Christopher M. Overall is a Full Professor at the University of British Columbia in the Faculty of Dentistry, Department of Oral Biological and Medical Sciences . He is also a Principal Scientist at the Centre for Blood Research and holds associate memberships in UBC's Biochemistry & Molecular Biology , Obstetrics and Gynecology , and Bioinformatics Graduate Program departments. As a Canada Research Chair Laureate , he pioneered the field of degradomics to study proteases in vivo. B.D.S., University of Adelaide Ph.D., University of Toronto Postdoctoral Fellowship, UBC (with Nobel Laureate Michael Smith) Dr. Overall’s research focuses on protease proteomics and systems biology , particularly degradomics to analyze protease substrates in diseases like COVID-19 and immunodeficiency . His work on matrix metalloproteinases has revealed new therapeutic strategies for inflammatory diseases and cancer . His 15 most recent articles (2015–2008) demonstrate expertise in TAILS proteomics , protein terminomics , and protease network analysis with applications in arthritis , antiviral immunity , and precision medicine . Scientific Awards 2022 Helmut Holzer Award 2018 Royal Society of Canada Fellow 2014 Tony Pawson Canadian Proteomics Award 2013 IADR Distinguished Scientist Award Dr. Overall has mentored 61 trainees , including 9 full professors with department chairs, and received the UBC John McNeill Mentorship Award (2023). He leads the HUPO Chromosome-centric Human Proteome Project and consults for Genentech and Novartis .
Reed Maxwell is the William and Edna Macaleer Professor of Engineering and Applied Science in the Department of Civil and Environmental Engineering and the High Meadows Environmental Institute at Princeton University. He serves as Director of the Integrated Groundwater Modeling Center (IGWMC) and leads a research group comprising graduate students, postdoctoral researchers, and staff. His academic appointments include concurrent roles in both the School of Engineering and Applied Science and the High Meadows Environmental Institute. Maxwell's research focuses on understanding connections within the hydrologic cycle and how they relate to water quantity and quality under anthropogenic stresses. His work centers on hard problems in hydrology including groundwater, evapotranspiration and snow. His research group uses integrated hydrologic modeling, field observations, and remote sensing products to study terrestrial freshwater systems. Key research areas include surface water and the terrestrial hydrologic cycle; interactions of the land-surface, surface water and groundwater; and human health risk assessment. Maxwell has authored more than 185 peer-reviewed journal articles with an H-Index of 66 and over 19,000 citations. His recent work emphasizes machine learning applications in hydrology, continental-scale modeling, and physically rigorous scenario generation through projects like HydroFrame and HydroGEN. He teaches courses including CEE 306/ENV 318 Hydrology: Water and Climate and CEE 586/ENV 586 Physical Hydrology. 2020 Distinguished Henry Darcy Lecturer American Geophysical Union Fellow (2019) 2018 Boussinesq Lecturer Belle van Zuylen Chair (visiting), University of Utrecht 2017 School of Mines Research Award recipient Maxwell has mentored 17 PhD students and 20 MS thesis students throughout his career. His current research group includes multiple postdocs, research software engineers, and graduate students working on projects spanning continental-scale hydrologic modeling, groundwater-stream interactions, and machine learning applications in hydrology. The IGWMC maintains an active education and outreach program including STEM fairs, school visits, and digital educational tools like the HydroFrame Education Team's virtual sandtank aquifer model.
Jeroen Tromp serves as the Blair Professor of Geology and Professor of Geosciences and Applied and Computational Mathematics at Princeton University, where he also directs the Princeton Institute for Computational Science and Engineering (PICSciE). His work centers on theoretical and computational seismology with applications across Earth and planetary sciences. His research interests focus on imaging Earth's interior through advanced computational techniques. Key areas include surface waves, free oscillations, body waves, seismic tomography, numerical simulations of 3-D wave propagation, and seismic hazard assessment. His group develops open-source software for acoustic, elastic and poroelastic wave propagation, addressing problems in exploration geophysics, regional and global seismology, and helioseismology. Current research trends show strong emphasis on Mars seismology (InSight mission), iron spin crossover in the lower mantle, tilted transverse isotropy in Earth's inner core, and crosstalk-free waveform inversion techniques across multiple scales. Tromp actively mentors graduate students and leads collaborative projects involving seismic wavefield imaging across planetary bodies. His group maintains strong connections with NASA's InSight mission and develops computational frameworks for global centroid moment tensor inversions. The research team operates within the Department of Geosciences, leveraging high-performance computing resources through PICSciE to tackle large-scale inverse problems in seismology.
