Milad Pourrahmani

PhD in Physics and Machine Learning | U.S. Citizen

Research & Data Scientist

Last Edit: 210917_140036


Pasadena, CA, USA


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Scientist and developer with 7+ years of research experience leading to novel software products, discoveries, and impactful publications. Expert in statistical and computational techniques: statistical learning, machine and deep learning, data simulation, signal processing. Interested in future technologies and research in machine learning and AI/AGI, soft robotics, microcontrollers, computational neuroscience, and quantitative finance.


University of California, Irvine

Doctor of Philosophy, Physics Dec 2019
Ph.D. Thesis: Deep Mining and its Astronomical Applications
Master of Science, Physics Dec 2016

Purdue University, Indianapolis

Bachelor of Science in Physics Dec 2012

Hard Skills

  • Advanced Python Programming
  • Algorithm Design
  • High Performance Computing
  • NumPy / SciPy / Plotly / Matplotlib
  • Scikit-learn / Pandas / Astropy
  • PyTorch / Tensorflow / Keras
  • Machine Learning / Statistical Learning
  • Deep Learning / Computer Vision
  • Network Architecture / NLP
  • Computer Vision / CNNs / GANs
  • Statistics & Mathematical Modeling
  • Signal Processing / Time Series Analysis
  • Data Analysis / Data Visualization
  • Data Wrangling / Data Pipeline
  • Image Processing / Digital Imaging
  • Infrared Imaging / CCD/CMOS
  • Anomaly Detection / Sensors / Robotics
  • JavaScript / HTML / CSS / JinJa
  • C++ / Matlab / Mathematica / LaTex
  • Linux Power User / Git
  • Cloud Computing / AWS / TACC
  • Scientific Writing and Speaking
  • Project Leadership and Management
  • Jira / Confluence

Soft Skills

Experienced with Data

Data representation, simulation, collection, curation, and processing describe my day to day activities. Data modeling with conventional statistical technics or modern ML algorithms with customized architectures have become my expertise.

Artistic Data Visualizer

I take pride in creating informative, artistic, and inviting data visualizations. I believe data visualization is the key to understand, communicate, solve, and debug any complex problem. Not to mention it is often the face of a project when it comes to marketing.

Creative and Collaborative Project Scientist & Developer

I enjoy working on new and exciting projects, solving technical challenges and learning along the way. I'm seasoned working independently or in a large team. I find it rewarding to collaborate with team members and teams outside the company.


  • Machine Learning
  • Statistical Analysis
  • Data Science
  • Computational Science
  • Algorithm Design
  • Data Visualization
  • NLP
  • Quantitative Finance & Option Trading
  • Communication, Writing, & Education

Projects & Work Experiences

R&D at Caltech and JPL

  • Research and development of scientific software for the next generation of space telescopes (SPHEREx).
  • Lead scientist for designing the algorithm for strategising deep field observations involving ML techniques and high dimensional and large volume data visualization.
  • Development of production-quality data pipeline for processing TBs of images on distributed systems.
  • Experiment design and data simulation for quality assessment.
  • $250 MM of funding from NASA.

Researcher at University of California, Irvine

  • Deep Learning Image Classification on Astronomical Data with 50 Training Examples
  • Designed, tested, and published a new deep CNN architecture with PyTorch for biased and limited datasets (less than 50 examples) to identify rare astronomical events known as gravitational lenses among millions of common galaxy images.
  • Ph.D. Thesis: Deep Mining and its Astronomical Applications
  • Mentored graduate and undergraduate students for various projects with leveraged interpersonal and communication skills and by advocating critical and independent thinking.

Using a Generative Adversarial Network (GAN) to Synthesize Gravitational Lenses

  • Used for boosting the training examples for image classification.
  • A generator was constructed from a series of transposed convolutions to upsample a latent vector to an RGB image.
  • A discriminator was used to identify synthesised images from real images.
  • A loss function was assigned to be maximized by the generator and minimized by the discriminator.
  • The discriminator was re-trained on synthesised data from 20 differently initialized GANs and used for data mining.

Wolfram Summer Internship

Summer 2018
  • Designed a chess engine, GUI, and API using the Wolfram Language, enabling users to explore and filter the decision tree of moves in a visual manner (Link')
  • Reviewed by Stephen Wolfram and approved to be part part of the Wolfram|Alpha

Student/Tutor Matching App Design

  • Recruited a group of undergraduate and master student software developers.
  • Set up a project development environment for the group.
  • Structured the backend and frontend and communicated its design to the developers.
  • Applied for grant funding and pitched the demo to entrepreneurs.

Image Processing Pip Package

  • ezimage, a library published on pip repository to facilitate loading, reformating, and displaying images in Python.
  • Smart input recognition to load images from URLs, local paths, or data in different formats without any specifications.
  • Useful for machine learning to convert between CHW and HWC format for PyTorch and TensorFlow.

Publication Highlights (Google Scholars)

LensFlow: A convolutional neural network in search of strong gravitational lenses 2018
Bringing manifold learning and dimensionality reduction to SED fitters 2019
SPHEREx: NASA's near-infrared spectrophotometric all-sky survey 2020
Deep Mining and Its Astronomical Applications 2019

Awards and Honors

Honorary fellow in UC-Irvine Machine Learning and Physical Sciences program 2017-2018
Graduate Assistance in Areas of National Need (GAANN) 2017-2019
First Place Poster Presentation Award, UCI Dept. of Phys. & Astronomy at Research Presentation 2018
Scholar's List Recipient for Outstanding Academic Performance from School of Science Fall 2011 & 2012
Outstanding Learning Assistance from AAPT 2012
Student Employee of the Year for Outstanding Performance Spring 2012