جستجوی استاد راهنمای دانشگاهی

Yingfei Xiong is an active Associate Professor at Peking University, China, specializing in software engineering and programming languages. With a consistent research trajectory from 2013 through 2026, Xiong has established themselves as a prominent figure in the software engineering research community, regularly contributing to top-tier conferences including SPLASH, ICSE, ASE, and PLDI. Dr. Xiong's research primarily focuses on program synthesis, automated program repair, and software analysis techniques. Their work bridges theoretical programming language concepts with practical software engineering applications, particularly in developing novel approaches for code generation, bug fixing, and program optimization. The research demonstrates strong interdisciplinary connections between traditional software engineering, programming languages theory, and emerging AI techniques. Analysis of Xiong's publication trends reveals a clear evolution in research focus, beginning with foundational work in API transformations and program adaptation around 2013-2016, shifting toward program repair techniques from 2017-2020, and most recently incorporating machine learning and neural approaches into program synthesis and repair (2021-2026). The work consistently addresses practical challenges in software development while maintaining theoretical rigor, with increasing integration of AI techniques in recent years. Dr. Xiong has served in various leadership roles across the software engineering conference ecosystem, including program committee membership and session chair positions at major conferences. Their extensive service demonstrates recognition by peers as a subject matter expert in software engineering and programming languages research. While specific grant information isn't detailed in the provided text, the sustained publication record suggests successful research funding.

• Software Engineering
• Programming Languages

Benjamin Recht is a Professor in the Department of Electrical Engineering and Computer Sciences and Department of Statistics at the University of California, Berkeley. Previously, he was an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin-Madison. Recht received his BS in mathematics from the University of Chicago and his MS and PhD from the MIT Media Laboratory, followed by a postdoctoral fellowship at Caltech's Center for the Mathematics of Information. His research interests span Machine Learning, Optimization, Control Theory, and Statistics , with a focus on both theoretical foundations and practical applications. Recht's work addresses fundamental questions in reproducibility, generalization, and robustness of machine learning systems, while also developing novel methods for control, computer vision, and data analysis. Recht's recent publications reveal a strong focus on reproducibility in machine learning , with papers like "The Mechanics of Frictionless Reproducibility" (2024), alongside continued contributions to statistical learning theory ("Interpolating Classifiers Make Few Mistakes", 2023) and computer vision ("Plenoxels", 2022; "K-planes", 2023). His work increasingly addresses societal implications of AI , including papers on systemic harm detection and post-deployment evaluation. NSF Career Award Alfred P. Sloan Research Fellowship 2012 SIAM/MOS Lagrange Prize in Continuous Optimization Presidential Early Career Award for Scientists and Engineers 2014 Jamon Prize 2015 William O. Baker Award for Initiatives in Research 2017 and 2020 NeurIPS Test of Time Awards Recht has advised numerous PhD students who have gone on to faculty positions at top universities and research roles at leading technology companies. His work on optimization algorithms has been widely influential, including the development of methods like HOGWILD! for parallel stochastic gradient descent. He co-founded the Conference on Learning for Decision and Control and has served on editorial boards for the Journal of Machine Learning Research and Mathematical Programming. His research group spans both theoretical and applied work, with connections to healthcare (adaptive medication tapering), computer vision (radiance fields), and social impact (systemic harm detection in deployed systems).

• Machine Learning
• Optimization
• Statistics
• +۵ مورد دیگر

Dr. Arno Onken is a Lecturer (Assistant Professor) in Data Science for Life Sciences at the School of Informatics, University of Edinburgh, where he is also affiliated with the Institute for Adaptive and Neural Computation. He leads a research group focused on developing machine learning and statistical methods for modeling neural activity and analyzing large-scale neuroscience data. His work bridges artificial intelligence and computational neuroscience. His research interests lie at the intersection of machine learning, statistics, and neuroscience. He develops flexible probabilistic models such as copulas and Gaussian processes, deep learning architectures like Vision Transformers for brain activity prediction, and matrix/tensor factorization techniques for dimensionality reduction in neural datasets. His group aims to uncover interpretable structure in complex neural recordings and understand how behavior and cognition are encoded in population activity. The recent publications reflect a strong trend in combining modern deep learning with classical statistical modeling to analyze large-scale neural recordings. His work spans from foundational methods in copula modeling and information theory to applications in predicting visual cortex responses and modeling brainstem-hippocampus interactions across sleep states. The research has been published in top venues including NeurIPS, CVPR, eLife, and PLoS Computational Biology. Dr. Onken actively supervises PhD students and has developed several open-source scientific software packages, including the Mixed Vine Toolbox and Population Spike Train Factorization Toolbox. He teaches core courses in Machine Learning and Pattern Recognition and Data Mining and Exploration at the University of Edinburgh.

