Siddharth Vohra

Siddharth Vohra

Machine Learning Engineer & Researcher

Research

Academic Publications

Hearsay: Vision-Language Medical Diagnoses Without an Image

1st Workshop on Toward Trustworthy Vision-Language Models in the Wild, co-located with ICMR 2026

Vohra, S. (2026). Hearsay: Vision-Language Medical Diagnoses Without an Image. The 1st Workshop on Toward Trustworthy Vision-Language Models in the Wild: Theory, Algorithm and Application, co-located with the 16th ACM International Conference on Multimedia Retrieval (ICMR 2026), Amsterdam, The Netherlands.

Audits frontier vision-language models under missing-image medical prompts and identifies structured diagnostic confabulation, demographic sensitivity, and structured-output failure modes.

Interactive project page · Paper (PDF)

TEEMIL: Towards Educational MCQ Difficulty Estimation in Indic Languages

The 31st International Conference on Computational Linguistics (COLING 2025)

Ravikiran, M., Vohra, S., Verma, R., Saluja, R., & Bhavsar, A. (2025). TEEMIL: Towards Educational MCQ Difficulty Estimation in Indic Languages. Proceedings of the 31st International Conference on Computational Linguistics (COLING 2025), pp. 2085-2099. Association for Computational Linguistics.

Introduces the TEEMIL-H and TEEMIL-K datasets for Hindi and Kannada MCQ difficulty estimation, with multilingual model baselines and ablations over context, answer options, and none-of-the-above options.

You Reap What You Sow—Revisiting Intra-class Variations and Seed Selection in Temporal Ensembling for Image Classification

International Conference on Frontiers in Computing and Systems (COMSYS 2023)

Ravikiran, M., Vohra, S., Nonaka, Y., Kumar, S., Sen, S., Mariyasagayam, N., & Banerjee, K. (2023). You Reap What You Sow—Revisiting Intra-class Variations and Seed Selection in Temporal Ensembling for Image Classification. In Proceedings of International Conference on Frontiers in Computing and Systems (pp. 73-82). Springer, Singapore.

Studies how intra-class variability, seed size, and seed selection affect semi-supervised Temporal Ensembling performance across image-classification datasets.

(Manikandan Ravikiran and Siddharth Vohra contributed equally. Names are ordered alphabetically)

Investigating the Effect of Intraclass Variability in Temporal Ensembling

arXiv preprint (2020)

Vohra, S., & Ravikiran, M. (2020, August 21). Investigating the effect of intraclass variability in temporal ensembling. arXiv.org

Academic Service

  • Ethics Reviewer, 40th Conference on Neural Information Processing Systems (NeurIPS 2026)
  • Reviewer, Context Beyond the Window: Persistent Knowledge in Language Models (CBW) Workshop, COLM 2026
  • Program Committee Reviewer, Workshop on Agentic Software Engineering (AgenticSE), ACM KDD 2026
  • Reviewer, 2nd Workshop on Compositional Learning: Safety, Interpretability, and Agents (CompLearn), ICML 2026

Technical Blogs

Using WAF with App Runner in Copilot

February 23, 2023

Vohra, S. (2023, February 23). Using WAF with app runner in copilot. AWS Copilot CLI Blog