Dr. Rachel Surowiec is currently an Assistant Professor at the Weldon School of Biomedical Engineering (Purdue University) and the Department of Radiology and Imaging Sciences (Indiana University School of Medicine, Courtesy Appointment). Following her Master’s in Biomechanics, she was a Senior Research Scientist dually appointed within the Department of Imaging Research and Biomedical Engineering at the Steadman Philippon Research Institute in Vail, Colorado. She returned to academia and received her Ph.D. in Biomedical Engineering with a Biomedical Imaging Concentration at the University of Michigan in Ann Arbor, Michigan. Her first Postdoctoral appointment was at the Center for Molecular Imaging, Department of Radiology at the University of Michigan, followed by a postdoctoral appointment within the Department of Anatomy, Cell Biology, and Physiology at the Indiana University School of Medicine. She is passionate about research and mentorship. Her lab, the Quantitative Biomedical Imaging and Spectroscopy Lab, operates at the intersection of novel medical imaging, deep and machine learning, biomechanics, and musculoskeletal biology to solve complex problems with clear translational value. 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.

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. Linkedln: https://www.linkedin.com/in/pjalaie

Youngjun is a Ph.D. student within the Weldon School of Biomedical Engineering and a research assistant in the QBIS lab. Born and raised in Incheon, South Korea, he also received his BS in Radiology at Shingu University and MS in Biomedical Engineering from Korea University. He studies medical imaging with AI (Deep Learning and Machine Learning), including bone imaging and feature analysis based on radiomics. When not engaged in research he enjoys yoga, working out, and meditation.

Wikum is a Ph.D. student and research assistant in the Weldon School of Biomedical Engineering. Wikum has joined the QBIS lab from Sri Lanka and has a background in medical imaging. His current research focuses on chronic kidney disease (CKD) and its effects on bone health, specifically examining the interactions between bone and bone water. Wikum is particularly interested in combining Thermogravimetry analysis and FT-NIR microscopy to enhance our understanding of bone pathology and refine diagnostic techniques. Through his work he aims to contribute to advancements in medical imaging biomarkers that guide treatment for CKD-related bone disorders

As a Graduate Research Assistant at Purdue University, West Lafayette (Fall 2023 – present), I develop deep learning and generative models for multimodal medical image reconstruction and restoration under complex, real-world degradations. My research emphasizes physics-informed learning for modeling motion, noise, and artifacts in high-resolution computed tomography and magnetic resonance imaging. I build physics-based motion simulation pipelines and design transformer- and generative adversarial network-based frameworks for motion and artifact correction in high-resolution computed tomography. I am also working on generalized medical image restoration under complex and mixed degradations, including motion, blur, undersampling, and domain shift, using domain adaptation, self-supervised learning, and transformer-based architectures. This includes developing dual-cycle domain-adaptive generative models and adversarial transformer frameworks for robust motion artifact correction. In magnetic resonance imaging, I have implemented the PETALUTE lung sequence at Riley Children’s Hospital and developed compressed sensing and transformer-based reconstruction methods for free-breathing lung magnetic resonance imaging, in collaboration with the University of North Carolina at Chapel Hill, the University of California, San Francisco. Additionally, I work with foundation models such as DINO and MedSAM for motion severity classification and mixed-domain semi-supervised segmentation. LinkedIn - https://www.linkedin.com/in/farhan-sadik-a37645236/ GitHub - https://github.com/fsa125

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. 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. 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. When not engaged in research, she enjoys reading and traveling. LinkedIn: https://www.linkedin.com/in/mohseu-rashid-subah/

My research focus is on spectroscopy and imaging techniques for diagnostic applications. I am also interested in scientific research communication.

Recipient of the First Time Researcher Fellowship

I’m a first-year undergraduate in biomedical engineering. I am incredibly interested in bioimaging technologies, hence why I joined QBIS! MRI machines are literally one of my favorite things ever. My end goal is to become a researcher in biomedical engineering someday. In my free time, I watch football and F1, as well as learn about and read anything pertaining to science or mathematics.