Find Academic Supervisors

Giridhar Reddy BojjaView profile

Assistant Professor

• Business Analytics
• Machine Learning
• Deep Learning
• +7 more

Giridhar Reddy Bojja is an Assistant Professor of Information Systems & Analytics at the College of Business, Michigan Technological University. His academic office is located in Academic Office Building G018. He holds a strong focus on interdisciplinary research and teaching in data-driven fields. Research Interests: Information Systems Capabilities and Firm Performance Social Media Analytics and Healthcare Applications Econometric Modeling in Technology Impact Design Science Research Methodologies Teaching Specializations: Business Analytics (ML, DL, Text Mining) Generative AI and Its Applications Management Science and Social Network Analysis Research Themes: His work spans AI-driven IoT security frameworks, healthcare data analytics, blockchain-enabled healthcare systems, and organizational impact of IT investments. Recent projects include predictive models for patient outcomes and frameworks for resilient IoT networks. Professional Background: His research has been published in top-tier journals and conferences, with a focus on healthcare IT, cybersecurity, and AI applications. He actively contributes to both theoretical and applied research in information systems.

Jingfeng JiangView profile

Professor

• Biomechanics
• Medical Imaging
• Machine Learning
• +3 more

Jingfeng Jiang is a Professor of Biomedical Engineering and Graduate Program Director at Michigan Technological University. His research focuses on translational biomedical engineering, particularly in biomechanics, medical imaging, and machine learning applications. He holds a PhD in Civil Engineering (Computational Mechanics) from the University of Kansas, along with MS degrees in Computer Science and Structural Engineering, and a BS in Civil Engineering from Zhejiang University, China. Research Interests : Dr. Jiang’s work bridges biomechanics, medical imaging, and computational intelligence. He develops tools for transforming biomedical imaging data into clinically relevant parameters, such as soft tissue elasticity and blood flow characteristics. His current projects include real-time elastography systems, AI-driven medical imaging analysis, and precision medicine tools for cardiovascular and oncological applications. He collaborates closely with clinical and industrial partners, notably contributing to Siemens’ real-time elastography system. Education : PhD, Civil Engineering (Computational Mechanics), University of Kansas MS, Computer Science, University of Kansas MS, Structural Engineering (Structural Dynamics), Zhejiang University, China BS, Civil Engineering, Zhejiang University, China Grants and Collaborations : Dr. Jiang’s work involves clinical and industrial collaborations, including contributions to commercial medical imaging systems. His lab focuses on advancing ultrasound technologies, computational fluid dynamics modeling, and AI-driven diagnostics for cardiovascular diseases and cancers. Labs and Future Work : His research lab aims to improve precision medicine through tool development in medical imaging and biomechanics. Current open positions include doctoral students and post-doctoral researchers to support projects in aneurysm rupture prediction, liver fibrosis characterization, and AI-driven diagnostic systems.

