Data Analyst Secretary

AAYUSHI VAISH

advms6@r.postjobfree.com aayushivaish.github.io aayushivaish https://www.linkedin.com/in/aayushi-vaish/ Education

Northeastern University GPA: 3.47 / 4.00

MS in Physics Minor- Data Analytics

Research Assistantship

Related courses: Quantum Theory, Classical Mechanics, Statistical Mechanics, Advanced Astrophysics, Statistical Interference-Data Analysts, Electromagnetic theory, Computational Physics

Sep 2021 May 2023

JSS Academy of Technical Education, Noida GPA: 3.56 / 4.00 B.Tech. In Electronics and Communications Engineering (HONS.) Aug 2016 - Sep 2020 Related courses: Data science, Data Structures, Programming in C, Digital Logic Design, Electronics Devices and Circuits Skills

Programming Languages: Python, R, C, C++, JavaScript, MATLAB Databases: MySQL, MS SQL, PostgreSQL, MongoDB

Frameworks/Tools: Anaconda, NumPy, Pandas, Keras, Matplotlib, Seaborn, scikit-learn, Jupyter, Git, R Studio Other: Excel / Google Sheets, Google Analytics, Data Modeling, Data Mining, IoT, Vivado, Digilent, Root-CERN Experience

Research Assistantship – GRAMS (gamma ray and antimatter survey) Project Boston, MA Technologies: Python, Jupyter, Pandas, NumPy, Matplotlib, MS Excel, Vivado 2022 May 2022- present Worked on SiPM test- Prepared Blackbox for the dark experiment and was able to detect the dark count rate on oscilloscope with needed gain. Made astrophysical observations with MeV gamma rays and indirect dark matter searches with antimatter using a LArTPC (Liquid Argon Time Projection Chamber) detector. Studying its series, hybrid, parallel connections to form the most suitable state to dark count so that it can remain more stable in chamber. Antimatter survey can provide background-free dark matter signatures, which may allow to verify the possible dark matter detection.

Worked on FPGA- Established Eclypse Z7 Xilinx's Zynq-7000 APSoC (Digilent) FPGA in lab with ADC- ADC setup and performed the simulations on the Vivado 2022 software.

MEV SKYMAP- Currently working on GRAMS first prototype launch location. I was able to create the Python code for the suggested places using the Astropy module. The coordinates from Earth's position are defined by the code. I collected and processed a year's worth of data for the sites using data from the Swift BAT and Fermi LAT. Doing calculations using Compton formula and comparing background data with the data I collected and analyzing the best location. Data Analyst: Massachusetts Institute of Technology Cambridge,MA GAPS (General Antiparticle Spectrometer) Project May2022 Sept2022 Technologies: Python, Jupyter, Pandas, NumPy, Matplotlib Worked on Thermal System Installation, Analysis and Validation at MIT Bates Linear accelerator. Which includes detection and mitigation of possible leaks from the Thermal cooling system to achieve the equipment operation temperature of -70 degrees Celsius.

Communities

E-Board Member -

Secretary- New England Graduate Women in Science and Technology September 2022- present Working on supporting all women graduate students in the New England area. We are a student-led cooperative of individual graduate women’s groups.

Secretary- Graduate Women in Science and Technology Northeastern University September 2022- present

GWISE strives to provide women with the support and resources necessary to be successful, as well as to create a sense of community. To achieve this, we sponsor events covering a wide variety of topics including networking, career options, balancing work and personal life, and other issues affecting women in the sciences. Member- American Physical Society November 2021-present Nominated for APS FGSA Executive Committee 2023.

Elected for the Winter conference ASPEN 2022 and 2023 for science talk. Projects

Exoplanet-Exploration-with-Data-Science: - This project utilizes ML techniques to predict exoplanets from Hubble-Data. Directional Stock Prediction using News Articles and Viral Tweets: - This project performs sentiment analysis on tweets to predict Stock Price Direction.

KrishAI- An AI based Plant Disease Detection System, trained on over 10000 images to recognize plant diseases from images.

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