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Tomasz MałolepszyView profile

Lecturer

• Nonlinear Integral Equations
• Fixed Point Theory
• Wave Propagation
• +7 more

Dr. Tomasz Małolepszy is a faculty member at the Department of Mathematics within the Faculty of Mathematics, Computer Science and Econometrics at the University of Zielona Góra. His academic work spans multiple specialized research domains in pure and applied mathematics, with particular emphasis on nonlinear analysis and mathematical modeling. His research interests form a cohesive portfolio centered around: Volterra's nonlinear integral equations (existence of nontrivial/explosive solutions, explosion time estimation) Iterative methods for fixed point problems in Hilbert spaces Nonlinear wave propagation including Korteweg-de Vries equation generalizations Game theory applications in multigenerational and stochastic contexts Approximation theory using Fourier series and orthogonal expansions His teaching portfolio demonstrates deep mathematical expertise through courses in differential equations, partial differential equations, optimization methods, mathematical programming, operational research, and numerical methods. The research profile indicates strong methodological connections between his theoretical work in operator theory on locally convex spaces and practical applications in solidification processes and data transmission security. His scientific activity bridges pure mathematics with interdisciplinary applications, particularly evident in projects involving: Secure data transmission in e-business and space communication Privacy standards for supply chain data exchange Mathematical modeling of nonlinear wave dynamics Applications of computer science in educational contexts using free tools

• Epidemiology
• Public Health
• Network Science
• +4 more

Jessica Davis serves as a Research Assistant Professor in the Department of Public Health and Health Sciences at Northeastern University's Bouvé College of Health Sciences and is an active member of the MOBS Lab within the Network Science Institute. Her interdisciplinary work integrates network science methodologies with epidemic modeling to develop practical tools for public health policy and pandemic preparedness, with significant contributions during the COVID-19 pandemic. Dr. Davis earned her PhD in Network Science from Northeastern University, where her dissertation research examined cryptic transmission patterns and the efficacy of travel restrictions during the initial phases of the COVID-19 outbreak. This foundational work established her expertise in bridging theoretical network analysis with real-world public health applications. Her research program centers on two interconnected domains: Biological and Health Systems , which investigates biological interdependencies through network medicine, systems epidemiology, and public health infrastructure; and Complex Systems Forecasting , which develops network-based computational models for predicting epidemic trajectories and supporting evidence-based decision-making. These areas converge in her development of open-source tools like Epydemix and innovative surveillance frameworks using aircraft wastewater networks. Analysis of her 15 most recent publications (2021-2025) reveals consistent methodological evolution toward real-time modeling with explicit uncertainty quantification, increasing focus on multi-scale heterogeneity in disease transmission, and growing emphasis on practical implementation of forecasting systems. Her work demonstrates strong continuity in applying network science to epidemic dynamics while expanding into influenza surveillance, variant tracking, and vaccine impact assessment. No scientific awards or major honors are documented in the available materials, though her research has generated significant policy impact through presentations at venues like the Rhodes Policy Summit and collaborations with governmental agencies. Dr. Davis has secured substantial research funding including a Gates Foundation partnership investigating equitable global vaccine distribution strategies. While specific advisee relationships aren't publicly listed, she actively contributes to academic training through the MOBS Lab's hiring of computational statistics researchers and participation in the Network Science Student Research Symposium. The MOBS Lab, directed by Alessandro Vespignani and jointly affiliated with Physics, Health Sciences, and Computer Sciences departments, operates as a hub for computational epidemiology with physical locations in Boston, London, and Portland. Current lab initiatives focus on developing mathematical models for complex network systems with immediate applications to pandemic response, including the aircraft-based wastewater surveillance network featured in her 2025 Nature Medicine publication.

