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Quantum Algorithms Researcher @ QuantumBasel & QC2 Universität Basel
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Stanford University
Doctor of Philosophy (Ph.D.), Applied Physics
January 1, 2011 – January 1, 2017
Stanford University
Master’s Degree, Applied Physics
January 1, 2011 – January 1, 2014
Imperial College London
BSc, Physics
January 1, 2007 – January 1, 2010
International School of Geneva
High School
January 1, 1993 – January 1, 2007
University of Basel
Staff Researcher at the Center for Quantum Computing and Quantum Coherence (QC2)
December 1, 2024 – Present
Basel, Switzerland
QuantumBasel
Quantum Scientist
May 1, 2023 – Present
Arlesheim, Basel-Country, Switzerland · Hybrid
QAI Ventures
Accelerator Investment Committee Member
May 1, 2023 – December 1, 2025
Arlesheim, Basel-Country, Switzerland · Hybrid
Goldman Sachs
Executive Director in the R&D Engineering Team
August 1, 2020 – March 1, 2023
Goldman Sachs
Vice President in the R&D Engineering Team
January 1, 2020 – July 1, 2020
Goldman Sachs
Associate in the R&D Engineering Team
September 1, 2019 – December 1, 2019
Goldman Sachs
Associate in the Franchise Analytics, Strategy & Technology (FAST) Team
March 1, 2018 – August 1, 2019
Caltech
Postdoctoral Research Fellow in Quantum Machine Learning
October 1, 2017 – March 1, 2018
Palo Alto, California
AT&T Palo Alto Foundry
Intern in the Intelligent Quantum Networks and Technology (INQNET) Group
June 1, 2017 – September 1, 2017
Palo Alto, California Area
Indian Institute of Technology, Madras
Intern in the Quantum Information Research Group
March 1, 2011 – July 1, 2011
Chennai Area, India
Swiss Armed Forces
Soldier in the Military Security Division
June 1, 2010 – November 1, 2010
Switzerland
CERN
Intern in the CMS Group
July 1, 2009 – August 1, 2009
Geneva Area, Switzerland
CERN
Intern in the CMS Group
July 1, 2008 – August 1, 2008
Geneva Area, Switzerland
Machine learning for thyroid cancer diagnosis
December 1, 2015 – Present
We investigate the use of high throughput gene expression data in the diagnosis of thyroid cancers. Using logistic regression and support vector machines (SVMs), we develop a classifier which gives similar performance (89% sensitivity and 80% specificity) to the currently bestknown classifier, but uses significantly fewer features. We used two different techniques, principal components analysis and mutual information score, to select features. The results do not depend significantly on which method is used for feature selection.
Securities Industry Essentials (SIE)
FINRA
June 24, 2026 – Present
Series 63 – Uniform Securities Agent State Law
FINRA
June 24, 2026 – Present
Series 7 – General Securities Representative Examination
FINRA
June 24, 2026 – Present
Sequences, Time Series and Prediction
DeepLearning.AI
June 24, 2026 – Present
Introduction to Time Series
Illinois Institute of Technology
June 24, 2026 – Present
Quantum Programming 101 - Core
D-Wave
June 24, 2026 – Present
IBM Quantum Challenge 2021 Achievement - Advanced
IBM
June 24, 2026 – Present
IBM Quantum Challenge
IBM
June 24, 2026 – Present
Qiskit Advocate
IBM
June 24, 2026 – Present
IBM Certified Associate Developer - Quantum Computation using Qiskit v0.2X
IBM
June 24, 2026 – Present
Cultural Fit Analysis
The candidate has a strong academic and research background, with significant experience in quantum computing and physics. While there is a project and a role at Goldman Sachs involving machine learning and data analysis, the overall profile leans heavily towards quantum science. The target role of 'Data Analyst' requires a stronger, more consistent demonstration of core data analysis skills, tools, and business application beyond the few mentioned instances. The candidate's recent roles are primarily in quantum computing, which may not align directly with a typical Data Analyst role's day-to-day responsibilities. The project diversity is high in scientific research but less so in traditional data analysis business applications.
Soft Skills & Operational Fit
The candidate's experience leading a team at Goldman Sachs and participating in collaborative projects suggests leadership and teamwork abilities. The diverse research background indicates strong problem-solving and analytical skills. However, specific details on communication style, stress handling, or direct team collaboration are not explicitly provided in the resume.