Amish Sethi

I'm an undergraduate student at the University of Pennsylvania, currently in my junior year pursuing a Bachelor's and Master's degree in Computer Science, with an expected graduation for both in 2026.

I work with Professor Mayur Naik on projects that push the boundaries of deep learning and neurosymbolic AI. My interests span neural-network optimization, scalable model compression, and integrating symbolic reasoning with neural architectures. I enjoy building efficient, interpretable systems that extend the capabilities of large language and vision models.

I’m incredibly grateful to Professor Mayur Naik for his continuous support and guidance throughout my research journey. I also deeply appreciate the mentorship and inspiration from the PhD students I’ve worked closely with: Neelay Velingker, Oscar Xu, Aaditya Naik, and Jiani Huang.

Email  /  CV  /  Scholar  /  Github

News

• (2025/05/01) Our paper Dolphin: A Programmable Framework for Scalable Neurosymbolic Learning is accepted to ICML 2025! 🎉

Research

I'm interested in deep learning, generative AI, and neurosymbolic AI. Most of my research focuses on optimizing large language models through efficient finetuning, quantization, and pruning, and on exploring how symbolic reasoning can be woven into neural networks for greater interpretability and control.

DOLPHIN is a novel framework combining symbolic reasoning and neural computation using CPU-GPU hybrid execution. Its execution of vectorized probabilistic computations on the GPU allows it to achieve up to 62× faster convergence than baselines across 13 benchmarks spanning text, image, and video modalities.

CLAM is a framework unifying parameter-efficient finetuning, quantization, and pruning for LLMs. It enables chaining of adapters with low overhead and high modularity, outperforming state-of-the-art methods by up to 6.5%. CLAM achieves superior trade-offs in compression and downstream performance, beating QLoRA while effectvely halving the number of active bits

We provide a semantically-equivalent computation graph reformulation for LoRA and other PeFT techniques that saves memory and accelerates training. Under practical conditions, this leads to up to a 1.4× reduction in max memory usage and latency for LoRA finetuning across language and diffusion transformers, without degrading predictive performance.

This project utilized machine learning, clustering, and dimensionality reduction algorithms in scikit-learn to identify which genes are expressed differently between those with Alzheimer’s and a control group. A model trained on this gene expression data could predict likelihood of Alzheimer’s with 98% accuracy.

Teaching and Mentorship

In the Fall of 2024, I served as the Head Teaching Assistant (TA) for CIS 7000: Large Language Models, the University of Pennsylvania’s first dedicated course on LLMs. The course enrolled over 120 students and covered the theory, design, training, compression, deployment, and application of large language models.

As Head TA, I was responsible for:

• Planning the course cirriculum
• Designing and implementing homework assignments
• Holding office hours and supporting students throughout the semester
• Creating several lecture slide decks
• Delivering some lectures on efficient finetuning, adaptation, and evaluation

In the Summer of 2024, I mentored five undergraduate students through the Penn Undergraduate Research Mentoring Program (PURM) on the CLAM project, focusing on efficient finetuning, quantization, and pruning.

I taught these students how to conduct research in machine learning, work with LLMs, and develop scalable optimization frameworks. The students I mentored were:

• Annabella Tian
• Cedric Chan
• Ani Petrosian
• Andrew Chang
• Matthew Kuo