Vidhi Ramesh Lalchand
Born in India
Lives & works in Cambridge, UK
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
ML Libraries: STAN, pymc3, PyTorch
GPU Programming: CUDA
Others: Unix, Shell and Latex
Citadel LLC, London, 2014-2015
High-Frequency Prop Trader
Credit Suisse Securities (Europe), London, 2012 - 2014
Quantitative Analyst for electronic FX market-making.
Qualcomm Innovation Fellowship (Europe), 2020.
Alan Turing Doctoral Fellowship for International Students, 2017-2020
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
(please see pdf CV for a upto-date list)
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 Subset-of-Data (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.
Deconstructing Gaussian Processes.