جست‌وجوی استاد و پژوهشگر

Youssef Marzoukمشاهده پروفایل

استاد

• Uncertainty Quantification
• Bayesian Computation
• Inverse Problems
• +۴ مورد دیگر

Youssef Marzouk is a Professor of Aeronautics and Astronautics at MIT, serving as co-director of the MIT Center for Computational Engineering and director of the Aerospace Computational Design Laboratory. His research focuses on integrating physical modeling with statistical inference, emphasizing Bayesian computation, uncertainty quantification, and optimal experimental design. He holds a SB, SM, and PhD from MIT and has been recognized with prestigious awards including the DOE Early Career Award and the Junior Bose Teaching Prize. Education: PhD in Aeronautics and Astronautics, MIT SM in Aeronautics and Astronautics, MIT SB in Aeronautics and Astronautics, MIT Research Interests: Uncertainty Quantification techniques for complex systems Bayesian computational methods and inverse problem solutions Optimal experimental design strategies Interdisciplinary applications in geophysics, environmental science, and engineering Awards: 2022: Report to the President, Center for Computational Science and Engineering 2021: Bayesian Inference Software Framework (hIPPYlib-MUQ) 2012: MIT School of Engineering Junior Bose Award 2010: DOE Early Career Research Award Labs & Leadership: Aerospace Computational Design Laboratory (Director) MIT Center for Computational Engineering (Co-Director) Editorial Board roles: SIAM Journal on Scientific Computing, Advances in Computational Mathematics

Henry Chaiمشاهده پروفایل

عضو هیئت علمی

• Machine Learning
• Bayesian Methods
• Probabilistic Numerics
• +۳ مورد دیگر

Henry Chai is an Assistant Teaching Professor in the Machine Learning Department at Carnegie Mellon University (CMU). His role focuses on teaching core machine learning courses such as Introduction to Machine Learning (10-301/601) and Bayesian Methods in Machine Learning (10-424/624). He co-teaches courses with faculty like Matt Gormley and Charlie Garrod. Chai holds a PhD from Washington University in St. Louis (2021), advised by Roman Garnett. His research explores Bayesian machine learning, probabilistic numerics, and active learning, aiming to address computational challenges in reasoning about intractable quantities. He has contributed to methods like BINOCULARS and Bayesian quadrature optimization, published in top venues like ICML and AISTATS. He actively engages in pedagogical research and K-12 CS education advocacy. His teaching responsibilities include advanced graduate courses (e.g., 10-701) and undergraduate introductory courses, with a focus on scalable Gaussian processes and Bayesian optimization. Office hours are held in GHC 8133.

Mostafa Milaniمشاهده پروفایل

استادیار

• Data Management
• Databases
• Data Cleaning
• +۷ مورد دیگر

Mostafa Milani is an Assistant Professor in the Department of Computer Science at Western University. His research focuses on data management, databases, and their applications in data cleaning, privacy, provenance, and fairness. Before joining Western, he held postdoctoral positions at the University of British Columbia and McMaster University, and earned his Ph.D. from Carleton University under Dr. Leopoldo Bertossi. Education: Ph.D. in Computer Science from Carleton University (supervised by Leopoldo Bertossi), Postdoctoral Fellowships at University of British Columbia and McMaster University. Research Interests: Data Quality, Privacy, Provenance, Fairness, Entity Matching, Query Optimization, and Database Systems. His work emphasizes ethical data practices and integrates machine learning for improved database interactions. He has contributed to projects like Building Trust in Data (privacy/fairness integration) and Unified Data Exploration (provenance and query recommendations). Courses taught include Databases I/II, Applied Logic, and Web Systems. Current advisees include 7 MSc and 1 PhD student. Former students have graduated across MSc and undergraduate programs. His research is supported by grants and collaborations, and he actively participates in program committees for top conferences like SIGMOD and VLDB.

