Mohamed Noureldin is an Assistant Professor at the Department of Civil Engineering, Aalto University , Finland, with prior academic roles at Sungkyunkwan University, South Korea (2015–2022). His expertise lies in integrating Artificial Intelligence (AI) with Structural Health Monitoring (SHM) , Structural Digital Twin , Predictive Maintenance , and Seismic Retrofitting . Research Focus : AI-powered sustainable structural design, smart retrofitting, predictive maintenance, structural material innovation, and next-generation performance-based seismic/wind design. Industrial Experience : 20+ years in offshore/onshore structural engineering (Hyundai Heavy Industries, Samsung Engineering, Arab-Swiss Engineering Company, Zuhair Fayez Partnership). Teaching : Courses in structural analysis, seismic design, dynamics, and reinforced concrete at Aalto and Sungkyunkwan Universities. Laboratory : Leads the Structural Design AI Lab (SDAI), focusing on AI-driven resilient infrastructure. Contact : mohamed.noureldin@aalto.fi , +358504544861. His publications explore cutting-edge applications of AI, ML, and DL in seismic retrofitting, structural durability, soil stabilization, and hybrid damping systems. Collaborative work emphasizes life-cycle cost assessment and augmented reality for predictive maintenance.
Daniel Adelman is the Charles I. Clough, Jr. Professor of Operations Management at the University of Chicago Booth School of Business. He joined the faculty in 1997 after completing his PhD in industrial engineering and operations research at Georgia Tech. Adelman is a leading expert in Business Analytics and Management Analytics, helping companies deploy data and decision analysis to build world-class strategic and tactical management capabilities. Adelman received his PhD in industrial engineering and operations research in 1997, along with a bachelor's degree in industrial engineering and a master's degree in operations research, all from the School of Industrial and Systems Engineering at the Georgia Institute of Technology. Daniel Adelman's research focuses on applying analytical models to solve complex business problems across multiple industries. He has worked with firms from diverse sectors including internet services, chemical distribution, airlines, third party logistics, fiber-optics manufacturing, semiconductor manufacturing, oil, and healthcare. His research integrates real-world data with analytical models to bring structure and discipline to decision and control processes, enabling firms to achieve higher profits with lower risk. Adelman's recent work has concentrated heavily on healthcare analytics, where he leads the Healthcare Analytics Laboratory at Chicago Booth. This lab works with teams of doctoral and MBA students on projects with major healthcare institutions to optimize clinical, operational, and financial outcomes. His research spans foundational operations research including approximate dynamic programming, inventory theory/supply chain management, and revenue management/pricing optimization, as well as examining the linkage between operational performance metrics and financial performance of firms. Adelman's publications show a clear trend toward increasing focus on healthcare applications while maintaining strong theoretical foundations in operations research. His earlier work focused more on general operations management problems like inventory control and supply chain optimization, while his recent publications demonstrate a strategic shift toward healthcare analytics, particularly examining surgical team dynamics, hospital performance metrics, and resource allocation during public health emergencies like the COVID-19 pandemic. George B. Dantzig Prize (1998) for the best dissertation in operations research and management sciences that is innovative and relevant to practice Adelman regularly advises doctoral and MBA students through the Healthcare Analytics Laboratory at Chicago Booth. He has served as Associate Editor for Management Science, currently serves as Associate Editor for Manufacturing and Service Operations Management, and is the Area Editor for Operations and Supply Chain at Operations Research. His industry collaborations include significant projects with Akamai on internet pricing, with GE Global Research Labs on the electricity smart grid, with BP on gasoline supply contract portfolio optimization, and with Symantec on software release planning. Adelman leads the Healthcare Analytics Laboratory at Chicago Booth, which brings together interdisciplinary teams of doctoral and MBA students to work on a portfolio of projects with major healthcare institutions. The lab focuses on optimizing clinical, operational, and financial outcomes through advanced analytics and decision modeling.
