Dr. Rachel Surowiec is an Assistant Professor in the Weldon School of Biomedical Engineering at Purdue University, with a courtesy appointment in Radiology and Imaging Sciences at Indiana University School of Medicine. Following her Master's in Biomechanics, she worked as a Senior Research Scientist dually appointed in Imaging Research and Biomedical Engineering at the Steadman Philippon Research Institute in Vail, Colorado. She returned to academia to complete her Ph.D. in Biomedical Engineering with a Biomedical Imaging Concentration at the University of Michigan, followed by postdoctoral training at the Center for Molecular Imaging (University of Michigan) and in Anatomy, Cell Biology, and Physiology at IUSM. Her lab - the Quantitative Biomedical Imaging and Sciences (QBIS) Lab - is based in Indianapolis, embedded in the clinical and research ecosystem of IU Health, Riley Hospital for Children, and the Regenstrief Institute. That geography matters. The QBIS Lab moves constantly between controlled experiments and real patients, using advanced imaging, machine learning, and biomechanics to ask questions about tissue quality that conventional tools simply can't answer. A lot of that work centers on bone - not just how much there is, but what it's actually made of. Bone water and collagen turn out to be powerful but largely invisible markers of skeletal health, and the lab has spent years developing imaging and spectroscopy tools to measure them. That same curiosity about tissue quality is now driving work on how obesity and rapid weight loss - including GLP-1 therapies like semaglutide - affect the adolescent skeleton during the very window when kids are still building it. In collaboration with Dr. Brian DeBosch at IUSM, the lab is deeply phenotyping adolescents starting semaglutide at Riley, trying to understand who loses bone and muscle during treatment and who doesn't - and what the imaging can tell us about why. On the preclinical side, the same DeBosch collaboration has produced some genuinely unexpected findings about how circadian and metabolic clock genes regulate bone quality in ways that don't show up on a standard DXA scan. Meanwhile, the lab's AI-driven radiomics pipeline - originally built for bone disease in chronic kidney disease - is being extended into muscle, lung, and pediatric imaging, with a firm conviction that if you can't see what the model is doing, it doesn't belong in a clinic. Beyond the lab, she loves hanging with her three amazing kids, running (slowly), and gardening (totally subpar).

Alfaj Uddin Ahmed is a Ph.D. student in the Weldon School of Biomedical Engineering at Purdue University, where his research focuses on AI-driven medical image analysis and decision-support systems. Prior to Purdue, Alfaj earned his B.Sc. in Biomedical Engineering from the Bangladesh University of Engineering and Technology (BUET) and gained industry experience as a Software Engineer in Augmedix. His work lies at the intersection of biomedical imaging, deep learning, multimodal data integration, and clinical translation. He has experience developing end-to-end machine learning pipelines for CT, MRI imaging. He is being co-advised by Dr. Christopher Newman. In the lab, Alfaj works on advanced deep learning frameworks for pediatric fracture detection, including diffusion-based data synthesis, few-shot learning, and ranking-based diagnostic models to address data scarcity and class imbalance in clinical imaging. His broader research interests include multimodal learning, generative models, and interpretable AI for biomedical applications.

I graduated from Wabash College in 2024 and currently an MS 2 at Indiana University School of Medicine (IUSM): West Lafayette with an interest in Orthopedic Surgery. My interests in medicine most stem from my interest in sports as a child and all throughout school. My work within the QBIS Lab focuses on novel UTE MRI imaging of bone micro architecture and characterizing bone water to develop predictive screening measures for qualitative bone disease and injuries.

He received his undergraduate degree in Biomedical Engineering from the Weldon School of Biomedical Engineering in West Lafayette, Indiana, in December 2025. Peter has been involved with the lab as an undergraduate research assistant (primarily during the summers) since the lab’s founding in 2023. His research interests focus on understanding the effects of various disease models on bone health, with the long-term goal of translating engineering insights into clinical impact. He plans to attend medical school following the completion of his graduate studies. Outside of the lab, Peter enjoys playing drums in a band at Purdue University.

Youngjun is a Ph.D. candidate in the Weldon School of Biomedical Engineering at Purdue University and a research assistant in the QBIS Lab. He is scheduled to defend his dissertation in October 2026 and will complete his degree in December 2026. He earned his B.S. in Radiology from Shingu University and his M.S. in Biomedical Engineering from Korea University. Before starting his Ph.D., he worked as a clinical radiographer at Samsung Medical Center and Seoul Medical Center, and as a staff researcher at Seoul National University Hospital, where he contributed to cardiovascular imaging device development. His doctoral research focuses on AI driven quantitative imaging biomarkers, applying radiomics and machine learning to CT, MRI, and ultrasound images to extract clinically meaningful information about tissue and bone health. His work has been recognized with the ASBMR Young Investigator Award (2024) and the Bottorff Graduate Fellowship (2025). Outside the lab, Youngjun enjoys yoga, working out, and meditation.