• Modernism
• Italian Literature
• 20th Century Literature
• +4 more

Massimiliano Tortora is an Associate Professor in the Department of Modern Literature and Culture at Sapienza University of Rome. He teaches Contemporary Italian Literature across multiple degree programs including Modern Philology, Modern Letters, and Cultural Studies. His institutional role extends to national educational leadership as he serves as the national coordinator for MOD-Scuola. His research centers on 20th century Italian literature with particular emphasis on modernism, novella studies, and cultural policies. Tortora approaches these fields through both theoretical-comparative frameworks and philological-textual analysis. His work frequently examines the intersection of literary form with historical context, especially regarding the transmission and publication of texts. Tortora's editorial leadership includes directing L'Ellise. Studi storici di Letteratura italiana and Allegoria. Per uno studio materialistico della letteratura , both ranked in Category A. He co-directs the international Centre for European Modernism Studies (CEMS) which connects approximately twenty Italian and international universities. His research output demonstrates consistent engagement with modernist theory, author studies (particularly Svevo, Tozzi, and Montale), and 20th century narrative forms. As an academic leader, he serves on multiple editorial boards including Levia Gravia and OBLIO , and co-directs the "European Modernism" book series. He is also co-scientific director of the National Edition of Federigo Tozzi's Works and manages the Caetani Literary Manuscript Collection at Fondazione Camillo Caetani.

N. Bora KeskinView profile

Associate Professor

• Dynamic pricing
• revenue management
• statistical learning
• +5 more

N. Bora Keskin serves as Associate Professor of Business Administration at Duke University's Fuqua School of Business, specializing in data-driven optimization for dynamic pricing, revenue management, and operational systems. His work bridges theoretical operations research with practical applications in evolving market environments. His research focuses on developing machine learning and statistical methods for pricing under demand uncertainty, with emphasis on perishable inventory, platform operations, and service management. Current investigations include blockchain-enabled supply chain transparency, smart meter-based electricity pricing, and multi-agent learning in competitive markets, demonstrating consistent innovation in integrating high-dimensional data with classical optimization frameworks. Recent publications reveal a trajectory toward interdisciplinary applications, combining reinforcement learning with stochastic modeling to address challenges like reference price effects, information asymmetry in insurance, and congestion in two-sided platforms. Key themes involve personalization, nonstationary demand learning, and incentive design in complex systems. Dr. Keskin's scientific contributions have been recognized with prestigious awards including: Winner, MSOM Young Scholar Prize (2024) Winner, Lanchester Prize (2019) Winner, Triangle Impact Challenge (2021) Markov Lecture Discussant, INFORMS Applied Probability Society (2023) Multiple best paper awards across INFORMS conferences (2020-2024) While specific doctoral student mentorship details and grant funding information are not provided in available materials, his collaborative research spans institutions including Chicago Booth and UNSW, with works featured in Duke Fuqua Insights and INFORMS publications. No dedicated research labs or teams are explicitly referenced in the source text.

Renato BruniView profile

Associate Professor

• Optimization
• Machine Learning
• Bioinformatics
• +10 more

Renato Bruni is Associate Professor at the Department of Computer, Control and Management Engineering of Sapienza University of Rome , Italy. He teaches in the Management Engineering and Bioinformatics study programs. His research focuses on Optimization, Machine Learning, and Bioinformatics , with applications spanning spacecraft control, biomedical engineering, and financial portfolio management. Research Interests His scientific activity is centered on: Combinatorial Optimization Data Mining and Classification Derivative-free Optimization Computational Molecular Biology Information Reconstruction Recent Research Trends The analysis of his publications reveals a strong focus on: Power distribution network optimization Spacecraft attitude control via mathematical programming Higher education data quality and institutional heterogeneity Robust classification with limited training data Stochastic dominance in portfolio selection Physician scheduling optimization Grants and Collaborations Principal Investigator in Sapienza-funded projects (2013–2019) Member of EU H2020 RISIS 2 project (2019–2022) Collaboration with international experts: Peter L. Hammer (Rutgers), Fabio Tardella (Sapienza), and Istat (Italian National Statistical Institute)