Dr. Monica E. McCallum is an Assistant Professor of Chemistry in the Department of Chemistry at the University of Pennsylvania’s School of Arts & Sciences. Her research focuses on understanding the biochemical origins of natural products and their roles in microbial communication. She leads the McCallum Lab, which employs interdisciplinary approaches combining organic synthesis, biochemistry, microbiology, and microscopy to study microbial natural products in their native contexts. Education: 2016 – Postdoctoral Fellow at Harvard University under Prof. Emily P. Balskus 2016 – PhD in Organic Chemistry from Baylor University 2013 – PhD Candidate at Colorado State University 2011 – B.S. in Chemistry from University of California, Irvine Research Interests: Dr. McCallum’s work bridges organic synthesis and microbiology to decode microbial metabolite functions. Key themes include: Synthesizing complex natural products and their biosynthetic precursors Discovering novel enzyme-catalyzed reactions Unraveling microbial communication mechanisms via natural products Investigating environmental microbial community dynamics Lab & Collaborations: The McCallum Lab emphasizes interdisciplinary collaboration, integrating techniques from organic chemistry, molecular biology, and microscopy to study natural products in situ. Current projects focus on diazeniumdiolate biosynthesis pathways and enzymatic detoxification of marine toxins.
Kathryn Roeder is the UPMC University Professor of Statistics and Life Sciences at Carnegie Mellon University (CMU), affiliated with the Dietrich College of Humanities and Social Sciences and the Departments of Statistics & Data Science and Computational Biology. Her research focuses on developing statistical methods for genetic and genomic data, particularly in identifying autism risk genes and analyzing single-cell multi-omic data. She earned her Ph.D. in Statistics from Penn State University and has been at CMU since 1994, previously serving as Vice Provost for Faculty (2015–2019). Education: Ph.D. in Statistics, Penn State University (1988) B.S. in Wildlife Resources, University of Idaho (1982) Research Interests: Her work integrates modern statistical techniques (high-dimensional statistics, machine learning, networks) to study complex diseases like autism and schizophrenia. Recent efforts include tools for analyzing single-cell RNA-seq and proteomic data, such as UNICORN, DAWN, and SCEPTRE. Key Awards: COPSS Distinguished Achievement Award (2020) National Academy of Sciences Member (2019) COPSS Presidents’ Award (1997) AAAS Fellow (2020) Advising & Grants: She has advised over 20 Ph.D. students, many contributing to landmark studies in autism genetics. Her grants include NIH funding for projects like the Autism Sequencing Consortium. Current research teams focus on computational biology and statistical genetics. Labs & Collaborations: Her lab develops software tools (e.g., TADA, MIND) and collaborates with the Autism Sequencing Consortium and iPSYCH-BROAD Consortium on large-scale genomic studies.
Eduardo Azevedo is the John M. Bendheim and Thomas L. Bendheim Professor of Business Economics and Public Policy at the Wharton School , University of Pennsylvania. He holds a courtesy appointment as Professor of Economics and was awarded the 2016 Sloan Foundation Fellowship. His research integrates economic theory with practical applications across science and business domains. His research interests include: Market design Selection markets Social science genetics Experimental economics Game theory Recent publication trends focus on: Economic theory applications to healthcare and digital markets Empirical Bayes methods in A/B testing Adverse selection in insurance markets Strategic behavior in two-sided matching Evolutionary behavioral economics He serves as an instructor for BEPP2500 - Managerial Economics , emphasizing real-world application of microeconomic theory to business problems. His work also involves software development for economic research, including MATLAB-based empirical Bayes tools for analyzing treatment effects in large-scale experiments. Scientific awards : Sloan Foundation Fellow (2016)
Dr. Jane Gair is a tenured Teaching Professor in the Division of Medical Sciences at the University of Victoria, where she teaches first and second-year medical students in the Island Medical Program (IMP). She serves as IMP Site Lead for Case-Based Learning (CBL) and Director for CBL Faculty Development, leading provincial curriculum implementation and faculty training. Her academic credentials include: BSc from McMaster University BSc from University of British Columbia (UBC) PhD from University of British Columbia (UBC) Her research centers on medical education methodologies including Problem-Based Learning (PBL) and Case-Based Learning (CBL), with emphasis on small-group pedagogy effectiveness. She maintains active scholarship in medical genetics and personalized medicine, as demonstrated through public lectures on epigenetics and genetic testing. International collaborations extend her work to the University of Newcastle (Australia), Patan Academy of Health Sciences (Nepal), and National University of Defense Technology (China). Dr. Gair directs UVic's branch of the national Let's Talk Science Outreach Program, engaging medical students in K-12 STEM education across Vancouver Island. This initiative serves as a core component of the FLEX course curriculum where she mentors students as Advisor, Assessor, and Supervisor. Her provincial faculty development work indicates ongoing grant-supported activities though specific funding sources aren't detailed. She maintains affiliations with the Centre for Biomedical Research (CBR) at UVic and the Centre for Health Education Scholarship (CHES) at UBC. Her leadership in Let's Talk Science coordinates medical student volunteers and community partners in delivering science literacy programs, with recent activities including 2023-2024 Mini Med School sessions on AI in healthcare, diabetes management, and genetic testing.
