جست‌وجوی استاد و پژوهشگر

Gurprit Singhمشاهده پروفایل

پژوهشگر

• Monte Carlo Integration
• Markov Chain Monte Carlo Sampling
• Generative AI
• +۳ مورد دیگر

Gurprit Singh is a Researcher at the Max Planck Institute for Informatics, Saarbrücken, Germany. His work focuses on advancing Monte Carlo integration techniques and their applications in generative AI, physically based rendering, and optimization. He has contributed to conferences such as SIGGRAPH, Eurographics (EG), and Pacific Graphics (PG), serving in roles like Technical Program Committee member and co-chair for Doctoral Consortium programs. Roles: Associate Senior Researcher, Conference Co-chair (EGSR 2021), and active in academic service. Research Interests: Monte Carlo methods, MCMC sampling, gradient-based optimization, and generative models. His research bridges rendering, optimization, and AI, with notable work on noise optimization in diffusion models and perceptual error minimization. He has received the Best Student Paper Award at ICPRAM 2025.

• Personality Psychology
• Social Psychology
• Behavioral Decision Theory
• +۳ مورد دیگر

Jasna Hudek-Knežević is a Professor at the University of Rijeka's Faculty of Humanities and Social Sciences, specializing in personality psychology, social dynamics, and evolutionary behavioral science. Her research focuses on the Dark Triad traits, sociosexuality, emotion regulation, and their impacts on relationships and health outcomes. Key Research Areas: Personality-environment interactions, dyadic decision-making, and stress coping mechanisms. Teaching: Leads courses in Group Productivity and Decision-Making at the postgraduate level. Her recent publications explore how personality traits influence mate poaching, relationship satisfaction, and health behaviors, utilizing dyadic analysis and interdependence models. Despite no explicit mention of scientific awards, her work demonstrates interdisciplinary impact across psychology, education, and health sciences.

Mohamed Iskandaraniمشاهده پروفایل

استاد

• Oceanography
• Climate Modeling
• Data Assimilation
• +۳ مورد دیگر

Mohamed Iskandarani is a Professor in the Department of Ocean Sciences at the Rosenstiel School of Marine, Atmospheric, and Earth Science, University of Miami. His research focuses on oceanographic modeling, data assimilation, and uncertainty quantification in geophysical fluid dynamics. He specializes in developing advanced numerical methods for climate and ocean circulation studies, including polynomial chaos frameworks, Gaussian process regression, and spectral element models. His work integrates machine learning techniques with traditional oceanography to improve predictions of submesoscale structures, storm surges, and hurricane impacts. Iskandarani has contributed to understanding Loop Current dynamics in the Gulf of Mexico, fish larval connectivity in Florida Keys, and the propagation of uncertainties in coupled atmosphere-wave-ocean systems. Key research areas include: (1) High-resolution ocean velocity field reconstruction using Lagrangian drifter data, (2) Quantifying uncertainties in biophysical and climate models, (3) Developing statistical interpolation codes for ocean forecasting, and (4) Exploring multiscale ocean-atmosphere interactions. His methods are applied to real-world challenges like hurricane forecasting, oil spill trajectory modeling, and coastal engineering design criteria. Collaborations involve institutions such as NOAA, NASA, and international oceanographic consortia. Published extensively in journals like Journal of Marine Science and Engineering and Monthly Weather Review , his work bridges computational science with environmental applications. Recent projects analyze submesoscale turbulence, drifter-based velocity field reconstructions, and ensemble-based statistical emulators for ocean current predictions.

Lixin Shenمشاهده پروفایل

استاد

• Applied and Computational Harmonic Analysis
• Optimization
• Imaging Science
• +۴ مورد دیگر

