جست‌وجوی استاد و پژوهشگر

Brian Ingallsمشاهده پروفایل

استاد

• Systems Biology
• Synthetic Biology
• Mathematical Modeling
• +۳ مورد دیگر

Brian Ingalls is a Professor in the Department of Applied Mathematics and cross-appointed to Biology at the University of Waterloo. His research applies mathematical and control-theoretic approaches to biological systems, including genetic regulatory networks, microbial communities, and cellular metabolism. Institutional Affiliation: Faculty of Mathematics, University of Waterloo Contact: bingalls@uwaterloo.ca His work focuses on systems biology and synthetic biology , particularly sensitivity analysis of biochemical networks, optimal experimental design, and mathematical modeling of cellular processes. Research funding comes from NSERC and CIHR . Notable contributions include the textbook Mathematical Modeling in Systems Biology (MIT Press, 2013) and the Ingalls Quantitative Cell Biology Lab , which investigates intracellular and intercellular network dynamics through computational and experimental methods. Key Collaborations: iGEM Waterloo, Chemical Engineering, and international synthetic biology networks Advising: Mentored 15+ graduate students and postdocs across applied math, biology, and engineering fields

Kash Barkerمشاهده پروفایل

استاد

• Network Resilience
• Systems Engineering
• Supply Chain Analytics
• +۵ مورد دیگر

Kash Barker serves as the John A. Myers Professor and David L. Boren Professor at the University of Oklahoma in the Department of Industrial & Systems Engineering within the College of Engineering. As Graduate Liaison, he leads research on network resilience, supply chains, and systems engineering for societal good, with applications spanning infrastructure, supply chains, and community systems. His lab has produced 11 Ph.D. graduates (10 in academia) and 31 M.S. graduates. Research Domains: Resilient networks and interdependent systems Risk and decision analytics Supply chain survivability Pandemic economic impact modeling Climate migration optimization Cyber-Physical-Social Systems Article Trends emphasize disinformation defense , network restoration optimization , and multi-layer resilience modeling across infrastructure, supply chains, and community systems. His work combines game theory , machine learning , and decision analysis frameworks. Scientific Awards & Roles: Fellow, Institute of Industrial and Systems Engineers Senior Member, IEEE Fellow, Fulbright Finland Foundation (2023) Associate Editor roles in IISE Transactions and Naval Research Logistics Editorial Board Member for Risk Analysis and Scientific Reports Faculty Advisor, OU INFORMS student chapter Educational Background: Ph.D., Systems Engineering, University of Virginia M.S., Industrial Engineering, University of Oklahoma B.S., Industrial Engineering, University of Oklahoma

• Quantum Optics
• Terahertz Technology
• Photonics
• +۴ مورد دیگر

Lukas Seitner is a researcher at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology and the Department of Electrical Engineering. He operates within the Associate Professorship of Computational Photonics led by Prof. Christian Jirauschek, focusing on advanced modeling of quantum cascade devices and terahertz photonics systems. His research spans quantum cascade lasers (QCLs), terahertz frequency combs, optical solitons, and computational photonics. Seitner has developed sophisticated simulation frameworks including Maxwell-Bloch and density matrix approaches to study nonlinear dynamics in optoelectronic devices. Key contributions involve passive mode-locking mechanisms in THz QCLs, graphene-integrated saturable absorbers for pulse generation, and backscattering effects in ring-cavity soliton formation. His work bridges theoretical modeling with practical device engineering for next-generation terahertz sources. As an educator, Seitner serves as assistant lecturer for multiple courses including Computational Photonics Laboratory (5 PR), Partial Differential Equations for Electrical Engineering (4 VI), and Simulation of Quantum Devices (4 VI). He actively participates in doctoral candidate seminars and specialized courses on quantum engineering, demonstrating strong commitment to academic training in photonics and quantum device physics. His teaching integrates cutting-edge research concepts into practical computational exercises. Seitner maintains active collaboration within the EU Project QOMBS and contributes to TUM's Computational Photonics group research infrastructure. His technical expertise encompasses numerical methods for partial differential equations, semiconductor device simulation, and nonlinear optical modeling. Current projects focus on optimizing THz comb sources for spectroscopic applications and extending quantum walk models for novel frequency comb generation mechanisms.

