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Dr. Camila Aquino serves as Assistant Professor in both the Department of Clinical Neurosciences and Department of Community Health Sciences at the University of Calgary's Cumming School of Medicine, and is a Full Member of the Hotchkiss Brain Institute. As Medical Director of the Deep Brain Stimulation Program, she specializes in Parkinson's disease, essential tremor, dystonia, and chorea management, including EMG-guided botulinum toxin injections. Her educational background includes: M.D. in Medicine from Escola Superior de Ciencias da Santa Casa (2005) M.S. in Neurosciences from Federal University of Sao Paulo (2012) Ph.D. in Neurosciences from Federal University of Sao Paulo (2019) M.S. in Health Research Methods, Clinical Epidemiology from McMaster University (2020) Dr. Aquino's research addresses unmet needs in movement disorders through innovative clinical trial designs for Parkinson's disease, focusing on precision-medicine approaches, early intervention, and outcome optimization. She actively evaluates neuromodulation therapies including Deep Brain Stimulation, Spinal Cord Stimulation, and MRgFUS, with emphasis on expanding indications and improving patient outcomes. As an International Parkinson and Movement Disorder Society member, she chairs the web-based learning committee and serves on evidence-based medicine and outcome measure committees. Analysis of her 15 most recent publications (2021-2025) reveals a cohesive research trajectory centered on Parkinson's disease management, with dominant themes in motor fluctuation treatments, deep brain stimulation methodology, and clinical trial innovation. Her work bridges fundamental clinical practice guidelines with advanced neuromodulation technologies, demonstrating consistent commitment to evidence-based solutions for movement disorders. Her scientific recognition includes: Parkinson Canada research grant Mohammed Al Zaibak Foundation scholarship CAPES scholarship from Brazilian Ministry of Education Dr. Aquino has secured significant research funding supporting clinical trials in Parkinson's disease neuroprotection and neuromodulation therapies. While specific student advisees aren't listed, her leadership of the DBS Program and active clinical trial portfolio indicate substantial mentorship responsibilities. Her grant portfolio emphasizes translational research with direct clinical impact. She directs the University of Calgary's Deep Brain Stimulation Program and contributes to the MRI-guided focused ultrasound initiative. Through her International Parkinson and Movement Disorder Society committee roles, she shapes global educational standards and evidence-based practices in movement disorders.

• Movement Disorders
• Parkinson's Disease
• Deep Brain Stimulation
• +5 more

Professor Maria Craig is a distinguished academic and researcher at the University of New South Wales, Faculty of Medicine & Health, specializing in childhood diabetes research. She holds a prominent position as a Professor with extensive contributions to the field of pediatric endocrinology and diabetes, particularly focusing on type 1 diabetes in children. Professor Craig's educational background includes: MB BS from the University of Melbourne MMedSc(ClinEpid) from the University of Newcastle PhD from the University of Sydney FRACP (Fellow of the Royal Australasian College of Physicians) Professor Craig's research primarily focuses on childhood diabetes, with special emphasis on prediction and prevention of type 1 diabetes. She has a significant interest in the association between viruses and type 1 diabetes, collaborating with the Virology Research Group at Prince of Wales Hospital (POWH). Together with Professor Bill Rawlinson, she leads the viral theme for the multicentre ENDIA study (endia.org.au). As principal investigator for the CoRD trial, she is conducting a world-first phase 1 study using autologous cord blood for prevention of type 1 diabetes in children with islet autoimmunity. Additionally, she serves as principal investigator for the Australasian Diabetes Data Network (ADDN). Her research portfolio also encompasses the epidemiology of various forms of childhood diabetes (type 1, type 2, cystic fibrosis related diabetes and monogenic diabetes) and diabetes complications, in collaboration with Professor Kim Donaghue at the Children's Hospital at Westmead. Professor Craig's extensive publication record, including over 361 journal articles, demonstrates her leadership in advancing our understanding of childhood diabetes. Her recent work shows increasing focus on early detection methods, risk prediction models, technological interventions for diabetes management, and the complex interplay between viral infections and autoimmune diabetes development. She has been instrumental in developing clinical practice guidelines through her role as co-editor of the International Society for Pediatric and Adolescent Diabetes (ISPAD) guidelines. Professor Craig has received numerous prestigious awards recognizing her contributions to pediatric endocrinology and diabetes research: Australian Paediatric Endocrine Group Young Investigator's Award (1997) Asia Pacific Paediatric Endocrine Society Clinical Teaching Award (2008) Lifetime Honorary Member, Caring and Living as Neighbours (2013) Australian Diabetes Society Jeff Flack Diabetes Data Award (2019) Australian Paediatric Endocrine Group Norman Wettenhall Award for Research and Innovation (2019) Throughout her career, Professor Craig has demonstrated exceptional leadership in professional societies, having served as former president/treasurer of the Australasian Paediatric Endocrine Group (APEG) and currently as Scientific Convenor of the Asia Pacific Paediatric Endocrine Society Fellows school. Her work with the ENDIA study and Australasian Diabetes Data Network represents significant collaborative research efforts involving multiple institutions across Australia and internationally. Her principal investigator roles for major studies indicate substantial research funding support. Professor Craig leads several important research initiatives including the ENDIA study, the CoRD trial, and the Australasian Diabetes Data Network. These programs involve multidisciplinary teams of researchers, clinicians, and support staff working collaboratively to advance understanding and treatment of childhood diabetes. Her work at the intersection of virology and diabetes represents a unique and innovative approach to understanding the environmental triggers of type 1 diabetes.