Lixin Shen is a Professor in the Department of Mathematics at Syracuse University, part of the College of Arts and Sciences. His research focuses on applied and computational harmonic analysis, optimization, imaging science, and information processing. Shen holds a Ph.D. in Mathematics from Sun Yat-Sen University (1996), an M.Sc. from Peking University (1990), and a B.Sc. from Peking University (1987). Education: Ph.D., Mathematics, Sun Yat-Sen University, 1996 M.Sc., Mathematics, Peking University, 1990 B.Sc., Mathematics, Peking University, 1987 Research Interests: Shen’s work emphasizes sparse optimization, image and signal processing, computational mathematics, and their applications. Notable areas include wavelet analysis, tensor decomposition, and robust algorithms for noise removal and high-resolution imaging. Grants & Awards: NSF Grant: Collaborative Research: Sparse Machine Learning and Sparse Optimization (2022–2025) Excellence in Graduate Education Faculty Recognition Award, Syracuse University (2024) Air Force Summer Faculty Fellowship (2024, 2023, 2021, 2020) Service & Leadership: Chair of Graduate Committee, Mathematics Department (2024–2025) Editor, Frontiers in Applied Mathematics and Statistics Organizer, SIAM Conferences on Optimization and Imaging (2023–2020) Teaching: Recent courses include Numerical Linear Algebra, Numerical Methods with Programming, Sparse Optimization, and Partial Differential Equations.

Markus Heglandمشاهده پروفایل

استاد

• Computational Mathematics
• High-Dimensional Problems
• Ill-Posed Problems
• +۴ مورد دیگر

Markus Hegland is a Professor and Head of the Centre for Mathematics and its Applications (CMA) at the Australian National University (ANU). He holds a PhD from ETH Zurich (1988) and has been affiliated with ANU since 1992, focusing on High-Performance Computing (HPC) and numerical analysis. As a Hans Fischer Senior Fellow at TUM-IAS, his research emphasizes high-dimensional problems, ill-posed systems, and data mining applications. His work bridges computational mathematics with practical domains like systems biology and spectral enhancement. Research interests include sparse grid techniques, regularization methods, and algorithm development for HPC. Notable contributions include the OPTICOM method for stable sparse grid solutions and convergence theory for variable Hilbert scales regularization. He has led projects on fault-tolerant HPC algorithms and collaborated with Fujitsu on HPC applications. Publications span numerical analysis, bioinformatics, and computational physics. His work on the chemical master equation and gyrokinetics showcases interdisciplinary impact. Currently, he explores resilient grid-based solvers and machine learning integration with HPC frameworks. No awards are explicitly listed, but his senior fellowship underscores recognition in his field. Grants and collaborations include ARC-funded research in bioinformatics and HPC resilience. His work on digital twins and algorithm optimization reflects broader interests in advanced computational modeling. He is actively involved in teaching and supervising in computational mathematics and data science at ANU.

Morteza Lahijanianمشاهده پروفایل

دانشیار

• Control Theory
• Formal Methods
• Robotics
• +۱۰ مورد دیگر

Morteza Lahijanian is an Associate Professor in the Aerospace Engineering Sciences department at the University of Colorado Boulder , with a courtesy appointment in Computer Science. He received his PhD (2013), MS (2009), and BS (2005) in Mechanical Engineering and Bioengineering from Boston University and UC Berkeley, respectively. His research focuses on safe autonomy through correct-by-construction algorithmic approaches, integrating control theory and formal methods to address challenges in robotics, hybrid systems, and human-robot interaction. Education PhD, Mechanical Engineering (Control Theory and Robotics), Boston University, 2013 MS, Mechanical Engineering (Control Theory and Robotics), Boston University, 2009 BS, Bioengineering, University of California, Berkeley, 2005 Lahijanian leads the ARIA Systems Group , which develops computational frameworks for reliable and intelligent autonomy. His work emphasizes stochastic systems , temporal logic , and multi-agent planning , with applications in autonomous vehicles and safety-critical robotics. Recent research trends include Bayesian verification, deep reinforcement learning, and polynomial barrier function analysis for uncertain environments. His students, including Anne Theurkauf, Justin Kottinger, and Kandai Watanabe, have contributed to advancements in motion planning, strategy synthesis, and stochastic control. Lahijanian's group has published extensively at top venues like IEEE CDC, ICRA, and NeurIPS, though no specific scientific awards are highlighted in the provided texts.