Kallol Settمشاهده پروفایل

دانشیار

• Risk and Reliability
• Civil Infrastructure
• Extreme Events
• +۵ مورد دیگر

Kallol Sett is an Associate Professor in the Department of Civil, Structural and Environmental Engineering at the University at Buffalo (SUNY), within the School of Engineering and Applied Sciences. His research focuses on risk and reliability analysis of civil infrastructure under extreme events, with expertise in uncertainty quantification, multi-hazard resilience, and geomechanics. He leads the Risk and Reliability Research Group, which develops computational tools integrating physics-based and data-driven modeling, stochastic calculus, and high-performance computing. Education includes a PhD from the University of California, Davis (2007), an MS from the University of Houston (2003), and a BE from Jadavpur University (1997). His work is funded by NASA, NSF, USDOT, NIST, and industry partners. Key research themes include probabilistic geotechnical site characterization, stochastic simulation of seismic ground motion, and life-cycle cost-benefit analysis of infrastructure systems. Advising includes mentoring over 10 PhD and MS students, with notable alumni now in academia and industry roles such as Assistant Professors at Embry-Riddle Aeronautical University and Tianjin University. His lab’s recent studies address real-time decision support systems for hurricane-impacted infrastructure, resilience deficit indices, and multi-hazard financial risk assessment of integrated infrastructure systems.

• Computer Graphics
• Human-Computer Interaction
• Robotics
• +۳ مورد دیگر

Dr. Daniel Berio is a researcher at Goldsmiths, University of London, specializing in computational models for human-like movement in digital art and robotics. His work bridges computer graphics, cognitive psychology, and robotic manipulation, focusing on stylized stroke generation, graffiti analysis, and kinematic modeling. He collaborates with Frederic Fol Leymarie and Rejean Plamondon, utilizing the Sigma Lognormal model to simulate human handwriting dynamics. Education : Doctoral thesis on AutoGraff (2021), exploring computational understanding of graffiti and calligraphy. Research Themes : Human-like motion in digital art, kinematic reconstruction from static traces, robotic graffiti generation, and perceptual fluency in aesthetic evaluation. Publications : 15+ works since 2015, spanning ACM Transactions on Graphics, British Journal of Psychology, and conferences like MOCO and IROS. Applications : Font stylization tools, synthetic graffiti generation, compliant robot control, and semantic typography systems.

Aleksandar Dimitrovskiمشاهده پروفایل

دانشیار

• Power Systems
• Microgrid Protection and Control
• Parallel Simulation of Power Systems
• +۵ مورد دیگر

Dr. Aleksandar Dimitrovski is an Associate Professor at the University of Central Florida (UCF) in the Department of Electrical and Computer Engineering, College of Engineering and Computer Science. Holding a Ph.D. in Power Engineering from Saints Cyril and Methodius University (1997), his research focuses on power system modeling under uncertainty, microgrid protection, parallel simulation techniques, and renewable energy integration. Ph.D., Power Engineering, Saints Cyril and Methodius University (1997) Active research areas: Power System Protection, Parallel Simulation, Solar Integration, Magnetic Amplifiers His publications emphasize advanced simulation algorithms like Parareal, probabilistic forecasting for solar power, and magnetic reactor applications in grid control. He has led DOE-funded projects on solar integration and real-time grid protection. Fulbright Scholar (2016) R&D 100 Award (2014) Prof. Dimitrovski serves as an IEEE Senior Member, CIGRE member, and reviewer for journals like IEEE Transactions on Power Systems and Smart Grid. He developed the open-source RAPID simulation toolbox and contributed to cyber-physical security frameworks for smart grids.

