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Takeshi ShirabeView profile

Associate Professor

• Geographic Information Science
• Spatial Optimization
• Geodesign
• +4 more

Takeshi Shirabe is an Associate Professor in the Division of Geoinformatics at KTH Royal Institute of Technology, Sweden. He holds positions in the Department of Urban Planning and Environment, School of Architecture and the Built Environment (ABE), and is part of the Digital Futures cross-disciplinary research center. His research focuses on spatial optimization, geographic information science (GIS), and geodesign, with particular emphasis on raster-based models for spatial decision support and route improvisation. He has taught numerous courses including GIS Architecture and Algorithms, Computational Methods in GIS, and Spatial Planning with GIS. Shirabe's academic journey includes a PhD from the University of Pennsylvania (USA), a Master’s in City and Regional Planning from the same institution, and a Bachelor of Engineering from the University of Tokyo. Before joining KTH in 2010, he served as an Assistant Professor at Vienna University of Technology, Austria, where he earned his Habilitation in Geoinformation. His research interests span combinatorial optimization in geography, spatial decision support systems, and GeoDesign. Notable projects include the Space Time Alarm Clock (STAC), an Android app for pedestrian route improvisation, developed with Adrian C. Prelipcean and Falko Schmid. This tool uses real-time spatial-temporal analysis to guide users toward destinations efficiently. Shirabe has contributed to over 30 peer-reviewed publications since 2002, focusing on raster-based GIS methods for corridor design, least-cost path analysis, and spatial allocation modeling. His work bridges theoretical computational geometry with practical urban planning applications. Courses he oversees emphasize algorithmic and computational foundations of geospatial technologies.

• Visual Media Compression
• Machine Learning
• End-to-End Optimization
• +5 more

Jona Ballé is an Associate Professor in the Department of Electrical and Computer Engineering at New York University Tandon School of Engineering. His research focuses on visual media compression, leveraging machine learning and end-to-end optimization to advance compression techniques for traditional and emerging modalities (e.g., AR, plenoptic imaging). He holds a PhD in signal processing from RWTH Aachen University (2012), followed by postdoctoral work at NYU’s Center for Neural Science and a Research Scientist role at Google (2017–2024). His contributions include foundational work on JPEG AI and leadership in conferences such as CLIC and DCC. Education: PhD in Signal Processing, RWTH Aachen University (2012) Master’s in Signal Processing, RWTH Aachen University (2007) Research: Ballé bridges machine learning and perceptual science to improve compression efficiency. Key areas include perceptual metrics, end-to-end optimization, and distributed coding. His work on Wasserstein distortion and Fourier basis models reflects cutting-edge advancements in compression theory. Impact: As co-organizer of the Challenge on Learned Image Compression (CLIC) and program committee member of the Data Compression Conference (DCC), he drives community progress. His industry collaborations and leadership in the JPEG AI standard highlight his translational research.

Robert LundeView profile

Assistant Professor

• Statistical Network Analysis
• Time Series Analysis
• Resampling Methods
• +2 more

Dr. Robert Lunde is an Assistant Professor in the Department of Mathematics and Statistics at Washington University in St. Louis. He holds a PhD in Statistics and Data Science from Carnegie Mellon University and completed postdoctoral research at the University of Michigan and University of Texas at Austin. His research encompasses statistical inference for network data, resampling methods, and distribution-free inference. Key areas include conformal prediction for network-assisted regression, bootstrap methods for streaming algorithms, and theoretical analysis of network resampling techniques. His work bridges high-dimensional statistics with computational efficiency in network analysis. Dr. Lunde has taught courses including Mathematical Statistics at Washington University and Probability Theory at Carnegie Mellon. His instructional approach emphasizes foundational theory and practical applications of statistical methods.

Shabnam AkhtariView profile

Professor

• Number Theory
• Geometry of Numbers
• Diophantine Analysis
• +3 more

Shabnam Akhtari is a Professor of Mathematics at the Department of Mathematics, Pennsylvania State University, affiliated with the Eberly College of Science. Her research focuses on Number Theory, particularly Diophantine Analysis and the Geometry of Numbers, with a strong emphasis on Thue equations, S-unit equations, and the study of number field properties. She co-organizes the Pittsburgh Number Theory Day and actively contributes to collaborative research initiatives like WIN6 (Women in Number Theory 6). Her work involves analyzing solutions to Diophantine equations, exploring properties of algebraic numbers, and investigating local-global principles in arithmetic contexts. Recent research highlights include studies on regulators of number fields, quartic index form equations, and the distribution of solutions to Thue equations. She has published prolifically on topics such as sparse binary forms, monogenizations, and Mahler measure bounds. Dr. Akhtari’s academic contributions extend beyond research to fostering gender equity in mathematics through conferences like WIN6. Her email is sma6998@psu.edu , and she holds office at 339 McAllister Building. No specific awards or grants are listed in the provided texts, but her involvement in organizing academic events underscores her role in advancing mathematical discourse.

