Parham Hamouni
Verified Expert in Engineering
Data Scientist and Developer
Parham is a data and applied research scientist with 4+ years of machine learning (ML) and deep learning experience. With solid understanding and hands-on experience in Graph ML, natural language processing (NLP), computer vision, time series, generative adversarial networks (GAN), and statistics, he has been teaching Graph ML online and publishing academic papers. Parham successfully led projects using data engineering, clarifying ambiguous problems, and driving insight for the client.
Portfolio
Experience
Availability
Preferred Environment
Unix, Python, SQL, Deep Learning, Graphs, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), GPT, Azure, Amazon Web Services (AWS), Pandas
The most amazing...
...result I've achieved improved the accuracy of a knowledge graph entity prediction problem from 35 to 95 percent using a simple yet effective technique.
Work Experience
Applied Research Scientist
Crater Labs Inc
- Trained a self-supervised language model on the sentences that accurately assigned relative risk in the news to the companies, using news about companies in the stock market and their relative 10-K forms in the SEC database.
- Improved edge pair prediction by more than 60 percent to 95 percent Hits@1 score by reframing a knowledge graph entity prediction problem as a sentence pair classification.
- Predicted the best price discount for a startup company with a regression problem approach using multi-relational graph embedding techniques to create relative features.
- Used question answering data scraped from the client's forum to create a dialog generation network based on GPT-2 with a persona context feature. Also applied spectral regularization, which improved the perplexity score.
- Handled manufacturing defect detection in highly imbalanced data. Using conditional GANs and image-to-image translation techniques augmented the data so the data imbalance was managed and the classifier overcame overfitting. Used Azure environment.
- Applied unsupervised image encoding techniques such as adversarial autoencoders and CNN models to find similar images in a proprietary photoshoot dataset. Used AWS environment.
Experience
TF-MF: Improving Multiview Representation for Twitter User Geolocation Prediction
https://ieeexplore.ieee.org/document/9073316In this paper, we take into account user-shared tweets as well as their social network and run extensive comparative studies to systematically analyze the impact of a variety of language-based, network-based, and hybrid methods in predicting user geolocation. In particular, we evaluate different text representation methods to construct text views that capture the linguistic signals available in tweets that are specific to and indicative of geographical locations. In addition, we investigate a range of network-based methods, such as embedding approaches and graph neural networks, in predicting user geolocation based on user interaction networks.
Our findings provide valuable insights into the design of effective and efficient geolocation identification engines. Ultimately, our best model, called TF-MF, substantially outperforms state-of-the-art approaches under minimal supervision.
Skills
Languages
Python, SQL
Libraries/APIs
Pandas
Other
Deep Learning, Graphs, Natural Language Processing (NLP), Computer Vision, Statistics, Artificial Intelligence (AI), GPT, Generative Pre-trained Transformers (GPT), Time Series Analysis
Platforms
Unix, Azure, Amazon Web Services (AWS)
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