Erkut SönmezView profile

Associate Professor

• Operations Management
• Supply Chain Management
• Agribusiness
• +6 more

Erkut Sönmez serves as Associate Professor of Supply Chain Management and Analytics in the College of Business at the University of Nebraska-Lincoln. His research integrates operations management with real-world applications in food systems, energy supply chains, and financial services, utilizing advanced analytical methods to solve complex capacity management problems. Education: Ph.D., Operations Management, Carnegie Mellon University, Tepper School of Business M.S., Operations Management, Carnegie Mellon University, Tepper School of Business B.S., Industrial Engineering, Middle East Technical University, Turkey Sönmez's research focuses on capacity management challenges arising from new business models and technologies, particularly in food and energy supply chains and banking operations. He employs queueing theory, simulation, and stochastic optimization to derive counter-intuitive managerial insights for strategic and tactical decision-making. His work reveals critical tradeoffs in volunteer staffing for gleaning operations, LNG supply chain optimization, and risk-efficiency balances in banking systems. Publication trends show consistent contributions to high-impact journals including Operations Research , Manufacturing and Service Operations Management , and Food Policy , with emphasis on sustainable operations in food waste reduction (40% of U.S. food is wasted while 14 million households face food insecurity) and energy logistics. His research bridges theoretical rigor with practical applications, frequently covered by media outlets like Huffington Post and Perishable Pundit . Scientific Awards: Distinguished Research Award, College of Business, University of Nebraska-Lincoln (2019) William Larimer Mellon Fellowship for doctoral studies Yasar Holding High Academic Achievement Fellowship Sönmez actively contributes to academic service as reviewer for Operations Research , Management Science , and other top journals, and as judge for POMS and M&SOM competitions. His research on LNG supply chains received support from Qatar National Research Fund's NPRP award. He collaborates extensively with organizations including Boston Area Gleaners, Food Bank of the Southern Tier, Cornell University, and Boston College to translate research into operational improvements for food rescue systems. As an educator, he teaches Supply Chain Logistics Management and Operations and Supply Chain Management courses while serving on university committees including Recruiting, Grade Appeal, and as Independent Study Advisor. His work demonstrates how community-focused operational innovations can simultaneously address food waste and insecurity through efficient gleaning systems.

Aris Filos-RatsikasView profile

Lecturer

• Theoretical Computer Science
• Artificial Intelligence
• Social Choice Theory
• +11 more

Aris Filos-Ratsikas is a Lecturer (Assistant Professor) at the University of Edinburgh, School of Informatics, and a member of the Laboratory for Foundations of Computer Science. His research integrates theoretical computer science and economics, focusing on algorithms, computational complexity, social choice, fair division, game theory, and mechanism design. Education: PhD in Computer Science, Aarhus University, Denmark (2015) – Supervisor: Peter Bro Miltersen MSc – Advisor: Ioannis Caragiannis Research Interests: His work spans social choice theory , fair division , consensus halving , necklace splitting , PPA-completeness , distortion in voting and matching , and market algorithms . He develops new techniques combining convex optimization, topology, and query complexity to solve foundational problems in economics and computation. Recent Publication Trends: Recent papers focus on distortion minimization in social choice and matching under limited information, FIXP-completeness for fair-division problems like envy-free cake cutting, and randomized voting rules that balance explainability and efficiency. His work frequently appears in NeurIPS, FOCS, EC, AAAI, IJCAI, and SODA. Awards & Service: Fellow of the Higher Education Academy Teaching Assistant of the Year, Aarhus University (2014) Outstanding/Senior PC Member awards: AAMAS 2022, IJCAI 2018, AAAI 2017, IJCAI 2016 Program (co-)Chair: SAGT 2023; Area Chair: EC 2023; Senior PC: WINE, AAMAS, AAAI Teaching & Grants: He has designed and delivered large MSc modules (Advanced Algorithmic Techniques, Programming Fundamentals) at Liverpool, and taught Algorithmic Game Theory at Oxford. His tutorials on “Total Search Problems” and “Auctions & Markets” are regularly invited to international summer schools. He holds a fellowship recognizing excellence in higher-education teaching and has produced reflective scholarship on teaching theoretical courses. Workshops & Visits: He co-organizes the UK-wide “Indoor Games” workshop series and multiple international workshops on fairness, distortion, and total search problems. Long-term visits have included Oxford (hosted by Elias Koutsoupias) and Tsinghua University (hosted by Andrew Yao).

