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Vidhi Ramesh Lalchand

Born in India

Lives & works in Cambridge, UK

 

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EDUCATION
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  • 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    

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  • M.Sc in Applicable Mathematics  (Distinction), 2010      

      London School of Economics & Political Science (LSE), UK

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  • B.Sc. Mathematics (First), 2008                                

      University of London External System

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COMPUTING

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Scripting: Python

ML Libraries: STAN, pymc3, PyTorch

GPU Programming: CUDA

Databases: SQL

Others: Unix, Shell and Latex

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INDUSTRY

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  • Citadel LLC, London, 2014-2015

      High-Frequency Prop Trader  

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

      Quantitative Analyst for electronic FX market-making.

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ACADEMIC GRANTS

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  • 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

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HONORS & ACTIVITIES

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  • 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

A meta-algorithm for classification using random recursive tree ensembles: A high energy physics application (MPhil Thesis, 2016).

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PUBLICATIONS

(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.

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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.

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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.

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P Treleavan, M Galas and V Lalchand. Algorithmic Trading Review. Association of Applied Computing Machinery, November 2013.

Probabilistic Decision Trees for binary classification on the Higgs dataset (Report, 2016).

TALKS

Analyzing the Higgs signal using Support Vector Machines (Report, 2016).

Machine Learning Meets Particle Physics Workshop Weizmann Institute of Science, Israel Aug 2019. Hierarchical Probabilistic Mixture Models for density estimation in HEP.

The Bayesian Approach to Linear Regression. (A scientific report, 2017)

Cavendish Laboratory Graduate Student Conference University of Cambridge Nov 2018. Bayesian Machine Learning in High Energy Physics.

38th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science & Engineering, July 2018. A progressive framework for Gaussian Process Regression.

 CogX 2018, Research Stage, June 2018

 Deconstructing Gaussian Processes.

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