• Data Science
• Machine Learning
• Computational Neuroscience
• +۷ مورد دیگر

Arash Asadpour Rahimabadi is an Associate Professor at the N. P. Loomba Department of Management within the Zicklin School of Business at Baruch College, CUNY . His research bridges Operations Research and Management Science , focusing on algorithmic design, dynamic pricing, and optimization in gig economy platforms. Education: Ph.D. in Operations Research, Stanford University (2010) BSc in Computer Engineering, Sharif University of Technology (2004) His research interests include stochastic optimization , submodular maximization , marketplace stability , and fair allocation . Recent work explores dynamic pricing in extreme value regimes , shared ride sustainability , and regulation of gig economy platforms . His scientific contributions span algorithmic game theory, combinatorial optimization, and resource allocation, with key publications in Management Science and Operations Research . Current projects analyze escrow payment mechanisms , shared mobility efficiency , and hotel reservation systems . Scientific Awards Best Paper Award, ACM-SIAM Symposium on Discrete Algorithms (SODA), 2010 1st Rank in Iran’s National Graduate Entrance Exam in Computer Engineering, 2004 Silver Medals in Iranian National Olympiads in Informatics, 1999–2000 He serves on graduate and PhD committees at CUNY and has reviewed for journals including Management Science and Operations Research . His teaching includes courses like Decision Models and Analytics and Advanced Discrete Optimization .

• Operations Research
• Management Science
• Dynamic Pricing
• +۴ مورد دیگر

Jan Draisma is a full professor of Mathematics at the University of Bern and a part-time full professor of Applied Algebra and Geometry at Eindhoven University of Technology (TU/e), where he is affiliated with the Department of Mathematics and Computer Science, specifically in Discrete Algebra and Geometry and Coding Theory and Cryptology. He obtained his Master's and Ph.D. degrees from TU/e cum laude and held a postdoctoral position at the University of Basel (2002–2005). He returned to TU/e as an assistant professor, later advancing to associate professor (2011–2016), and served as a part-time full professor at VU Amsterdam (2015–2016). His research focuses on the interplay between combinatorics, statistics, and algebraic geometry. Key areas include tropical geometry, algebraic statistics, symmetric systems of polynomial equations in infinitely many variables, and representation stability. His work often explores the structure of infinite-dimensional algebraic objects and their finite approximations. The most recent publications highlight trends in polynomial functors, topological Noetherianity, amoebas of linear spaces, and the geometry of tensor representations. These works reflect a deep integration of algebraic geometry with combinatorics and category theory, emphasizing stabilization phenomena and symmetry in algebraic structures. NWO Vici Award: Stabilisation in Algebra and Geometry (2015) NWO Vidi Award: Finite thanks to symmetry (2010) Draisma has received significant research funding, including the NWO Vidi and Vici grants, and two NWO Free Competition grants (2008, 2012). He has supervised 16 students and is actively involved in the academic community as an associate editor for Experimental Mathematics, SIAM Journal on Applied Algebra and Geometry, and Linear and Multilinear Algebra. He has held leadership roles in major conferences such as MEGA 2015 and SIAM AG 19. He is affiliated with the research groups in Discrete Algebra and Geometry and Coding Theory and Cryptology at TU/e and leads research activities centered on algebraic methods in discrete mathematics and statistics.

