Sebastian Seung is a Professor at Princeton University , affiliated with both the Department of Computer Science and the Princeton Neuroscience Institute . His career spans Harvard University (Ph.D., 1990), Bell Laboratories, and Massachusetts Institute of Technology before joining Princeton in 2014. An External Member of the Max Planck Society and 2008 Ho-Am Prize recipient, Seung merges machine learning with neuroscience . Research Focus : Pioneering connectomics , Seung developed technologies for reconstructing neural circuits from high-resolution brain images, including FlyWire for collaborative brain mapping. His work explores brain function, development, and plasticity , drawing parallels between fly visual systems and convolutional networks . Awards & Affiliations : 2008 Ho-Am Prize in Engineering External Member, Max Planck Society Technical Contributions : Led breakthroughs in 3D connected component labeling and high-throughput EM imaging for mammalian brains, partnering with NIH’s BRAIN Initiative to scale connectomics to whole mouse brains. Seung’s team has shifted from EM analysis to interpreting connectomes , focusing on neural circuit function and biological mechanisms in flies and mice. His lab alumni network spans institutions, advancing AI and neuroscience globally.
Dr. Mehdi Mirakhorli is the Director of Research at ESL Global Cybersecurity Institute (GCI) and holds the Kodak Endowed Chair as an Associate Professor in the Software Engineering Department at Rochester Institute of Technology (RIT). His work bridges Software Engineering and Cybersecurity , focusing on secure system design and software supply chain transparency. Recipient of the prestigious NSF CAREER award Awarded multiple Distinguished/Best Paper Awards He has led large-scale research projects funded by the Department of Defense, U.S. Air Force, Department of Homeland Security, and NSF, with total grants exceeding $15 million.
Craig Lee is a Professor of Oceanography at the University of Washington, where he also serves as Senior Principal Oceanographer and Assistant Director for Research at the Applied Physics Laboratory. His work focuses on physical oceanography with emphasis on observational studies and instrument development. Lee leads research programs studying upper ocean dynamics, coastal processes, and high-latitude oceanography across diverse regions including the Arctic, North Atlantic, and South China Sea. Dr. Lee's educational background includes: B.S. in Electrical Engineering and Computer Science from the University of California, Berkeley (1987) Ph.D. in Physical Oceanography from the University of Washington (1995) Lee's primary research interests center on three interconnected areas: (1) upper ocean dynamics, particularly mesoscale and submesoscale fronts and eddies; (2) interactions between biology, biogeochemistry and ocean physics; and (3) high-latitude oceanography in changing Arctic environments. His work often combines field observations with instrument development to address fundamental questions about ocean circulation and its role in climate systems. He has pioneered approaches using autonomous platforms to study difficult-to-access regions like ice-covered waters. Analysis of Lee's recent publications reveals a strong focus on Arctic oceanography, upper ocean mixing processes, and the application of autonomous observing technologies. His research spans multiple ocean basins with particular emphasis on the Arctic, North Atlantic, and western Pacific. A notable trend is the increasing integration of biogeochemical measurements with physical oceanography to understand coupled systems. His work often addresses climate-relevant questions about ocean circulation, heat transport, and ecosystem responses to environmental change. Dr. Lee provides leadership through service on science steering committees for large research programs and advisory panels for U.S. Arctic efforts. He actively supports and advises graduate students while teaching courses on ocean circulation observations and experimental design. His team has developed innovative technologies including autonomous gliders for ice-covered waters, high-performance towed vehicles, and lightweight mooring systems. Lee leads a research team pursuing diverse field programs including Arctic PISCES, Stratified Ocean Dynamics of the Arctic (SODA), and studies of the Kuroshio Current. His group collaborates extensively with institutions worldwide and contributes to major international research initiatives focused on understanding ocean processes and their climate implications.
