جست‌وجوی استاد و پژوهشگر

Zhiyu Quanمشاهده پروفایل

استادیار

• Actuarial Science
• Data Science
• Computational Statistics
• +۳ مورد دیگر

Zhiyu (Frank) Quan is an Assistant Professor at the University of Illinois at Urbana-Champaign (UIUC), holding positions in the Department of Mathematics, Department of Statistics, and National Center for Supercomputing Applications (NCSA). He is also an affiliate faculty member at Discovery Partners Institute as InsurTech Lead and serves as an ORMI Faculty Fellow in Finance. His research focuses on data science applications in actuarial science, including tree-based models, natural language processing, and deep learning for insurance risk modeling, predictive analytics, and InsurTech innovation. Education: Ph.D. in Actuarial Science (University of Connecticut, 2019), MS in Applied Statistics (Michigan State University, 2014), and BS in Mathematics and Applied Mathematics (Xiamen University, 2012). Research interests include computational statistics, insurance analytics, and leveraging machine learning for actuarial challenges such as claim prediction, rate-making, and cyber risk modeling. He leads the Illinois Risk Lab, bridging academic research with industry needs, and has pioneered hybrid tree-based models to address imbalanced data in insurance. Notable achievements include the Arnold O. Beckman Research Award and Society of Actuaries Research Institute recognition. He advises two doctoral students and teaches advanced predictive analytics courses, emphasizing practical applications in actuarial science and data ethics. Key collaborations involve InsurTech companies and NCSA, focusing on NLP-driven academic paper repositories (CyLit) and federated learning for privacy-preserving insurance data sharing. His work addresses real-world challenges in cyber insurance and automated machine learning systems.

Erik Hjalmarssonمشاهده پروفایل

استاد

• Empirical Finance
• Financial Econometrics
• Empirical Asset Pricing
• +۴ مورد دیگر

Erik Hjalmarsson is a Professor of Banking and Financial Economics at the Department of Economics, University of Gothenburg. He holds the Felix Neubergh Chair and previously served as Director of the Centre for Finance. His research focuses on empirical asset pricing, financial econometrics, and long-run stock returns. Hjalmarsson earned his PhD from Yale University and has held roles at the Federal Reserve Board and Winton Capital Management. Education: PhD (Yale University, 2005), M.Sc. in Econometrics (London School of Economics, 2000), B.Sc. in Mathematical Statistics (University of Gothenburg, 1999). Research interests include stock return predictability, high-frequency trading, and econometric methods. His work has been published in top journals like the Journal of Finance and Journal of Financial Economics . Key grants include funding from the Swedish Research Council and Marianne and Marcus Wallenberg Foundation. Awards include the Carl Anderson Prize (2004) and multiple stipends for doctoral education. He supervises PhD students and advises on central bank policies. His recent studies explore long-horizon returns, inflation expectations, and portfolio strategies. Teaching includes PhD courses in econometrics and financial economics. He serves on editorial boards and as a referee for leading journals.

David L. Darmofalمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Numerical Analysis
• Uncertainty Quantification
• +۳ مورد دیگر

