Harsh Poonia
I am a fourth year undergraduate student at the Indian Institute of Technology Bombay, where I am majoring in Computer Science and Engineering with honors.
My research interests lie broadly in machine learning and natural language processing, with a focus on generalizability and fairness. More specifically, I am interested in Large Language Models, Causal Machine Learning, Speech Recognition, and Interpretability in Sequence Models.
I have been fortunate to be advised by Prof. Devendra Singh Dhami at TU Eindhoven and Hessian Center for AI on my research around causal machine learning. I worked with him on a novel tractable probabilistic circuit that enabled causal inference in hybrid domains, as real world datasets commonly have both discrete and continuous random variables (published at UAI 2024). I am now working with him on neural granger causality in the context of multivariate time series data. At IIT Bombay, I have the privilege of working with Prof. Preethi Jyothi for my bachelor's thesis. We aim to compute invariant and equivariant representations of speech, to improve recognition performance for non-native speakers. I have also worked with Prof. Sunita Sarawagi on leveraging the in-context learning ability of LLMs for few-shot learning in Text-to-SQL tasks.
In my spare time, I love to play all kinds of sports: football, basketball, badminton, and table tennis being my favorites. I also love to write, and I sketch sometimes.
Research
Neural Granger Causality for Multivariate Time Series
Harsh Poonia, Felix Divo, Kristian Kersting, Devendra Singh Dhami
OpenReview, 2025
arxiv /
A novel method for predicting sparse granger causal relations between a set of time series variables, using an xLSTM based architecture and a dynamic lasso penalty for inducing sparsity. Sepp Hochreiter recognised our work!
chiSPN: Characteristic Interventional Sum-Product Networks for Causal Inference in Hybrid Domains
Harsh Poonia, Moritz Willig, Zhongjie Yu, Matej Zečević, Kristian Kersting, Devendra Singh Dhami
Uncertainty in Artificial Intelligence (UAI), 2024
arxiv / code /
We compute post-intervention likelihoods based on true causal relationships between variables. We developed a novel tractable probabilistic circuit, that enabled causal inference for hybrid domains, that is, on systems with both discrete and continuous random variables. By using characteristic functions to represent distributions, we were able to better model asymmetric and long-tailed distributions, which are especially prevalent in real-world datasets.
Equivariant Representations of Speech
Guide: Prof. Preethi Jyothi
Bachelor's Thesis I, 2024
report /
I am working towards bridging the performance gap in speech recognition for non-native speakers across underrepresented accents and dialects, to create more inclusive models through equivariant learning approaches. We have also been working on efficient wakeword detection (”Hey Alexa!”), where equivariance is desired with respect to shifts within the audio. Our implementation of a shift-equivariant transformer has shown promising results on command recognition, matching state-of-the-art accuracy while displaying significantly less variance with shifts in input.
In-Context Learning in LLMs for Text-to-SQL
Guide: Prof. Sunita Sarawagi
Research and Development Project, 2024
report /
In collaboration with Amazon, I worked on a project to explore the theoretical underpinnings of the in-context learning capability of LLMs, and to better leverage it for something that can aid fast, natural language based retrieval in databases: the domain of Text-to-SQL models. Our approach combined constrained decoding with question decomposition to answer queries represented by sub-trees of the syntax tree of a complex query. We were able to reduce schema hallucinations and improve coherence of generated SQL through this bottom-up approach.