• Childhood Diabetes
• Type 1 Diabetes
• Virology and Diabetes
• +4 more

Yannis Paschalidis is a Distinguished Professor at Boston University with appointments in Electrical and Computer Engineering, Systems Engineering, Biomedical Engineering, and Computing & Data Sciences. He serves as Director of the Rafik B. Hariri Institute for Computing and Computational Science & Engineering. He holds a PhD (1996) and MS (1993) in Electrical Engineering and Computer Science from MIT, and a Diploma (1991) from the National Technical University of Athens. His interdisciplinary research spans optimization, control systems, machine learning, and data science with applications in healthcare, autonomous systems, and networks. Key focus areas include developing algorithms for autonomous navigation, healthcare analytics for clinical decision support, energy demand optimization, and computational biology for protein interaction modeling. Recent publications demonstrate strong focus on AI robustness (adversarial defenses, distributional robustness), healthcare applications (cognitive impairment detection, epidemic control), and sustainable systems (power networks, ecological forecasting). Methodological innovations center on reinforcement learning, distributionally robust optimization, and geometric analysis of classical algorithms. CAREER Award (NSF) IEEE Fellow (2014) IFAC Fellow (2022) IBM/IEEE Smarter Planet Award IEEE Computer Society Crowd Sourcing Prize IMIA Best Paper Award Charles DeLisi Award (2020) Distinguished Professor of Engineering As primary advisor to 35 PhD graduates, he leads the Network Optimization & Control (NOC) Lab. His research is funded by NSF, NIH, DoD, ARPA-E, and industry partners, including major grants on Neuro-Autonomy (ONR MURI), pandemic preparedness (ARPA-E NewRAMP), and healthcare AI (NIH QuBBD). He directs the Network Optimization & Control Lab focusing on optimization, learning, and control for autonomous systems, healthcare, and networks. The lab develops fundamental methodologies with applications in robotics, computational medicine, and infrastructure systems.

• Optimization
• Control Systems
• Stochastic Systems
• +8 more

Aleksandar Jeremic is an Associate Professor in both the Electrical & Computer Engineering department and the McMaster School of Biomedical Engineering at McMaster University. His research focuses on biomedical signal processing, statistical signal processing, and biometrics, with applications in healthcare and biomedical systems. He holds a Dipl. Ing. from the University of Belgrade, and an M.S. and Ph.D. from the University of Illinois at Chicago. His expertise spans physiological signal analysis (e.g., ECG/EEG), medical imaging, and machine learning for biomedical applications. He has authored over 50 technical articles and two book chapters, and has been recognized with a teaching award from the McMaster Electrical and Computer Engineering Society (2018). He supervises graduate students in both theoretical and applied research areas. Dr. Jeremic teaches advanced courses such as Biomedical Signal Modeling and Processing and Advanced Probability and Random Processes , emphasizing practical applications of signal processing in healthcare. His work includes clinical implementations like neonatal seizure monitoring software and microwave imaging for breast cancer detection.