Hua Ge is a Professor in the Department of Building, Civil and Environmental Engineering at Concordia University's Faculty of Engineering and Computer Science. She holds a Tier II Concordia University Research Chair in High Performance Building Envelope for Climate Resilient Buildings and leads extensive research in building science and climate adaptation. Her research focuses on wind-driven rain analysis , hygrothermal performance of building envelopes , advanced building facades , innovative wood-frame construction , and low-energy buildings . Current work examines climate change impacts on wind-driven rain loads, urban micro-climate effects, climate-resilient building envelopes, dynamic facades, and low-carbon healthy buildings. Her methodology combines large-scale laboratory testing, field monitoring, and computational modeling. Her 15 most recent publications demonstrate strong trends in nature-based climate resilience solutions , overheating risk mitigation in educational buildings , advanced hygrothermal modeling of wood-frame systems , and carbon sequestration strategies for buildings. The work spans multiple sub-disciplines including computational fluid dynamics, life cycle assessment, stochastic modeling, and field validation studies across Canadian climates. Tier II Concordia University Research Chair (CURC) in High Performance Building Envelope for Climate Resilient Buildings Professional Engineers of Ontario American Society of Heating, Refrigerating and Air-conditioning Engineers ASHRAE TC4.4 Building materials and building envelope performance (Subcommittee Chair) Professor Ge has supervised 42 graduate students (26 PhD, 16 MASc), including current advisees working on nature-based solutions, climate-resilient envelopes, and building integrated photovoltaics. Her research is supported by Concordia University Research Chair funding and collaborative projects with institutions like BCIT. She directs activities at Concordia's Building Envelope Test Facility and contributes to national standards through ASHRAE.
David J. Stensrud is a Professor of Meteorology and Atmospheric Science at Pennsylvania State University, where he has been a faculty member in the Department of Meteorology and Atmospheric Science within the College of Earth and Mineral Sciences. His research focuses on advancing our understanding of severe weather phenomena and improving numerical weather prediction capabilities. Dr. Stensrud received his academic training at Penn State, earning his M.S. in Meteorology in 1985 and his Ph.D. in Meteorology in 1992. His educational background has provided the foundation for his extensive research career focused on atmospheric dynamics and prediction. Dr. Stensrud's research spans several critical areas in atmospheric science, with particular emphasis on mesoscale meteorology , numerical weather prediction , and synoptic meteorology . He is internationally recognized for his work on ensemble forecasting , where he explores how groups of numerical weather prediction models can provide probabilistic forecasts of severe weather events. His research on convective-scale data assimilation aims to improve how observations from radar and satellites are incorporated into high-resolution weather models. Additional research interests include the physical processes behind severe weather phenomena like derechos and heavy rainfall events, the predictability of convective-scale phenomena, and the dynamics of the North American monsoon system. He has made significant contributions to understanding how urban environments influence thunderstorms and how convective systems interact with their larger-scale environment. Analysis of Dr. Stensrud's recent publications reveals a consistent focus on improving severe weather prediction through advanced data assimilation techniques. His work primarily centers on integrating radar and satellite observations into convection-allowing models to enhance forecasting capabilities for thunderstorms and other severe weather phenomena. A notable trend in his research is the increasing sophistication of ensemble approaches to address uncertainties in both initial conditions and model physics. His publications demonstrate a progression from fundamental studies of mesoscale phenomena to increasingly operational applications with potential for real-world forecasting improvements. Dr. Stensrud has served in several important professional capacities that highlight his standing in the meteorological community: Chair, Storm-scale Radar Data Assimilation Workshop, Norman, Oklahoma, October 2011 Member, NOAA/NWS Functional Weather Radar Requirements Integrated Working Team, 2012-2013 Guest Editor, Advances in Meteorology, Special Issue on "Storm-scale data assimilation and NWP", 2013 Commissioner, Scientific and Technological Activities Commission, American Meteorological Society, 2016-2017 Dr. Stensrud has authored more than 150 peer-reviewed publications and a textbook entitled "Parameterization Schemes: Keys to Understanding Numerical Weather Models." He has been actively involved in mentoring graduate students, though specific names of advisees are not provided in the available information. In collaboration with colleagues at Penn State, he helped create a 20-station environmental monitoring network across Pennsylvania with plans to expand to 50+ stations. His research has been supported by various grants that have enabled field campaigns such as the Mesoscale Predictability Experiment (MPEX) in 2013, where his team intercepted severe thunderstorms to collect critical observational data. Dr. Stensrud is involved with several research teams and facilities at Penn State, including work with the Joel N. Myers Weather Center and the Bob and Charlotte Landis Broadcast Room. His research group focuses on analyzing data from dual-polarization radar systems and developing improved techniques for assimilating these observations into convection-allowing models. He collaborates extensively with other researchers at Penn State and beyond, particularly in studies involving the interactions between urban environments and thunderstorms, and the upscale effects of deep convection on larger-scale weather patterns.