Aliaksandr Hubinمشاهده پروفایل

دانشیار

• Statistics
• Artificial Intelligence
• Econometrics
• +۴ مورد دیگر

Aliaksandr Hubin is an Associate Professor of Statistics at Østfold University College (OUC), affiliated with the Section for Research Administration. He provides statistical support to researchers at OUC while conducting methodological research in Bayesian statistics, machine learning, and operations research. His academic background includes a PhD from the University of Oslo (2014–2018), a Master's from Molde University College (2012–2014), and a Specialist degree from Belarusian State University (2008–2013). His research focuses on Bayesian model selection, nonlinear regressions, neural networks, and weak supervision in machine learning. Hubin has held positions at the Norwegian Computing Center (2018–2020) and the University of Oslo as a postdoc (2021–2022). His work spans methodological advancements in MCMC algorithms, variational inference, and applications in epigenetics, econometrics, and healthcare. Key achievements include developing the GMJMCMC algorithm for Bayesian logic regression and the 'skweak' framework for weak supervision in NLP. His awards include the NIMA 2014 award for best MSc graduate, Belarusian Ministry of Education First Award (2013), and recognition at the Graybill 2017 conference. His research has been published in journals like Bayesian Analysis , Neural Computing & Applications , and Scientific Reports , with a focus on model uncertainty quantification and scalable Bayesian methods.

Ba Chuمشاهده پروفایل

استاد

• Econometrics
• Time-Series Econometrics
• Nonparametric and Semiparametric Econometrics
• +۷ مورد دیگر

Professor Ba Chu is a faculty member in the Department of Economics at Carleton University, Ottawa, Canada. He holds a Ph.D. from the University of London and has a strong academic background in econometrics and financial economics. His research is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), including a 2020 Discovery Grant for work on simulation-based methods in large dynamic latent variable models. B.A. (Hanoi) M.Sc., M.Phil., Ph.D. (London) His research focuses on optimal asset allocation , time-series econometrics , and nonparametric and semiparametric methods . He has made significant contributions to dependence modelling using copulas , asymptotic theory , and large deviations approaches to risk assessment . His work bridges theoretical econometrics with practical applications in finance and macroeconomics. The recent publications highlight a consistent focus on high-dimensional and dynamic models, with applications in financial markets, portfolio optimization, and macroeconomic data analysis. His methodological contributions include advanced inference techniques in moment restriction models, duration dependence in high-frequency trading, and robust estimation under dependence and structural breaks. The research trend shows a deep integration of probability theory, statistical inference, and financial economics. NSERC Discovery Grant (2020), $90,000 over five years Professor Chu teaches courses such as Econometrics I (Econ 5027) , Applied Time Series Analysis (Econ 5713w) , and Statistical Methods in Economics . While no formal advisees are listed, his collaborative research involves prominent econometricians such as D.T. Jacho-Chavez, O.B. Linton, and S. Satchell. He has no known lab or research team listed publicly, but his work is theoretically rigorous and mathematically sophisticated, often involving simulation-based and asymptotic methods.

• Solar Energy Optimization
• Control Systems
• Remote Laboratories
• +۷ مورد دیگر

Fariba Moghaddam is an Ordinary Professor at HES-SO Valais-Wallis - Haute Ecole d'Ingénierie, leading the Power & Control orientation under the Institut Systèmes industriels. She specializes in renewable energy systems, control engineering, and educational technology, with a focus on solar energy optimization, remote laboratories, and sustainable development initiatives in the Global South. Her work spans academic leadership, research, and pedagogical innovation. Education: PhD in Control Engineering from École Polytechnique Fédérale de Lausanne (EPFL), 1996. Additional academic background in electrical and mechanical engineering. Research Interests: Solar tracking systems, photovoltaic-thermal (PVT) integration, machine learning applications in energy systems, remote experimentation platforms, and collaborative educational infrastructure. Recent projects include the Solar Energy Optimization via Remote Platforms and DEAR MENA (Digital Education & Research for MENA). Grants & Projects: Solar Energy Optimization (2022-2026): Swiss National Science Foundation-funded project developing cost-effective sun tracking solutions. DEAR MENA (2019-2024): Cluster fostering digital education partnerships between Swiss and MENA institutions. Sustainable Laboratories (2020-2021): Platform for redistributing lab equipment to emerging economies. MOOLs (2018-2021): Massive Open Online Laboratories for global engineering education. Labs & Infrastructure: Spearheaded the Remote Real-Time Research Platform for control engineering, enabling global access to lab equipment via secure web interfaces. Collaborates on IoT-integrated systems for remote experimentation and industrial automation. Awards: None explicitly listed, but recognized for contributions to sustainable energy and educational equity through numerous funded projects.