• Biomedical Signal Processing
• Statistical Signal Processing
• Biometrics
• +7 more

Jonathan W. Friedberg, M.D., M.M.Sc. is the Director of the Wilmot Cancer Institute and Professor in the Department of Medicine, Hematology/Oncology at the University of Rochester School of Medicine and Dentistry. He holds the Samuel E. Durand Chair in Medicine and leads one of the nation's premier lymphoma programs. Dr. Friedberg is internationally recognized for his expertise in lymphoma treatment, particularly in developing novel therapies and clinical trial approaches. Dr. Friedberg's research focuses on lymphoma, with particular expertise in Hodgkin lymphoma, non-Hodgkin lymphoma, Waldenstrom macroglobulinemia, and chronic lymphocytic leukemia. His work spans the entire treatment continuum from diagnosis through novel therapies including autologous stem cell transplantation and CAR-T cell interventions. He has built a comprehensive lymphoma program with expertise spanning hematopathology, radiation oncology, dermatology, and neurology, in addition to hematology and medical oncology. His research interests include developing risk-adapted treatment strategies, investigating novel therapeutic agents, and improving outcomes for patients with various lymphoma subtypes. Dr. Friedberg's extensive publication record demonstrates his leadership in lymphoma research, with recent work focusing on immunotherapy combinations, predictive modeling, risk stratification, and novel treatment approaches for various lymphoma subtypes. His research has significantly contributed to the understanding and treatment of lymphomas, particularly in developing more personalized and effective treatment strategies. Scientific Awards: Faculty Academic Mentoring Award (2012) Scholar in Clinical Research (2008) America's Top Doctors Selection (2008) Jacob Gitelman Award (2007) Lawrence A. Kohn Senior Teaching Fellow (2004-2006) Clinical Investigator Career Development Award (2003) Clinical Oncology Research Fellowship "Immunotherapy of Hodgkin's Disease" (2001-2003) Rising stars program for innovative research (2001-2003) As Director of the Wilmot Cancer Institute, Dr. Friedberg leads numerous clinical trials and research initiatives focused on advancing lymphoma treatment. He is actively involved in SWOG and has served in leadership roles for multiple clinical trials investigating novel therapies for lymphoma patients. His practice team includes Anna Morrison, R.N., and Kerri Hugelmaier, N.P., providing comprehensive care for lymphoma patients. Dr. Friedberg has established the Lymphoma Epidemiology of Outcomes (LEO) Consortium, a large observational cohort study supporting broad research on NHL prognosis and survivorship. His leadership extends to national organizations where he contributes to developing clinical practice guidelines and consensus recommendations for lymphoma treatment.