Professor Hassan Rivaz is a Full Professor and Concordia University Research Chair in Medical Imaging with Deep Learning at Concordia University's Gina Cody School of Engineering and Computer Science. He holds appointments in the Department of Electrical and Computer Engineering and is cross-appointed to the Department of Computer Science & Software Engineering. Dr. Rivaz serves as the Founding Director of the IMPACT Lab and actively supervises PhD students in Electrical and Computer Engineering and Computer Science programs. Dr. Rivaz received his PhD from Johns Hopkins University in 2011, Master's degree from the University of British Columbia, and Bachelor's degree from Sharif University, followed by postdoctoral training at McGill University. His academic journey includes prestigious awards such as the NSERC Post-Doctoral Fellowship and Jeanne Timmins Costello Post-Doctoral Award. His research focuses on advancing medical image analysis through deep learning techniques, particularly in ultrasound imaging applications. Dr. Rivaz has made significant contributions to quantitative ultrasound, cancer detection, lymphedema assessment, and ultrasound elastography. His work bridges theoretical algorithm development with practical clinical applications, addressing challenges in medical image denoising, segmentation, registration, and tissue characterization. The IMPACT Lab under his direction develops innovative solutions for medical imaging problems with direct clinical relevance. Analysis of his recent publications reveals a strong emphasis on deep learning applications for ultrasound image processing, with particular focus on denoising techniques, elastography improvements, and segmentation algorithms. His work consistently addresses the challenge of working with real clinical data rather than simulated environments, contributing to more practical medical imaging solutions. Dr. Rivaz has received numerous prestigious awards including: Concordia University Research Chair in Medical Imaging with Deep Learning (2023–2028) QBIN/RBIQ Rising Star in Bio-Imaging in Quebec (2022) Concordia University Research Chair in Medical Image Analysis (2018-2023) Petro-Canada Young Innovator Award (2016–2018) He actively mentors graduate students, with many recipients of competitive scholarships including NSERC CGS, FRQNT, and FRQS awards. Dr. Rivaz serves on editorial boards for top journals including IEEE Transactions on Medical Imaging (since 2017), Medical Image Analysis (since 2025), and IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control (since 2018). He has organized major conferences including IEEE EMBC 2020, ISBI 2021, and IEEE IUS 2023, and served as Area Chair for MICCAI from 2017 to 2024. As Founding Director of the IMPACT Lab, Dr. Rivaz leads a multidisciplinary research team focused on innovative medical imaging solutions. The lab maintains strong collaborations with hospitals and research institutions to translate imaging technologies into clinical practice. Current projects include developing AI-powered ultrasound analysis tools, quantitative imaging biomarkers for cancer diagnosis, and advanced techniques for ultrasound elastography with applications in tissue characterization and disease detection.
Pierre-Philippe Combes is a CNRS Research Professor and Head of Doctoral Studies in Economics at Sciences Po since 2021. He also serves as Co-Editor of the Journal of Urban Economics and Director of Graduate Programs in Economics at Sciences Po. Previously, he held positions at Aix-Marseille University (2003-2016), Boston University (2001-2003), and CERAS-École des Ponts et Chaussées (1998-2001). He holds a PhD in Economics from CREST (Paris School of Economics). Education PhD in Economics, Paris School of Economics (CREST), 1996 Habilitation à Diriger des Recherches (HDR), Aix-Marseille University, 2004 Research Interests : Urban economics and economic geography, focusing on local labor markets, housing markets, firm and household location choices, and resulting spatial disparities. His work uses machine learning for historical data recovery and examines agglomeration economies, migration externalities, and discrimination in urban contexts. Scientific Output Trends : Recent publications analyze urbanization in Sub-Saharan Africa, agglomeration economies, real income inequality in China, and historical land use through machine learning. Collaborative work spans France, China, and international institutions, addressing housing costs, peer effects in research, and urban poverty metrics. Scientific Awards : NATO Advanced Fellowship Grant (2001-2002) PhD Thesis Award - Association Française de Science Économique (AFSE) PhD Thesis Award - Association de Science Régionale de Langue Française (ASRDLF) Mention très honorable avec félicitations du jury (PhD thesis) Teaching & Editorial Roles : Teaches urban economics at Sciences Po and ENS Lyon. Serves on editorial boards of the Journal of Economic Geography , Regional Science and Urban Economics , and Journal of Urban Economics . Participates in evaluation committees for PhDs and habilitations across Europe. Policy Contributions : Advises on urban poverty measurement for the World Bank and contributes to French regional policy reports, including the Grand Paris Express project and CNIS (National Statistical Council).