Adrian SampsonView profile

Associate Professor

• computer architecture
• programming languages
• compilers
• +5 more

Adrian Sampson is an Associate Professor in the Department of Computer Science at Cornell University, where he is part of the Computer Systems Laboratory and the programming languages group. He joined Cornell in 2016 as an Assistant Professor and was promoted to Associate Professor in 2022. Prior to Cornell, he was a Visiting Researcher at Microsoft Research (2015-2016). He received his Ph.D. from the University of Washington in 2015 under advisors Luis Ceze and Dan Grossman, with a dissertation on Hardware and Software for Approximate Computing. His research focuses on breaking down abstraction barriers and rethinking the hardware-software interface. He is particularly known for his work on approximate computing, which explores how computers can be more efficient by allowing them to make controlled mistakes. He leads the Capra research group at Cornell, which investigates programming languages and computer architecture. Sampson's recent publications demonstrate a strong focus on hardware acceleration, FPGA programming, compiler design, and programming language theory. His work often bridges the gap between high-level programming abstractions and low-level hardware implementation, with particular attention to predictability, verification, and energy efficiency. He has made significant contributions to geometry types for graphics programming, timeline types for modular hardware design, and virtual machines for FPGA programming. Among his notable recognitions are the IEEE TCCA Young Computer Architect Award (2021), NSF CAREER award (2019), and multiple Distinguished Artifact Awards at major conferences. He has advised numerous Ph.D. students who have gone on to positions at institutions like Wellesley College, Northwestern University, and Amazon. Sampson is actively involved in academic service, serving on program committees for major conferences including PLDI, ASPLOS, and ISCA. He has also held leadership roles such as ACM SIGARCH Board of Directors (2023-2025) and SIGPLAN Information Director. His teaching at Cornell includes courses on computer systems, programming languages, and advanced compilers.

Giacomo NanniciniView profile

Associate Professor

• Optimization
• Quantum Computing
• Algorithms
• +3 more

Giacomo Nannicini is an Associate Professor in the Daniel J. Epstein Department of Industrial & Systems Engineering at the University of Southern California's School of Advanced Computing, with a courtesy appointment in the Ming Hsieh Department of Electrical & Computer Engineering. His research focuses on optimization broadly defined, with particular interests in algorithms, software, models of computation, and quantum computing applications. His research interests span optimization theory , quantum algorithms , computational methods , and mixed-integer programming . Nannicini has made significant contributions to both classical optimization techniques and emerging quantum computing approaches, developing algorithms that bridge theoretical foundations with practical applications in various domains. Nannicini has received numerous prestigious awards including the 2021 Beale–Orchard-Hays prize, the best paper award at IEEE QCE 2021 (quantum algorithms track), the 2016 COIN-OR Cup, the 2015 Robert Faure prize, and the 2012 Glover-Klingman prize. His publication record demonstrates consistent high-impact contributions across top journals in operations research, computer science, and quantum computing. He has successfully advised multiple PhD students who have gone on to prominent positions including research scientists at Saint-Gobain and SINTEF, as well as tenure-track faculty positions. Nannicini actively recruits PhD students interested in computational optimization, quantum optimization, GPU-enabled computational optimization, and mixed-integer derivative-free optimization.

