inference
Your notes
Save this lab to keep notes.
Data
Some numbers could not be verified:
- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Not collected
- Alumni
- Not collected
Tiers are venue classifications, not lab rankings.