Saeed AmalView profile

Research Professor

• Artificial Intelligence
• Healthcare Applications
• Deep Learning
• +7 more

Saeed Amal is an Assistant Research Professor at Northeastern University's College of Engineering and Department of Bioengineering. He leads the Amal Lab for Precision Medicine, focusing on AI-driven healthcare solutions. His primary affiliation is with Northeastern's Roux Institute and he holds academic leadership roles in cardiovascular and cancer research. Previously, he was a Postdoctoral Fellow at Stanford University School of Medicine and held industry roles as VP of R&D at Cardiacx Medical (cardiology startup) and Data Scientist positions at companies like General Electric and Dynamic Yield. Education: B.Sc. in Computer Science, Technion - Israel Institute of Technology (2005) M.Sc. in Computer Science (Information Retrieval), University of Haifa (2009) Ph.D. in Computer Science (Machine Learning), University of Haifa (2019) Postdoctoral Research, Stanford University (2021–Present) Research Interests: AI applications in healthcare, including deep learning for medical imaging, NLP for clinical text analysis, and multimodal data fusion for cardiovascular and cancer care. His work emphasizes precision medicine, automated disease diagnosis (e.g., prostate and breast cancer), and improving patient outcomes through AI-driven tools. Key Contributions: Developed AI tools for cardiovascular disease prediction using multimodal data fusion, led initiatives in digital pathology for cancer grading, and pioneered the use of generative AI in Gleason pattern segmentation. His lab has published extensively on machine learning models for adverse event prediction in surgery and healthcare decision-making visualization. Awards: Emerging Visionary Award (Northeastern University NAI, 2025) Grants and Collaborations: Partnered with Santovia Path AI and Prima CARE on AI tools for cancer diagnosis. His work is supported by Northeastern's research initiatives and collaborations with institutions like Stanford University School of Medicine. Labs/Teams: Director of The Amal Lab for Precision Medicine, focusing on AI and multimodal data integration for disease care.

Wei ZhuView profile

Professor

• Biostatistics
• Medical Imaging
• Clinical Research
• +5 more

Wei Zhu is a Professor and Deputy Chair in the Department of Applied Mathematics and Statistics at Stony Brook University. She holds a Ph.D. in Biostatistics from UCLA (1996), an M.S. in Statistics from UIC (1992), and a B.S. in Mathematics from East China Normal University (1989). Her research focuses on biostatistics, brain image analysis, clinical trial design, and environmental modeling. Key areas include medical imaging algorithms, climate prediction systems, and statistical methodologies for biomedical studies. Her work spans interdisciplinary applications such as predictive analytics for kidney disease outcomes, synthetic data-driven climate forecasting, and cryptocurrency crash prediction frameworks. She has developed machine learning models for medical image segmentation and decision tree frameworks for risk stratification in pandemic contexts. Teaching includes courses like AMS 312 and AMS 572. Zhu’s research portfolio emphasizes translational statistics, with contributions to genomic modeling, financial market dynamics, and public health policy evaluation. Her lab addresses pressing challenges in healthcare analytics, environmental science, and computational biomedicine through rigorous statistical innovation.

• Biomedical Informatics
• Data Science
• Health Systems Innovation
• +7 more

Vikrant G. Deshmukh is an Adjunct Assistant Professor at the University of Utah's School of Medicine and College of Nursing. He serves as a leader in Data Science Services within the Health Sciences Cores, leveraging over 18 years of expertise in healthcare data warehousing and analytics to drive innovation in health systems, operations, and translational research. Education: PhD, JD, MS, MSc Specialties: Biomedical Informatics, Population Health Sciences, Data Science, Law His research focuses on data-driven solutions for healthcare outcomes, with emphasis on cancer survivorship, mental health, cardiovascular disease, and rural-urban health disparities. He integrates machine learning, clinical informatics, and population-based cohort analysis to improve evidence-based practice and patient quality of life. His publications highlight trends in cancer survivorship , mental health comorbidities , and machine learning applications in clinical decision-making. Topics span prostate cancer treatment algorithms, cardiovascular risks post-cancer, and rural healthcare access disparities. As an educator, he mentors students in biomedical informatics and health systems innovation, bridging interdisciplinary knowledge between technology, regulation, and clinical research. His work underscores the importance of cross-sector collaboration in advancing translational medicine.

• Pharmacoepidemiology
• Comparative Effectiveness Research
• Medical Informatics
• +3 more

Brian C. Sauer, PhD, MS is a researcher affiliated with the Department of Internal Medicine (primary) and adjunct departments including Biomedical Informatics , Family & Preventive Medicine , and Population Health Sciences . His work focuses on improving observational research methodologies through transparency and reproducibility frameworks. Education: PhD from University of Florida MS from University of Utah BS from University of Florida Dr. Sauer's research spans pharmacoepidemiology , medical informatics , and causal inference , with a particular emphasis on database research transparency and reproducibility. He leads the development of TRUST (Transparent ReUsable database and Statistical Tools) modules funded by the Veterans Health Administration and OMOP , enabling automated, shareable clinical workflows. His recent work examines cardiovascular risks in rheumatoid arthritis patients, drug safety monitoring via electronic health records, and multimorbidity pattern analysis using machine learning. Dr. Sauer collaborates extensively with epidemiologists, biostatisticians, and health services researchers across the Veterans Affairs system.