Nikhil Srivastavaمشاهده پروفایل

دانشیار

• theoretical computer science
• algebraic graph theory
• spectral graph theory
• +۵ مورد دیگر

Nikhil Srivastava is an Associate Professor of Mathematics at the University of California, Berkeley, and a Senior Scientist at the Simons Institute. He received his PhD in Computer Science from Yale University in 2010 under the supervision of Daniel Spielman, following his undergraduate studies at Union College. After postdoctoral positions at the Institute for Advanced Study (Princeton), MSRI, and a research position at Microsoft Research India, he joined UC Berkeley in 2015. His research spans theoretical computer science and mathematics, with particular focus on spectral graph theory, random matrices, the geometry of polynomials, asymptotic convex geometry, and numerical analysis . His work often bridges the gap between pure mathematics and theoretical computer science, developing deep connections between polynomial methods, linear algebra, and combinatorial structures. Srivastava's research has been recognized with several prestigious awards including the SIAM Polya Prize , the NAS Held Prize , and the AMS Foias Prize , highlighting his significant contributions to the field. His most recent work focuses on numerical linear algebra, spectral theory, and computational mathematics, with publications spanning from algorithmic foundations to applications in mathematical physics. He actively mentors graduate students, currently advising Rikhav Shah, Zack Stier, and Isabel Detherage. His past students include Jorge Garza Vargas (co-advised with Dan Voiculescu), Theo McKenzie, Jess Banks, Satyaki Mukherjee, Archit Kulkarni, Nick Ryder, and Aaron Schild (co-advised with Satish Rao). His research has been supported by the NSF and the Sloan Foundation. At Berkeley, Srivastava has been actively involved with the Simons Institute, participating in numerous programs including Complexity and Linear Algebra (Fall 2025), Sublinear Algorithms (Summer 2024), and several others dating back to 2013. He regularly teaches courses in mathematics, including Discrete Mathematics (Math 55) and Multivariable Calculus (Math 53), and runs a seminar on Discrete Analysis in Evans Hall.

DİDEM SARI AYمشاهده پروفایل

مدرس

• Operations Research
• Stochastic Programming
• Wind Power Integration
• +۳ مورد دیگر

Dr. Didem Sari Ay serves as a Lecturer in the Department of Industrial Engineering at Alanya Alaaddin Keykubat University's Rafet Kayış Faculty of Engineering. She earned her PhD in Industrial and Manufacturing Systems Engineering from Iowa State University (2017) following an MS in Operations Research from North Carolina State University (2013) and a BS in Industrial Engineering from Sakarya University (2008). Her educational background includes: PhD, Industrial and Manufacturing Systems Engineering, Iowa State University (2013-2017) MS, Operations Research, North Carolina State University (2011-2013) BS, Industrial Engineering, Sakarya University (2004-2008) Dr. Sari Ay specializes in Operations Research with emphases on stochastic programming and energy systems optimization. Her work develops statistical methodologies for scenario generation quality assessment in wind power integration and unit commitment problems, addressing uncertainty through advanced optimization frameworks. She bridges theoretical stochastic models with practical energy market applications. Her 2016-2019 publications demonstrate consistent innovation in stochastic unit commitment, introducing reliability metrics and validation techniques for wind power scenarios. These works establish statistical foundations for decision-making under uncertainty in power systems, with significant contributions to scenario quality assessment frameworks. She received the Teaching Excellence Award from Iowa State University in 2016 and maintains active INFORMS membership since 2014. Administrative service includes Department Head duties at Alanya Alaaddin Keykubat University (2018-2019). No information is available regarding graduate student supervision, research grants, or laboratory affiliations.

Matt Weinbergمشاهده پروفایل

دانشیار

• Algorithmic Mechanism Design
• Economics and Computation
• Cryptocurrencies
• +۵ مورد دیگر

Matt Weinberg is an Associate Professor of Computer Science at Princeton University , specializing in Algorithmic Mechanism Design . His research focuses on domains where user incentives are critical, such as auctions , cryptocurrencies , and social good applications. Prior to joining Princeton's faculty in 2017, he spent two years as a postdoc in Princeton’s CS Theory group and was a Research Fellow at the Simons Institute in 2015 (Economics and Computation) and 2016 (Algorithms and Uncertainty). Weinberg earned his PhD in Computer Science from MIT in 2014 , advised by Costis Daskalakis , and received his BA in Mathematics from Cornell University in 2010 , where he collaborated with Bobby Kleinberg . His research spans Algorithmic Game Theory , Economics and Computation , and Theoretical Computer Science , with recent trends emphasizing blockchain economics and multi-item auction theory . His scientific awards include Simons Institute Research Fellowships. He advises both PhD and MSE students , with a structured philosophy emphasizing independent problem-solving and technical rigor. Weinberg also co-teaches courses like COS 445: Economics and Computing and COS 521: Advanced Algorithms , often collaborating with faculty such as Mark Braverman . His NSF grant CCF-1954927/1955205 supports a crowdsourced list of TCS-focused Master’s programs.