Johan Meyers is a full Professor at KU Leuven's Faculty of Engineering Science, Department of Mechanical Engineering, where he heads the Applied Mechanics and Energy conversion (TME) research unit. He serves as a contact person for TME and is an active member of the KIES – KU Leuven Institute for Energy and Society. His administrative roles include membership on the Council of the Faculty of Engineering Science, the Mechanical Engineering Department Council and Board, and chairing the HPC Steering Committee. Professor Meyers' research focuses on turbulent flow simulation and optimization, with particular emphasis on wind energy applications, atmospheric pollutant dispersion, and computational methods. His work spans Direct Numerical Simulation (DNS), Large-Eddy Simulation (LES), and model reduction techniques for applications in energy engineering. Current research categories include flow control & optimization, wind farm engineering, and atmospheric pollutant dispersion modeling, with specific applications in radioactive release scenarios and wind turbine system optimization. His recent publications demonstrate a strong trend toward wind energy applications, particularly in optimizing wind farm layouts and operations through advanced computational methods. The research shows significant emphasis on Large-Eddy Simulation techniques to study atmospheric boundary layer interactions with wind farms, with growing interest in hybrid wind-solar energy systems and the effects of surface temperature heterogeneity on flow patterns. His work increasingly integrates machine learning approaches to enhance computational efficiency in wind farm modeling. Professor Meyers actively supervises numerous PhD students including Bon, T., Janssens, N., Jamaer, S., and ALREWENY, A., among others. His research is supported by multiple ongoing projects through 2028, including 'Wind-farm co-design in the North-Sea basin given climate and market uncertainty' and 'Reconstruction of turbulence from partial observations,' primarily funded by research councils and industry partnerships. He leads the Turbulent Flow Simulation and Optimization (TFSO) research group, which develops efficient supercomputing simulation tools for turbulent flow applications in energy engineering. The group specializes in wind farm optimization, atmospheric pollutant dispersion modeling, and airborne wind energy systems, with a particular focus on LES studies of wind farm interactions with the atmospheric boundary layer.
Dr. Charles Rougé is a Senior Lecturer in Water Resilience at the Department of Civil and Structural Engineering, School of Mechanical, Aerospace and Civil Engineering, University of Sheffield. He holds an MSc and PhD, and his career spans top institutions in France, the US, Canada, and the UK. 2018–present: University of Sheffield (Lecturer → Senior Lecturer) 2023–2026: Principal Investigator, EPSRC-funded project on water-energy systems under climate change and energy transition Research Focus: Modelling complex water resource systems to enhance resilience against climate change, with a growing emphasis on water-energy nexus challenges. His work integrates hydrology, power systems engineering, economics, and decision theory. Key Trends: 15 most recent articles span climate-perturbed hydrological models, water-energy system coupling, socio-hydrology applications, and transboundary water governance. Many showcase interdisciplinary approaches to water infrastructure flexibility and uncertainty quantification. Scientific Awards: 2019 Quentin Martin Best Practice Award (JWRPM) 2015 Editor's Citation for Excellence (WRR) Grants: EPSRC grant (UKRI) for 'Flexible design and operation of water resource systems' (2023–2026) Team Leadership: Leads the 'Water resilience' research group at Sheffield, mentoring early-career researchers in water system sustainability and low-carbon energy transition.
