Elham Saraee
Department of Computer Science, Boston University

Welcome to my webpage

About me

Research Interest

  • Deep learning
  • Computer Vision
  • Applied Machine Learning
  • Human Computer Interaction
  • Work Experience

    Adhark Incorporation (Sept 2017 - Present)

    Role: Data Scientist Project: Image analysis with deep learning, to evaluate the visual preferences of different audience groups
    Deep learning platform: Tensorflow
    Programming Language: Python

    Research Projects

    Unsupervised Deep Feature Extraction for Visual Complexity Analysis

    In this work, we propose a new direction in unsupervised information extraction from intermediate convolutional layers of deep neural networks. We focus on visual com- plexity, an image attribute that humans can subjectively evaluate based on the level of detail in the image. We derive an activation energy (AE) metric that combines convolutional layer activations to quantify visual complexity. In addition, we introduce Savoias, a visual complexity dataset that compromises of more than 1,400 images from seven diverse image categories.

    ExerciseCheck: Remote Monitoring and Evaluation Platform for Home Based Physical Therapy

    We present ExerciseCheck, a remote monitoring and evaluation platform for individuals involved in a home exercise program. The goal of the platform is to give patients feedback about their performance and, if needed, and how they should adjust their movements. ExerciseCheck is designed for a therapist to remotely monitor a patient in real time, enabling the therapist to give instant feedback or further instructions.

    Dynamic Adjustment of Physical Exercises Using Reinforcement Learning

    In this project, we propose a system for divculty adjustment of exercises in physical therapy. We use pattern recognition algorithms (Dynamic Time Warping and Spectral Arc Length), to extract performance measures; then we design our online reinforcement learning algorithm to dynamically adjusts the divculty con guration of exercises based on the users performance.

    Posture Modeling for Exercise Assessment

    We propose a method to capture and model the postures of a user for during a physical therapy exercise. We capture the skeletal data using the Microsoft Kinect, extract features and train a Gaussian Mixture Model (GMM) to represent postures of the user during an exercise. Our model enables autonomous detection of incorrect postures during an exercise.


    Teaching Experience

    • Boston University (2013 - Current)
      • Graduate Teaching Fellow, Image and Video Computing ,( Fall 2016).
      • Graduate Teaching Fellow, Introduction to Electronics,( Fall 2015).
      • Graduate Teaching Fellow, Introduction to Logic circuits, (Spring 2014).
      • Graduate Teach- ing Fellow, Introduction to Logic circuits, (Fall 2013).

    • Sharif University of Technology(2008 - 2013)
      • Coordinator and Lab TA, Principals of Electrical Engineering, (Fall 2012).
      • Lab Teaching Assistant, Principals of Electronics, (Fall 2012).
      • Lab Teaching Assistant, Analog Circuits, (Spring 2011).
      • Lab Teaching Assistant, Principals of Electronics, (Fall 2010).