Markdown

Build aesthetically pleasing ‘personal websites’ for free…

Jun 14, 2025

Design/optical engineer at ASML Netherlands 04/2024 – Present Developed data-driven reports on optical module performance, helping senior leadership make informed decisions for ramp-up production. Assessed and derisked algorithmic tools through scenario testing and impact simulations using Excel and Matlab. Supported cross-sector stakeholder coordination involving engineering, factory teams, and customer support for diagnostic alignment. Industrial engineer at ASML Netherlands 11/2022 – 03/2024 Led multiple initiatives to improve service availability by conducting root cause analysis and collaborating across cross-functional teams to implement scalable solutions. Designed and facilitated training workshops for internal stakeholders, supporting service knowledge capture and rollout of new operational standards. Contributed to system reliability modeling and diagnostics planning to support post-release performance assessments. Documented procedures, risk analyses, and troubleshooting workflows to streamline service operations and reduce support escalations.

Jun 10, 2025

Capstone Project 1 This project provides solution to real-life business problems: ‘Housing complaints in New York city’ solved using Python. Problem Statement The people of New York use the 311 system to report complaints about the non-emergency problems to local authorities. In the last few years, the number of 311 complaints coming to the Department of Housing Preservation and Development has increased significantly. Although these complaints are not necessarily urgent, the large volume of complaints and the sudden increase is impacting the overall efficiency of operations of the agency. Therefore, I have developed a solution to help the Department of Housing Preservation and Development to manage their large volume of 311 complaints they are receiving every year. The project tries to answers four questions: Which type of complaint should the Department of Housing Preservation and Development of New York City focus on first? (Solution: https://github.com/AmitVSingh/capstone_python_complaints/blob/master/Capstone_python_edx_prob_1.ipynb) Should the Department of Housing Preservation and Development of New York City focus on any particular set of boroughs, ZIP codes, or street (where the complaints are severe) for the specific type of complaints identified in response to Question 1? (Solution: https://github.com/AmitVSingh/capstone_python_complaints/blob/master/Capstone_python_edx_prob_2.ipynb) Does the Complaint Type that have been identified in response to question 1 have an obvious relationship with any particular characteristic or characteristics of the houses or buildings? (Solution: https://github.com/AmitVSingh/capstone_python_complaints/blob/master/Capstone_python_edx_prob_3.ipynb) Can a predictive model be built for a future prediction of the possibility of complaints of the type that have been identified in response to question 1? (Solution: https://github.com/AmitVSingh/capstone_python_complaints/blob/master/Capstone_python_edx_prob_4.ipynb) The project contains 4 jupyter notebooks each for one problem. It contains data analysis along with nice visualisations. Datasets Two datasets have been used from the Department of Housing Preservation and Development of New York City to address their problems. 311 complaint dataset (https://data.cityofnewyork.us/Social-Services/311-Service-Requests-from-2010-to-Present/erm2-nwe9) PLUTO dataset for housing (https://data.cityofnewyork.us/City-Government/Primary-Land-Use-Tax-Lot-Output-PLUTO-/xuk2-nczf) The complete solutions can be found on my github page: https://github.com/AmitVSingh/capstone_python_complaints (The details of the project guidelines can be found in the following link https://courses.edx.org/courses/course-v1:IBM+DS0720EN+1T2019/course/) Skills developed through this professional data science certificate program Understand Python language basics and apply to data science Practice iterative data science using Jupyter notebooks on IBM Cloud Analyze data using Python libraries like pandas and numpy Create stunning data visualizations with matplotlib, folium and seaborn Build machine learning models using scipy and scikitlearn Demonstrate proficiency in solving real life data science problems It is always a good idea to solve the same problem using different tool. I have further used ‘R’ to investigate the the same problem. One can find the code snippets (.R file), markdown file (.Rmd) and a report on the project (.pdf) in my Github page (https://github.com/AmitVSingh/Capstone_Housing_complaints_R) Certificate: Python: See certificate Capstone Project 2 Smart manufacturing (ongoing) with MIT Professional Education

Oct 26, 2023

My Ph.D. research focuses on the topic ‘Spatiotemporal evolution of non-diffracting plasmonic pulses’. It has been divided into two phases ‘test-out research’ and ‘exploratory research’. In the so called ‘test-out phase’, a small incremental step has been taken to the established framework of published research on ‘Airy surface plasmons’. Airy plasmons are non-differacting electromganetic solution that can propagate on metal-dielectric interface. So far, researchers had investigated the non-diffracting properties only in spatial domains in 3 dimensions. My team have further investigated the 4th dimension the ‘time’ and it’s interdependence on space. The new phenomenon has been given a name ‘Airy plasmon pulses’. It studies the effect of pulsed excitation on non-diffracting properties of Airy plasmons. The graphics shows the Airy plasmon propagation on metal dielectric interface investigated by photoemission electron microscopy (PEEM). The image has been rendered using open source ray tracing software PovRay. As the name suggest it is a 4 dimensional complex scientific problem. The problem has been broken down into two parts. The first part numerical simulation and second, experimental verification of the numerically calculated results. My contribution to the project has been in devising and managing very large scale numerical simulations of light scattering into various forms of surface plasmon polaritons on structured gold surfaces. To this end, I have been using two large software packages, one open-source MEEP and one commercial Lumerical FDTD Solutions, implementing the numerical finite-difference time-domain method which has so far been the only method that could provide rigorous solutions for the electromagnetic fields in the femtosecond domain. The use of these software packages is complex and years of dedicated work are required for reaching the expertise level. Aforementioned methods are very time and memory consuming numerical tools. My research institute ‘Institute of Applied Physics’ maintains several multi-CPU compute servers and a high performance compute cluster consisting of ~2000 cores. I used the availble resources to perform this challenging computational task. The post processing of the data was performed in Matlab. A colleague from our research team has further verified the numerical results experimentally and a good agreement has been found between numerical and experimental results. The results have been published in OSA Continnum. The second phase of the research which I have made an original contribution to science by introducing the concept of ‘Airy plasmon pulses.’ This project investigates the spatiotemporal evolution of Airy plasmon pulses under very short fs pulse. The non-diffracting properties of Airy plasmon remains intact even under short pulse excitation. This is an interesting finding and implies large bandwidth of information can be carried without significant diffraction and dispersion over 30 µm on a metal-dielectric interface. This can find an interesting application in optical interconnects and communication devices. The proof of the concept has been provided using numerical, analytical, semi-analytical methods. The work is published in Optics Express. I defended my Ph.D. thesis on 01.02.2022 and have been awarded a Ph.D. degree (Dr. rer. nat.).

Oct 26, 2022

A parallel between machine learning and human learning!

Jul 9, 2019