CV

Education

• M.S. in Computer Science, University of Southern California, 2024
• B.S. in Computer Engineering, Mumbai University, 2022

Work experience

• August 2022 - May 2024: Research Assistant
  • Information Sciences Institute
  • Duties include: Conducting novel research on argumentation, and logical and commonsense reasoning in dialogue systems and LLMs.
  • Advisors: Filip Ilievski, Fred Morstatter, Jonathan May

Skills

• Languages: Advanced Python, CUDA, C, Java, SQL, JavaScript, HTML/CSS
• Machine Learning: Pytorch, Tensorflow, Keras, JAX, Hugging Face, Sklearn, XgBoost, Pandas, NumPy, Matplotlib
• Deployment: Docker, Flask, Django, RestAPI, FastAPI
• Developer Tools: Git, Docker, Google Cloud Platform, Amazon AWS, Microsoft Azure
• Database Management: MongoDB, MySQL, PostgreSQL, SQLite

Publications

Contextualizing Argument Quality Assessment with Relevant Knowledge

Deshpande, D., Sourati, Z., Ilievski, F., & Morstatter, F. (2023). Contextualizing Argument Quality Assessment with Relevant Knowledge. ArXiv, abs/2305.12280.

Robust Text Classification: Analyzing Prototype-Based Networks

Sourati, Z., Deshpande, D., Ilievski, F., Gashteovski, K., & Saralajew, S. (2023). Robust Text Classification: Analyzing Prototype-Based Networks. ArXiv, abs/2311.06647.

Robust and explainable identification of logical fallacies in natural language arguments

Sourati, Z., Venkatesh, V. P. P., Deshpande, D., Rawlani, H., Ilievski, F., Sandlin, H., & Mermoud, A. (2023). Robust and explainable identification of logical fallacies in natural language arguments. Knowledge-Based Systems, 266, 110418. https://doi.org/10.1016/j.knosys.2023.110418

Talks

Diffusing Away from GANs and Transformers

June 08, 2022

Talk at TFUG Meetup, 2022, Mumbai, India

Projects and Reports

• Exploring Diffusion Models with JAX - Python, JAX, TPU (June 2022)
  • Explores the mathematics and code of discrete diffusion models, their effectiveness, applicability, and limitations.
• Review Classification using Active Learning - Tensorflow, Keras, GPU (Oct. 2021)
  • Demonstrates the effectiveness of active learning in text classification tasks by implementing a ratio-based sampling approach, improving performance and suitability for industrial applications.
• The Reality Behind the Optimization of Imaginary Variables - I and II - Research (Jun 2021 – Oct 2021)
  • Leverages complex-valued neural networks to enhance representations by experimenting with traditional mappings and more efficient complex vector techniques.
  • Explores circularity and holomorphicity constraints in complex-valued neural networks, showcasing the effectiveness of linear and widely linear networks for image-denoising.
• MelGAN based Spectrogram Inversion using Feature Matching - Reproducibility (Sep. 2021)
  • Showcases a non-autoregressive model for spectral inversion utilizing a feature-matching discriminator, highlighting fast inference on dynamic inputs.

Awards and Grants

• Google OSS Expert Award (2022): For releasing open-source articles with code for exploring diffusion models.
• Papers With Code Top Contributor Award (2022): For open-source and reproducibility contributions with JAX.
• Google Research Grant (2021): A $5,000 grant to investigate the robustness of ML-based steganography methods.