• Head and Neck Oncology
• Surgical Oncology
• Thyroid and Parathyroid Disorders
• +3 more

Marcus M. Monroe, MD is an Associate Professor in the Department of Otolaryngology—Head and Neck Surgery at the University of Utah School of Medicine. He serves as a surgeon and investigator at the Huntsman Cancer Institute (HCI), where he holds leadership roles including Division Chief of Head and Neck Surgical Oncology, Leader of the Head and Neck Disease Center, and Physician Lead for the Head and Neck Clinical Trials Program. Education: MD from University of Missouri; Fellowship at MD Anderson Cancer Center; Residency and Internship at Oregon Health and Science University Dr. Monroe specializes in treating advanced head and neck cancers (skin cancers, thyroid/parathyroid tumors, salivary gland tumors, and upper aerodigestive tract malignancies) through a multidisciplinary approach involving medical/radiation oncologists, dermatologists, endocrinologists, pathologists, and radiologists. His research focuses on cancer survivorship , radiation toxicity , prognostic modeling , and multidisciplinary treatment outcomes . His recent publications analyze cardiovascular risks in cancer survivors, AI accuracy in patient education, recurrence patterns of cutaneous carcinoma, and radiation therapy trends. He has contributed to book chapters on Cancer of the Head and Neck and Targeted Therapy in Translational Cancer Research , and participates in public health podcasts on thyroid cancer and HPV-related head and neck diseases.

Sarah ZhengView profile

Associate Professor

• Service operations
• Data-driven healthcare operations
• Healthcare policy
• +3 more

Sarah Zheng is an Associate Professor at the Peter B. Gustavson School of Business, University of Victoria. She holds a PhD in Management from Boston University, an MA in Economics from Boston University, and a Bachelor of Economics from Peking University, China. Her research focuses on healthcare operations, service quality, and data-driven solutions to improve organizational performance. She employs econometric and machine learning techniques to analyze healthcare datasets, aiming to create evidence-based policies for managers and policymakers. Before joining UVic in 2018, she served as an assistant professor at Ithaca College and a lecturer at Boston University. She also contributed to Massachusetts health reform studies at Boston Medical Center. Her work has been published in journals like Production and Operations Management and Medical Care , and featured in media like U.S. News & World Report. Her current research explores how healthcare organizations can enhance performance through process-level improvements and patient engagement strategies. A key project investigates the moderating role of operational failures on workarounds and patient outcomes, using surveys of 4,000 nurses and patient data from 262 nursing units. She has received the 2022 Excellence in Research Award from Gustavson School of Business. She teaches Operations Management and Service Operations at the undergraduate level. She leads a Canadian Institutes of Health Research (CIHR) grant on childhood obesity management using digital technology, totaling $443,700 (2020–2024).

Balakrishnan PrabhakaranView profile

Professor

• Multimedia Systems
• Video and Healthcare Data Analytics
• 3D Video Streaming
• +5 more

