Tejas Rana

Curriculum Vitae

View / Download CV (PDF)

Education

Integrated Master of Science (BS-MS) Aug 2021 – Present

Indian Institute of Science Education and Research (IISER) Mohali, India

CPI: 8.3

Relevant courses

  • PHY401: Nuclear and particle physics
  • PHY424: Relativistic quantum mechanics and quantum field theory
  • PHY302: Quantum mechanics; PHY306: Advanced quantum mechanics; PHY403: Atomic and molecular physics
  • PHY301: Classical mechanics; PHY303: Electrodynamics
  • IDC410: Machine learning; IDC409: Introduction to data science
  • IDC402: Nonlinear dynamics, chaos and complex systems; MTH407: Algorithms and complexity

Research experience

Quantum decoherence measurement in \(B^0 \to D^{*-}\,\ell^+\,\nu_{\ell}\) via the \(\Upsilon(4S)\) resonance. Jun 2025 – Present

Master thesis under Dr. Hans-Günther Moser - Max-Planck-Institut für Physik, Munich, Germany.

  • Generated signal and $500\ fb^{-1}$ generic MC samples with EvtGen, processed via the DIRAC Grid, performed full reconstruction and analyzed distributions of key variables.
  • Implemented selection optimisation and background suppression; performed a binned 2D template fit using pyhf, extracted sWeights and applied them to combined MC to separate signal, true-D* and fake-D* background components; validated against truth-matched distributions.
  • Performed unbinned maximum-likelihood fits with zfit to obtain analytic component PDFs; next steps include systematic-uncertainty evaluation and applying the analysis to Belle II data.
Muon g-2 experiment - Fermilab Oct 2024 – Jan 2025

Under Dr. Satyajit Jena - IISER Mohali

  • Studied Fermilab's muon g-2 experiment and the muon anomalous magnetic moment ($a_{\mu}$), focusing on analysis methods that test Standard Model predictions.
  • Worked with Monte Carlo simulations for spin dynamics (Geant4), Fourier analysis, and ROOT for event reconstruction and systematic uncertainty studies.
Three-flavoured neutrino oscillations using PMNS theory Jun 2023 – Aug 2023

Under Prof. Kavita Dorai - IISER Mohali

  • Implemented three-neutrino oscillations with and without CP violation (vacuum and matter cases) on a 4×4 subspace of a two-qubit Hilbert space.
  • Constructed quantum circuits for the PMNS matrix on IBM Qiskit and QasmSimulator.
Embedded systems & PCB design for wireless biomedical EIT system May 2024 – Aug 2024

Under Dr. Mayank Goswami - IIT Roorkee

  • Designed and built a wireless Electrical Impedance Tomography (EIT) system using nine ESP32 modules; engineered custom PCBs in Proteus and performed SMD assembly.
  • Developed firmware and a real-time IoT data pipeline (Wi-Fi/MQTT, I²C), local MQTT broker with Node-RED, and efficient DAQ/impedance preprocessing for EIT integration.

Honors

  • Selected for MPI for Physics master’s-thesis fellowship via the MPG-IISER '25 program (competitive selection across 7 IISERs).
  • Selected for SPARK '24 internship at IIT Roorkee (competitive selection among ~20,000 applicants).

Projects

Face recognition attendance system ML, Neural networks

Code

  • Automated face-attendance system using MTCNN for detection/alignment and face_recognition embeddings with cosine-similarity matching; logs attendance to Excel.
  • Evaluated using confusion matrix, precision-recall and ROC. Uses pretrained models (no custom NN training).
Question identification using NLP NLP, ML

Code

  • Preprocessed text and implemented TF-IDF, LSA, LDA, word & sentence embeddings to distinguish questions from general sentences.
  • Evaluated Logistic Regression, Decision Trees, Random Forest, Naive Bayes via ROC; built a Flask REST API and Dockerized the service.
Multi-layer perceptron (MLP) using NumPy ML, Neural networks

Code

  • Built an MLP from scratch to classify MNIST digits: backpropagation, multi-layer support, various activations, and momentum-based gradient descent.
Linear & logistic regression from scratch ML, Regression models

Code

  • Implemented gradient descent, L1/L2 regularization; studied impact of noise and dataset size on parameter learning using synthetic datasets.

Skills

Languages

Python (scientific & ML ecosystem), PyTorch, TensorFlow, Bash, C++, R

High-energy physics (HEP)

ROOT (TTree/TChain, histogramming, pyROOT, RooFit/RooStats); basf2 (analysis steering, reconstruction, ntuple production); Geant4 (simulations, detector geometry & digitization); Fitting - pyhf & zfit (binned template fits, unbinned ML fits, extended/profile likelihoods, sWeighting, nuisance-parameter systematics).

ML & AI

Supervised & unsupervised learning (Decision Trees, SVM, Naive Bayes, K-Means); neural networks (MLP, backpropagation); Bayesian methods (Bayesian networks, Markov models, Monte Carlo); model evaluation (Precision, Recall, AUC, MAPE). Proficient with PyTorch, TensorFlow, NumPy.

Electronics

Microcontrollers (Arduino Uno, Mega 2560, ESP32), FPGA basics, PCB design (Proteus), oscilloscopes, multimeters, signal conditioning (amplifiers, op-amp chains), SMD soldering.

Other tools

MATLAB, LaTeX, Mathematica.