Projects & Industry Applications

From Laboratory Tools to Measurable Impact: systematic thinking applied to solve real problems

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Laboratory Tools

Understanding the physical problem & hardware constrains

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Code & Automation

Building robust pipelines & automated controls

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Measurable Impact

Delivering efficiency gains & reliable data

Twin-Foci Laser Polarization Control System

Twin-Foci Laser Polarization Control System

Completed & In Use

Automated Experimental Control • Python • PyQt5

Challenge

Manual rotation of quarter-wave plates and Glan-Taylor polarizers for polarization scans (±45° and full 360°) took over 2 hours per dataset, with inconsistent angular precision and high risk of human error.

Solution

Developed a Python-based GUI application (PyQt5) for automated hardware control, real-time power monitoring, and precise angular positioning. Inspired by self-referencing techniques for circular dichroism measurements, the system ensures reproducible polarization quality for twin-foci beam profiles.

Impact

  • •Reduced scan time from 2+ hours to ~5 minutes (96% faster)
  • •Eliminated manual positioning errors
  • •Enabled multiple high-quality datasets per day instead of 1–2
PythonPyQt5NumPyMatplotlibSerial Communication
96%
time reduction
PEPICO Data Analysis Pipeline - Image 1
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PEPICO Data Analysis Pipeline

In Active Use

High-Throughput Spectroscopy Analysis • Python • Jupyter

Challenge

Time-resolved PEPICO experiments produce complex binary data from TDC acquisition. Manual conversion and analysis limited throughput and introduced inconsistencies.

Solution

Built a Python/Jupyter-based pipeline that converts raw TDC binary files to calibrated time-of-flight spectra, performs electron kinetic energy and mass calibration, coincidence analysis (TOF-vs-TOF), and statistical evaluation. Users specify data location and basic parameters; the pipeline handles the rest.

Impact

  • •Processes multiple datasets reliably and consistently
  • •Generates publication-ready plots and statistical summaries
  • •Removes analysis as a bottleneck, experiment pace now drives progress
PythonPandasNumPyMatplotlibBinary Parsing
10x
throughput increase
Raw Spectra(200+ files/day)Automation• Baseline correction• ML classificationAnalysis Ready(hours vs weeks)

FTIR/Raman Spectroscopy Automation Pipeline

In Progress

Planned Development • Data Science • Machine Learning

Challenge

High-throughput spectroscopic analysis requires processing 200+ spectra per day with minimal manual intervention.

Solution

Creating a robust, semi-automated workflow featuring automated baseline correction, peak fitting, machine-learning classification for material identification, outlier detection, quality scoring, and LIMS integration.

Impact

  • •Reduce processing time from weeks to hours for large batches
  • •Standardize analysis across datasets
  • •Enable parallel research streams in materials characterization
Pythonscikit-learnTensorFlowStreamlitSQL
200+
spectra/day target
INSPIRE Fellowship Research Program

INSPIRE Fellowship Research Program

Completed

5-Year National Research Project • Multi-Institution Coordination

Challenge

Manage a prestigious national fellowship (one of ~1,000 awarded annually in India) as principal researcher while completing PhD research.

Solution

End-to-end program delivery including annual financial reporting, procurement planning, progress tracking for government review, and multi-institution collaboration across Germany and India over 5 years.

Impact

  • •Delivered 12 first-author publications
  • •Contributed to one granted patent (gas-separation membranes)
  • •Maintained regulatory compliance across 5-year program
  • •Coordinated with stakeholders across multiple institutions
Project ManagementBudget PlanningRegulatory ComplianceStakeholder Communication
5 yrs
program duration
+40%
Efficiency Gain
Workflow Optimized

Laboratory Workflow Optimization

Ongoing

Process Improvement • Team Coordination • Resource Planning

Challenge

In a shared ultrafast laser laboratory, scheduling conflicts, documentation gaps, and workflow handoffs limited overall productivity.

Solution

Team-based implementation of shared equipment booking calendar, standardized maintenance checklists, experimental protocols, and Obsidian-based shared lab notebook. Weekly planning meetings align schedules and priorities.

Impact

  • •Improved equipment availability and reduced downtime
  • •Enabled smoother onboarding and better collaboration through superior documentation
  • •Increased overall group throughput (estimated ~40% from combined improvements)
ObsidianProcess DocumentationTeam CoordinationResource Planning
~40%
efficiency gain

From Research to Real-World Impact

The same systematic approach that drives these projects applies directly to data science, process optimization, and technical project management in industry.

Real Code. Real Impact.

I don't just talk about automation, I build it. With efficient, cleaner, and well-documented code that solves actual problems.

Explore My Repositories