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)
Professor John Pickett serves as Chair of the School of Chemistry at Cardiff University, where he holds the position of Professor of Biological Chemistry. His research program has established him as a leading figure in chemical ecology with significant contributions to understanding chemically mediated interactions between organisms. Professor Pickett's research focuses on chemical ecology involving chemically mediated interactions between various organisms, particularly pests attacking plants and animals. His work encompasses the chemical characterisation of pheromones and other semiochemicals across plants and animals, including insect pests and beneficial organisms. He investigates stress-related chemical signalling by plants and vertebrates (including humans and farm animals), elucidates biosynthetic pathways to pheromones and other semiochemicals, and explores how these compounds can be exploited in pest management through ecological approaches and genetic modification. Analysis of Professor Pickett's recent publications reveals a sustained and impactful research trajectory spanning chemical ecology, agricultural science, and sustainable development. His work demonstrates remarkable continuity in investigating semiochemicals and pheromones while expanding into interdisciplinary areas including AI applications for sustainable futures and the economic impacts of agricultural innovations. A consistent theme throughout his career is the practical application of chemical ecology principles to develop sustainable pest management solutions, particularly through the development and refinement of push-pull farming systems that have transformed agricultural practices in Africa.
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.
Farnoush Banaei-Kashani is an Associate Professor (Tenured) in the Department of Computer Science and Engineering at the University of Colorado Denver. She also holds an Adjunct Associate Professor position in the Department of Mathematical and Statistical Sciences. As the founder and director of the Big Data Management and Mining Lab (BDLab), she leads multiple GAANN Fellowship Programs, including BDSE (Big Data Science and Engineering), DDC (Data-Driven Cybersecurity), and II (Infrastructure Informatics). She directs the 'Data Science in Biomedicine' MS Track and focuses on data-driven decision systems (DDSs), integrating machine learning and big data analytics into healthcare, energy, transportation, and environmental applications. Education: Details not explicitly provided in the text. Her research spans data management cycles for DDSs, addressing challenges like big data volume, velocity, and variety. Key projects include iWatch (crime surveillance), POCM (mobility monitoring), and GeoSIM (urban texture documentation). She teaches courses such as Machine Learning Systems, Big Data Science, and Data Mining. Publications highlight advancements in sea ice classification, federated learning, proteomic networks, and privacy-preserving AI. Her work is funded by NSF, NIH, DOT, and industry partners like Google and IBM. She has advised numerous students and contributes to academic leadership as editor, conference chair (ACM SIGSPATIAL 2018/2019), and program committee member for venues like SIGMOD and KDD.
Mingchen Gao is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo, SUNY. He serves as Program Director for the Engineering Sciences (Artificial Intelligence) MS Program and is affiliated with the Institute for Artificial Intelligence and Data Science. Previously, he was a Postdoctoral Fellow at the NIH Clinical Center's Radiology and Imaging Science Department (2014–2017). His research focuses on medical imaging informatics, computer vision, and machine learning applications in healthcare. Notable projects include NSF-funded work on continual learning and federated domain adaptation. He teaches advanced courses like CSE674 (Advanced Machine Learning) and CSE703 (Deep Learning for Medical Imaging). Dr. Gao earned his Ph.D. in Computer Science from Rutgers University (2014), advised by Dimitris N. Metaxas, and a B.S. from Southeast University, China (2007). His lab develops AI systems for medical diagnosis, with recent work on robust neural networks and federated learning frameworks. His team has produced impactful algorithms for segmentation, classification, and domain adaptation in imaging tasks. Current research includes NSF CAREER Award (2023–2028) for deployable medical diagnosis systems and collaborations on drug discovery and toxicity prediction. He advises four PhD students and has authored over 60 peer-reviewed publications in top venues like NeurIPS, CVPR, and MICCAI.
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).
