© 2018 by Vidhi Lalchand

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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, 2014-2015

      High-Frequency Prop Trader  

  • Credit Suisse Securities (Europe), London, 2012 - 2014

      Quantitative Analyst for electronic FX market-making.

ACADEMIC GRANTS

  • 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

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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 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.

TALKS

 CogX 2018, Research Stage, June 2018

 Deconstructing Gaussian Processes.