Ioannis KyriakouView profile

Professor

• Actuarial Science
• Quantitative Finance
• Financial Mathematics
• +13 more

Professor Ioannis Kyriakou is a leading academic in actuarial finance and quantitative methods at Bayes Business School, City St George's, University of London, where he serves as Professor of Actuarial Finance and Director of the MSc in Actuarial Science and MSc in Actuarial Management. He holds a visiting professorship at the University of Eastern Piedmont and has previously served as affiliate faculty at the Cyprus International Institute of Management. His research spans actuarial science, derivatives pricing, risk management, computational finance, and machine learning applications in finance and insurance. His research interests focus on quantitative finance , stochastic modeling , numerical methods , and machine learning , particularly in the context of derivative pricing , pension product design , energy and commodity markets , and investor sentiment . He has developed advanced computational techniques such as moment-based approximations and transform methods for financial modeling. His work integrates simulation, optimization, and data-driven approaches to solve complex financial and actuarial problems. The recent publications reflect a strong trend toward interdisciplinary research, combining machine learning with financial economics , energy efficiency forecasting , mutual fund performance , and climate risk . His work frequently appears in top journals like Operations Research , Journal of Financial and Quantitative Analysis , and European Journal of Operational Research , showcasing expertise in both theoretical and applied finance. European Journal of Operational Research (2020) Editors' Award for Excellence in Reviewing Cass Business School (2014) Prize for Excellence in Teaching and Learning Dimitris N. Chorafas Foundation (2009) Prize for outstanding PhD research work EPSRC Doctoral Training Award (2008) He supervises several PhD students in areas such as derivatives pricing, machine learning in actuarial science, and pension optimization. He has received research support through editorial leadership, consultancy (e.g., with Lloyd’s Treasury), and academic collaboration. He is actively involved in organizing conferences and workshops, including the Finance and Business Analytics Conference. He is affiliated with research groups focusing on financial modeling , actuarial computation , and machine learning in insurance , contributing to both academic and industry-facing initiatives. His co-authored book Machine Learning in Insurance (MDPI, 2020) highlights his leadership in bridging data science with actuarial applications.

Afrooz JalilzadehView profile

Assistant Professor

• Stochastic Optimization
• Variational Inequalities
• Nash Games
• +7 more

Afrooz Jalilzadeh is an Assistant Professor in the Department of Systems and Industrial Engineering at the University of Arizona, part of the College of Engineering. She is also a member of the Applied Mathematics and Statistics Graduate Interdisciplinary Programs (GIDP), highlighting her strong cross-disciplinary research profile. She leads the Optimization and Mathematical Analysis (OPTIMA) Lab, which focuses on algorithmic innovation for stochastic optimization and variational problems. PhD in Industrial Engineering and Operations Research, The Pennsylvania State University BS in Mathematics, University of Tehran, Iran Her research lies at the intersection of stochastic optimization , variational inequalities , and machine learning , with applications in game theory, healthcare, and power systems. She develops and analyzes algorithms such as stochastic approximation, primal-dual methods, and variance-reduced schemes to solve complex minimax and equilibrium problems. Her work emphasizes theoretical convergence guarantees and computational efficiency. The recent publications show a strong trend in nonconvex-concave saddle-point problems , stochastic Nash games , and projection-free optimization . These are central to modern machine learning and adversarial training. Keywords across her work include stochastic approximation, accelerated methods, risk aversion, and distributed computing, indicating a deep engagement with both theoretical and applied aspects of optimization. Her scientific recognition includes: Teacher of the Year, College of Engineering, University of Arizona (Spring 2022) Gerald J. Swanson Prize for Teaching Excellence NSF Grant: Generalized Stochastic Nash Equilibrium Framework James E. Marley Graduate Fellowship Max and Joan Schlienger Graduate Scholarship Third Place in INFORMS Poster Competition (2018) University Graduate Fellowship, Penn State (2015) H. Marcus Dean’s Chair Scholarship, Penn State (2015) She actively advises students and researchers in her OPTIMA Lab, with multiple publications co-authored with graduate students. She has secured competitive grants, including an NSF award, supporting her research group. Her lab seeks students with strong mathematical and coding skills (MATLAB/Python) for PhD-level research in optimization and mathematical analysis. The OPTIMA Lab conducts cutting-edge research in algorithm design for stochastic variational inequalities , Nash equilibrium computation , and minimax optimization . The lab emphasizes theoretical rigor and practical implementation, with applications spanning machine learning, healthcare, and energy systems. It has published in top venues such as NeurIPS, ACM TOMACS, and Mathematical Programming.