Kilian Q. Weinberger is a Professor of Computer Science at Cornell University's College of Engineering, focusing on Machine Learning, Deep Learning, and AI applications. He has held previous roles as Associate Professor at Washington University in St. Louis and Research Scientist at Yahoo! Research. His research spans metric learning, resource-constrained learning, Gaussian Processes, and advancements in 3D perception for autonomous systems. Education : Ph.D. in Machine Learning (University of Pennsylvania), BA in Mathematics and Computing (University of Oxford) Key Research Areas : AI in Science, Computer Vision, Autonomous Vehicles, and Neural Network Efficiency His recent work emphasizes interpretable machine learning, large language models, and multimodal applications. Awards include NSF CAREER (2012) Daniel M Lazar '29 Teaching Award (2016) Ann S. Bowers Excellence Award (2024) ACM and AAAI Fellow (2024) He teaches advanced courses like CS6784 (Cornell) and has mentored numerous PhD students across institutions. Current affiliations include the Sloan Research Fellowships Selection Committee since 2024.
Emanuele Colonnelli is the Joseph L. Gidwitz Professor of Finance and Entrepreneurship at the University of Chicago Booth School of Business. He serves as Board Member and Co-Chair of the Finance Sector at J-PAL, leads the Finance and Entrepreneurship theme at PEDL, co-directs BFI LATAM, and is a Research Associate at NBER, CEPR, and BREAD. He founded the Chicago Booth Entrepreneurship Research Lab and directs Undergraduate Studies in Entrepreneurship at the University of Chicago. Education: PhD in Economics, Stanford University (2018) MSc in Economics, Bocconi University BSc in Economics, University of Siena Visiting Scholar, Pembroke College, Oxford University Research Interests: Colonnelli's work focuses on the intersection of finance, development economics, and political economy. His primary investigations examine high-growth entrepreneurship, government-firm-investor dynamics, venture capital in emerging markets, corruption mechanisms, ESG impact, and bankruptcy systems. His methodology emphasizes large-scale field experiments across 16+ emerging economies. Publication Trends: His research consistently employs field experiments and administrative microdata to analyze institutional impacts on firms. Recent work demonstrates growing emphasis on ESG polarization, public procurement integrity, bankruptcy stigma, and political influences in labor markets – frequently set in Brazil, China, and Uganda. Awards and Honors: 2023 Carlo Alberto Medal (top Italian economist under 40) Poets & Quants 40 Under 40 Best MBA Professors Leadership and Grants: As founding director of the Chicago Booth Entrepreneurship Research Lab, he oversees investigations into global entrepreneurial ecosystems. He secured multiple grants through PEDL, BFI, and J-PAL to support field experiments in developing economies. At Booth, he developed the first MBA course on emerging markets venture capital.
Ronny Scherer is Center Director and Professor at CEMO (Center for Educational Measurement) and Deputy Director at CREATE (Center for Research on Equality in Education) at the University of Oslo's Faculty of Educational Sciences. His work bridges educational measurement, assessment, and evaluation with a focus on research syntheses and complex sampling surveys. Dr. Scherer's research spans two interconnected domains: substantive areas including digital divides, equity and equality in education, and measurement of complex cognitive skills (such as complex problem solving, adaptability, computational thinking, and executive functioning); and methodological areas focusing on advanced meta-analytic techniques, multilevel structural equation modeling, and spatial analysis of complex survey data. His work frequently utilizes international large-scale assessment data from PISA, ICILS, TIMSS, PIRLS, PIAAC, and TALIS. His publication record demonstrates a clear trajectory toward increasingly sophisticated meta-analytic approaches, with recent work focusing on second-order meta-analyses, AI-assisted screening methods, and advanced techniques for handling complex survey data. His research consistently addresses critical educational challenges related to equity, digital literacy, and measurement of 21st century skills. Dr. Scherer has secured significant research funding for projects including ARISE (Academic resilience in mathematics and science among vulnerable students), DiDiRes (Digital inequalities in education), and ADAPT21 (Educational assessments of the 21st century: Measuring and understanding students' adaptability in complex problem solving situations). Co-director of CREATE (Centre for Research on Equality in Education) since 2023 Professor of Educational Assessment and Measurement at CEMO since 2019 Extensive experience with international large-scale assessments including ICILS, TALIS, and PIAAC As an educator, Dr. Scherer teaches advanced courses in measurement models, multilevel models, meta-analysis, and equity in education. He actively supervises graduate students interested in his research areas and has developed numerous workshops on structural equation modeling and meta-analytic methods for international audiences.
