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Meet Jing-Ping Lin, PhD!
Assistant Professor, Tenure Track
Department of Physiology and Department Neurology
Specialty Areas:
- Marmoset models of neuroinflammatory disorders
- White matter development & aging
- Medical imaging & multiomics integration
Recognition:
- University of Arizona nomination for 2027 Pew Scholar
- National MS Society Career Transition Award (K99/R00 equivalent)
- Young Investigator Award
- NINDS Director's Award
Why you should know Dr. Lin:
- Transformative Platform
- Next-generation marmoset disease
- Multi-scale integration: MRI pathology, spatial omics
- Translational Impact
- Identify early MRI biomarker before lesion onset (Science, 2025)
- Recognized as landmark study (Neurology Today, 2025)
- Strategic Value UofA
- Developing distinctive infrastructure: marmoset colony, biobank, multi-modal datasets.
- Enabling cross-scale, molecular-to-physiology discoveries.
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Meet Fakrul Islam Tushar, PhD!
Assistant Professor, Department of Radiology & Imaging Sciences
iTrialSpace
Synthetic framework for controlled assessment of lung CT
- 13,140 human lung nodules
- 54 attribute nodule profiling
- Generates synthetic cohorts for controlled trial design
- 55,469-sample virtual lesion study spanning three medical VLMs, four spatial-guidance conditions and three clinical task produces synthetic cohorts that remain within the range of real clinical inter-dataset variability while supporting controlled evaluation beyond fixed retrospective benchmarks.
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Meet Jennifer Stern, PhD!
Associate Professor with Tenure
Department of Medicine, Division of Endocrinology
- Internationally recognized investgator studying glucoregulatory hormone signaling in aging and metabolic disease
- Recent major discovery demonstrating glucagon signaling regulates aging and mediates calorie restriction - induced health span extension.
- Dr. Stern's seminal work established that glucagon signaling was critical to normal aging and mediates the health span benefits of caloric restriction, identifying a new target to prevent age-related disease and slow aging.
- The Stern Laboratory established that a moderate lifelong 15% restriction improves metabolic and physical function in aging rodents, providing promising evidence that the level of restriction achieved in human clinical trials could improve aging.
- Her lab is focused on translating her group's findings in mice to that of human participants in the nationally renowned CALERIE clinical trial.
- For decades, it was assumed that glucagon stimulated fasting-induced lipolysis in white adipose tissue. Through a series of ex vivo and in vivo studies, Dr. Stern and her group established that knocking out glucagon receptors in white adipose tissue had no effect on lipolysis or lipid homeostasis, thereby eliminating this misconception.
Accomplishments and Research:
- $4M+ in NIHZ funding secured since joining faculty in September 2018.
- Principal investigator on 4 NIH grants (NIA R01, R21, R56, R00).
- Two additional NIH R)! proposals currently under review
- Additional grant support from ABRC and multiple University of Arizona internal grants
- 2025 COM-T Faculty excellence award - Basic Science - Translational Investigator
- 2025 Research featured on NPR KJZZ
- 2021 Top Cited Author, Journal of Endocrinology
- Internationally and Nationally recognized in the aging and metabolism fields
- Recent invited talks:
- Translational Medicine for Cardiometabolic Diseases - Copenhagen, 2025
- Masoro-Barshop Conference on Aging - Texas 2025
- European Association for the Study of Diabetes - Madrid 2024
- Recent invited talks:
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Meet Sarah Pungitore, PhD!
Assistant Professor, Department of Emergency Medicine
- Developed LLM for Cerner EHR for computational phenotyping to create digital twin models:
- Respiratory failure interventions
- Pulmonary hypertension therapy
- Noninvasive assessment of complex respiratory mechanics
- Designed community-level pandemic surveillance models
- Analysis of Human-AI interaction in clinical decision making.
Why you should know Dr. Pungitore:
- AI data scientists will be crucial key members of research teams
- AI data scientists understand WHY an AI generated model is or isn't appropriate for specific scenarios
- AI data scientists are extremely helpful in understanding why surprising AI-generated results might be occurring.