• Algebraic Geometry
• Combinatorics
• Algebraic Statistics
• +۸ مورد دیگر

Professor Christine Currie is a Professor of Operational Research within the School of Mathematical Sciences at the University of Southampton. She is a Fellow of the Alan Turing Institute and previously served as Director of the Centre for Operational Research, Management Science and Information Systems (CORMSIS). Her research is primarily funded by EPSRC and spans healthcare, disaster relief, and pricing optimisation. Research Interests: Simulation Optimisation Healthcare Management Decision Making Under Uncertainty Disaster Relief Logistics Optimal Pricing and Revenue Management Her recent work focuses on real-time simulation for emergency departments, infectious disease modelling, patient flow optimisation, food relief procurement in Indonesia, and robust pricing models in transportation and leisure sectors. The trend in her publications shows a strong emphasis on applied operational research with societal impact, particularly in digital twin integration and stochastic optimisation under uncertainty. Scientific Awards: Companion of Operational Research (2024) Professor Currie actively supervises PhD students and has secured multiple research grants, primarily from EPSRC. She has led projects such as 'Dial-a-Ride', 'CREST-OR', and 'Designing a resilient food supply network for natural disasters in West Java, Indonesia'. She also collaborates with external organisations and has supervised student projects with industry partners. Leadership and Editorial Roles: Editor-in-Chief, Journal of Simulation (2015–2024) Member, Editorial Board, Royal Society Open Science (2021–present) Co-chair, OR63 National Operational Research Conference (2021) Member, Operational Research Society Research Panel She is actively involved in the operational research community and leads interdisciplinary research teams including the Operational Research group, Institute for Life Sciences, CORMSIS, and the Centre for Healthcare Analytics.

• Operational Research
• Simulation Optimisation
• Healthcare Management
• +۹ مورد دیگر

Pascal Vincent is an Associate Professor at the Department of Computer Science and Operational Research , University of Montreal, and a key member of the Montreal Institute for Learning Algorithms (MILA) . He holds a PhD in Computer Science from the University of Montreal and has been pivotal in advancing machine learning and artificial perception. Education: PhD in Computer Science (University of Montreal, 2003) His research spans machine learning , deep learning , representation learning , and neural networks , focusing on unsupervised methods and geometrically inspired algorithms. He explores how intelligent systems can autonomously build meaningful representations from raw data, driven by principles like the manifold hypothesis . Key projects include generative stochastic networks , contractive autoencoders , and high-dimensional sequence transduction . His work has resulted in 15+ recent publications in top venues like NIPS, ICML, and CVPR. Scientific Awards : Best student-paper award at ICML 2012 Honorable mention at NIPS 2011 Funded by FCI, FRQNT, CRSNG, CIFAR, and IBM Pascal has supervised 15+ doctoral and Master’s students , including Florian Bordes, Tom Bosc, and Nicolas Boulanger-Lewandowski, across topics like representation learning and generative models . He is also a co-founder of the UNIQUE (Union Neurosciences & Intelligence Artificielle Québec) research consortium.

• Machine Learning
• Representation Learning
• Deep Learning
• +۶ مورد دیگر

Chaopeng Shen is a Professor in the Department of Civil and Environmental Engineering at Pennsylvania State University. His research bridges hydrology with state-of-the-art deep learning and differentiable modeling techniques, focusing on advancing our understanding of hydrologic cycles and their interactions with ecosystems, energy, and carbon cycles. He leads the Multi-scale Hydrology, Processes and Intelligence group (MHPI) and has developed the Process-based Adaptive Watershed Simulator (PAWS) for large-scale hydrologic modeling. Shen's work emphasizes physics-informed machine learning , where deep learning components are integrated with process-based equations through differentiable modeling. This approach enables training neural networks using big data while respecting physical laws, leading to improved generalizability and robustness. His group has demonstrated advantages of differentiable models in rainfall-runoff prediction, routing, ecosystem modeling, and water quality studies. Notably, his team's deepLDB project addresses landslide prediction using AI and big datasets. Recent publications highlight his contributions to global water modeling (grid-LSTM, differentiable Muskingum-Cunge routing), extreme flood forecasting (probabilistic diffusion models), and hydrologic uncertainty quantification . Shen actively engages in interdisciplinary collaborations through the PRISM Cooperative Institute, which aims to integrate multi-domain data for systemic risk assessment. His group has advised students including Dapeng Feng, Wen-Ping Tsai, Kuai Fang, Xinye Ji, and Tasnuva Mahjabin. Shen's research is supported by the National Science Foundation (NSF), Department of Energy (DoE), USGS, Google.org, and the Gates Foundation. He serves as Editor for the Journal of Geophysical Research - Machine Learning & Computation and Chief Editor for Frontiers in Water: Water & AI. His open-source software tools like PAWS and deepLDB are available through dedicated project websites.