Ann B. Lee is a Professor and Co-Director of the PhD Program in Statistics at Carnegie Mellon University , with a joint appointment in the Department of Statistics & Data Science and the Machine Learning Department. Prior to joining CMU, she held positions as a J.W. Gibbs Assistant Professor at Yale University and a visiting research associate at Brown University. PhD in Physics, Brown University MSc/BSc in Engineering Physics, Chalmers University of Technology, Sweden Her research focuses on statistical methodology for complex data in the physical sciences , emphasizing trustworthy inference, uncertainty quantification, and integration of classical statistics with machine learning. Recent work includes likelihood-free inference, calibrated forecasting, and diagnostics for generative models. The STAMPS research group , which she co-founded in 2018, hosts weekly meetings and public webinars. In Fall 2024, STAMPS will transition into a CMU Research Center. Recent publications span likelihood-free inference , climate modeling , and astronomy . Notable collaborations include applications to hurricane intensity guidance , galaxy redshift estimation , and cosmological parameter biases . She mentors PhD students and has advised multiple award-winning researchers, including ASA Best Student Paper Award winners. Her teaching includes advanced courses on probability, regression, and AI for climate sciences.
Emmanuel J. Candès is the Barnum-Simons Chair in Mathematics and Statistics at Stanford University, with joint appointments in the Institute of Computational and Mathematical Engineering and as Professor of Statistics and Electrical Engineering (by courtesy). His research spans mathematical signal processing , high-dimensional statistics , and data science , focusing on compressive sensing, inverse problems, and applications to imaging sciences. 2021 IEEE Jack S. Kilby Signal Processing Medal 2020 Princess of Asturias Award for Technical and Scientific Research 2017 MacArthur Fellow His recent work on conformal prediction and uncertainty quantification has advanced machine learning reliability, particularly in high-dimensional settings. Publications include breakthroughs in medical imaging, gravitational wave detection, and AI validation frameworks. Key collaborations include Terence Tao (UCLA) and Justin Romberg (Georgia Tech) for IEEE Kilby Medal recognition. He serves as Co-chair of Stanford's Data Science Institute and previously as Statistics Department Chair (2016–2019).
Giuseppe Carlo Marano is a Full Professor at the Department of Structural, Building and Geotechnical Engineering at Politecnico di Torino. He is also a component of the SISCON Interdepartmental Center for Infrastructure Safety. With expertise in civil and structural engineering, his work focuses on machine learning applications, seismic risk reduction, and sustainable structural optimization. Education Graduated cum laude in Structural Engineering from Polytechnic University of Bari PhD in Structural Engineering from University of Florence (2000) Research Interests Marano's research spans structural optimization, seismic engineering, and machine learning applications in civil infrastructure. He develops advanced computational models for: Seismic retrofitting of existing structures Optimization of steel and masonry structures Recycled materials in concrete production AI-driven structural health monitoring Multiobjective design methodologies Publication Trends His recent work emphasizes: Machine learning for concrete mix design and damage assessment Optimization of gridshells and arch structures Seismic isolation systems and vibration control Sustainable construction practices with recycled materials Multiobjective genetic algorithms for structural design Scientific Recognitions National Scientific Qualification - First Band (2013, MIUR Italy) Certificate of Appreciation for Outstanding Lecture (2012, China) Academic Contributions As an educator, he teaches: Consolidamento Strutturale (Structural Consolidation) Dinamica delle Vibrazioni Random (Random Vibration Dynamics) Progettazione Generativa (Generative Design) He also leads Challenge@PoliTo initiatives and contributes to national infrastructure safety regulations. Research Projects ADAPT4CE - Adaptive Digital Systems for Circular Economy (2025-2028) AI-ENVISERS - AI for Seismic Retrofit Environmental Impact (2023-2025) ADDOPTML - Additive Manufacturing Optimization (2021-2025)