Abigail L. Beckمشاهده پروفایل

پژوهشگر

• Civil Engineering
• Infrastructure Resilience
• Disaster Recovery
• +۳ مورد دیگر

Abigail L. Beck is a Research Assistant Professor at the University of Houston in the Department of Civil and Environmental Engineering , transitioning to Assistant Professor in September 2025. Her work bridges civil engineering with social systems, focusing on probabilistic infrastructure analysis and equity-driven disaster recovery. Education: Ph.D. in Civil Engineering, University of Illinois Urbana-Champaign (2024) M.S. in Civil Engineering, University of Illinois Urbana-Champaign (2020) B.S. in Civil Engineering, University of Texas at Austin (2019) Her research develops models connecting infrastructure failures to socioeconomic impacts, emphasizing equity in decision-making. She specializes in probabilistic analysis , community stakeholder engagement , and critical infrastructure prioritization , particularly for post-disaster scenarios. Recent publications analyze socially-aware retrofit strategies for power networks (2023) and probabilistic frameworks for quantifying social impacts of infrastructure nodes (2022). Themes include equity, resilience, and stakeholder-driven mitigation planning. Scientific Awards: NSF Graduate Research Fellowship (2020-2023) Student Best Paper/Poster Awards (ICOSSAR 2022, Natural Hazards Summit 2024) Mavis Future Faculty Fellowship (2023-2024) Her outreach includes game-based education tools like "Cookout Community" (2023) and NSF-funded travel awards for international conference participation. She’s advised by Dr. Eun Joo Cha in her research.

Daniel McKenzieمشاهده پروفایل

استادیار

• Derivative-free optimization
• Implicit neural networks
• Geometric methods in data science
• +۳ مورد دیگر

Daniel McKenzie is an Assistant Professor in the Department of Applied Mathematics and Statistics at the Colorado School of Mines. His research focuses on derivative-free optimization, implicit neural networks, and geometric methods in data science. He holds a B.Sc.(hons) and M.Sc. in Mathematics from the University of Cape Town (2010, 2014) and a PhD in Mathematics from the University of Georgia (2019). B.Sc.(hons): Mathematics and Applied Mathematics, University of Cape Town, 2010 M.Sc.: Mathematics, University of Cape Town, 2014 PhD: Mathematics, University of Georgia, 2019 His research explores the intersection of optimization theory and machine learning, with applications in spatial data modeling, geometric data analysis, and high-dimensional clustering. Recent work emphasizes curvature-aware algorithms, comparison-based optimization, and implicit network architectures like LatticeVision. His methods address challenges in non-stationary spatial data and convex game equilibria prediction. Key contributions include Fermat distance metrics for clustering, Jacobian-Free Backpropagation (JFB) for implicit networks, and zeroth-order algorithms for black-box optimization. While no scientific awards are listed, his publications reflect a strong focus on advancing optimization techniques for modern data science problems. No specific grants or advising roles are detailed in the provided text. His work bridges computational mathematics and applied AI, with potential applications in robotics, spatial statistics, and algorithmic game theory.

• Earthquake Engineering
• Structural Dynamics
• Steel Structures
• +۱ مورد دیگر

Dr. David Roke is an Associate Professor in the Department of Civil Engineering at the University of Akron's College of Engineering and Polymer Science. A member of the American Society of Civil Engineers (ASCE) and Chi Epsilon, Dr. Roke was honored with the 2019 Excellence in Teaching Award from The National Society of Leadership and Success (Sigma Alpha Pi). His research program focuses on earthquake engineering and structural resilience, with particular emphasis on developing advanced structural systems for seismic resistance. Key research areas include self-centering structural systems, buckling-restrained elements, soil-structure interaction effects, and life-cycle cost analysis of resilient infrastructure. His investigations employ experimental testing combined with computational methods including evolutionary algorithms, sensitivity analysis, and machine learning techniques. Dr. Roke's recent publications demonstrate significant contributions to economical seismic-resistant design, with innovative work on self-centering concentrically braced frames and rocking core systems. His research integrates structural performance assessment with economic feasibility studies to advance practical applications in earthquake engineering. His educational background includes a Ph.D. in Structural Engineering from Lehigh University, an M.S. in Civil Engineering from University of Pittsburgh, and a B.S. in Civil Engineering from University of Pittsburgh.

