HomeExperienceGalleryProjectsBlog
AM
कर्मण्येवाधिकारस्ते मा फलेषु कदाचन। मा कर्मफलहेतुर्भूर्मा ते सङ्गोऽस्त्वकर्मणि॥
You have a right to your actions alone, never to their fruits. Let not the fruit of action be your motive, nor let your attachment be to inaction.

Bhagavad Gita 2.47

In [1]: resume

Aditya Mishra

B. Tech, Instrumentation & Control Engineering at Netaji Subhas University of Technology (2023-2027), focused on machine learning, computer vision, and real-time inference systems.

download pdf

##Snapshot

New Delhi, India
B. Tech, Instrumentation & Control Engineering at Netaji Subhas University of Technology (2023-2027) | CGPA: 7.4
ML Engineer Intern at DUSQ (formerly InnerGize) | Nov 2025-Present
Chairperson, IEEE NSUT Student Branch | Jun 2025- Present

##Core Skills

Languages

PythonSQLMATLABJava

Machine Learning

TensorFlowscikit-learnXGBoostOpenCV

CV & Deployment

FastAPIStreamlitTime-Series ForecastingComputer Vision

Hardware & IoT

Raspberry PiMQTT

Tools

GitJupyter

# publications

A Bayesian-Tuned LSTM Approach to State of Charge Forecasting in Electric Vehicle Batteries↗

IEEE SEFET 2025 | Published.

State-of-Charge Forecasting Versus Persistence in Electric Vehicles: A Horizon-Resolved Benchmark with Conformal Deferral

Journal Paper | In-Review.

# awards & certifications

  • IEEE Outstanding Student Volunteer Award for Academic & Extracurricular Excellence (June 2026)
  • IBM Data Science Professional Certificate (2025)
  • Lala Ram Kishan Das Jain Memorial Award for Initiative (2022)

ML Engineer Intern

DUSQ (formerly InnerGize)
Nov 2025-Present
  • Cut end-to-end inference latency by 88.9% (7.7 s to 0.86 s) and 9x throughput via parallel and async execution.
  • Built a 27-keypoint placement API with visibility-aware training for robust side-view guidance at sub-second speeds.
  • Optimised preprocessing to 26 ms mean latency (64% faster; P99 37 ms, -66%), then packaged low-latency pipelines for production.

ML-IoT Intern

Indian Meteorological Department
Jun 2025 - Jul 2025
  • Automated thermometer readings using a Raspberry Pi vision system and OpenCV pipeline.
  • Trained models on a custom dataset of 1000+ images captured across diverse weather conditions.
  • Implemented MQTT-based real-time transmission enabling low-latency environmental monitoring.