Joseph is an MD-PhD student at Purdue University's Weldon School of Biomedical Engineering and the Indiana University School of Medicine. He earned his B.S.E in Chemical Engineering at the University of Michigan. His current research focuses on using near-infrared spectroscopy and thermal analysis to study bone composition and track changes across pathologies. Joseph is interested in utilizing spectroscopy and other imaging modalities to study the compositional effects of glucocorticoids on the pediatric population.

Peter is a PhD Candidate of Biomedical Engineering at Purdue University and a Graduate Research Assistant in the QBIS Lab. He is passionate about mathematics and the intricacies of research, which led to the study of bone water and its relationship with its mechanical properties. He is currently investigating bone water dynamics and its role as a therapeutic target - both in exercise regimen and medication. This bone water dynamicity will be tracked via its electric properties, and the study will be expanded to incorporate the development of a mechanical loading device. This loading device will target bone water to improve bone strength and its overall mechanical properties. The study is translatable and the loading device would be MRI-safe, lightweight, and patient friendly. Peter holds an MSc in Medical Imaging from the University of Dundee (Scotland, United Kingdom), and a BSc in Physics from Covenant University (Nigeria). Outside of research, he is a huge fan of art, history, and soccer.

Wikum Roshan Bandara is a PhD candidate in Biomedical Engineering at Purdue University, working in the QBIS Lab under the mentorship of Dr. Rachel Surowiec. His research focuses on how chronic kidney disease alters cortical bone quality, with particular interests in bone water, cortical porosity, mineral-matrix composition, and mechanical behavior. He combines medical imaging and spectroscopy methods, including high-resolution peripheral quantitative computed tomography (HR-pQCT), micro-computed tomography, ultrashort echo time magnetic resonance imaging (UTE-MRI), and Fourier-transform near-infrared (FT-NIR) spectroscopy, with image registration, three-dimensional image analysis, and mechanical testing. His current work includes longitudinal tracking of individual cortical pores and investigation of relationships among bone hydration, composition, and mechanical function. Wikum holds an MSc in Medical Physics from the University of Colombo and a BSc (Hons) in Radiography/Medical Imaging from the University of Peradeniya. His broader goal is to develop quantitative imaging methods that improve the non-invasive assessment of bone quality and fracture risk. Longitudinal Cortical Pore Tracking: Cortical porosity is an important contributor to bone fragility, but total porosity alone does not show how the pore network changes over time. Our work uses longitudinal HR-pQCT imaging to track individual cortical pore between scans and classify their structural fate. After three-dimensional registration and cortical pore segmentation, pores are matched across time points and categories as newly formed, enlarged, reduced, infilled, or stable. We then quantify changes in pore number, volume, morphology, spatial distribution, and network connectivity. This approach converts cortical porosity from single summary value into a set of biologically interpretable events that framework to investigate whether chronic kidney disease shifts pore-level remodeling. We are applying this framework to investigate whether chronic kidney disease shifts pore-level remodeling towards new pore formation and enlargement and whether these changes help explain deterioration in cortical bone quality. The long-term goal is to develop a repeatable, non-invasive imaging approach for monitoring intracortical remodeling and identifying patients at increased risk of skeletal fragility. Bone water, Spectroscopy, and mechanics: Bone strength depends not only on mineral density but also on water and the organic Metrix. In Chronic kidney disease, changes in hydration and matrix composition may occur before substantial mineral loss and may contribute to reduced mechanical performance. Our research combines UTE-MRI, FT-NIR spectroscopy, thermogravimetry analysis, micro-CT, and mechanical testing to examine how water is distributed within cortical bone and how this distribution relates to composition, porosity, and function. UTE-MRI provides non-invasive measures sensitive to bone water, while FT-NIR spectroscopy maps water-related spectral features across the context and identifies regional differences in tissue composition. Thermal analysis provides complementary measurements of free, loosely bound, matrix-associated water, and mechanical testing determines how these changes related to stiffness, deformation, and failure. By integrating imaging, spectroscopy, composition, and mechanics, this work aims to identify hydration-based markers of early bone-quality deterioration in chronic kidney disease and improve the assessment of skeletal fragility beyond bone density alone.