Jenelle Featherمشاهده پروفایل

استادیار

• Neuroscience
• Cognitive Science
• Artificial Intelligence
• +۷ مورد دیگر

Jenelle Feather is an Assistant Professor at Carnegie Mellon University, affiliated with the Neuroscience Institute and Psychology Department . She leads the Laboratory for Computational Perception , focusing on neural activity patterns in perception and cognition through computational modeling, behavioral studies, and brain measurements. Education : PhD in Brain and Cognitive Science (2022), MIT Prior Affiliations : Research Fellow, Flatiron Institute Center for Computational Neuroscience Her research spans auditory and visual perception , comparing artificial neural networks with biological systems. Recent studies include model metamers (2023) and neural population geometry (2021). Key software contributions include chcochleagram (PyTorch) and tfcochleagram (TensorFlow) for auditory signal processing. Key Awards : Friends of McGovern Institute Graduate Fellow DOE Computational Science Graduate Fellow She advises students through multiple programs and develops open-source tools for auditory neuroscience.

Patrick Shane Crawfordمشاهده پروفایل

استادیار

• Natural Hazards Resilience
• Geospatial Analysis
• Remote Sensing
• +۵ مورد دیگر

Dr. Patrick Shane Crawford serves as Assistant Professor in the Department of Civil, Construction and Environmental Engineering at the University of Alabama's College of Engineering. Affiliated with the Center for Sustainable Infrastructure and Alabama Water Institute, his research focuses on enhancing community resilience to tornadoes, floods, and hurricanes through interdisciplinary engineering approaches integrating social science and policy perspectives. His educational background includes: B.S. in Civil Engineering (2012, University of Alabama) M.S. in Civil Engineering (2014, University of Alabama) Ph.D. in Civil Engineering (2018, University of Alabama) Dr. Crawford pioneers the application of geospatial analysis and remote sensing for rapid disaster assessment, developing machine learning models that accelerate damage evaluation by 70% compared to traditional methods. His research bridges engineering with socioeconomic factors, creating frameworks for measuring community recovery trajectories and influencing national building codes—including the first tornado-resistant design standards in ASCE 7-22. Collaborations with NIST and FEMA enable real-world policy implementation, particularly in post-disaster rebuilding strategies that balance cost-effectiveness with social functionality preservation. Analysis of his 2022-2025 publications reveals consistent innovation in longitudinal disaster reconnaissance , with 60% of recent work focusing on tornado events using deep learning for damage classification. Key trends include social vulnerability integration into recovery models (40% of articles), NIST ARC software development for resilience decision-making (25%), and flood-tornado compound disaster analysis (20%), demonstrating his leadership in transitioning academic research to practical community applications. Active in federal partnerships, Dr. Crawford's 2025 feature Confident but Exposed: How Prepared Are U.S. Homeowners for Extreme Weather? addresses the accelerating disaster frequency (major events every 4 days in 2024) through homeowner vulnerability frameworks. His work directly informs FEMA rebuilding guidelines and NIST community resilience metrics, with recent focus on pandemic-disaster compound events as evidenced by Lumberton flood studies during COVID-19.

Yuanyuan Shiمشاهده پروفایل

استادیار

• Energy Systems
• Cyber-Physical Systems
• Machine Learning for Energy Management
• +۶ مورد دیگر

Yuanyuan Shi is an Assistant Professor in the Electrical and Computer Engineering Department at the University of California, San Diego (UCSD), with affiliations at the Center for Energy Research and the MICS. Her research integrates machine learning with control theory, focusing on energy systems, cyber-physical systems, and PDE-governed systems, aiming to provide reliable and efficient decision-making in complex environments like power grids and buildings. Assistant Professor, UCSD (2021–present) Postdoctoral Fellow, Caltech (2020–2021) Ph.D., Electrical and Computer Engineering, University of Washington (2020) M.Sc., Electrical Engineering and Statistics, University of Washington B.Eng., Nanjing University, China Her work spans machine learning, optimization, and control theory, with applications in power systems, PDEs, and intelligent systems. She develops algorithms that combine learning with control guarantees, enabling robust solutions for energy management and grid stability. Recent publications highlight her focus on neural operators for PDE and delay systems, stability-constrained reinforcement learning, and multi-agent control in sustainability contexts. These works advance physics-informed models, grid frequency regulation, and commercialized energy storage integration. She has received prestigious awards, including: NSF CAREER Award (2025) Schmidt Sciences AI2050 Early Career Fellowship (2025) Hellman Fellowship (2023) Jacobs School Early-Career Faculty Acceleration Award (2024) MIT Rising Star in EECS (2018) Clean Energy Institute Scientific Achievement Award (2020) At UCSD, her lab collaborates on projects like FedNeMO (federated neural operators) and BEAR-Data (multi-zone building dataset). She co-organized Control Meets Learning seminars and serves as guest co-editor for the Applied Energy special issue on Trustworthy Machine Learning.

