Daniel Sitompul

Intro

I am an engineer, innovator, and entrepreneur passionate about shaping the future of technology for social impact. With a Master's in Development Engineering from UC Berkeley and a Bachelor's in Electrical Engineering from the Bandung Institute of Technology, I specialize in data analytics, machine learning, and sustainability. My work spans across sectors—leading emissions analytics at the International Council on Clean Transportation and conducting machine learning research at the Renewable and Appropriate Energy Laboratory (RAEL), UC Berkeley to detect generator use in health facilities in DR Congo. I bring over four years of industry experience from Telkom Indonesia, where I managed digital infrastructure deployment and data-driven telecom solutions.

Beyond my professional pursuits, I’m a dedicated musician and sports enthusiast, finding balance and creativity through rhythm and motion. Whether I'm building emissions models, designing user interfaces, or exploring new applications of AI, I aim to bridge technology and society. I’m always eager to connect with fellow changemakers and collaborators. Please find my contact information bellow.

• Linkedin
• Instagram
• GitHub

Portfolio

1. Generator Emission ML Modeling

This machine learning model is created to assess the use of machine learning to predict generator usage within a time period. The data that we have is voltage and frequency (within a 2 minute period) from several locations in healthcare facilities. This model has achieved more than 95% accuracy, precision, and recall for a singular location. Find out more about the project here:

• IEEE Global Humanitarian Technology Conference paper
• Check GitHub repo: Generator ML Emission Modeling

2. Aircraft Local Impact Footprint Tool (AIRLIFT)

AIRLIFT presents data from the 5,000 airports globally with the highest number of flights in 2023, covering about 49.4 million flights. Flight activity was identified using Automatic Dependent Surveillance–Broadcast (ADS-B) data from Spire, which include aircraft type, International Civil Aviation Organization (ICAO) designator code, tail number, and departure and arrival airports. Find out more about the project here:

• Request data: ICCT landing page
• 500 airport visualization: icct-airlift.azurewebsites.net

3. Aviation Vision 2050

Working along the ICCT aviation team, we create this report as an update to the ICCT’s Vision 2050 decarbonization roadmap for the aviation sector by quantifying how SLCP mitigation can complement greenhouse gas (GHG) strategies to align aviation with the Paris Agreement. The report concludes that in the Full Breakthrough scenario, which blends maximum GHG and SLCP mitigation, additional aviation warming could be cut by more than 90% below the Historical Trends scenario and limits aviation’s share of additional contribution to the remaining 1.7 °C budget to 2%. Find out more about the project here:

• Check the paper here: Aviation Vision 2050 (ICCT)

4. Private Jets Pollution

Working along Dan Rutherford, we worked on analyzing the air and greenhouse gas pollution from private jets. Find out more about the project here:

• Check the paper here: Air and GHG pollution from private jets (ICCT)
• Featured in: Washington Post , Safe Landing , ESG Post

5. Runway Edge Light Photometry

As a student researcher in my Electrical Engineering undergraduate program at Bandung Institute of Technology, I researched runway edge light photometry. We created a new methodology to check runway edge light quality using drones. Check the 3 IEEE publications here:

• Runway Edge Light Photometry System by Using Drone-Mounted Instrument
• Horizontal Scanning Method by Drone Mounted Photodiode Array for Runway Edge Light Photometry
• Runway Edge Light Photometry by Vertical Scanning Method Using Drone Mounted Photodiode Array

6. Air Transport Emission Calculator

Calculates operational variables, comparing air taxi vehicles and ground transportation. It features sliders to customize variables such as fuel consumption, emissions, and more. As input, the application uses the Google Maps API to determine distances and travel times. Find out more about the project here:

• AirTransport Comparison Calculator

7. Amazon Data Science Analysis

This project explores the predictive potential of e-commerce review data by analyzing Amazon beauty product reviews (2004–2018). It employs machine learning to predict product ratings and builds a recommendation system to provide insights into customer preferences and product popularity. Find out more about the project here:

• Data-Science Amazon Analysis

Resume

Last updated: 28th June 2025

Contact

Shoot me a message here and I'll contact you:

• Linkedin
• Instagram
• GitHub

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