Dr. Balakrishnan Prabhakaran is a Professor of Computer Science at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. He specializes in multimedia systems, focusing on areas like video and healthcare data analytics, 3D video streaming, wireless network QoS, and collaborative virtual environments. His work integrates technical innovation with healthcare applications, including telemedicine and rehabilitation systems. Education: PhD (Computer Science & Engineering) from Indian Institute of Technology, Chennai (1995); MSc (CSE) from IIT Chennai (1990); BEng (Electronics & Communication) from Madurai-Kamaraj University (1986). Research Interests: Current projects include intelligent medical imaging platforms (e.g., IntelliCardiac), mixed reality systems for pain management (MR.MAPP), and robotic grasp synthesis. Past work involved multimedia databases, web-based presentation servers, and QoS in ad-hoc networks. Notable Achievements: Recipient of NSF CAREER Award (2003), 2007 School of Engineering Service Award, and recognition as an ACM Distinguished Scientist. He chairs the PhD Studies program at UT Dallas and has led numerous academic initiatives including graduate admissions committees. Teaching: Courses span multimedia systems, advanced operating systems, and animation programming. He has taught at institutions including National University of Singapore and University of Maryland. Labs/Teams: Active in developing immersive healthcare technologies through projects like H-TIME (haptic tele-examination) and VirTePeX (virtual tele-physical exam systems). Collaborates on cloud-based body sensor networks (BSNCloud) and mixed reality frameworks (ScanToVR).

Sandeep SinghalView profile

Associate Professor

• Cancer Genomics
• Biomarker Discovery
• Radiogenomics
• +2 more

Sandeep Singhal is Associate Professor of Pathology and Biomedical Engineering at UND. His research develops multi-omics biomarkers for cancer prognosis/therapy response, focusing on breast cancer disparities and prostate radiation toxicity. Key contributions include PIK3CA mutation signatures for endocrine therapy response and DNA methylation-based immune classifiers. Recent work explores arsenic-related bladder carcinogenesis and radiogenomics models. Published in Nature Genetics and Journal of Clinical Investigation, he serves on Scientific Reports editorial board. Secured grants for clinical genomics and toxicity prediction. Holds adjunct appointments at Columbia University and directs bioinformatics for North Dakota INBRE.

• Software Engineering
• Empirical Software Engineering
• Mining Software Repositories
• +3 more

Abram Hindle is a Professor in the Department of Computing Science within the Faculty of Science at the University of Alberta. He holds a Ph.D. from the University of Waterloo (2010), an M.Sc. from the University of Victoria (2005), and a B.Sc. (Honours with distinction) from the University of Victoria (2003). His research focuses on evidence-based software development, leveraging techniques from data mining, machine learning, and empirical analysis. Hindle's research spans multiple domains including software repository mining, energy efficiency in software systems, and interdisciplinary applications like computer music and ECG analysis. His work integrates statistical analysis, NLP, and visualization to study software processes, maintenance, and metrics. His recent publications demonstrate a strong focus on healthcare applications of machine learning (particularly ECG-based diagnostics), software defect prediction, container orchestration, and energy-aware development practices. These reflect an ongoing commitment to empirical validation and real-world impact.

• Air Pollution Research
• Climate Change and Health
• Environmental Toxicology
• +3 more

Professor Frank Kelly holds the Battcock Chair in Community Health and Policy at Imperial College London's School of Public Health. Previously, he served as Chair of Environmental Health at King’s College London, leading the Environmental Research Group and multiple national research units. His work focuses on air pollution's health impacts, including mechanisms of toxicity, climate change interactions, and indoor air quality. He has published over 450 papers and advises global health bodies like the WHO and the Health Effects Institute. Research interests include oxidant/antioxidant imbalances in vulnerable populations, microplastics' health risks, and the effects of urban policies like London’s Ultra Low Emission Zone. He leads projects such as the WellHome study on indoor environments and the CHILL cohort assessing children’s health. Awards include Fellowships from the Royal Society of Chemistry and Commander of the British Empire (CBE). He supervises PhD students researching topics like indoor air quality and microplastics. His grants include funding from NERC, NIHR, and the Wellcome Trust for studies on air pollution’s metabolic effects, urban health equity, and biomarkers of exposure. Labs/Teams: Directs the Environmental Research Group and co-leads the NIHR Health Protection Research Unit in Environmental Exposures and Health. Collaborates with the MRC Centre for Environment and Health and the Grantham Institute.