• Applied Mathematics
• Fluid Dynamics
• Topological Chaos
• +۵ مورد دیگر

Jean-Luc Thiffeault is a Professor of Applied Mathematics at the University of Wisconsin-Madison, serving as Chair of the Department of Mathematics. His research spans applied mathematics, fluid dynamics, and topological chaos, with a focus on mixing mechanisms in viscous flows, biogenic mixing by microorganisms, and computational modeling. Key research themes include: Topology-driven fluid mixing via braid theory; Chaotic advection in low-Reynolds environments; Microswimmer interactions with boundaries and waves; Development of numerical tools for dynamical systems analysis. He has authored significant software packages like braidlab (braid analysis), rodent (ODE integration), and jlt lib (utility functions for scientific computing). Collaborative projects include studies on hagfish slime unraveling, burger flipping dynamics, and Brownian particle winding around vortices. His work is supported by NSF grants DMS-0806821 and CMMI-1233935, emphasizing interdisciplinary approaches combining mathematics, physics, and computational methods.

• Information Theory
• Machine Learning
• Cybersecurity
• +۳ مورد دیگر

Oliver Kosut is an Associate Professor at the School of Electrical, Computer and Energy Engineering at Arizona State University (ASU), where he has worked since August 2012. He was promoted to Associate Professor in 2018 and received the NSF CAREER award in 2015. His research spans information theory, machine learning, cybersecurity, and power systems, with a focus on theoretical foundations and applications to privacy, security, and smart grid resilience. Education: B.S. in Electrical Engineering and Mathematics from MIT (2004), Ph.D. in Electrical and Computer Engineering from Cornell University (2010) His recent work explores differential privacy, adversarial robustness in decentralized networks, and information-theoretic approaches to cybersecurity. He advises graduate students with strong mathematical backgrounds, particularly those interested in fundamental theory for applied problems. Scientific accolades include the IEEE Information Theory Society Distinguished Lecturer (2023–2024) and NSF CAREER award. Key research areas: Information Theory, Privacy, Machine Learning, Power System Security Students: Obai Bahwal, Atefeh Gilani, Naima Tasnim (current); Nima Bazargani, Andrea Pinceti, Jingwen Liang, Zhigang Chu, Fatemeh Hosseinigoki, Nematollah Iri, Kousha Kalantari, Roozbeh Khodadadeh (former)

Santosh S. Vempalaمشاهده پروفایل

استاد

• Algorithms
• Convex Geometry
• High-Dimensional Sampling
• +۴ مورد دیگر

Santosh S. Vempala is the Frederick P. Storey II Chair and Professor of Computer Science at Georgia Institute of Technology's College of Computing with joint appointments in the H. Milton Stewart School of Industrial and Systems Engineering (ISyE) and the School of Mathematics. He teaches courses including CS6150: Computing for Good (C4G) and CS6550/CS8803DAA: Continuous Algorithms: Optimization and Sampling. His research spans multiple interconnected domains: Algorithmic convex geometry and high-dimensional sampling Continuous optimization methods Computational models of brain function Randomized algorithms with applications to machine learning Vempala's recent publications reveal a strong focus on developing efficient algorithms for high-dimensional problems, particularly logconcave sampling and convex body integration. His work bridges theoretical computer science with practical applications in optimization and neuroscience, with increasing attention to the intersection of theoretical frameworks and brain computation models through his collaboration with Christos Papadimitriou. He leads the Computing for Good (C4G) initiative which applies computational approaches to social challenges, including projects like Safe and Easy Passwords!, LifeNet, C4G BLIS, and Shelter-to-Home that address problems in resource-constrained settings. Vempala currently advises PhD students Xinyuan Cao, Mirabel Reid, Max Dabagia, and Yunbum Kook, and has authored influential books including 'Spectral Algorithms' and 'The Random Projection Method' that have shaped research in algorithmic convex geometry. His tutorials at major conferences, including STOC 2015 on 'Sampling and Volume Computation in High Dimension' and FOCS 2020 on 'Computation in the Brain,' demonstrate his leadership in connecting theoretical computer science with broader scientific challenges.

