Dr. Suvra Pal is an Associate Professor of Statistics at the University of Texas at Arlington. He holds a PhD from McMaster University and specializes in survival analysis, cure rate modeling, and machine learning applications in biostatistics. His NIH-funded research develops novel statistical methods for cancer prognosis and wound healing. Dr. Pal received the College of Science Outstanding Teaching Award (2022) and was nominated twice for UTA's President’s Teaching Excellence Award. Key research areas include: Machine learning-enhanced cure models Interval-censored survival data Statistical methods for oncology Optimization algorithms His publications demonstrate consistent innovation in survival analysis, with recent focus on SVM-based cure models and Fokker-Planck frameworks for cancer therapy optimization. Articles appear in Statistics in Medicine and Nature Communications. Honors include: College of Science Teaching Award (2022) NSA Travel Award (2019) Best Paper Award, International Indian Statistical Association (2013) Dr. Pal chairs dissertation committees for PhD students in statistics and serves as Associate Editor for multiple statistical journals. He secured over $1.2M in federal grants for projects integrating machine learning with biomedical research.
Alexander Tsodikov is a Professor in the Department of Biostatistics at the University of Michigan School of Public Health. His career spans multiple institutions including the Curie Institute, University of Leipzig, University of Utah, and University of California, Davis. Education: PhD in Applied Mathematics (1991), M.Sc. in Applied Mathematics (1988), both from St. Petersburg State Technical University. Research focuses on biostatistical methodology for cancer studies, including: Survival analysis and cure models Semiparametric inference Frailty/mixture models Age-period-cohort modeling Statistical consulting in clinical research Methodological contributions include generalized self-consistency approaches and computational methods like EM/MM algorithms. Research supported by National Cancer Institute grants. Scientific Awards: Elected Member, International Statistical Institute (2011-present) Key affiliations: Member, American Statistical Association Member, International Biometric Society Co-Investigator, Cancer Interventions and Surveillance Modeling Network (CISNET) Students advised include Quoc Tran , Kelley Kidwell , Jonathan Rice , and Chin-Shang Hu .
Souvik Roy is an Associate Professor in the Department of Mathematics at the University of Texas at Arlington (USA). His primary research focuses on inverse problems in medical imaging, stochastic models for cancer dynamics, and optimization algorithms for cure rate models. He leads the Center for Integrative Math-Bio and Experimental Research (CIMBEX) and has secured major grants from NSF, NIH, and UTA. His work spans interdisciplinary areas including medical imaging, signal processing in chromatography, and PDE optimal control frameworks. Education: Ph.D. in Mathematics (Tata Institute of Fundamental Research, India, 2015); B.Sc. (Honors) in Mathematics (University of Calcutta, India). Research Interests: Medical imaging algorithms, stochastic cancer modeling, numerical optimization, and mathematical biology. Awards: 2024 College of Science Teaching Award, Top Cited Article in Statistica Neerlandica (2023), Best Paper Award (2022). His lab develops algorithms for quantitative photoacoustic tomography and Fokker-Planck models for cancer treatment optimization. He has supervised 8+ Ph.D. students and co-developed software tools like CERTS and ADICC for imaging and stochastic simulations.
Dr. Donglin Zeng is an Adjunct Professor in the Department of Biostatistics at the University of North Carolina at Chapel Hill's Gillings School of Global Public Health. He holds a PhD in Statistics from the University of Michigan (2001) and earlier degrees in Mathematics from the University of Science and Technology of China (B.S., 1993; M.S., 1995). His research focuses on modern empirical process theory, semiparametric efficiency, survival analysis, causal inference, and high-dimensional data. He has published over 100 papers in top-tier statistical journals and is a Fellow of both the American Statistical Association and the Institute of Mathematical Statistics. Dr. Zeng has taught advanced courses in probability, statistical inference, and machine learning. His service includes roles on editorial boards and NIH study sections. His research interests span personalized medicine, machine learning applications in healthcare, biomarker discovery, and semiparametric modeling of clinical trial data. Notable contributions include methodologies for electronic health records analysis, survival data inference, and genetic association studies. His awards include the 2011 ASA Fellowship and 2010 IMS Fellowship, alongside early-career recognitions like the 2008 Roy Kuebler Fund Award. His work bridges theoretical statistics with practical applications, emphasizing rigorous inference in high-dimensional and complex data settings. Dr. Zeng's technical contributions include algorithms for semiparametric transformation models and meta-analysis simulations. He actively explores interdisciplinary collaborations, particularly in biomedical research and health informatics.
