Puria Azadi Mohadam
pazadimo {at} sfu {dot} ca
Simon Fraser University
My name is Puria Azadi, a M.Sc. student in Computing Science at Simon Fraser University(SFU), and I am working under supervision of the Prof. Hefeeda. Before joining Simon Fraser University, I was an undergraduate Electrical Engineering (Telecommunication) student with minor in Computer Engineering at the Department of ECE, University of Tehran.
My research interest is at the intersection of multimedia, computational photography, computer vision with strong connections to optimization and machine learning.
Publications
“Revealing True Identity: Detecting Makeup Attacks in Face-based Biometric Systems,” ACM MM, 2020
A. Arab, P. A. Moghadam, M. Hussein, W. Abd-Almageed, and M. Hefeeda“Deep Reinforcement Learning for Dynamic Reliability Aware NFV-Based Service Provisioning,” IEEE Globecom, 2019
H. R. Khezri, P. A. Moghadam, M. K. Farshbafan, V. Shah-Mansouri, H. Kebriaei, and D. NiyatoEducation
M. Sc. in Computer Science
Graduate Research Assistant, SFU - Huawei Visual Computing Joint Lab and NSL Lab, School of Computing Science, Simon Fraser University, Canada Advisor: Prof. M. Hefeeda GPA: 4.33/4.33 (A+)
Courses: Statistical Machine Learning, Deep Learning, Computational Photography, Machine Learning, Design Algorithms
B. Sc. in Electrical Engineering
Minor in Computer Engineering
Undergraduate Research Assistant, Advanced Mobile Communication Lab, School of Electrical and Computer Engineering, University of Tehran, Iran Advisor: Prof. V. Shah-Mansouri GPA: 3.72/4. (17.42/20)
Courses: Statistical Inference, Intelligent Systems , Discrete Signal Processing, Signals and Systems, Numerical Computation, Linear Control Systems, Engineering Mathematics, Advanced Programming, Discrete Mathematics, Data Structure and Algorithm, Computer Architecture
Research Experiences
Revealing True Identity: Detecting Makeup Attacks in Face-based Biometric Systems:
NSL Lab at Simon Fraser University and ISI at the University of Southern California
- Face-based authentication systems are among the most commonly used biometric systems, because of the ease of capturing face images at a distance and in non-intrusive way. Makeup attacks are the hardest to detect in such systems because makeup can substantially alter the facial features of a person.
Paper - Code(Available Soon)
More Info
- In our solution, we design a generative adversarial network for removing the makeup from face images while retaining their essential facial features and then compare the face images before and after removing makeup. Also, we collect a large dataset of various types of makeup, especially malicious makeup that can be used to break into remote unattended security systems. This dataset is quite different from existing makeup datasets that mostly focus on cosmetic aspects.
- Our results show that the proposed solution produces high accuracy and substantially outperforms the closest works in the literature.
Improving Visual Question Answering Using Semantic Analysis and Active Learning:
- In this work, we aimed to train a model for the task of visual question answering, using only a small number of labeled data. In order to do so, we proposed a solution influenced by the active learning. We defined an oracle to provide a label for the question that is asked about an image. This oracle is an image captioning network that given an image as its input, generates a sentence describing the objects which are visible in that image.
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- We used a semantic similarity calculator to connect the result of the image captioning model and interpret that to become a potential label for the visual question answering task. By using this structure and defining a new loss function, we became able to train a visual question answering model using a small number of labeled data.
- The simulations of our proposed method shows that this approach can result in a model, which performs in the same standard of classical approaches.
Deep Reinforcement Learning for Dynamic Reliability Aware NFV-Based Service Provisioning:
Advanced Mobile Communication Lab at University of Tehran
- Network function virtualization (NFV) is referred to the technology in which softwarized network functions virtually run on commodity servers. Such functions are called virtual network functions (VNFs). One of challenges is to meet the reliability requirement of the requested services considering the reliability of the commodity servers.
More Info
- To address such an issue, in this paper, we employ Deep Reinforcement Learning (Deep-RL) to model NFV placement problem considering the reliability requirement of the services.
- Numerical evaluations show that the introduced model can significantly improve the performance of the network operator.
Notable Projects
Iterative Edge Aware Filtering and Cross Filtering
The implementation of spatial filtering proposed in "Temporally Coherent Local Tone Mapping of HDR Video". The most relevant parts are sections 4.1 and 4.2 of the paper.
Texture Synthesis and Texture Transfer
The implementation of the texture synthesis and texture transfer method proposed in: Efros and Freeman , "Image Quilting for Texture Synthesis and Transfer".
Poisson Image Blending
The implementation of the Poisson image blending method proposed in: Perez et al., "Poisson Image Editing"
Naive Bayes Classifier to Predict Party Affiliation
Implementing and benefiting the NB classifiers to predict the party affiliation of either Democrat or Republican of Congressmen based on their votes for 16 different measures in python.
Gibbs Sampling for the Image Restoration
Estimating the posterior probabilities of pixels value and restoring the noisy images by using the Monte Carlo estimate and Gibbs Sampling which iterates and samples in Ising model of the image in python.
Factor Graphs and Loopy Belief Propagation
Error correcting codes based on highly sparse, low density parity check (LDPC) matrices, and using the sum-product variant of the loopy belief propagation to estimate partially corrupted message bits in python.
Implementing and Analyzing Autoencoders
Implementing, analyzing, and comparing the Fully Connected, Convolutional, and Variational Autoncoders using Keras in python.
RNN Sequence Processing
Implementing RNN-based language models and compare extracted word representation from different models using Keras in python. Designing Vanilla RNN to capture word representations for classifying the 20Newsgroups dataset.
Implementation and Visualization of CNNs
Design and implementation the LeNet using Keras in python to classify the Caltech101 containing 101 categories. Visualization the network's weights and analyzing them was the final objective.