Andreas AlvermannView profile

Researcher

• Quantum Physics
• Condensed Matter Physics
• Theoretical Physics
• +7 more

Dr. Andreas Alvermann is a researcher at the Institute of Physics , University of Greifswald, Germany. His work spans quantum physics, condensed matter theory, and computational methods, with a focus on non-Hermitian systems, Floquet dynamics, and polaronic effects. Develops advanced numerical techniques for eigenvalue problems and quantum transport Investigates symmetry-protected topological phases in photonic systems Applies Chebyshev expansions to quantum impurity problems and disordered systems His research areas include: Non-Hermitian quantum mechanics Topological materials and edge states Quantum-classical crossover phenomena Optomechanical stability and chaos Electron-phonon coupling in polarons Stochastic Green's function methods Recent publication trends emphasize non-Hermitian topological phases (2021-2020), Floquet system engineering (2019-2020), and quantum transport in nanostructures (2015-2010). His collaborations with H. Fehske and G. Wellein highlight interdisciplinary computational physics efforts.

Yingzhen LiView profile

Senior Lecturer

• Machine Learning
• Probabilistic Modelling
• Bayesian Deep Learning
• +7 more

Yingzhen Li is a Senior Lecturer in Machine Learning at the Department of Computing, Imperial College London, within the Faculty of Engineering. She holds a Turing Fellowship at The Alan Turing Institute (2024). Her research focuses on building reliable machine learning systems through probabilistic methods, emphasizing uncertainty quantification, robustness, explainability, and adaptive techniques. Key areas include trustworthy ML models, generative modeling (especially sequential and spatiotemporal data), and approximate inference applied to Bayesian deep learning. Education: PhD in Engineering from the University of Cambridge, supervised by Prof. Richard E. Turner. Previously a senior researcher at Microsoft Research Cambridge and intern at Disney Research. Research interests span uncertainty-aware AI, causal representation learning, and scalable inference techniques. Her work has influenced industrial applications in deep learning frameworks like TensorFlow Probability and Pyro. Notable contributions include tutorials on approximate inference at NeurIPS 2020 and organizing conferences like AABI and AISTATS. Her recent publications address challenges in generative AI, calibration of vision-language models, and energy-based models. Awards include the Turing Fellowship recognizing her leadership in foundational AI research. Collaborations include affiliations with The Alan Turing Institute and prior industry experience. She actively mentors students and advises on adaptive learning systems through her academic and industrial networks.

Mostafa NasriView profile

Lecturer

• Mathematical Analysis
• Operations Research
• Functional Analysis
• +3 more

Mostafa Nasri serves as an Instructor in the Department of Mathematics and Statistics at the University of Winnipeg within the Mathematics and Statistics Faculty. Holding a Ph.D. from IMPA, he completed postdoctoral fellowships at Laval University, University of Montreal, and McGill University before joining his current institution. His academic credentials include: Ph.D. in Mathematics, IMPA- Instituto Nacional de Matemática Pura e Aplicada (2008) M.Sc. in Mathematics, Amirkabir University of Technology (2004) B.Sc. in Mathematics, Sharif University of Technology (2002) Dr. Nasri's research integrates Mathematical Analysis and Operations Research with specialized expertise in Functional Analysis, Optimization, and Applied Mathematics. His theoretical work examines Schauder bases, composition operators, and Dirichlet spaces, while his Optimization research develops augmented Lagrangian methods for equilibrium problems and variational inequalities. Applied contributions include contact dynamics modeling for multibody systems and HIV infection dynamics. Analysis of his 2023-2025 publications reveals intensified focus on Functional Analysis (particularly Dirichlet spaces and operator theory) alongside persistent development of optimization algorithms, with applied mechanics and mathematical biology research continuing at reduced frequency. No scientific awards are documented in available materials. Current information indicates no graduate student advising or active research grants at the University of Winnipeg. Prior industry collaboration with CM Labs Simulations Inc. and Canadian university departments demonstrates applied research experience, though no current laboratories or dedicated research teams are specified.