David L. Darmofal is the Vice Chancellor for Undergraduate and Graduate Education and the Jerome C. Hunsaker Professor of Aeronautics and Astronautics at MIT. He leads the Aerospace Computational Science & Engineering (ACSEL) Lab and contributes to the MIT Center for Computational Science & Engineering (CCSE). His research focuses on computational methods for PDEs (especially fluid dynamics) and engineering education innovation. He holds a BS from the University of Michigan and SM/PhD from MIT, with postdoctoral work at the University of Michigan. Notable awards include the MacVicar Faculty Fellow (2004), Earll M. Murman Award (2011), and NSF CAREER Award (1998). Education: B.S.E., University of Michigan, 1989 S.M., MIT, 1991 Ph.D., MIT, 1993 Affiliations: MIT Schwarzman College of Computing Aerospace Computational Design Laboratory (ACSEL) His research emphasizes higher-order adaptive finite element methods, space-time mesh adaptation, and turbulence modeling. He teaches courses in computational methods and fluid dynamics. Recent projects include the Metris open-source meshing software and studies on sonic boom propagation. His work bridges computational science and engineering education, with a focus on evidence-based pedagogy. Awards & Recognition: Michael M. Byram Visiting Professorship (2021) Common Ground Excellence in Teaching Award (2024) AIAA Student Chapter Teaching Awards (2005, 2013) Bisplinghoff Fellow & Alumni Merit Award (2012) Advising & Grants: Over 130 peer-reviewed publications, leadership in MIT’s Common Ground initiative, and mentorship of postdocs/UROPs (e.g., Emily Williams, Lucien Rochery). Active in interdisciplinary collaborations, including DOE projects on physics-informed PDEs and NASA’s CFD Vision 2030 study.

• Data Mining for Business
• Data Visualization
• Large-Scale Experimental Design
• +۳ مورد دیگر

Ben Collier is an Assistant Teaching Professor of Business Analytics at the Tepper School of Business , Carnegie Mellon University. He holds a PhD in Information Systems and Organizational Behavior from Carnegie Mellon and has extensive experience in data science leadership roles in industry. PhD, Information Systems and Organizational Behavior (2012), Carnegie Mellon University MS, Information Systems and Organizational Behavior (2009), Carnegie Mellon University MBA, Information Systems (2007), University of Wisconsin-Madison BBA, Management Computer Systems and Mathematics (2004), University of Wisconsin-Whitewater His research focuses on data mining for business , data visualization , and large-scale experimental design , with applications in healthcare analytics, online community dynamics, and gender equity in technology. His recent work includes monetization data science for Duolingo's $6.5 billion IPO and developing UPMC's CognitiveRx analytics engine. Ben's publications span topics including gender gaps in Wikipedia , leadership in open collaboration communities , and conflict resolution in crowdsourced platforms . He has served on CMU committees for curriculum review and summer summit planning, and actively advises MSBA capstone projects.

Ram Bajpaiمشاهده پروفایل

مدرس

• Epidemiology
• Biostatistics
• Survival Analysis
• +۷ مورد دیگر

Dr. Ram Bajpai is a Lecturer in Epidemiology/Applied Statistics at Keele University's School of Medicine. He joined in 2019 as part of the Research Institute for Primary Care and Health Sciences, combining active research and teaching roles. Previously, he worked at the Lee Kong Chian School of Medicine (Nanyang Technological University, Singapore) and the Army College of Medical Sciences (India). Education: BSc in Statistics/Mathematics (University of Lucknow), MSc Health Statistics (Banaras Hindu University), PhD in Medical Statistics (Guru Gobind Singh Indraprastha University). Research focuses on cross-domain applications of statistical/epidemiological methods, including survival analysis, Bayesian methods, risk prediction modelling, and evidence synthesis. Teaching experience includes biostatistics modules for medical students at multiple institutions. Current research interests span prognostic studies, meta-analysis, complex data analysis, and design of epidemiological studies. Key contributions include systematic reviews on gout prophylaxis safety, dementia prognostic factors, and long-term outcomes of pediatric COVID-19. Active in collaborative projects on aging populations, musculoskeletal health, and public health interventions.