Ali ZamaniView profile

Research Fellow

• Biomedical Engineering
• Medical Imaging
• Antenna Design
• +3 more

Dr. Ali Zamani is an Honorary Research Fellow at the School of Electrical Engineering and Computer Science, The University of Queensland. His research focuses on biomedical engineering, medical imaging, and electromagnetic systems. He holds a PhD in frequency-based microwave medical imaging from UQ (2018). His work combines antenna design, signal processing, and machine learning to develop innovative diagnostic tools for conditions like hepatic steatosis, stroke, and pulmonary edema. Key projects include the HepNet neural network for liver disease detection and a portable electromagnetic brain scanner for acute stroke diagnosis. Research interests span microwave medical imaging, wearable sensors, and electromagnetic tomography. His contributions include over 45 peer-reviewed publications and patents on medical imaging probes and systems. Collaborations involve interdisciplinary teams in biomedical engineering, electrical engineering, and clinical medicine. His recent publications highlight advancements in AI-driven diagnostics, non-invasive imaging techniques, and hardware innovations for clinical applications. He has pioneered electromagnetic systems for 3D head imaging and torso scanning, with emphasis on real-time and portable solutions.

Mirco RampazzoView profile

Assistant Professor

• Machine Learning
• Control Systems
• Smart Grids
• +5 more

Mirco Rampazzo is an Assistant Professor in the Department of Information Engineering at the University of Padova. His research focuses on advanced control systems, smart grid stability, machine learning applications in energy systems, and fault detection in medical devices like artificial pancreas systems. He leads projects on energy-efficient HVAC systems, real-time optimization of heat pumps, and adversarial robustness in critical infrastructure. His work integrates machine learning with traditional control engineering, addressing challenges in thermal systems (e.g., CO2 heat pumps), grid resilience against cyberattacks, and automation in industrial processes. Key contributions include the GAN-GRID framework for smart grid security and FaultGuard for resilient fault prediction. Rampazzo also explores extremum-seeking control algorithms for optimization in complex systems, such as data center cooling and sludge drying processes. His publications span journals and conferences, emphasizing interdisciplinary approaches to energy, healthcare, and industrial automation. He maintains an active role in editorial and academic activities, including peer review and course development for engineering education. Current research activities include reinforcement learning for anesthesia control and elastic shape analysis for textile quality assessment.

Le ZhangView profile

Assistant Professor

• Medical Image Computing
• Machine Learning
• Generative AI
• +6 more

Dr. Le Zhang is an Assistant Professor in Machine Learning/AI for Digital Healthcare at the University of Birmingham's School of Engineering, part of the College of Engineering and Physical Sciences. He holds an honorary role as Research Fellow at Queen Mary University of London's William Harvey Research Institute. His work focuses on computational modeling, pattern recognition, and machine learning applied to biomedical imaging and data analysis, particularly in Cardiac MRI, Neuroimaging, and Retinal Image Analysis. Education: PhD in Medical Image Computing (University of Sheffield, 2019), Postdoctoral Researcher at University of Oxford (2022), and Research Fellowships at UCL and Queen Mary University of London. Research Interests: Generative AI for Medical Imaging, Explainable Machine Learning in Cardiovascular Applications, Multimodal Learning for Population Health Studies, and Computational Imaging Methods. His work bridges medical imaging, computer vision, and data science to advance clinical decision-making. Key Projects: Generative AI for Medical Imaging (using GANs/Transformers), Learning from Limited Labeled Data in Segmentation Tasks, and Medical Image Quality Control via AI-driven Imputation. Award: MICCAI Young Scientist Award Nominee (2019) Funding: EPSRC PhD/Summer Studentships, Research Kick Starter Grants Collaborations: University of Oxford, Johns Hopkins, Novo Nordisk, Alibaba, Tencent Teaching: Leads modules on Computing for Engineers and Software/Systems Development at Birmingham.