Vidhi Ramesh Lalchand
Born in India
Lives & works in Cambridge, UK
EDUCATION

Ph.D. in Machine Learning, 2017 Current University of Cambridge (Christ's College), UK

M.Phil in Scientific Computing (Distinction), 2016
University of Cambridge (Clare Hall), UK

M.Sc in Applicable Mathematics (Distinction), 2010
London School of Economics & Political Science (LSE), UK

B.Sc. Mathematics (First), 2008
University of London External System
COMPUTING
Scripting: Python
ML Libraries: STAN, pymc3, PyTorch
GPU Programming: CUDA
Databases: SQL
Others: Unix, Shell and Latex
INDUSTRY

Citadel LLC, London, 20142015
HighFrequency Prop Trader

Credit Suisse Securities (Europe), London, 2012  2014
Quantitative Analyst for electronic FX marketmaking.
ACADEMIC GRANTS

Alan Turing Doctoral Fellowship for International Students, 20172020

The University of London Award for Academic Excellence for External Students, 2008
HONORS & ACTIVITIES

Cambridge University: Women In STEM Interview, Nov 2019

The Cambridge Union: Immigrant Identities Panel, Nov 2019

Presentation to the Scientific Advisory Board, The Alan Turing Institute, London. Algorithms for rare event classification in High Energy Physics, June 2018

Asian Voice  An Unconventional Journey from Banking to Science
The Cambridge Union: Immigrant Identities Panel (link) Nov 2019 Presentation to the Scientific Advisory Board, The Alan Turing Institute, London. Algorithms for rare event classification in High Energy Physics. June 2018 Asian Voice  An Unconventional Journey from Banking to Science. (link)
THESIS & OTHER WRITING
PUBLICATIONS
V. R. Lalchand. Extracting more from boosted decision trees: A high energy physics case study. Second Workshop on Machine Learning and the Physical Sciences. NeurIPS 2019, Vancouver, Canada.
V. R. Lalchand, A.C. Faul. A Fast and Greedy SubsetofData (SoD) scheme for Sparsification in Gaussian Processes. 38th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. August 2018.
V. R. Lalchand and Carl E Rasmussen. Approximate inference for fully bayesian gaussian process regression. In Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference, volume 118 of Proceedings of Machine Learning Research, pages 1–12. PMLR, 2020.
P Treleavan, M Galas and V Lalchand. Algorithmic Trading Review. Association of Applied Computing Machinery, November 2013.
TALKS
CogX 2018, Research Stage, June 2018
Deconstructing Gaussian Processes.