We are excited to announce that Pallavi Tiwari, PhD will be joining the University of Wisconsin – Madison as part of a cross-campus collaboration to expand the UW’s leadership in the field of artificial intelligence (AI) through a university-wide cluster hire. Dr. Tiwari’s cluster hire was supported by investments made by the Department of Radiology, School of Medicine and Public Health (SMPH), the Vice-Chancellor for Research and Graduate Education (VCRGE), the Carbone Cancer Center (UWCCC), and the Department of Medical Physics.
Dr. Tiwari earned her bachelor’s degree in biomedical engineering from Shri G.S. Institute of Technology and Science, India and her master’s and PhD in biomedical engineering from Rutgers – The State University of New Jersey. She will be joining us from Case Western Reserve University where she is currently an Assistant Professor of Biomedical Engineering and the Director of the Brain Image Computing Laboratory. Dr. Tiwari is a leader in machine learning in medical imaging and has been a recipient of several scientific awards, most notably being named as one of 100 women achievers by the Government of India for making a positive impact in the field of Science and Innovation. Dr. Tiwari’s research is funded through the National Cancer Institute, Department of Defense, as well as multiple foundations, and state grants.
Department of Radiology Chair, Thomas Grist, MD, FACR said, “We are delighted to have recruited Dr. Tiwari to join UW Radiology to help us accelerate our efforts in developing applications of artificial intelligence in medical imaging. Her scientific and engineering excellence, coupled with her collaborative nature, make her an ideal candidate to fulfill the intent of the Cluster Hire for AI in Precision Medical Imaging and Diagnostics. Pallavi brings an active NIH funded program in the application of AI to improve imaging and diagnostic accuracy in cancer and emerged as our top candidate after an extensive international search. We are grateful that Pallavi recognizes that UW is an outstanding environment for her to work with colleagues across campus in this exciting area and translate her innovative research into clinical practice to ultimately benefit patient care. Many thanks to the search committee and all our UW family who contributed to her recruitment.”
Learn more about Dr. Tiwari below!
Q: How would you describe your research interests?
A: “The research in my group focuses on developing artificial intelligence and machine learning approaches for solving challenging clinical problems in oncology and neurological disorders. For instance, we focus on addressing questions such as who to treat? How to treat? Did the treatment work? These are critical, time-sensitive questions; the answers to which are often nebulous due to the lack of sufficient diagnostic information based on current clinical care. One area of interest in our group is Glioblastoma (GBM), an aggressive brain cancer with a median survival of 15 months. Unlike some of the other cancers, treatments for GBM have not changed much in the last two decades and still consists of chemoradiation treatment. However, roughly 50% of patients fail chemotherapy within 6-months of treatment initiation. Our group is interested in developing image-based biomarkers that can allow for optimizing treatment decisions for these patients so patients who are not suited for chemotherapy are not being subjected to this drug and perhaps may be more suited for other experimental treatments. In our recent work, we demonstrated that building ‘sex-specific’ image-based signatures may lead to improved survival prognosis and prediction of response to therapy in GBM tumors. These findings could have significant implications in personalizing treatment decisions. Through our clinical collaborations and research efforts, we seek to build technologies with a potential for near-term clinical impact in customizing personalized treatments and ultimately improving patient survival.”
Q: Why did you choose to study AI?
A: “It happened organically for me. During my undergraduate years, I worked on projects in machine learning applied to healthcare, which gravitated me towards this field. I then had the opportunity to work with an outstanding mentor on the use of AI and Machine Learning for prostate cancer characterization during my MS and PhD, which helped me appreciate the potential of these approaches in augmenting treatment decisions in oncology. Towards the end of my PhD, I started interacting with a neurosurgeon who helped me recognize the challenges with limited treatment options in patients with brain tumors. Since then, I have been focused on developing AI approaches in the context of brain-related disorders. I am constantly fascinated by the potential that AI approaches can have in the near future in driving and augmenting treatment decisions and look forward to integrating some of the approaches developed in our group into clinical workflows and experimental clinical trials.”
Q: What are your goals for your new position at UW-Madison?
A: “I am really excited about the prospects of growing the Machine Learning for Medical Imaging (ML4MI) initiative at UW-Madison. Given my background in Biomedical Engineering and Radiology, I am looking forward to augmenting the already outstanding AI and Machine Learning community across the medical and engineering schools. I am also excited about my role as the co-director of imaging sciences within the Carbone Cancer Center, through which I look forward to being more involved in applying AI and machine learning within multiple oncology applications.”
Q: What about coming to work at UW-Madison is most exciting for you?
A: “UW-Madison has very strong medical and engineering programs, but what I am most impressed with is the collaborative spirit that percolates through the University. I really like the fact that there are no borders or silos, and I have heard from many people that one could start collaborations rather easily. This is particularly relevant for the field of AI in healthcare to flourish. We need cross-disciplinary collaborations across engineering, radiology, pathology, oncology, informatics, and other disciplines to come together and work in tandem, to ultimately lead these technologies towards clinical impact. Personally, I’ve also experienced the kindness of many colleagues who have reached out to offer help with the lab’s transition. I am looking forward to being a part of this collaborative and vibrant community. On Wisconsin!”