• Hydrology
• Deep Learning
• Physics-Informed Machine Learning
• +۹ مورد دیگر

Dr. Alexia Sampri is a Research Associate in Health Data Science at the University of Cambridge, affiliated with the Department of Public Health and Primary Care and the Cardiovascular Epidemiology Unit (CEU). Her work explores the intersection of AI, epidemiology, and population health data integration. Education : BEng and MEng in Environmental Engineering (Democritus University of Thrace), MSc in Big Data with distinction (University of Stirling), PhD in Health Informatics (University of Manchester). Her research focuses on leveraging electronic health records, probabilistic methods, and machine learning to study long-term effects of diseases like COVID-19, environmental health impacts, and prediction modeling. She has collaborated on international projects, presented at conferences, and contributed to teaching UG/PG courses. Awards : Advance HE Fellowship for teaching contributions.

• Health Data Science
• Epidemiology
• Machine Learning
• +۶ مورد دیگر

Andrzej Murawski is a Professor of Computer Science at the University of Oxford and a Tutorial Fellow at Worcester College . His research focuses on the semantics of programming languages and software verification, with applications in automata theory, probabilistic computation, and concurrency. Current affiliations: University of Oxford, SIGLOG Vice-Chair, FoSSaCS Steering Committee Research interests: Game semantics, higher-order recursion, probabilistic systems, differential privacy Recent publications address probabilistic verification, equivalence checking, and game semantics for concurrent systems. He has chaired program committees for conferences such as ESOP and PERR, and his work has earned recognition like the POPL 2025 Distinguished Paper Award . Current students : Benedict Bunting, Haoxuan Yin Past students : Conrad Cotton-Barratt, David Hopkins, Guanyan Li, Dominik Wagner, Fabian Zaiser

• Semantics of Programming Languages
• Automata Theory
• Software Verification
• +۴ مورد دیگر

Dr. Partha Narayan Mishra is a Senior Lecturer at the School of Civil Engineering, The University of Queensland, with expertise in geotechnical engineering and electromagnetic soil characterization. He holds a PhD in Geotechnical Engineering (2020) and dual master's/bachelor's degrees (2015) from National Institute of Technology Rourkela, India. PhD Thesis: Soft soil characterization and improvement for reclaimed land application (2020) Dual Degree: B.Tech. Hons. in Civil Engineering and M.Tech. in Geotechnical Engineering His research focuses on soft soil improvement, unsaturated soil mechanics, electromagnetic characterization of geomaterials, biomediated geotechnical engineering, and clay barrier systems for waste disposal. He has published 30+ articles in top-tier journals and conferences, with recent work on mine waste utilization, MSE wall stability, and electromagnetic dewatering techniques. He co-supervises 2 PhD and 5 Master's students at UQ, having previously guided 1 PhD, 1 Master's, 2 Bachelor's, and 3 summer research theses. He initiated the global AGERP lecture series (2020), reaching 125+ countries, and holds teaching certifications from the Higher Education Academy (UK). Professional affiliations include the Australian Geomechanics Society, ISSMGE, IGS, and ASCE. He has reviewed 50+ journal articles and held leadership roles in UQ committees.

• Geotechnical Engineering
• Soft Soil Improvement
• Unsaturated Soil Mechanics
• +۴ مورد دیگر

Priya L. Donti is an Assistant Professor at MIT's Department of Electrical Engineering and Computer Science (EECS) and Laboratory for Information and Decision Systems (LIDS). She co-founded and chairs Climate Change AI , a global nonprofit focusing on climate-AI intersections. Education: Ph.D. in Computer Science & Engineering & Public Policy, Carnegie Mellon University (advised by Zico Kolter and Inês Azevedo) Undergraduate in Computer Science & Math, Harvey Mudd College Research Focus: Machine learning for high-renewables power grids, incorporating physics and constraints into deep learning. Key areas include robust optimization, control systems, and climate-AI alignment. Article Trends: 2024-2022 works emphasize climate mitigation through AI (keywords: environmental science, AI ethics, policy modeling), while 2019-2021 studies focus on grid stability (power engineering, optimization) and hybrid AI-logic systems (symbolic reasoning, constraint handling). Scientific Recognition: MIT Technology Review 35 Innovators Under 35 (2021) Vox Future Perfect 50 (2023) Schmidt Sciences AI2050 Early Career Fellowship ACM SIGEnergy Doctoral Dissertation Award (2022) Best paper/poster awards at ACM e-Energy 2021 and PECI 2019 Her group at MIT develops physics-informed ML for power grids, with funding from NSF, DOE, and Center for Climate and Energy Decision Making. She actively advises prospective students through MIT EECS applications.