Jonas Striaukasمشاهده پروفایل

استادیار

• Econometrics
• Statistical/Machine Learning Methods
• Mixed Frequency Data Econometrics
• +۳ مورد دیگر

Jonas Striaukas is an Assistant Professor of Statistics and Finance at the Copenhagen Business School (CBS), Department of Finance, and a Marie Skłodowska-Curie Action fellow. His research focuses on econometrics, statistical/machine learning methods, and mixed frequency data modeling. He holds a PhD from Université Catholique de Louvain (2022), supervised by Professors Andrii Babii and Eric Ghysels. Prior to CBS, he was a research fellow at Fonds de la Recherche Scientifique—FNRS in Belgium. Research Interests: High-dimensional econometric modeling, nowcasting techniques, factor-augmented regressions, and applications of machine learning to financial and macroeconomic data. His work emphasizes robust statistical methods for time series analysis and sparse/dense model comparisons. Key Projects: Developed the 'midasml' and 'FAS' R packages for econometric analysis. Conducted a 2022 project at the National Bank of Belgium on machine learning-based nowcasting models. Affiliations: Department of Finance, Center for Statistics, CBS. Member of the Marie Skłodowska-Curie fellowship program.

Inge Kochمشاهده پروفایل

استاد مدعو

• Statistics
• Data Science
• Multivariate Analysis
• +۷ مورد دیگر

Inge Koch serves as Adjunct Professor and head of Statistics and Data Science in the Department of Mathematics and Statistics at the School of Physics, Maths and Computing, The University of Western Australia (UWA), a position she commenced in early 2019. Her research centers on multivariate and high-dimensional data analysis with specific expertise in: Dimension reduction and feature extraction methodologies Clustering and classification algorithms Sparsity-constrained statistical models Applications in medical imaging (flow cytometry, cancer proteomics), neuroimaging (fMRI signal processing), and astronomy (source detection) Recent publications reveal a methodological focus on advancing statistical techniques for pattern recognition in complex datasets, bridging theoretical statistics with real-world applications across biomedical and astronomical domains while maintaining strong educational research components. Koch supervises graduate students and actively shapes academic discourse through leadership roles including chairing the UWA Data Science Forum and organizing the Statistics Seminar Series. Her prior tenure as Executive Director of the Australian Mathematical Sciences Institute (2015-2019) featured leadership of the BHP-funded CHOOSEMATHS initiative promoting women's participation in STEM fields.

Ilias Zadikمشاهده پروفایل

استادیار

• High-Dimensional Statistics
• Machine Learning Theory
• Computational-Statistical Trade-offs
• +۵ مورد دیگر

Ilias Zadik is an Assistant Professor at Yale University's Department of Statistics and Data Science. His research focuses on computational-statistical trade-offs in modern machine learning, high-dimensional statistics, and probability theory. He has held postdoctoral positions at MIT (2021-2023) and NYU (2019-2021), and earned his PhD from MIT (2019), advised by David Gamarnik. He teaches courses like Stochastic Processes and has contributed to numerous conferences and workshops. His awards include MIT's Best Student Paper Honorable Mention (2017) and scholarships from Trinity College and the Onassis Foundation. Education: PhD in Operations Research, MIT (2019) MASt in Mathematics (Part III), University of Cambridge (2014) BA in Mathematics, University of Athens (2013) Internship at Microsoft Research New England (2017) Research Interests: Computational-statistical trade-offs, phase transitions in inference (e.g., All-or-Nothing phenomena), cryptographic methods in statistics, privacy-cost analysis, and algorithmic lower bounds. Awards: MIT Operations Research Best Student Paper Honorable Mention (2017) Trinity College Senior Scholarship (2014) Onassis Foundation Scholarship (2013-2014) SEEMOUS Gold Medal (2011), IMC First Prize (2011) Teaching & Service: Instructor for Yale's S&DS 351 (Stochastic Processes) and advanced courses on computational-statistical trade-offs. Co-organized the MaD+ seminar during the pandemic. Served on program committees for COLT, NeurIPS, and FOCS. Labs/Teams: Active in MIT's NSF/Simons Collaboration on Theoretical Foundations of Deep Learning and NYU's Math and Data group.