Kevin A. Haasمشاهده پروفایل

استاد

• Coastal Engineering
• Numerical Modeling
• Sediment Transport
• +۵ مورد دیگر

Kevin A. Haas is a Professor of Civil Engineering and Associate Chair for Undergraduate Programs at the Georgia Institute of Technology's School of Civil and Environmental Engineering, part of the team developing Coastal Engineering programs in Savannah. He holds a B.S. (Ohio State University, 1994), M.S. (Ohio State University), and Ph.D. (University of Delaware, 2001) in Civil/Coastal Engineering, followed by a postdoctoral fellowship at the University of Delaware. Education: B.S. in Civil Engineering (Distinction), The Ohio State University, 1994 M.S. in Civil Engineering, The Ohio State University, 1997 Ph.D. in Coastal Engineering, University of Delaware, 2001 Research Interests: Focuses on coastal dynamics, numerical modeling of nearshore circulation, sediment transport, and hydrodynamics of rip currents. Explores wave energy extraction, tidal marsh sediment transport, and applications of video-based field observations. His work bridges environmental engineering and renewable energy, emphasizing sustainable coastal systems. Key Achievements: Recipient of the Delaware Sea Grant Award for outstanding Ph.D. research on rip currents Lead in securing a $1M educational grant for undergraduate programs at Georgia Tech Active in developing tidal and wave energy assessment methodologies Grants & Labs: Principal investigator on projects funded by federal agencies and industry partnerships Conducts fieldwork and modeling at Georgia Tech's Savannah campus and coastal sites Collaborates with USGS, NOAA, and international teams on marine energy initiatives

Amirali Abariمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Computational Decision Making
• +۷ مورد دیگر

Dr. Amirali Abari is an Associate Professor in the Faculty of Business and Information Technology at Ontario Tech University, specializing in Artificial Intelligence, Machine Learning, and Computational Decision Making. His research focuses on decision-making under uncertainty, including preference learning, group recommendation systems, and probabilistic inference. He holds a PhD in Computer Science from the University of Toronto (2016) and was an NSERC Postdoctoral Fellow at the University of Toronto and University of Waterloo. Dr. Abari's research has been recognized with awards including the NSERC Postdoctoral Fellowship and a U.S. patent. He serves on program committees for top AI conferences (e.g., AAAI, IJCAI) and reviews for journals like IEEE Transactions and ACM Transactions. His work bridges computational methods with real-world applications in supply chain optimization, risk management, and sustainable infrastructure planning. Key contributions include integrating machine learning with queueing theory for EV charging stations, stochastic optimization for resilient supply chains, and decision support systems for industrial processes. His research group collaborates with industry partners to apply AI-driven solutions to business analytics and operations challenges. Education: PhD in Computer Science, University of Toronto (2016) Awards: NSERC Postdoctoral Fellowship, Bell Scholarship, Student Best Paper Award Service: Program committees for AAAI, IJCAI; reviewer for IEEE, ACM, and Algorithmica