• Lymphoma
• Hodgkin Lymphoma
• Non-Hodgkin Lymphoma
• +5 more

Professor Nagi Gebraeel serves as the Georgia Power Early Career Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology, where his research integrates predictive analytics, machine learning, and optimization for industrial IoT applications. His work focuses on real-time equipment diagnostics, prognostics, and operational decision-making in critical infrastructure systems. Education: Ph.D. in Industrial Engineering (2003), Purdue University M.S. in Industrial Engineering (1998), Purdue University Research Focus: Dr. Gebraeel develops statistical learning algorithms for IoT-enabled maintenance, repair, and operations (MRO), with emphasis on federated learning frameworks for distributed fault diagnosis and cybersecurity protection against Industrial Control System (ICS) attacks. His research spans manufacturing, power generation, and deep space habitats through NASA's HOME Space Technology Research Institute, where he pioneers self-aware habitat systems. Recent work addresses data heterogeneity in high-consequence industrial environments using causal-informed analytics. Publication Trends: His 2024-2025 publications demonstrate a strong trajectory toward distributionally robust optimization for maintenance logistics, federated learning architectures for distributed fault diagnosis, and prognostics for complex systems like offshore wind farms and industrial robots. Key themes include handling imbalanced data in fault diagnosis, state-space representations for interdependent systems, and cybersecurity integration in manufacturing networks. Awards and Recognition: NSF CAREER Award (2007) SAE Aircraft Electrical Power System Recognition Award (2008) SAE Materials Modeling and Testing Recognition Award (2006) IEEE-AUTOTESTCON Certificate (2006) Fellow of the Institute of Industrial and Systems Engineers Advising and Funding: Dr. Gebraeel mentors doctoral students including Michael Ibrahim (2025 IISE Best Student Paper winner), Heraldo Rozas (now Assistant Professor at University of Chile), Ayush Mohanty, and Nazal Mohamed. He secured a $500,000 NSF grant in August 2025 for AI-driven cybersecurity in distributed manufacturing networks and leads NASA-funded research on deep space habitat systems. His work bridges academic research with industry applications through Georgia Tech's Strategic Energy Institute collaborations. Research Infrastructure: He directs the Analytics and Prognostics Systems laboratory at Georgia Tech's Manufacturing Institute and leads the Predictive Analytics and Intelligent Systems (PAIS) research group. Previously, he served as associate director of Georgia Tech's Strategic Energy Institute (2014-2019), fostering data science applications in energy systems.

• Predictive Analytics
• Machine Learning
• Prognostics and Health Management
• +5 more

Zhonghai Lu is a Professor of Electronic Systems Design (specializing in Dependable and Autonomous Systems) at KTH Royal Institute of Technology, part of the Department of Electrical Engineering in the School of Electrical Engineering and Computer Science (EECS). He serves as Program Director for KTH's Embedded Systems master's program and Director of Studies at the Division of Electronics and Embedded Systems. His research focuses on Network-on-Chip (NoC), computer architecture, embedded systems, and Prognostics and Health Management (PHM) of power electronics. He leads a research group exploring in-network processing and embedded intelligence, transforming passive networks into active computational frameworks. Lu holds a BSc from Beijing Normal University (1989), MSc and PhD from KTH (2002, 2007), and an MBA in Innovation and Growth from the University of Turku (2012). He has authored over 240 scientific papers, including journal articles and peer-reviewed conferences, with notable recognitions such as Best Paper Awards at NOCS’2015 and EU HiPEAC, and a Featured Paper in IEEE Transactions on Computers (2020). He serves as Associate Editor for ACM Transactions on Architecture and Code Optimization (TACO) and has chaired major conferences like HiPEAC’2017 and NOCS’2018. His research group’s recent work includes integrating AI into hardware acceleration, fault-tolerant neural networks, and RUL estimation for power electronics using recurrent neural networks. Lu has secured grants from the Swedish Research Council and Intel Corporation and developed courses like IL2230 (Hardware Architectures for Deep Learning) and IL2233 (Embedded Intelligence), pioneering embedded AI education at KTH. Education: BSc (Beijing Normal University), MSc/PhD (KTH), MBA (University of Turku) Awards: Best Paper Awards (NOCS, EU HiPEAC), Swedish Research Council Grants, Intel Research Gifts Labs/Teams: Research Group on In-Network Processing and Embedded Intelligence