Weiqiang Chen is a Professor of Mechanical and Biomedical Engineering at New York University's Tandon School of Engineering and Director of Research and PhD Programs. He holds a joint appointment at NYU Langone's Perlmutter Cancer Center as a Faculty Member of the Tumor Immunology Research Program. B.S. in Physics (Nanjing University, 2005) M.S. in Electrical Engineering (Shanghai Jiao Tong University, 2008) M.S. in Electrical and Computer Engineering (Purdue University, 2009) Ph.D. in Mechanical Engineering (University of Michigan, 2014) His research focuses on Lab-on-a-Chip , Organ-on-Chip systems, Biomaterials , and Mechanobiology , with applications in cancer biology, stem cell engineering, and immune monitoring. He pioneers microfabrication technologies for real-time observation of cellular interactions, including CAR T-cell immunotherapy efficacy and tumor microenvironment dynamics. Recent grants include NSF funding for leukemia bone marrow niche modeling, NIH Trailblazer Awards for glioblastoma immunotherapy research, and collaborations with the Arthritis Foundation for synovium-on-chip rheumatoid arthritis studies. His work has been supported by over $2M in federal and institutional research funding. National Science Foundation (NSF) grants for leukemia-on-chip and glioblastoma modeling National Institutes of Health (NIH) awards for immunotherapy research American Heart Association fellowships and institutional training programs Chen's scientific awards include the American Heart Association Fellow distinction, multiple Young Investigator Awards from Lab on a Chip and Biomedical Engineering Society, and recognition for his dissertation on nanotopography in stem cell differentiation. He leads the Applied Micro-Bioengineering Laboratory (AMBL) , which develops microphysiological systems for drug testing and personalized medicine. His team has created the first immunocompetent leukemia-on-a-chip for CAR T-cell therapy screening and glioblastoma models that enable patient-specific immunotherapy validation.
Professor Carlo Pappone is a Full Professor of Cardiology at Vita-Salute San Raffaele University (since 2019) and Director of the Arrhythmology Department at IRCCS Policlinico San Donato Hospital (since 2015). He has held previous academic/clinical leadership roles at IRCCS San Raffaele Hospital (2000-2010), Villa Maria Cecilia Hospital (2010-2015), and University of Naples Federico II (1990-2000). With 212 publications in top journals like NEJM, JAMA, and Circulation, he has made significant contributions to cardiac arrhythmia research. Current Positions Vita-Salute San Raffaele University (2019-present): Full Professor of Cardiology IRCCS Policlinico San Donato Hospital (2015-present): Director of Arrhythmology Department Previous Roles University of Naples Federico II (1990-2000) University of Michigan Ann Harbor (1990-2000) IRCCS San Raffaele Hospital (2000-2010) Villa Maria Cecilia Hospital (2010-2015) Research Focus: Specializing in cardiovascular diseases, his work spans atrial fibrillation ablation techniques, Brugada syndrome pathogenesis, heart failure device therapy, and ion channel disorders. His H-index of 53 and 18,407 citations reflect his substantial academic impact. Notable Scientific Contributions Author of 44 patents Principal Investigator in 14 clinical trials (clinicaltrials.gov) Developed circumferential pulmonary vein ablation technique Innovator in biventricular pacing systems for heart failure Pioneered research on non-excitatory current for cardiac contractility Scientific Recognition Awarded as Elite Reviewer of JACC (2005) Editorial Board Member of 6 leading journals Reviewer for NEJM, JAMA, Lancet, and Nature Medicine Education Medical Doctorate: University of Naples Federico II
John D Brennan is a Professor in the Department of Chemistry & Chemical Biology at McMaster University. He is affiliated with the Biointerfaces Institute and focuses on developing innovative biosensing technologies and functional nucleic acid-based assays. His research integrates materials science, biochemistry, and analytical chemistry to create practical diagnostic tools for healthcare applications. Key research areas include the design of DNA aptamers and DNAzymes for detecting biomarkers (e.g., eosinophil peroxidase, SARS-CoV-2 spike proteins), development of paper-based diagnostic platforms, and optimization of sol-gel materials for enzyme entrapment. His work emphasizes high-throughput screening, point-of-care testing, and CRISPR-based biosensing systems. Notable contributions include a rapid sputum-based assay for asthma biomarkers and a universal DNA aptamer for SARS-CoV-2 variants. His lab also explores functional nucleic acid circuits and their integration into scalable diagnostic devices. Brennan’s teaching includes advanced courses in analytical chemistry and biochemical assay development. His work has been featured in journals like *Angewandte Chemie*, *Analytical Chemistry*, and *ChemBioChem*, with a focus on translating fundamental research into practical clinical applications.