Farhan is a Graduate Teaching Assistant at the Weldon School of Biomedical Engineering, where his research focuses on developing advanced AI and deep learning methods for medical imaging. His work includes motion correction in high-resolution computed tomography (HR-pQCT) using generative models, simulation-to-real domain adaptation, and vision-language foundation models for automated motion scoring. He also investigates non-rigid motion correction in ultrashort echo-time (UTE) MRI using the PETAL-UTE sequence and advanced free-breathing dynamic reconstruction methods such as XD-GRASP-Pro. His research further includes implementing PETAL-UTE for musculoskeletal imaging and quantitative bone water and pore-water characterization, as well as radiomics-based feature analysis and machine learning classification for pulmonary nontuberculous mycobacterial (NTM) disease. Farhan has also worked as an AI/ML Summer Intern with the Computer Vision team at Johnson & Johnson Innovative Medicine, where he applied advanced AI techniques to accelerate clinical trial workflows and improve the efficiency of clinical data analysis.

Subah is a Ph.D. candidate at the Weldon School of Biomedical Engineering and an American Association of University Women (AAUW) International Doctoral Degree Fellow (2025–2026). She received her undergraduate degree in Electrical and Electronic Engineering (EEE) from the Bangladesh University of Engineering and Technology (BUET), with a major in Communication and Signal Processing. Her research investigates label-efficient learning strategies for medical data, where annotations are scarce and expensive. Specifically, she explores semi-supervised and few-shot learning frameworks for segmentation and disease characterization, including the use of foundation models such as DINO, MedSAM, and CLIP, to enable knowledge transfer under limited supervision. Her work spans a range of imaging modalities, including HR-pQCT, MRI, ultrasound, DXA, and OCT. In parallel, she studies domain adaptation using generative adversarial networks to improve generalization across datasets, and complements deep learning with interpretable, classical machine-learning approaches to support clinical relevance and translational impact. In summer 2026, she interned with the Product Development Data Science and Analytics group at Genentech within the Ophthalmology therapeutic area, where she developed a pretrained foundation model-based segmentation algorithm for medical imaging data. When not engaged in research, she enjoys reading and traveling. Our group aims to identify imaging biomarkers of risk and resilience to musculoskeletal decline in aging and fracture-prone conditions. We are advancing radiomics and machine learning techniques to extract critical imaging features, transforming high resolution peripheral quantitative computed tomography (HR-pQCT) images into rich, quantitative datasets. To achieve this, we developed a deep learning-based pipeline for multi-class HR-pQCT segmentation, which separates not only cortical and trabecular bone but also the adjacent soft tissues such as muscle and fat. Building on these segmentations, we extract quantitative radiomic features from each tissue region and use them to train machine learning classifiers for osteoporosis detection, benchmarked against dual-energy X-ray absorptiometry (DXA)-based diagnoses. Notably, features derived from muscles proved especially informative, offering strong discriminative power between osteoporotic and non-osteoporotic patients and, in some cases, exceeding the performance of bone-based radiomics. These findings suggest that soft tissue surrounding bone carries meaningful signals that could help advance future fracture risk and osteoporosis screening tools.

Aurelia works on the FT-NIR Spectroscopy and thermogravimetric analysis of rat bones. She investigates the effect of chronic kidney disease on bone water composition to diagnose fracture risk. Outside of the lab, she is a board member of PiMed and an (IN)SCRIBE Scholar.

Ishita Mukadam is an undergraduate Biomedical Engineering Honors student at Purdue University Indianapolis. She started her journey in the QBIS lab as a FTR Fellow, transitioned to an OUR Scholar, and is now an Undergraduate Research Assistant in the lab. She especially enjoys sharing her work and traveling to conferences across the country to share research, learn from other researchers, and connect with scientific communities. Outside the lab, Ishita is a Quality Control and Product Development Intern at Thrive Orthopedics, UR Resident Assistant, Weldon BME Ambassador, College of Engineering Peer Counselor, and Office of Admissions Ambassador. She is also the founder and president of the Purdue in Indianapolis Medical Association (PiMed) and a 2026 Golden Hammer Trailblazer Award recipient. Through her leadership and outreach work, she enjoys creating opportunities that connect students with medicine, STEM, and professional development. In her free time, Ishita enjoys tennis, yoga, henna, reading, watching good movies, and traveling.

I am a sophomore in biomedical engineering. I am incredibly interested in imaging technologies, hence why I'm a part of the QBIS lab! My end goal is to become a researcher in astrophysics or biomedical engineering. Outside the lab, I am the secretary of Purdue in Indianapolis Medical Association (PiMed), and the director of outreach for STEAM. In my free time, I watch football and F1, build Legos (that I can afford) as well as learn about and read anything pertaining to astronomy.