• Computer Architecture
• Embedded Systems
• Network-on-Chip (NoC)
• +7 more

Dr. Aria Fallah, MD, is an Assistant Professor in Neurosurgery at the University of California, Los Angeles (UCLA), with secondary appointments in Health Policy and Management. His research focuses on Evidence-Based Surgery , Clinical Trials , and Meta-Analyses for pediatric epilepsy surgery outcomes, including prognostic tools and comparative effectiveness studies . He actively participates in international collaborations like the CTSI and has published extensively in journals such as Epilepsia , Neurology , and Clinical Neurophysiology . University: University of California, Los Angeles Department: Neurosurgery Email: afallah@mednet.ucla.edu Dr. Fallah’s research integrates machine learning and deep learning to analyze high-frequency oscillations in epilepsy, aiming to refine diagnostic and therapeutic strategies. His recent work includes developing tools like PyHFO for oscillation detection and HOPS calculator for predicting seizure freedom after hemispherectomy. His publications highlight collaborations across institutions in pediatric epilepsy surgery , hemimegalencephaly , and thalamic neuromodulation . While no explicit awards or student advisement are listed, his contributions span clinical trials , neuroimmunology , and global neurosurgery initiatives , including work in Haiti.

• Evidence-Based Surgery
• Clinical Trials
• Pediatric Epilepsy Surgery
• +7 more

Annette Juul Vangsted serves as a Clinical Associate Professor in the Department of Clinical Medicine at the University of Copenhagen's Faculty of Health and Medical Sciences. Her clinical specialization is in Internal Medicine with a focus on Hematology, specifically multiple myeloma. Based at Blegdamsvej 9 in Copenhagen, she maintains an active research profile with 67 documented research outputs and extensive collaborations across Nordic countries and internationally. Dr. Vangsted's research primarily centers on multiple myeloma, with particular emphasis on genetic predisposition, treatment outcomes, epidemiological patterns, and bone disease management. Her work bridges clinical practice with genetic research, examining how genetic variants influence disease risk and progression. She investigates geographical variations in myeloma incidence and outcomes, as well as novel treatment approaches for elderly patients and those with specific genetic profiles. Analysis of her recent publications reveals a clear focus on multiple myeloma across several dimensions: genetic susceptibility (including large genome-wide association studies), treatment efficacy (particularly regarding bone disease management), epidemiological patterns across Nordic countries, and specialized care for elderly populations. Her collaborative approach is evident through numerous multi-center studies involving Nordic Myeloma Study Group and international research teams. Dr. Vangsted actively participates in clinical research through the Nordic Myeloma Study Group, contributing to registry studies and clinical trials that inform treatment guidelines for multiple myeloma. Her work has significant implications for understanding disease mechanisms and improving patient outcomes, particularly in the areas of genetic risk assessment and personalized treatment approaches. She maintains strong research collaborations across Denmark, Sweden, and international institutions, with particular focus on hematological malignancies. Her laboratory and clinical research team works within the Department of Clinical Medicine's hematology division, focusing on translational research that directly impacts clinical practice for myeloma patients.

• Hematology
• Multiple Myeloma
• Cancer Genetics
• +4 more

Ying Lu is an Associate Professor at the Department of Applied Statistics, Social Science, and Humanities within the Steinhardt School of Culture, Education, and Human Development at New York University. She holds dual PhDs in Public Policy and Demography from Princeton University (2005) and in Statistics from the University of North Carolina at Chapel Hill (2009). Before joining NYU, she was an Assistant Professor at the University of Colorado Boulder, affiliated with the Institute of Behavioral Science. Her research focuses on quantitative methodology in social and behavioral sciences, including applications in demography, health, and political behavior, as well as statistical methods like model selection and hypothesis testing for high-dimensional data. Her interdisciplinary work bridges statistical rigor with real-world societal challenges. Recent articles highlight her contributions to areas such as employee outcomes in HRM, gut microbiota-cardiometabolic disease links, and innovative clinical trial designs. She has also explored topics in food science, environmental catalysis, and sustainable development policy. Ying Lu’s academic journey reflects a commitment to advancing statistical methodologies while addressing pressing issues in health, policy, and environmental science. She has advised numerous projects but no specific students are listed in the provided texts.