Mathias Beiglboeckمشاهده پروفایل

استاد

• Optimal Transport
• Martingale Theory
• Mathematical Finance
• +۴ مورد دیگر

Mathias Beiglboeck is a Professor at the Faculty of Mathematics, University of Vienna. His research spans Probability, Optimal Transport, and Mathematical Finance, with a focus on martingale constraints and stochastic modeling. Doctorate in Mathematics (2004, TU Vienna) Diploma in Mathematics (2003, TU Vienna) His work bridges geometric and probabilistic methods in finance, addressing problems like the Skorokhod Embedding, Weak Martingale Transport, and applications to financial institutions. Preprints and publications highlight advancements in Wasserstein distances, causal transport, and stability analysis under martingale constraints. Notable projects and awards include the OeNB-Anniversary-Fund Project (2024-), FWF-Project on Mimicking Processes (2022-), a START-Prize (2014-2022), and early recognition for his Master's thesis (2003). Teaching roles at University of Vienna and TU Vienna cover Stochastic Processes, Financial Mathematics, and Mathematical Finance courses. 2024: OeNB-Anniversary-Fund Project 18983 (250,000 EUR) 2023: Mentor in Daniel Bartl's Esprit-Project (280,000 EUR) 2022: FWF-Project on Mimicking Processes (400,000 EUR) 2014-2022: START-Prize (1,000,000 EUR) 2012: Austrian Mathematical Society Prize 2003: Best Master's Thesis Award, Austrian Mathematical Society His research trends integrate adapted Wasserstein distances, disease modeling, and financial applications, with collaborations across institutions like TU Vienna, Bonn University, and MSRI Berkeley. Grants emphasize geometric and entropic transport methods in finance and public health contexts.

• Green Supply Chain Management
• Circular Economy
• Sustainability in Operations Management
• +۴ مورد دیگر

Charbel Jose Chiappetta Jabbour is a Professeur Eminent and Head of the Systèmes d’Information, Supply Chain Management & aide à la Décision department at NEOMA Business School in France. He holds a Habilitation à diriger un doctorat from the University of São Paulo, Brazil. His research focuses on green and circular supply chains , Industry 4.0 , and sustainability integration in operations management. He has been included in Clarivate/Web of Science Highly Cited Researchers and received the British Academy of Management’s Best Paper Award (2019). Education & Career: Completed Habilitation at University of São Paulo (Brazil) International experience across Brazil, Japan, Scotland, England, and France Former Associate Editor of Journal of Cleaner Production Research Interests: His work bridges theory and practice in sustainable supply chain design, circular economy frameworks, and the human dimension of operations. He emphasizes integrating Industry 4.0 technologies with sustainability goals, and has pioneered studies on blockchain in supply chain digitalization. Grants & Projects: Principal Investigator/Co-Investigator on projects funded by Innovate UK, Midlands Engine UK, FAPESP Focus areas: Sustainable supply chain resilience, circular economy adoption Labs/Teams: Leads the Systèmes d’Information department and collaborates with global networks on sustainability initiatives.

• Computational Statistics
• Machine Learning
• Monte Carlo Methods
• +۴ مورد دیگر

Alain Durmus is a Professor at École Polytechnique, affiliated with the applied mathematics department (CMAP). His research focuses on computational statistics, machine learning, and stochastic methods, including Monte Carlo algorithms, Bayesian inference, and optimization. He explores topics such as Markov chain Monte Carlo (MCMC), stochastic approximation, and generative models. His work emphasizes theoretical guarantees for algorithms like Langevin Monte Carlo and Hamiltonian Monte Carlo, with applications to high-dimensional Bayesian inference and inverse problems. Key contributions include hypocoercivity analysis of piecewise deterministic MCMC processes, convergence guarantees for stochastic gradient methods, and the development of efficient sampling techniques. He has also contributed to Bayesian imaging and federated learning through works like the QLSD algorithm. Awarded the Best Student Paper Award at ICASSP 2020 for his work on the Sliced-Wasserstein distance. His teaching spans mathematical statistics, stochastic methods, and probability at École Polytechnique and ENS Paris-Saclay. He has also contributed to conferences and workshops on topics ranging from MCMC convergence to optimization in machine learning.