• Stochastic Optimization
• Mixed-Integer Optimization
• Risk Management
• +4 more

Jim Luedtke is a Professor of Industrial & Systems Engineering at the University of Wisconsin-Madison, within the College of Engineering. His research focuses on stochastic optimization, mixed-integer programming, and their applications in risk management, energy systems, and data-driven decision-making. He holds a Ph.D. from Georgia Institute of Technology (2007) and postdoctoral experience at IBM Research. Luedtke has received notable awards, including the NSF CAREER Award (2010), the INFORMS Optimization Society Prize for Young Researchers (2013), and the IBM Faculty Award (2015). He currently serves on the editorial boards of Mathematical Programming Computation and chairs the Mathematical Optimization Society Publications Committee. Education Ph.D., Industrial & Systems Engineering, Georgia Institute of Technology, 2007 M.S., Industrial & Systems Engineering, Georgia Institute of Technology, 2004 B.S., Industrial Engineering, University of Wisconsin-Madison, 2001 Research Interests Luedtke’s work addresses challenges in stochastic optimization, particularly under risk constraints, and advances mixed-integer optimization methods. His research bridges theoretical developments with practical applications in power grids, supply chains, and data-driven systems. Key areas include: Bi-objective stochastic integer programming for power grid resilience Data-driven sample average approximation techniques Algorithmic enhancements for Benders decomposition and Lagrangian methods Recent Research Trends His publications emphasize balancing computational efficiency with robustness, such as sparse disjunctive cuts for binary variables and residuals-based distributionally robust optimization. Recent work extends stochastic programming to handle covariate information and missing data in subspace clustering. Awards & Recognition 2022: Best Paper Award, Mathematical Programming Computation 2018: Harvey D. Spangler Faculty Scholar (UW-Madison) 2015: Fulbright Scholar Advising & Grants Luedtke has secured grants from NSF and industry partners, including the NSF CAREER Award. While no formal advisee list is provided, his courses (e.g., Stochastic Programming, Operations Research) suggest active involvement in mentoring graduate students.

Marc CASTELLAView profile

Lecturer

• Signal Processing
• Blind Source Separation
• Neural Networks
• +3 more

Prof. Marc CASTELLA is a Lecturer at Telecom SudParis, part of the SOP (Signal and Optimization Processing) unit. His research focuses on signal processing, particularly in blind source separation, nonlinear reconstruction, and optimization techniques. He has extensively contributed to areas such as sparse signal recovery, neural networks, and tensor decomposition. His work often addresses challenges in noisy environments and nonlinear systems. Recent publications include advancements in clipped signal recovery, motor torque estimation, and global optimization methods. Though no specific awards are listed, his prolific publication record reflects his impactful contributions to signal processing and applied mathematics. He collaborates with researchers like Jean-Christophe Pesquet and Arthur Marmin on projects involving polynomial systems and neural network models. His work is applied in diverse fields from industrial electronics to data analysis.

Binxu WangView profile

Research Fellow

• Generative Models
• Neuroscience
• Machine Learning
• +3 more

Binxu Wang is an Inaugural NeuroAI Research Fellow at the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University, starting in Fall 2023. He holds a Ph.D. in Neuroscience from Harvard Medical School, advised by Dr. Carlos Ponce, after completing his doctoral studies at Washington University in St. Louis. His research focuses on using optimization, generative models, and geometry to bridge visual information processing in the primate brain and deep neural networks. Key interests include analyzing diffusion models' spectral bias, the geometry of image manifolds, and neural dynamics in ventral stream alignment. He has received the prestigious NeuroAI Research Fellowship at the Kempner Institute. His work spans theoretical insights into neural representations, practical applications of generative models, and interdisciplinary approaches linking neuroscience and AI. Notable contributions include uncovering hidden linear structures in score-based models and exploring the geometry of natural image manifolds. Wang has presented his research at venues like the Cognitive & Neural Computation Lab (Yale), NAISYS2024, and the KITP DL2023 Program. His research emphasizes a closed-loop approach, integrating computational theory with experimental neuroscience to advance understanding of brain function.