Liping Liuمشاهده پروفایل

دانشیار

• Machine Learning
• Data Science
• Deep Learning
• +۳ مورد دیگر

Liping Liu is an Associate Professor in the Department of Computer Science at Tufts University's School of Engineering. He holds a Ph.D. from Oregon State University and has held postdoctoral positions at Columbia University and Tufts. His research focuses on machine learning, generative models, graph learning, and their applications in biochemical data analysis and fluid dynamics simulation. His work on graph generative methods earned the NSF CAREER Award. Education: Ph.D. (Oregon State University, 2016), M.Sc. (Nanjing University, 2009), B.S. (Hebei University of Technology, 2006). Research Interests: Machine Learning, Deep Learning, Generative Models, Time Series, Graph Learning. Dr. Liu's research emphasizes probabilistic modeling and neural networks, addressing challenges in graph generation, data-driven physics simulation, and biochemical analysis. His recent work includes advancements in graph-based recommendation systems, turbulence modeling, and enzymatic reaction prediction. His publications span top AI conferences like NeurIPS, ICML, and ICLR. He has secured grants totaling over $9 million, including the NSF CAREER Award and NIH funding for metabolomics and enzymatic promiscuity studies. His teaching includes courses on generative models, deep learning, and machine learning for graph analytics. Awards: NSF CAREER Award (2023), NIH grants, DARPA ACT-NOW project (2019). Service: NSF panelist, program committee member for AAAI, NeurIPS, and IJCAI.

Kyle B. Reedمشاهده پروفایل

دانشیار

• Rehabilitation engineering
• Haptics
• Human-robot interaction
• +۴ مورد دیگر

Kyle B. Reed serves as an Associate Professor in the Department of Mechanical Engineering at the University of South Florida's College of Engineering. His academic career at USF has progressed from Assistant Professor (2009-2016) to his current position as Associate Professor (2016-present), following postdoctoral research at Johns Hopkins University. He teaches specialized courses including Haptics (EML 4593/6594), Mechanical Controls, and Advanced Engineering Mathematics. Dr. Reed earned his Ph.D. (2007) and M.S. (2004) in Mechanical Engineering from Northwestern University, and his B.S. in Mechanical Engineering from the University of Tennessee-Knoxville (2001). Prior to his faculty position, he completed postdoctoral research at Johns Hopkins University's Laboratory for Computational Sensing and Robotics (2007-2009) and worked as a researcher at Los Alamos National Laboratory (1998-2001). His research focuses on rehabilitation engineering, haptics, human-robot interaction, and medical robotics, with applications in medical devices and rehabilitation technologies. The REED Lab (Rehabilitation Engineering and Electromechanical Design Lab), which he directs, develops innovative solutions for human motion analysis and assistive technologies. His work bridges fundamental engineering principles with clinical applications, particularly in stroke rehabilitation and assistive device development. Analysis of his publication record shows consistent contributions to haptics research, human-robot interaction, and rehabilitation engineering. His work spans theoretical investigations of human motor control to practical applications in medical devices, with publications appearing in venues like IEEE Transactions on Haptics, EMBC, and Haptics Symposium. Recent work emphasizes wearable haptic devices, rehabilitation robotics, and human factors in medical technology. Developed the Gait Enhancing Mobile Shoe (GEMS) prototype during postdoctoral work Created minimally invasive steerable needle system for biopsies at Johns Hopkins Established REED Lab focusing on rehabilitation engineering and haptic technologies Developed innovative haptic devices for communication, rehabilitation, and education Dr. Reed actively mentors students through the REED Lab, supervising both graduate and undergraduate researchers. His teaching philosophy emphasizes building intuition while providing frameworks for logical problem-solving. He incorporates substantial project components into his courses, with students regularly developing haptic devices and robotics applications that sometimes lead to publications. The lab maintains strong outreach connections, working with K-12 students to promote engineering education. The REED Lab, located in the Interdisciplinary Research Building Room 114 on USF's Tampa campus, serves as the hub for his research activities. Current projects include wearable haptic devices for communication, diagnostic tools for Parkinson's disease, and rehabilitation technologies for gait analysis. The lab maintains strong connections with both clinical partners and industry, facilitating translational research from concept to application.