Hassan Hayajnehمشاهده پروفایل

استادیار

• Mechatronics systems
• Renewable energy sources
• Traditional energy systems
• +۴ مورد دیگر

Hassan Hayajneh is an Assistant Professor in Mechatronics Engineering Technology at Purdue University Northwest . He is a Certified Energy Manager (CEM ® ), LEED Green Associate, and Mechanical Engineering EIT with extensive academic-industry experience in renewable energy systems , mobile energy storage , and electric vehicle infrastructure . Education : Ph.D. in Sustainable Energy Systems Engineering (Texas A&M University-Kingsville) M.S. in Mechanical Engineering (Texas A&M University-Kingsville) B.S. in Mechatronics Engineering (The Hashemite University) Research Interests : Developing integrated frameworks for renewable energy adoption Optimizing electric vehicle charging infrastructure Designing hybrid energy storage solutions Advancing solar-powered desalination technologies Improving grid resiliency through decentralized systems Applying AI to energy forecasting and management Research Trends : Recent publications focus on cross-disciplinary approaches combining renewable energy , water treatment , and transportation electrification . Key themes include hybrid energy systems , storage optimization , and sustainable infrastructure for urban and remote applications. Scientific Awards : Certified Energy Manager (CEM ® ) LEED Green Associate accreditation Mechanical Engineering EIT license

Martina Kaledinمشاهده پروفایل

استاد

• Computational Chemistry
• Physical Chemistry
• Molecular Dynamics
• +۳ مورد دیگر

Martina Kaledin is a Professor in the Department of Chemistry & Biochemistry at Kennesaw State University, Georgia. Her research focuses on computational chemistry methods for molecular structure and dynamics, particularly in protonated clusters and atmospheric chemistry systems. She teaches undergraduate and graduate courses including General Chemistry, Physical Chemistry, and Directed Applied Research. Her research program develops molecular dynamics techniques using MP2 and DFT methods to analyze vibrational spectra, with recent work on permutationally invariant polynomial approaches for potential energy surfaces. Current projects aim to enhance computational efficiency through analytical potential energy surfaces while maintaining accuracy. Key trends in her 15 most recent publications (2012–2025) include: quantum Monte Carlo methods, polarizability-driven simulations, polynomial fitting techniques, and studies of carbon/nitrogen hydrides. She collaborates with Joel Bowman and Anatoly Kaledin, and has supervised numerous student researchers. Scientific Awards DAAD fellow (1999) Emerson Center Visiting Fellow (2000) Her advising program integrates undergraduate and graduate students into computational research projects, providing training in quantum methods, simulation tools, and spectral analysis. She has contributed to software development for molecular dynamics and polarizability calculations.

• Theoretical Computer Science
• Statistics
• High-Dimensional Estimation
• +۷ مورد دیگر

Tselil Schramm is an Assistant Professor in the Department of Statistics at Stanford University, with courtesy appointments in Computer Science and Mathematics. She is actively engaged in research and teaching in theoretical computer science and statistics. Department: Department of Statistics School: School of Humanities and Sciences University: Stanford University Office: CoDa E254 Email: tselil@stanford.edu She earned her PhD from UC Berkeley under Prasad Raghavendra and Satish Rao, followed by postdoctoral work at Harvard and MIT with Boaz Barak, Jon Kelner, Ankur Moitra, and Pablo Parrilo. Her research lies at the intersection of theoretical computer science and statistics, focusing on high-dimensional estimation, information-computation tradeoffs, sum-of-squares algorithms, and random graph theory. She develops algorithms for statistical problems and investigates the boundaries between what is statistically possible and what is computationally feasible. Her recent publications span topics including the overlap-gap property, discrepancy algorithms, robust message passing, semidefinite programming, spectral clustering, and random geometric graphs, appearing in top venues such as STOC, FOCS, COLT, NeurIPS, and The Annals of Statistics. She teaches a range of courses, including Introduction to Statistics (STATS 60), Theory of Statistics II (STATS 300B), and Machine Learning Theory (STATS 214 / CS 228M), reflecting her expertise in both foundational and advanced statistical theory. Runner-up for Best Paper at COLT 2021 Invited to STOC 2022 special issue of SICOMP Invited to SODA 2016 special issue of ACM Transactions on Algorithms Invited to CCC 2019 special issue of Theory of Computing Tselil Schramm advises and collaborates with numerous students and researchers, including Shuangping Li, Misha Ivkov, and Siqi Liu. She has been involved in multiple research grants and projects, particularly in the areas of high-dimensional inference and algorithmic robustness. Her work often bridges theoretical guarantees with practical algorithmic design. She is affiliated with Stanford’s theoretical computer science and statistics research groups, contributing to a vibrant academic environment. Her future work is expected to further explore the limits of efficient computation in statistical settings, with potential applications in machine learning, signal processing, and network analysis.