• Machine Learning
• Power Systems
• Physics-Informed Machine Learning
• +۳ مورد دیگر

Gregory F. Lawler is the George Wells Beadle Distinguished Service Professor in the Department of Mathematics at the University of Chicago. He also maintains appointments in the Department of Statistics and has affiliations with Computational and Applied Mathematics and Financial Mathematics programs. His academic background includes: B.A. (1976) from the University of Virginia Ph.D. (1979) from Princeton University under Edward Nelson Professor Lawler is a leading researcher in probability theory with a focus on conformally invariant processes , particularly the Schramm-Loewner Evolution (SLE) and various forms of random walks. His work bridges pure mathematics and statistical physics, establishing rigorous connections between discrete models and their continuous scaling limits. He has made fundamental contributions to understanding loop-erased random walks, Brownian motion, and the geometric properties of random curves, with applications in statistical mechanics for understanding two-dimensional critical phenomena. Analysis of his recent publications reveals a sustained focus on the mathematical foundations of SLE, with particular attention to natural parametrization, loop measures, convergence questions, and the relationship between discrete models and their continuous limits. His work consistently combines complex analysis with probabilistic methods to establish rigorous results about random curves. Professor Lawler is actively involved in mentoring through the University of Chicago's REU program and has developed extensive educational materials for students at all levels. His notes on probability theory and stochastic calculus are widely used resources within the mathematics community. He has authored several influential books including "Random Walk and the Heat Equation," "Conformally Invariant Processes in the Plane," and "Random Walk: A Modern Introduction" (with Vlada Limic), which have become standard references in probability theory.

• Probability Theory
• Random Walks
• Schramm-Loewner Evolution
• +۳ مورد دیگر

Alex Scott is a Professor of Mathematics at the Mathematical Institute, University of Oxford, with active research contributions evidenced by publications through 2025. His work is centered within the university's Mathematical Institute, a key hub for theoretical mathematics research. Scott's research specializes in combinatorics, particularly graph theory and probabilistic methods. His investigations span structural graph theory, extremal combinatorics, and random graph models, addressing fundamental questions about graph minors, forbidden substructures, and combinatorial optimization. This focus reflects deep engagement with discrete mathematical structures and their theoretical applications. Analysis of his publication timeline (2014-2025) reveals consistent contributions to graph theory, with recent work emphasizing subdivisions, stable sets, and graph reconstruction algorithms. Earlier research examined Erdős-Hajnal properties, deviations in random graphs, and dimensional properties of posets, demonstrating both depth and evolution in his scholarly focus. No scientific awards were documented in the source material. The available information does not specify any advised students or research grants, though his extensive publication record suggests significant collaborative research activity. Scott is affiliated with the Combinatorics research group at Oxford's Mathematical Institute, contributing to one of the world's leading centers for discrete mathematics research through theoretical investigations and academic collaboration.

• Combinatorics
• Graph Theory
• Probability

Huan Lei is an Assistant Professor at Michigan State University, holding a joint appointment in the Department of Computational Mathematics, Science and Engineering and the Department of Statistics and Probability. He earned his Ph.D. in Applied Mathematics from Brown University in 2012 under George Karniadakis and a B.S. in Special Class for the Gifted Young from the University of Science & Technology of China in 2005. His research integrates scientific machine learning with numerical analysis to develop structure-preserving algorithms for partial and stochastic differential equations arising in multi-scale systems. His work spans multi-scale modeling , non-Markovian dynamics , coarse-grained molecular simulations , and data-driven parameterization . Recent publications focus on learning generalized Langevin equations with state-dependent memory, consensus-based free energy surfaces, and non-equilibrium coarse-grained models. His team applies these methods to fluid dynamics, biomolecular solvation, and climate systems. NSF CAREER Award (2021) Brown University Dissertation Fellowship (2012) He advises graduate and undergraduate researchers and seeks Ph.D. candidates with expertise in numerical analysis or scientific computing. His group receives funding from NSF, DOE, Ford, and MSU Foundation.

• Computational Mathematics
• Numerical Analysis
• Scientific Machine Learning
• +۴ مورد دیگر