Dr. Huachao Mao serves as an Assistant Professor at Purdue University's School of Engineering Technology, where he directs the Additive & Intelligent Manufacturing (AIM) Lab. His research bridges nanoscale precision with additive manufacturing, focusing on applications in optics, microfluidics, and biomedical technologies through innovations in process engineering and computational modeling. His academic foundation includes: Ph.D. in Industrial and Systems Engineering, University of Southern California, 2019 M.S. in Computer Science, University of Southern California, 2018 M.S. in Control Science and Engineering, Tsinghua University, China, 2014 B.E. in Mechanical Engineering and Automation, Beihang University, China, 2011 Dr. Mao's research centers on precision Additive Manufacturing processes for optics, functional polymers, microfluidics, and structural composites, coupled with advanced computing for manufacturing including digital twin development, topology optimization, and AI/ML-driven process control. His work pioneers techniques like adaptive slicing, mask image optimization, and precision spin coating to achieve nanoscale accuracy in complex 3D geometries, enabling breakthroughs in optical devices and biomedical applications. Analysis of his recent publications reveals a dominant focus on vat photopolymerization advancements, particularly for optical and microfluidic applications. His work consistently integrates AI/ML for process optimization while expanding into functional materials like index-matching resins and conductive polymers, with growing emphasis on educational frameworks for smart manufacturing. His scientific contributions have been recognized with: Best Paper Award, ASME MSEC 2021 Best Paper Award, NAMRC 2016 Best Paper Award, NAMRC 2018 Purdue Departmental Achievement Award 2024 Outstanding Faculty in Discovery at Purdue Polytechnic 2025 Dr. Mao actively mentors graduate researchers and leads NSF-funded projects developing 3D-printed optical AI processors and robotic e-skin. His lab currently recruits Ph.D. students for Fall 2025 to advance multi-material printing and digital twin applications. As an editorial board member for Micromachines and Digital Manufacturing Technology, he shapes the field's scholarly discourse while reviewing for top journals including Nature and Science Robotics. The Additive & Intelligent Manufacturing Lab serves as the operational hub for Dr. Mao's research, driving innovations in liquid crystal display-based printing, microfluidic device fabrication, and precision optical manufacturing through interdisciplinary collaboration between materials science, robotics, and computational engineering.
Christopher Senseney is an Associate Teaching Professor and Associate Chair for Undergraduate Education in the Department of Civil, Environmental, and Architectural Engineering at the University of Colorado Boulder. He holds a Professional Engineer (PE) license and specializes in Construction Engineering Management. His roles emphasize teaching and undergraduate education leadership. Dr. Senseney earned a PhD in Civil Engineering from the Colorado School of Mines (2011), an MS in Civil Engineering from the University of Colorado Boulder (2004), and a BS in Civil Engineering from the U.S. Air Force Academy (1997). His career includes military and civilian engineering roles. His research focuses on sustainable construction practices , including pavement design , non-destructive testing , life cycle assessment (LCA) , and environmental product declarations (EPDs) . Recent work explores graphene-modified asphalt, Buy Clean procurement policies, and airport pavement rating systems. Dr. Senseney has received notable recognition, including the American Society of Civil Engineers’ Outstanding Faculty Advisor Region 7 Award (2023) and U.S. Air Force Academy’s Outstanding Academy Educator (2015-16) . His technical expertise spans civil engineering innovation and practical field applications, particularly in challenging environments like Afghanistan. He advises on cradle-to-gate environmental standards for construction materials and collaborates on projects involving precast concrete solutions and geotechnical modeling. His teaching and research bridge theoretical knowledge with real-world infrastructure challenges.
Dipak Dey is a Professor in the Department of Statistics at the University of Connecticut . His work bridges theoretical and applied statistics, with a focus on Bayesian methodologies and computational statistics. Affiliations : University of Connecticut, Department of Statistics Contact : dipak.dey@uconn.edu , Office: AUST 327, Phone: (860) 486-4755 His research interests span Bayesian analysis , Biostatistics , Computational statistics , Statistical genetics , and Spatio-temporal modeling . He has pioneered techniques in spatial curvature processes and scalable Bayesian algorithms for large datasets. Recent publications highlight his contributions to Bayesian spatial modeling (blockNNGP, curvature processes), survival analysis (skew-t distributions, cure rate models), and computational statistics (variable selection in Gaussian processes, fast inference algorithms). Applications include insurance data , epidemiology , and environmental statistics . Scientific Awards : Board of Trustees Distinguished Professor (University of Connecticut) He actively collaborates on interdisciplinary projects and mentors researchers in advanced statistical methodologies for complex data structures.
Zhangsheng Yu is an Adjunct Professor in the Biostatistics Department at Yale School of Public Health , Yale University. He specializes in clinical statistics methods and health science collaborations. Research Interests : Advanced survival analysis for disease risk Panel count and cure rate models Deep learning in medical imaging High-dimensional mediation analysis Spatial transcriptomics algorithms AI in psychiatric clinical trials Publications : Recent work spans multimodal cancer prognosis, neurodegenerative disease modeling, and spatial gene expression analysis, with methodologies applied to liver/kidney diseases and psychiatric conditions. Professional Service : Serves as Associate Editor for Statistics in Medicine , Heart Rhythm , and Journal of Digestive Disease . Former President of Central Indiana Chapter of American Statistical Association and current Vice-President of Clinical Statistics Chapter of World Congress of Chinese Medicine.