Walid Morsi Ibrahimمشاهده پروفایل

استاد

• Smart Grid
• Power Systems
• Signal Processing
• +۴ مورد دیگر

Dr. Walid Morsi Ibrahim is a Professor in the Department of Electrical, Computer and Software Engineering at Ontario Tech University, Faculty of Engineering and Applied Science. His expertise includes Smart Grid design, power systems analysis, and signal processing. He holds a PhD from Dalhousie University (2009), with prior academic positions including Adjunct Professor at the University of Waterloo and Assistant Professor roles at Ontario Tech University and the University of New Brunswick. Education: PhD (2009, Dalhousie), MSc (2002, Suez Canal University), BSc (1998, Suez Canal University) His research focuses on Smart Grid technologies, including signal processing for power systems, automation, and distributed energy resources. Notable contributions include work on electric vehicle integration, transformer aging analysis, and nonintrusive load monitoring using wavelet transforms and machine learning. He has authored over 50 publications in top-tier IEEE conferences and journals. Recent articles highlight his work on electric vehicle load forecasting during the pandemic, fault detection in photovoltaic systems, and transactive energy frameworks for prosumers. Awards include the IEEE PEDG 2010 Best Paper Award and multiple poster awards at Ontario Tech University. Dr. Ibrahim teaches courses on smart grid fundamentals, power systems operation, and information technology for engineers. He has secured grants for research on smart grid monitoring systems and holds patents related to energy management solutions. His work bridges theoretical advancements with practical grid applications, emphasizing resilience and sustainability.

• Actuarial science and mathematical finance
• Biostatistical methods
• High dimensional data analysis
• +۶ مورد دیگر

Narayanaswamy Balakrishnan is a Professor in the Department of Mathematics and Statistics at McMaster University, Canada. His research focuses on probabilistic and statistical models with applications in science, engineering, and medicine. Key areas include reliability analysis, statistical inference, high-dimensional data analysis, and actuarial science. He has authored/co-authored over 60 books and numerous research papers, contributing significantly to fields like order statistics, censored data analysis, and survival analysis. Notable works include Hybrid Censoring: Models, Methods and Applications (2023) and A First Course in Order Statistics (2008). His research has been recognized with prestigious honors, including election to the Royal Society of Canada in 2023. Dr. Balakrishnan mentors graduate students in statistics and has collaborated widely, with contributions to statistical methodologies in engineering, medical studies, and industrial problems. His work bridges theoretical developments with practical applications, emphasizing robust statistical techniques and innovative data analysis approaches.

Kevin Kellyمشاهده پروفایل

استاد

• Ockham's Razor
• Epistemology
• Formal Learning Theory
• +۷ مورد دیگر

Kevin Kelly is a Professor of Philosophy at Carnegie Mellon University and the Director of the Center for Formal Epistemology. His work bridges formal epistemology, computational learning theory, and philosophy of science, with a focus on Ockham's razor, belief revision, and the topology of inquiry. Key Research Areas: Ockham's Razor, Epistemology, Formal Learning Theory, Modal Epistemic Logic, and Interdisciplinary Applications of Topology. Grants: John Templeton Foundation grant for research on truth-finding efficiency and scientific simplicity. Scientific Awards: John Templeton Foundation grant (2018–2021) Kelly's publications emphasize connections between probabilistic reasoning and qualitative belief, solutions to the lottery paradox, and computational models of knowledge acquisition. His recent work explores lighting design, human-centric ergonomics, and machine learning epistemology, reflecting a deep interdisciplinary engagement with technology and science.

• Formal Verification and Control
• Safety-Critical Systems
• Machine Learning
• +۵ مورد دیگر

Dr. Yulong Gao is an Assistant Professor in the Department of Electrical and Electronic Engineering at Imperial College London, affiliated with the Control and Power Research Group. He holds a B.E. in Automation from Beijing Institute of Technology (2013), M.E. in Control Science & Engineering (2016), and a joint Ph.D. in Electrical Engineering from KTH Royal Institute of Technology and Nanyang Technological University (2021). He has held postdoctoral positions at Oxford University and KTH. His research focuses on formal verification and control, machine learning, and their applications to safety-critical systems, including autonomous systems and control synthesis under uncertainty. His work emphasizes robust control strategies, data-driven optimization, and formal methods to ensure safety in dynamic systems. Recent publications address challenges in autonomous vehicle motion planning, risk-aware Bayesian neural networks, and adaptive task planning using temporal logic. He has contributed to stochastic modeling, distributed MPC, and resilient control under cyber-physical threats. Research affiliations include the Control and Power Research Group at Imperial College London, leveraging interdisciplinary collaboration to advance theoretical and applied control systems research.