Gautham Krishna Gudur is a machine learning Ph.D. student in Electrical and Computer Engineering at The University of Texas at Austin, advised by Prof. Joydeep Ghosh (previously Prof. Edison Thomaz), and affiliated with IDEAL, WNCG, IFML, and iMAGiNE. His research focuses on resource-efficient and data-centric machine learning, foundation models, multimodal/multisensory learning, and health and human-centered AI. His interests include efficient pretraining and post-training of language models, continual learning, personalization and few-shot adaptation, modality gaps and cross-modal alignment, data-, parameter- and token-efficient learning, reasoning, and agentic AI, with applications across wearable, physiological, time-series, and acoustic data. He has held research internships at Bosch Research (BCAI), working on language models for multimodal time-series data, and Nokia Bell Labs (Cambridge, UK), working on foundation models for health. Before his Ph.D., he worked at Ericsson R&D on AI/ML for next-generation telecom systems and at SmartCardia, an EPFL spin-off, on AI-driven wearable healthcare. His work has appeared at leading conferences like NeurIPS, EMNLP Findings, esteemed journals, and numerous workshops co-located with leading venues including ICLR, NeurIPS, ICML, KDD, UbiComp, MobiCom, MobiSys, and IJCAI. He has also organized the WellComp workshop at UbiComp/ISWC 2026 and been on the program committees/reviewer for leading conferences and workshops in machine learning, signal processing, health, and ubiquitous computing.