Jianxi Gaoمشاهده پروفایل

دانشیار

• Network Science
• Artificial Intelligence
• Control Theory
• +۳ مورد دیگر

Jianxi Gao is an Associate Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI). His research focuses on network science, particularly network resilience, robustness, and control, integrating network theory, control theory, statistical physics, and operations research. He also explores the intersection of network science and AI, including applications of AI to network analysis and vice versa. His work aims to understand, predict, and control the resilience of complex systems against cascading failures. Key research areas include network resilience in transportation systems, quantum networks, and biological systems, with applications to pandemic response and infrastructure optimization. Gao's contributions span theoretical frameworks and computational tools, such as the NuRsE MATLAB package for network resilience analysis. His GitHub repositories (e.g., NuRsE and NON) showcase his open-source contributions to network science and computational methods. His recent publications address topics like AI-driven network analysis, quantum network percolation, and pandemic-induced healthcare system stress. He actively collaborates on interdisciplinary projects, emphasizing real-world applications of network science principles.

Kristen L. Corbosieroمشاهده پروفایل

استاد

• Tropical Meteorology
• Atmospheric Science
• Hurricane Dynamics
• +۳ مورد دیگر

Kristen L. Corbosiero is a Professor in the Department of Atmospheric & Environmental Sciences at the University at Albany, State University of New York. Her primary research focuses on understanding the structure, intensity changes, and environmental interactions of tropical cyclones, including processes like secondary eyewall formation and rapid intensification. She utilizes both observational data and numerical models to explore topics such as cloud microphysics, vertical wind shear effects, and the role of lightning in storm dynamics. Education: PhD, Atmospheric Science, University at Albany, SUNY, 2005 MS, Atmospheric Science, University at Albany, SUNY, 2000 BS with Distinction, Atmospheric Science, Cornell University, 1997 Research Interests: Dr. Corbosiero investigates tropical cyclone behavior, including the formation of hurricane rainbands and secondary eyewalls, the impact of environmental conditions on storm evolution, and the influence of cloud microphysical parameterizations. Her work also addresses the predictability of heavy rainfall events linked to tropical systems, such as atmospheric rivers and remnant cyclones. Articles & Research Trends: Her recent publications emphasize ventilation processes in tropical cyclones, diurnal pulsations in hurricane structure, and the climatological significance of downshear reformation. She collaborates on projects funded by NASA, NOAA, and UCAR, advancing the understanding of cyclone dynamics and forecast improvement. Advising & Students: Dr. Corbosiero has mentored numerous graduate and undergraduate students, many of whom have contributed to studies on tropical cyclone evolution, precipitation patterns, and mesoscale meteorology. Notable advisees include Nicholas Johnson (ventilation in sheared storms) and Alex Mitchell (eastern Pacific cyclone variability). Labs & Teams: Her research group actively explores topics like tropical cyclone predictability, lightning activity in storms, and the North American Monsoon System’s interaction with eastern Pacific systems. Current projects involve ensemble-based sensitivity analysis and high-resolution numerical simulations.

• Applied Mathematics
• Mathematical Biology
• Epidemiology
• +۴ مورد دیگر

Paul Tupper is a Professor in the Department of Mathematics at Simon Fraser University (SFU), part of the Faculty of Science. He holds a Ph.D. in Scientific Computing from Stanford University (2002). His research focuses on applied mathematics with emphasis on mathematical modeling in epidemiology, speech perception, neural networks, and computational linguistics. He teaches advanced courses in probability, numerical linear algebra, and calculus for social sciences. His work bridges theoretical mathematics and real-world applications, particularly in understanding complex systems like disease transmission dynamics and cognitive processes. Recent studies include modeling the transition of pandemics to endemic states, genomic analysis of viral spread, and audio-visual perception mechanisms in speech. He actively contributes to public health policy discussions through epidemic modeling research. Professor Tupper's research has been published in high-impact journals and conferences, with notable contributions to diversity metrics in biology and geometry, stochastic differential equations, and connectionist models of linguistic phenomena. His courses reflect interdisciplinary interests, integrating mathematical rigor with practical computational methods.