Rachel Hess, MD, MS is Professor of Population Health Sciences and Internal Medicine and Associate Vice President for Research-Health Sciences at the University of Utah Schools of the Health Sciences. She co-directs the Utah Clinical and Translational Science Institute and was founding Chief of the Division of Health System Innovation and Research (2014-2022). A board-certified General Internist and internationally recognized health-services researcher, she focuses on translating evidence into practice through health-information technology and patient-centered outcomes. Education & Training MD – University of New Mexico School of Medicine MS Clinical Research – University of Pittsburgh Fellowship – General Internal Medicine & Women’s Health, University of Pittsburgh / VA Pittsburgh Medical Center Chief Residency – Internal Medicine, Western Pennsylvania Hospital Residency – Internal Medicine, Temple University Hospital BA Mathematics – Washington University in St. Louis Research Focus Dr Hess’s program is dedicated to improving patient-centered outcomes by leveraging implementation science, health-information technology, and patient-reported measures. Her work spans: Design and nationwide deployment of EHR-integrated clinical decision support for cancer genetics, lung-cancer screening, heart-failure management, and antibiotic stewardship. Large multi-site pragmatic trials (ADAPTABLE, RECOVER, BRIDGE, MAINTAIN) examining effectiveness, equity, and scalability of digital-health interventions. Women’s health across the lifespan, including studies on menopause, sexual function, and post-COVID sequelae. Advanced analytics linking patient-reported outcomes (PROs) with healthcare utilization and cost. Scientific Awards & Recognition Board Certification, American Board of Internal Medicine (Internal Medicine) Leadership of NIH RECOVER Consortium adult cohort—one of the largest studies of Long COVID worldwide Principal investigator on >$50 million in federal and foundation funding (PCORI, NHLBI, NCI, AHRQ, CDC) Leadership & Service As Associate Vice President for Research she sets strategic priorities for the Schools of Medicine, Nursing, Pharmacy, Dentistry, and Health. She co-chairs the Utah Clinical and Translational Science Institute, oversees campus-wide clinical-trials infrastructure, and mentors interdisciplinary teams spanning informatics, behavioral science, epidemiology, and clinical medicine. Laboratories & Teams Dr Hess leads the Health System Innovation and Research (HSIR) group—an interdisciplinary unit of data scientists, implementation researchers, clinicians, and patient partners—dedicated to rapid-cycle testing and national scale-up of digital-health solutions.
Roop Aparajita Subhra Purushottam is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. His research focuses on machine learning foundations and applications, particularly in extreme classification, optimization techniques, robust learning, and educational technology. He has developed scalable algorithms for web-scale applications and innovative teaching tools for programming education. His research interests span: Design and analysis of machine learning algorithms Statistical learning theory and online optimization Non-convex optimization for large-scale problems Robust learning against adversarial corruptions Applications in information retrieval, education, and environmental monitoring Recent publications demonstrate a strong focus on extreme classification techniques, efficient deep learning architectures, and educational technologies. His work consistently appears in top-tier conferences including KDD, ICML, NeurIPS, and CVPR, with innovations in scaling machine learning systems to handle millions of labels and users. Significant Awards: Gopal Das Bhandari Distinguished Teacher Award (2024) PK Kelkar Faculty Fellowship (2024-2027) Microsoft Bing Ads Greatness Award (2021) Computer Society of India Faculty Award (2018) Multiple best paper awards and nominations at major conferences He leads several research grants and consults for industry partners including Microsoft Research and Tower Research. His team develops open-source tools like Prutor for programming education and DEFRAG for efficient feature agglomeration in extreme classification. He has advised numerous PhD and Master's students who have received prestigious awards for their research contributions.
