Minoo Taghavi, MSc

Washington DC · (202) 604-1905 · minoo.tgh1991@gmail.com

Data Scientist and Engineer with 5 years of experience in the transportation industry, machine learning techniques, deep learning algorithms, and large-scale datasets. Seeking to leverage my engineering expertise, programming and data analysis skills in data science field.


Experience

Data Science Fellow

General Assembly, Washington DC.

Completed 30+ projects and labs utilizing the entire data science workflow, including data acquisition, data cleaning, data visualization, exploratory analysis, feature selection, modeling, and evaluation. Conducted natural language processing (using NLTK library in Python) on thousands of scraped Reddit posts to train several classification models to predict related subreddit. Built geospatial analysis of areas at risk for COVID-19 and natural disasters in California using python (Clustering algorithm) and ArcGIS and Tableau. Developed Lane detection with deep learning (CNN architecture) with 88% accuracy on test data set using transfer learning and Pytorch library

August 2020 - December 2020

Project Engineer

Applied Research Associates Inc.

On-site consultant for Maryland Department of Transportation State Highway Administration (MDOT SHA). Worked mainly on addressing customers’ (divisions, districts and public) ad hoc requests including pavement network data inquiries. Worked on development and improvement of data quality management protocols and procedures saving hundreds of man-hours. Developed pavement network data analysis, reporting, and mapping using SQL, Excel and ArcGIS with data analysis team in pavement management division. Also worked on performance metric evaluation of MDOT SHA roadways using pavement condition and construction history data.

July 2018 - Present

PhD Graduate Research Assistant

University of Maryland, College Park

Studied transportation systems and conducted traffic modeling projects such as modeling traffic flow using PTV Vissim for prediction and improvement of traffic flow.

January 2018 - July 2018

Project Engineer

Milani Construction LLC

Prepared cost estimates for asphalt pavement and bridge construction projects. Also, worked with a team on construction bidding process, CPM scheduling, takeoffs and other construction procedures.

January 2017 - January 2018

Project Engineer

EBA Engineering Inc.

Worked as an on-site consultant with construction division in Maryland Department of Transportation, State Highway Administration. Mainly worked on preparing RFIs, material clearance, change orders and progress report. Also, worked on MCMS/MMS programs to manage cost and expense for labors and equipments.

June 2016 - January 2017

Project Manager Assistant Intern

PWE, City of Houston, Texas

Worked on cost estimation, scheduling and reviewing field material orders. Assisted in inspection and project management of waterline and traffic signalization.

April 2015 - April 2016

Education

University of Houston, Texas.

M.Sc. in Construction Engineering and Management.
2014- 2016

Ferdowsi University of Mashhad, Iran.

B.Sc. in Architectural Engineering.
2009- 2014

Skills

Programming Languages & Tools
  • Python (Pandas, Numpy)
  • SQL
  • Spark
  • Scala
  • Data Visulization (Matplotlib, Seaborn, ggplot, Plotly, geoplotlib)
  • Machine Learning
  • Natural Language Processing (NLP)
  • Webscraping, HTML
  • R programming
  • ArcGIS- ArcMap
  • Tableau
  • AutoCad
  • Microstation
  • Roadway Vision
  • Microsoft Excel/ Access

Projects

Lane Detection with Deep Learning

Earned the Best Project of the Week badge on Career Karma

"It is true that human beings can identify lane line markings while driving with basic training, but sometimes, based on the number of crashes and accidents,
it is understandable that they can have a disadvantage of not always being attentive. Although, it is not that easy for computers to learn identifying lane
line markings but after learning the task, there would not be any distractions
for them and they have this advantages that the rate for crashes and accidents caused by distraction would be less and computers can take over this task from human driver. Using deep learning, I have used transfer learning and re-train
a pre-trained model (PINet ).
The model is based on a Convolutional Neural Network (CNN) which perform well on image dataset. CNNs work well with images by looking at them in pixel level."
To see this project click here


Indexing Natural Disasters and Outbreaks


"The COVID-19 global pandemic has impacted all of our communities. In some areas, the impact of the pandemic has been exasperated by other natural disasters. In the state of California, wildfires have been devasating in recent years. Being part of the ring of fire and laying on top of large number of faults, earthquakes are also a constant threat. There is a shortage of tools available
for looking at the combined effect of the pandemic and other natural disasters.
This makes it challenging for decision makers to assess risk and make appropriate prepardness plans.
Here, we will provide a tool to visualize the concurrence of COVID-19 hot spots, wildfires, and earthquakes in California."
To see this project click here





Finding your Favorite Reddit Community


"There are about 138,000 active subreddits among a total of 1.2 million subreddits. But, how is it possible to find your own community with the topics you have interest in?! Well, that is why I am here! I try to help you out but how?
I collected more than 100,000 comments using webscrapping and APIs from different subreddit categories. By using CountVectorizer and TFIDF and Naive Bayes my Natural Language Processing (NLP) classification model here goes over all the subreddits that you have some interest in and BAM! It gives you the most key words of each subreddits and you can easily decide which community is for you! Why am I think my model is good? Since my model has an accuracy of %99 to identify the certain posts and vocabularies from any subreddits."
To see this project click here




Predicting Housing Market Prices


"The Ames Housing Data set contains information regarding the houses sold in the years 2006 to 2010 in Ames, Iowa. The main purpose of this project is to model the home prices and I used different techniques using regression
(of course!) and some feature engineering. The final accuracy of this model is 94%."
To see this project click here


Awards & Certifications

  • Data Science Immersive Certification- Completed 480 hours.
  • ArcGIS Training Certification.
  • Awarded full scholarship for PhD program in transportation systems at UMD.
  • Awarded full scholarship for Master's program in Construction Eng. and Mngt.
  • OSHA Construction Certification.