Ali Nazemiمشاهده پروفایل

دانشیار

• Hydrology
• Water Security
• Climate Change
• +۳ مورد دیگر

Dr. Ali Nazemi is an Associate Professor in the Department of Building, Civil, and Environmental Engineering at Concordia University, where he joined in 2015 as a Strategic Hire in Water Resources. He holds adjunct appointments at the University of Saskatchewan's School of Environment and Sustainability and is an Associate Member of the Global Institute for Water Security. His foundational education includes a PhD from the University of Birmingham (UK), an MSc from Ferdowsi University of Mashhad (Iran), and a BSc from KNT University of Technology (Iran). Dr. Nazemi's research advances methodologies for water security challenges under climate change, focusing on: Hydrological modeling and algorithm development Climate change vulnerability assessments Coupled human-water systems Hydroclimatic data diagnostics His recent publications predominantly explore climate model integration, uncertainty quantification in water systems, and hybrid modeling approaches, with consistent themes of climate adaptation and risk management across Canadian and international contexts. He has been recognized with several awards, including: Dorothy Hodgkin Postgraduate Award (UK government, 2005–2009) Best Presentation Award at IEEE World Congress on Computational Intelligence (2006) Post Graduate Teaching Award (University of Birmingham, 2004–2007) Dr. Nazemi actively supervises graduate students through his Water Security and Climate Change (WSCC) lab, with projects on climate downscaling, vulnerability assessment tools, and coupled human-water systems. He leads research collaborations with the Saskatchewan Water Security Agency, NSERC's Changing Cold Region Network, and the Global Water and Energy Cycle Experiment.

Miguel F. Anjosمشاهده پروفایل

استاد

• Mathematical Optimization
• Smart Grids
• Operations Research
• +۳ مورد دیگر

Miguel F. Anjos is Professor and Chair of Operational Research at the School of Mathematics, University of Edinburgh , and holds the NSERC-Hydro-Québec-Schneider Electric Industrial Research Chair on Optimization for Smart Grids at Polytechnique Montréal. He received his B.Sc. (1992), M.S. (1994), and Ph.D. (2001) from McGill, Stanford, and Waterloo respectively. Research Theme Head of Data and Decisions at Edinburgh Founding Director of Trottier Institute for Energy Editor-in-Chief of Optimization and Engineering Research Interests: His work bridges mathematical optimization with smart grid applications , focusing on conic optimization, optimal power flow, demand response, and facility layout. He applies these techniques to energy storage, electric transportation, and industrial systems. Scientific Awards: Méritas Teaching Award (2012) Humboldt Research Fellowship (2009) Queen Elizabeth II Diamond Jubilee Medal (2013) Elected Fellow of EUROPT and Canadian Academy of Engineering Academic Service: Served on Mathematical Optimization Society Council, SIAM Activity Group on Optimization, INFORMS Optimization Society Vice-Chair, and Mitacs Research Review Committee. Hosts benchmark datasets: QAPLIB, FLPLIB, Jones Benchmark.

Roland N. Horneمشاهده پروفایل

استاد

• Geothermal reservoir engineering
• Energy production optimization
• Well test interpretation
• +۸ مورد دیگر