Phillip J Ansell is an Associate Professor in the Department of Aerospace Engineering at the University of Illinois. He serves as the Director of the Center for High-Efficiency Electrical Technologies for Aircraft (CHEETA), focusing on advancing sustainable aviation through innovative propulsion and energy systems. His research interests include aerodynamics optimization, hydrogen propulsion, electric aircraft integration, and cryogenic technologies. Ansell has received prestigious awards such as the AFOSR Young Investigator Award (2015), ARO Young Investigator Award (2017), and the Lawrence Sperry Award (2023), recognizing his contributions to sustainable aviation and flow control technologies. His work spans interdisciplinary areas like hydrogen fuel cell systems, airfoil design, and electrified aircraft architectures. Recent research emphasizes sustainable aviation frameworks, cryogenic hydrogen storage, and propulsion-airframe integration. Ansell has collaborated on projects involving distributed propulsion systems, wind energy optimization, and advanced plasma actuators for flow control. His leadership in CHEETA drives innovations in superconductivity and high-temperature superconducting components for next-generation aircraft. Notable contributions include studies on laminar flow control, transonic aerodynamics, and the technical challenges of integrating MW-scale hydrogen propulsion systems. His publications reflect a blend of theoretical modeling, experimental validation, and systems engineering approaches to address aviation's sustainability challenges.
Dr. Kathryn Lester is an Associate Professor in Developmental Psychology at the University of Sussex's School of Psychology. She leads internationally recognized research on childhood anxiety, focusing on intergenerational transmission, cognitive biases, and school mental health interventions. Her work includes developing evidence-based programs for emotionally-based school avoidance and evaluating whole-school approaches to mental health. She holds leadership roles in Sussex’s senior management team, including Subject Group Lead for Developmental Psychology and Deputy Director for Postgraduate Research. She co-leads the Sussex Foundation Partnership Trust School Mental Health Research Team Clinic and has secured funding from the National Institute for Health Research, ESRC, and The National Lottery Community Fund. Her academic journey includes a D.Phil. in Psychiatry from the University of Oxford (2008) and postdoctoral research at the University of Sussex and King’s College London. Key research interests include anxiety prevention, school-based interventions, and the impact of parenting behaviors on child mental health. She has collaborated with organizations like the Sussex Wildlife Trust and provided consultancy for educational content development, such as children’s book series on emotions and ITV’s ‘Planet Child’ series. Her research spans mixed-methods approaches, including participatory methods with children and caregivers. Notable projects include NIHR-funded studies on digital mental health toolkits and Kavli Trust-funded interventions to reduce anxiety transmission from parents to children. She actively engages in knowledge exchange and mentoring early-career researchers.
David Hong is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Delaware. He holds a PhD from the University of Michigan, where he was an NSF Graduate Research Fellow, and previously served as an NSF Postdoctoral Research Fellow at the University of Pennsylvania. His research focuses on developing robust methods for analyzing heterogeneous and high-dimensional data, particularly through low-rank matrix and tensor techniques. Applications span medical imaging, radar systems, genomics, and astronomy. He emphasizes theoretical guarantees and practical algorithms for signal extraction and inverse problems. Education: PhD in Electrical Engineering and Computer Science (University of Michigan), NSF Postdoctoral Research Fellowship (University of Pennsylvania). Research Interests: Low-rank matrix/tensor methods, heterogeneous data analysis, unsupervised learning, and applications in healthcare, imaging, and sensor systems. His work addresses noise robustness, scalable algorithms, and real-world deployment challenges. Scientific Awards: Recipient of the NSF Postdoctoral Research Fellowship (2020) and NSF Graduate Research Fellowship (2015). Advising & Grants: Advisor to graduate students in machine learning and signal processing (no named advisees listed). Active NSF grant recipient for foundational and applied research in data science. Labs/Teams: Engaged in interdisciplinary collaborations through the University of Delaware's Center for Computational Research and Data Science initiatives.