Laurence Hunt is an Associate Professor of Experimental Psychology at the University of Oxford and a Tutorial Fellow in Psychology at St John's College. He leads the Laboratory of Decision Dynamics, focusing on neural mechanisms underlying decision-making. His work integrates mathematical models with electrophysiological techniques in humans and animal models to study behavioral and neural data. Key roles include the Wellcome/Royal Society Sir Henry Dale Fellowship and prior postdoctoral positions at UCL and Oxford's Department of Psychiatry. Education: D.Phil in Neuroscience from the University of Oxford (2007-2011), pre-clinical Medicine at Cambridge University. Research emphasizes cognitive computational neuroscience, particularly how neural systems process value, uncertainty, and learning. Collaborators include notable figures like Matthew Rushworth and Christopher Summerfield. Scientific Awards: Wellcome/Royal Society Sir Henry Dale Fellow Advising & Grants: His research is supported by fellowships, though specific grants or advisees are not detailed. The Hunt Lab actively explores topics such as reward processing, decision dynamics, and neural geometry using advanced analytical methods. Labs/Teams: Director of the Laboratory of Decision Dynamics, part of the Department of Experimental Psychology. Engaged in interdisciplinary collaborations across Oxford and international institutions.
Réka Albert is a Distinguished Professor of Physics at Pennsylvania State University, affiliated with the Eberly College of Science. Her research focuses on the application of network science to biological systems, including signal transduction networks, ecological interactions, and cancer systems biology. She holds editorial roles at npj Systems Biology and Applications , IET Systems Biology , and Bulletin of Mathematical Biology . Education: Ph.D. in Physics from the University of Notre Dame (2001), M.S. and B.S. from Babeș-Bolyai University, Romania (1995-1996). Research Interests: Modeling complex systems using network theory; Boolean network analysis of biological pathways; ecological community dynamics; systems-level understanding of disease mechanisms (e.g., cancer, AML). Her work bridges theoretical physics, computational biology, and experimental data to predict system behavior and therapeutic strategies. Awards: External member of the Hungarian Academy of Sciences (2016), APS Maria Goeppert-Mayer Award (2011), NSF CAREER Award (2007), and Alfred P. Sloan Fellowship (2004). Grants/Support: NSF awards (MCB 1715826, IIS 1814405), ARO MURI on hyperuniform systems, and collaborations with biologists like Sarah Assmann (plant signaling) and Katriona Shea (ecology). Labs/Teams: Leads a multidisciplinary research group at Penn State, mentoring over 20 PhD alumni and current students like Eli Newby and Fatemeh Nasrollahi. Active in developing tools like pystablemotifs for Boolean network analysis.
Mark Lee is an Adjunct Professor in the People Analytics department at NYU’s Tandon School of Engineering, specializing in Technology Management and Innovation. He holds a Ph.D. in Engineering Psychology from Georgia Institute of Technology (1996). Currently, he serves as Head of Research, Analytics, and Business Development at UL ComplianceWire, focusing on pharmaceutical and medical device manufacturing training. His research leverages large datasets to improve healthcare safety through regulatory compliance and best practices. Courses taught include Human Factors Engineering, Workplace Design, and Predictive Analytics. Education: Ph.D. in Engineering Psychology, Georgia Tech (1996) Key Roles: Adjunct Professor, Head of Research at UL ComplianceWire Research Focus: Human Factors, Training Systems Design, Healthcare Compliance His work spans auditory display systems for aviation (e.g., 3D audio cockpit interfaces) and ergonomic design for industrial products. Recent projects emphasize data-driven solutions for regulatory challenges in life sciences. Publications highlight studies on visual search strategies, age-related cognitive performance, and application of signal detection theory in decision-making. He actively collaborates with industry and government entities, exemplified by the FDA-UL Cooperative Research Agreement.
Maria Apostolaki is an Assistant Professor of Electrical and Computer Engineering at Princeton University. Her research focuses on designing secure, reliable, and high-performance networked systems, integrating expertise in networking, security, blockchain, and machine learning. She holds a Ph.D. from ETH Zurich (2021) and an M.Eng. from the National Technical University of Athens (2015). Education: Ph.D., ETH Zurich 2021 | M.Eng., NTUA 2015 Her work investigates challenges in network security, distributed systems, and adversarial resilience. Notable contributions include SABRE (protecting Bitcoin from routing attacks) and TANGO (collaborative route control). She has advised multiple graduate students in computer science and engineering. Key honors include the NSF CAREER Award and the IRTF/IETF Applied Networking Research Prize (2018). Her lab explores cutting-edge topics like contextual robustness in ML-driven network functions and formal methods for secure systems. Advising: 6 advisees in COS/ECE domains Grants: NSF CAREER Award, innovation grants for AI/robotics Her TANGO framework enables secure cross-domain routing, while recent work addresses vulnerabilities in Ethereum PoS and BGP routing protocols.