Roland N. Horne is the Thomas Davies Barrow Professor of Earth Sciences at Stanford University and Senior Fellow at the Precourt Institute for Energy. He holds positions in the Department of Energy Science & Engineering and is an Affiliate at the Stanford Woods Institute for the Environment. With degrees from the University of Auckland (BE, PhD, DSc), Horne has established himself as a leading expert in geothermal reservoir engineering and energy production optimization. His research focuses on inverse problems in reservoir modeling, including tracer analysis of fractures, computer-aided well test analysis, production schedule optimization, and automated history matching. Horne has made significant contributions to understanding geothermal reservoir engineering and multiphase flow of boiling fluids through porous materials and fractures. The analysis of his recent publications (2023-2025) reveals a strong emphasis on enhanced geothermal systems (EGS), with particular focus on flexible operations, economic modeling, and advanced characterization techniques. His work increasingly incorporates machine learning approaches for reservoir analysis and has expanded into microbial tracing methods for interwell connectivity assessment. There's also significant attention to US geothermal resource potential and integration into the broader energy transition. Honorary Member of the Society of Petroleum Engineers Member of the US National Academy of Engineering Multiple SPE Distinguished Lecturer appointments (1998, 2009, 2020) John Franklin Carl Award recipient Five Best Paper awards from Geothermal Resources Council Patricius Medal from German Geothermal Society Core Values Award from Women in Geothermal (2023) Horne has supervised 60 PhD and 135 MS students throughout his career. His current teaching includes undergraduate and graduate courses in Fundamentals of Energy Processes, Geothermal Reservoir Engineering, Mass and Energy Transport in Porous Media, and Well Test Analysis. He previously served as President of the International Geothermal Association (2010-2013) and Technical Program Chair for multiple World Geothermal Congress events. Horne maintains active research collaborations worldwide, including with the University of Tokyo (where he was a Fellow of the School of Engineering in 2016) and China University of Petroleum. His current research group focuses on advancing EGS technologies and developing more accurate reservoir characterization methods for geothermal applications.

Rajen Shahمشاهده پروفایل

استاد

• Statistical Inference
• High-Dimensional Data Analysis
• Machine Learning
• +۳ مورد دیگر

Professor Rajen Shah is a faculty member in the Statistical Laboratory at the University of Cambridge, part of the Department of Pure Mathematics and Mathematical Statistics (DPMMS) within the Faculty of Mathematics. His research focuses on statistical methodology, particularly in high-dimensional data analysis, machine learning, and robust statistical inference. He is known for contributions to areas such as change-point regression, inverse propensity score weighting, and efficient estimation techniques in complex models. Key research interests include developing novel methods for variable selection, robust hypothesis testing, and scalable algorithms for large-scale data. He has collaborated on interdisciplinary projects, such as functional genomics studies (e.g., screening conserved genes of unknown function). His work often emphasizes theoretical rigor alongside practical applications in fields like causal inference and computational statistics. Prof. Shah has published extensively in top-tier journals like The Annals of Statistics , Bernoulli , and Journal of the Royal Statistical Society Series B . His recent articles address challenges in cross-validation for change-point detection, rank-transformed subsampling, and sandwich boosting methods. He is actively involved in the academic community, contributing to the Cambridge Statistics Clinic and supervising research in statistical methodology. His research group is affiliated with the Statistical Laboratory at the Centre for Mathematical Sciences, Cambridge. The lab focuses on advancing statistical theory and applications, with a strong emphasis on high-dimensional and assumption-lean methods.

• Machine Learning
• Signal Processing
• Biomedical Engineering
• +۷ مورد دیگر

Professor Danilo Mandic is a leading academic in Machine Intelligence and Signal Processing at Imperial College London's Department of Electrical and Electronic Engineering. He holds roles including President of the International Neural Network Society and Distinguished Lecturer for IEEE Computational Intelligence and Signal Processing Societies. His research spans Statistical Learning, Wearable Sensing (Hearables), Financial Signal Processing, and Tensor Networks for Big Data. Key contributions include pioneering in-ear physiological sensing and developing quaternion-based adaptive filters. He has authored over 600 publications, including seminal monographs on neural networks and complex-valued signal processing. Education: PhD in Nonlinear Adaptive Signal Processing from Imperial College (1999). Professional accolades include the 2019 Dennis Gabor Award and multiple IEEE Best Paper Awards. His labs include the Financial Signal Processing & Machine Learning Lab and collaborations with the Centre for Neurotechnology. He advises numerous students and leads projects on AI ethics, graph signal processing, and biomedical applications. His work emphasizes translating research into educational curricula via participatory sensor-based learning.