I am a third year PhD student at UNSW Sydney working on conditional generative models, and being supervised by Dr. Lina Yao and Dr. Dong Gong. Prior to my PhD, I was a research assistant at the Computer Vision Center, Barcelona where I worked with Dr. Joost van de Weijer on continual learning.

I completed my Erasmus Mundus Joint Master’s Degree in Advanced Systems Dependability from the University of St Andrews, UK, and l’Université de Lorraine, France. During my master’s, I interned with the MULTISPEECH group at Inria Nancy where I worked with Dr. Emmanuel Vincent on training domain-specific language models for Automatic Speech Recognition. Before that, I  was a machine learning engineer at FactSet Research Systems Inc., India. I grew up in Eastern Nepal and earned my BS in Computer Science and Engineering from MNNIT Allahabad, India.

Experience

  • Tencent, Sydney, Australia (Sep 2024 – Present)
    AI Research Intern

Working on multi-modal generative AI.

  • Sony Group Corporation, Tokyo, Japan (May 2024 – Aug 2024)
    Research Scientist Intern

Worked on continual personalization of text-to-image diffusion models.

  • INRIA Nancy, France (Mar 2021 – Jun 2021)
    Research Intern

Worked on training domain-specific language models for Automatic Speech Recognition based on machine translation.

  • FactSet Research Systems, Hyderabad, India (Jun 2018 – Jul 2019)
    Machine Learning Engineer

Worked on Factset’s named entity recognition and topic modelling services.

Publications

Please check my google scholar profile for an up-to-date list.

Pre-prints:
  • Jha S., Yang S., Ishii M., Zhao M., Simon C., Mirza J., Gong D., Yao L., Takahashi S., Mitsufuji Y., “Mining Your Own Secrets: Diffusion Classifier Scores for Continual Personalization of Text-to-Image Diffusion Models”, 2024. [Work done at Sony Group, Tokyo] Paper
  • Mirza, M. J., Zhao, M., Mao, Z., Doveh, S., Lin, W., Gavrikov, P., Dorkenwald, M., Yang, S., Jha, S., Wakaki, H., Mitsufuji, Y., Possegger, H., Feris, R., Karlinsky, L., Glass, J., “GLOV: Guided Large Language Models as Implicit Optimizers for Vision-Language Models”, 2024. Project
  • Jha S., Gong D., Wang X., Turner R., Yao L., “The Neural Process Family: Survey, Applications and Perspectives”, 2022. Paper
Conferences:
  • Jha S., Gong D., Yao L., “CLAP4CLIP: Continual Learning with Probabilistic Finetuning for Vision-Language Models”, NeurIPS 2024. Paper  Code 
  • Jha S., Gong D., Zhao H., Yao L., “NPCL: Neural Processes for Uncertainty-Aware Continual Learning”, NeurIPS 2023. Blog Paper Code 
  • Li Y., Liu Z., Jha S., Cripps S., Yao L., “Distilled Reverse Attention Network for Open-world Compositional Zero-Shot Learning”, ICCV 2023. Paper
Workshops:
  • Joshi A., Renzella J., Bhattacharyya P., Jha S., Zhang X. “On the relevance of pre-neural approaches in natural language processing pedagogy”. ACL Workshop on Teaching NLP, 2024. Paper
  • Pelosin F.*, Jha S.*, Torsello A., Raducanu B., van de Weijer J. “Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight Regularization”. CVPR Workshop on Continual Learning, 2022. [* Equal Contribution] Paper Code [BEST RUNNER-UP PAPER!]
  • Jha S., Schiemer M., Ye J. “Continual learning in human activity recognition: an empirical analysis of regularization”. ICML Workshop on Continual Learning, 2020. Paper Code
Journals:
  • Jha S., Schiemer M., Zambonelli F., Ye J. “Continual learning in sensor-based human activity recognition: An empirical benchmark analysis”. Information Sciences, 2021. Paper Code
  • Ye J., Nakwijit P., Schiemer M., Jha S., Zambonelli F. “Continual Activity Recognition with Generative Adversarial Networks”. ACM Transactions on Internet of Things (TIOT), 2021. Paper
  • Jha S., Sudhakar A., Singh A.K. “Learning cross-lingual phonological and orthographic adaptations: a case study in improving neural machine translation between low-resource languages”. Journal of Language Modelling, 2019. Paper

Academic Services

  • Reviewer for: ICLR 2024, CPVR 2024, TPAMI, ACM Multimedia 2024, ICLR 2023, NeurIPS 2023
  • PC member for the Industry Track for Applied Research of the Web Conference 2025 (WWW 2025, Sydney, Australia).
  • PC member for Workshop Proposals, Conference on Information and Knowledge Management (CIKM 2023, Birmingham, UK)

Teaching at UNSW

  • COMP6713 (Natural Language Processing), taught by Dr. Aditya Joshi
  • COMP9418 (Advanced Topics in Statistical Machine Learning), taught by Dr. Gustavo Batista
  • ZZEN9444 (Neural Networks and Deep Learning), taught by Dr. Dong Gong
  • COMM5007 (Python Coding for Business), taught by Dr. Xiangyu Wang