جستجوی استاد راهنمای دانشگاهی

• Operations Research
• Management Science
• Dynamic Pricing
• +۴ 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 .

Jan Draismaمشاهده پروفایل

استاد

• Algebraic Geometry
• Combinatorics
• Algebraic Statistics
• +۸ 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.

Christine Currieمشاهده پروفایل

استاد

• Operational Research
• Simulation Optimisation
• Healthcare Management
• +۹ 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.

Pascal Vincentمشاهده پروفایل

دانشیار

• Machine Learning
• Representation Learning
• Deep Learning
• +۶ 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.

Chaopeng Shenمشاهده پروفایل

استاد

• Hydrology
• Deep Learning
• Physics-Informed Machine Learning
• +۹ 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.

Alexia Sampriمشاهده پروفایل

پژوهشگر

• Health Data Science
• Epidemiology
• Machine Learning
• +۶ 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.

Andrzej Murawskiمشاهده پروفایل

استاد

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

Partha Narayan Mishraمشاهده پروفایل

مدرس ارشد

• Geotechnical Engineering
• Soft Soil Improvement
• Unsaturated Soil Mechanics
• +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.

• Machine Learning
• Power Systems
• Physics-Informed Machine Learning
• +۳ 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.

Gregory F. Lawlerمشاهده پروفایل

استاد

• Probability Theory
• Random Walks
• Schramm-Loewner Evolution
• +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.

Alex Scottمشاهده پروفایل

استاد

• Combinatorics
• Graph Theory
• Probability

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.

Huan Leiمشاهده پروفایل

استادیار

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

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.

Kostas Papakonstantinouمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Civil Engineering
• Computational Mechanics
• +۹ مورد دیگر

Kostas Papakonstantinou is an Associate Professor in the Department of Civil Engineering at Penn State University, affiliated with the College of Engineering. His research bridges Artificial Intelligence (AI) with Civil Engineering, focusing on uncertainty quantification and decision-making under uncertainty. Research Areas: Uncertainty Quantification Stochastic Control Deep Reinforcement Learning Bayesian Analysis Nonlinear Filtering Computational Mechanics Infrastructure Management Rare Events Quantification His work emphasizes AI-driven solutions for structural life-cycle management, infrastructure systems, and autonomous operations. Funded by the NSF and USDOT, his projects include AI-enabled fiscally constrained life-cycle asset management and Deep reinforcement learning for multi-asset infrastructure management . Scientific Awards: NSF CAREER Award: Optimal engineering decision-making under uncertainties for enhanced structural life-cycle He teaches graduate courses on Uncertainty and Reliability in Civil Engineering (CE 566) and Computational Analysis of Randomness in Engineering (CE 597) .

• Conformal Field Theory
• Quantum Field Theory
• Renormalization Group
• +۴ مورد دیگر

Slava Rychkov is a Permanent Professor of Theoretical Physics at the Institut des Hautes Études Scientifiques (IHES), a position he has held since 2017. He specializes in strongly coupled quantum and conformal field theories, with applications across high energy physics, statistical mechanics, and condensed matter physics. His current research focuses on the conformal bootstrap and renormalization group techniques, including both perturbative and nonperturbative methods like tensor network renormalization. Education: Ph.D. in Physics, Princeton University (2002) Master of Science, Moscow Institute of Physics and Technology (1996) Recent research highlights include a groundbreaking connection between Deligne categories and symmetries of probabilistic loop ensembles in statistical physics, and a novel method for analytic continuation of Euclidean CFTs to Lorentzian signature. His work on the 2+ϵ expansion challenges established assumptions about critical exponents in 3D systems. Publications span topics from tensor renormalization group methods to rigorous mathematical approaches in the conformal bootstrap program. Scientific Awards: Jacques Solvay International Chair in Physics (2025) Grand Prix Mergier-Bourdeix, French Academy of Sciences (2019) New Horizons in Physics Prize (2014) As Deputy Director of the Simons Collaboration on the Nonperturbative Bootstrap, Rychkov leads efforts to rigorously analyze conformal field theories. His former advisees include prominent researchers at institutions like EPFL, Princeton, and Università di Genova. Current projects focus on resolving fundamental questions about critical phenomena and phase transitions using advanced mathematical physics tools.

• Computational Social Choice
• Algorithmic Game Theory
• Fairness in Machine Learning
• +۳ مورد دیگر

Anson Kahng is an Assistant Professor in the Department of Computer Science and the Goergen Institute of Data Science at the University of Rochester. He previously held postdoctoral positions at the University of Toronto and completed his PhD at Carnegie Mellon University under the supervision of Ariel Procaccia, focusing on computational social choice. PhD, Computer Science, Carnegie Mellon University Undergraduate degree, Computer Science, Harvard College His research explores the intersection of computer science and democracy, developing frameworks like virtual democracy and liquid democracy while analyzing fairness in participatory budgeting and voting systems. He combines theoretical analysis with empirical methods, emphasizing interdisciplinary collaboration. Recent work includes advancements in ranked choice voting optimization, fairness metrics for elections, and structural analysis in cryo-electron tomography. He has published in top venues such as IJCAI, AAAI, NeurIPS, and ACM Transactions on Economics and Computation. NeurIPS 2019 Spotlight Presentation (top 2.5% of submissions) Kahng advises PhD students Alina Chadwick and Joe Saber, and has mentored multiple undergraduate researchers. He teaches courses on algorithmic game theory and computational statistics at the University of Rochester.