Erik Willcutt is a Professor in the Department of Psychology and Neuroscience at the University of Colorado Boulder. His research focuses on the genetic and neurobehavioral underpinnings of ADHD, learning disabilities, and developmental psychopathologies. He holds positions at the Institute for Behavioral Genetics and the Center for Neuroscience. Education: PhD in Psychology from the University of Denver (1998). Research Interests: Etiology and assessment of ADHD, reading disabilities, and developmental psychopathologies. His work integrates behavioral genetics, neuroimaging, and longitudinal twin studies to understand cognitive and psychiatric disorders. Key topics include neuroanatomical correlates of ADHD, genetic influences on dyslexia, and comorbidity between learning disabilities and psychiatric conditions. Publications highlight advanced methods like genome-wide association studies (GWAS) and phenotype harmonization (e.g., Rosetta method). His work bridges molecular genetics with clinical psychology, emphasizing translational research. Lab/Affiliations: Active in the Institute for Behavioral Genetics and collaborates with interdisciplinary teams studying neurodevelopmental disorders. Office located at Muenzinger D451B.
Brian Greenfield, MD, FRCPC, ABPN is an Associate Investigator at the Research Institute of the McGill University Health Centre (RI-MUHC) and affiliated with the Brain Repair and Integrative Neuroscience (BRaIN) Program. He is a professor at McGill University and focuses on childhood suicide research through genetic predisposition, disease pathways, treatments, and predictive testing. His work intersects with mental health, borderline personality disorder (BPD), and externalizing disorders in youth. Email: brian.greenfield@muhc.mcgill.ca Dr. Greenfield's research explores the genetic and clinical dimensions of suicide risk in children and adolescents. He investigates biomarkers, predictive algorithms, and treatment models to address mental health crises, particularly in borderline personality disorder and suicidal behaviors. His publications highlight innovative approaches to suicide prevention in youth, including outpatient rapid-response systems, diagnostic tools for BPD, and population-level surveillance algorithms. Research keywords include psychiatry, genetics, health economics, and clinical psychology. As part of the BRaIN Program at RI-MUHC, Dr. Greenfield contributes to translational neuroscience and integrative mental health studies, bridging clinical practice with research to improve outcomes for vulnerable young populations.
Heping Zhang is the Susan Dwight Bliss Professor of Biostatistics at the Yale School of Public Health , with secondary appointments in the Child Study Center , Department of Statistics and Data Science , and Department of Obstetrics, Gynecology, and Reproductive Sciences . He directs the Collaborative Center for Statistics in Science (C²S²) and leads the Reproductive Medicine Network data coordinating center. Education: PhD in Statistics, Stanford University (1991) Postdoctoral Fellow, Mathematical Science Research Institute (1991) Research Focus : Zhang specializes in biostatistical methodology for genomic data analysis , clinical trials , and reproductive medicine . His work bridges genetics , mental health , and maternal-child health through innovative statistical approaches. Awards : 2023 Web of Science Highly Cited Researcher 2023 International Chinese Statistical Association Distinguished Achievement Award 2022 Institute of Mathematical Statistics Neyman Award and Lecture 2011 Royan Institute International Research Award 2011 Institute of Mathematical Statistics Medallion Award 2008 Harvard School of Public Health Myrto Lefokopoulou Distinguished Lecturer Professional Roles : He served as President of the International Chinese Statistical Association (2019) and Former Editor of the Journal of the American Statistical Association - Applications and Case Studies . His lab develops open-source software tools like ABESS , STREE , and modSaRa for genomic and clinical data analysis.