• Quantitative Methodology
• Demography
• Health Sciences
• +3 more

Professor Guy Williams is a leading academic at the University of Cambridge with a focus on imaging science and clinical neurosciences, affiliated with Downing College and the Wolfson Brain Imaging Centre . Holding a PhD in Physics from his initial Natural Sciences degree, he specializes in nuclear magnetic resonance (NMR) and MRI techniques for brain imaging. Education: BA, PhD in Physics His research centers on non-invasive imaging of brain structure and function, particularly in traumatic brain injury (TBI) and dementia. His work involves developing novel MRI pulse sequences and advanced data analysis algorithms, including AI-based diagnostic tools. He leads studies on white matter integrity post-trauma, longitudinal dementia assessment, and applications of MRI in disorders of consciousness and addiction. Recent publications highlight collaborations in traumatic brain injury outcomes, AI-guided dementia prediction, and neuroimaging of post-COVID cognitive deficits. His team's work on ultra-high field laminar fMRI and distortion correction methods has advanced clinical neuroscience applications. Key techniques include diffusion tensor imaging (DTI), 7 Tesla MRI, and positron emission tomography (PET/MR). His research spans from basic NMR physics to clinical translation, with a strong emphasis on multi-site studies and real-world diagnostic implementation.

• Nuclear Magnetic Resonance (NMR)
• In vivo imaging
• MRI pulse sequence development
• +7 more

Nida Latif is a Research Fellow in the Department of Internal Medicine at Yale School of Medicine. Her work focuses on understanding coronary microvascular dysfunction and ischemic heart disease in patients with nonobstructive coronary arteries, particularly in women. She is a key contributor to the DISCOVER INOCA multicenter registry, evaluating invasive coronary function testing protocols and diagnostic strategies. Her research integrates clinical, anatomical, and physiological data to improve diagnostic accuracy and patient outcomes. Key areas of investigation include coronary vasoreactivity testing, risk factor analysis in ischemic syndromes, and the impact of diabetes on angina pathophysiology. Latif's publications highlight advancements in coronary flow reserve measurement, comparison of diagnostic modalities (e.g., PET vs thermodilution), and the clinical utility of vessel-specific analysis. Her work emphasizes translational outcomes, bridging basic science insights with clinical practice improvements. While no specific awards are listed, her contributions to high-impact clinical registries and peer-reviewed publications reflect her active role in advancing cardiovascular medicine.

• Cardiovascular Disease
• Coronary Microvascular Dysfunction
• Invasive Coronary Function Testing
• +3 more

Mohammad Modarres is the Nicole J. Kim Eminent Professor at the University of Maryland within the A.J. Clark School of Engineering. He serves as Director of the Center for Risk and Reliability (CRR) and is a Professor of Nuclear Engineering in the Department of Mechanical Engineering. Dr. Modarres co-founded the world's first degree-granting graduate curriculum in reliability engineering at the University of Maryland and has established himself as an international expert in reliability and risk analysis. Dr. Modarres received his educational credentials from prestigious institutions: B.S. in Mechanical Engineering from Tehran Polytechnic M.S. in Mechanical Engineering from MIT M.S. and Ph.D. in Nuclear Engineering from MIT Dr. Modarres' research spans multiple critical areas in engineering risk and reliability. His primary interests include probabilistic risk assessment, uncertainty analysis, probabilistic physics of failure, and probabilistic fracture mechanics. His work encompasses both experimental investigations and sophisticated probabilistic model development. He has made significant contributions to materials degradation science, prognosis and health management systems, and nuclear safety analysis. His research bridges theoretical developments with practical applications in complex engineering systems, particularly in nuclear power and aerospace sectors. Analysis of Dr. Modarres' recent publications reveals a strong trend toward integrating advanced data science techniques with traditional reliability engineering. His work increasingly incorporates machine learning, deep learning, and entropy-based approaches to solve complex problems in prognostics and health management. There's a clear focus on multi-unit systems, particularly in nuclear power applications, and a growing emphasis on data-driven methodologies for remaining useful life estimation and failure prediction. His research maintains a strong foundation in probabilistic methods while embracing cutting-edge computational approaches. Dr. Modarres has received numerous prestigious honors and awards throughout his distinguished career: Nicole Y. Kim Eminent Professorship in Engineering Minta Martin Professorship in A.J. Clark School of Engineering University of Maryland Distinguished Scholar-Teacher (2019) Tommy Thompson Award for outstanding lifetime contributions to nuclear safety (American Nuclear Society) Fellow, American Nuclear Society Fellow, Institute of Electrical and Electronics Engineers (IEEE) Life Fellow of IEEE 1996 Maryland Inventor of the Year Award (in Information Sciences) FDA Commissioner Special Citation for Contributions to Risk Assessment Methods (2004) 2008 International Research Leadership Award (Society for Reliability Engineering, Quality and Operations Management) Honorary Doctorate from Universidad Da Vinci de Guatemala As the founding director of the Center for Risk and Reliability, Dr. Modarres has built a world-renowned program that has awarded over 500 Ph.D. and master's degrees. His research has been supported by significant grants from government agencies and industry partners, particularly in nuclear safety, aerospace reliability, and critical infrastructure protection. He has mentored numerous students who have gone on to become leaders in reliability engineering across various industries. His center collaborates extensively with the International Atomic Energy Agency (IAEA) and other international organizations on risk assessment methodologies. The Center for Risk and Reliability (CRR), which Dr. Modarres directs, serves as a hub for multidisciplinary research in risk and reliability engineering. The center has recently renovated its facilities to enhance collaboration among researchers from different engineering disciplines. CRR maintains strong partnerships with industry leaders including Amazon Lab126 (as evidenced by their collaboration on device durability research) and has been instrumental in advancing probabilistic risk assessment tools used in nuclear power plant safety analysis. The center hosts regular seminars, workshops, and international conferences, positioning itself at the forefront of risk and reliability research globally.

• Probabilistic risk assessment
• Uncertainty analysis
• Probabilistic Physics of Failure
• +5 more

Dan Lizotte is an Associate Professor jointly appointed to the Department of Computer Science in the Faculty of Science and the Department of Epidemiology and Biostatistics in the Schulich School of Medicine & Dentistry at Western University. Additional affiliations include the Schulich Interfaculty Program in Public Health and a cross-appointment to the Department of Statistics and Actuarial Sciences. Based in Middlesex College, London, Ontario, his contact email is dlizotte@uwo.ca. His research centers on machine learning and biostatistics for health decision support, with emphasis on sequential decision-making in chronic disease management where evolving patient health status and preferences inform adaptive interventions. Core contributions involve adapting reinforcement learning frameworks to model dynamic health decisions in public health and primary care settings, addressing methodological challenges in personalized medicine and risk prediction. Analysis of his publication record reveals consistent focus on healthcare applications of machine learning, particularly in chronic disease risk modeling using electronic medical records, intersectionality frameworks in public health AI, and Bayesian methods for dose personalization. His work bridges reinforcement learning with clinical decision support systems, advancing dynamic treatment regimes and statistical methodologies for evolving patient data. No scientific awards were mentioned in the provided text. The text does not specify any advisees, grant funding, or educational background details. Lizotte leads a research laboratory focused on machine learning applications in health, as evidenced by the dedicated lab site referenced in his contact information. His team likely explores intersections of statistical methodology, AI ethics, and clinical implementation for personalized health interventions.

• Machine Learning
• Biostatistics
• Health
• +4 more

Dr. Kaibo Liu is the Grainger STAR Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison and serves as Associate Director of the UW-Madison IoT Systems Research Center. He earned his B.S. from the Hong Kong University of Science and Technology (2009), and M.S. and Ph.D. from Georgia Tech (2011/2013). His research focuses on system informatics, big data analytics, and data fusion for process modeling, monitoring, and decision-making. He has been funded by NSF, ONR, DOE, and industry partners. Notable awards include the 2024 Hromi Medal (ASQ), 2021 IISE Technical Innovation Award, and multiple early-career recognitions. Recent work emphasizes real-time cyber-physical security, reinforcement learning for data streams, and Bayesian methods for prognosis. He edits IEEE Transactions on Automation Science and Engineering and IISE Transactions on Data Science.

• System Informatics
• Big Data Analytics
• Data Fusion
• +9 more