Cameron McLemanView profile

Associate Professor

• Algebraic Number Theory
• Elliptic Curves
• Class Field Tower Problem
• +4 more

Cameron McLeman is an Associate Professor of Mathematics at the College of Innovation and Technology , University of Michigan-Flint. His academic career spans roles including Division Director and Department Chair , with a focus on algebraic number theory , elliptic curves , and data science . Ph.D. in Mathematics, University of Arizona (2008) B.Sc. in Mathematics, Harvey Mudd College (2002) His research bridges algebraic number theory with data science , emphasizing pedagogical innovations. Recent work explores heavenly elliptic curves , class field tower problems , and network dynamics , while contributing to graph theory and K–12 mathematics education . Selected publications highlight trends in imaginary quadratic fields , graph invertibility , and mixing times for stochastic processes. His Teaching Excellence Award (2018) underscores a commitment to integrating linear algebra and statistics into real-world modeling.

Michael CochezView profile

Assistant Professor

• Machine Learning
• Knowledge Graph Embedding
• Semantic Web
• +5 more

Michael Cochez is an Assistant Professor in the Learning and Reasoning group at the Faculty of Science, Vrije Universiteit Amsterdam . His research focuses on Machine Learning , Knowledge Graph Embedding , and Prototype-Based Ontologies , with applications in Scalable Hierarchical Clustering , Ontology Matching , and Knowledge Evolution . He teaches Intelligent Systems , Data Mining , and Machine Learning courses. His research explores integrating Knowledge Graphs into end-to-end ML models , addressing challenges in Approximate Query Answering and Knowledge Representation . He has also contributed to Multi-Agent Systems and Cloud Communication through projects like Graphino.nl (his consultancy business). His students include Jiawen Chen (supervised thesis on Smart Semantic Multi-channel Communication ).

• Geometric Data Processing
• Computational Geometry
• Optimal Transport
• +7 more

Justin Solomon is an Associate Professor in the Department of Electrical Engineering & Computer Science at Massachusetts Institute of Technology, where he serves as Principal Investigator of the Geometric Data Processing Group. He maintains dual affiliations with the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the MIT Center for Computational Science and Engineering (CCSE), reflecting his interdisciplinary research bridging theoretical mathematics with practical applications in graphics and machine learning. His research interests center around geometric data processing, computational geometry, and optimal transport theory, with significant contributions to computer graphics, machine learning, and computer vision. Solomon's work spans fundamental mathematical theory to practical implementations, particularly in shape analysis, 3D reconstruction, and geometric deep learning. His research demonstrates consistent innovation in developing algorithms that bridge discrete and continuous geometry with applications in graphics, vision, and AI. The publication trends reveal Solomon's evolving research trajectory from foundational work in geometry processing toward increasing integration with modern machine learning techniques. His recent work shows strong emphasis on diffusion models, geometric deep learning, and applications of optimal transport in AI, with significant contributions to SIGGRAPH, NeurIPS, and ICML proceedings. The research demonstrates both mathematical rigor and practical impact, with applications spanning character animation, 3D reconstruction, and generative AI. Amazon Research Award (2017) for Large-Scale Geometrically-Structured Sampling Amazon Research Award (2023) for Lightweight Algorithms for Generative AI Ben Wegbreit Prize for Best Undergraduate Honors Thesis Firestone Medal for Excellence in Undergraduate Research Boothe Prize for Excellence in Writing 2nd place, SGP best paper awards (2010) Solomon has secured substantial research funding through awards like the Amazon Research Awards and maintains active collaborations across academia and industry. His group has produced numerous influential publications with students and collaborators, contributing significantly to both theoretical foundations and practical implementations in geometric data analysis. His textbook "Numerical Algorithms" demonstrates his commitment to education alongside research. As Principal Investigator of the Geometric Data Processing Group, Solomon leads a research team focused on developing mathematical foundations for analyzing and processing geometric data. The group maintains strong connections with both theoretical mathematics and practical applications, working at the intersection of computer graphics, machine learning, and computational geometry. Their work has significant implications for fields ranging from computer animation to medical imaging and scientific computing.

Dan SteinbergView profile

Academic

• Probabilistic Machine Learning
• Variational Inference
• Bayesian Deep Learning
• +7 more

Dan Steinberg is a senior research scientist and team leader of the Decisions & Statistical Learning team at CSIRO Data61 in Canberra, Australia. His expertise lies in probabilistic machine learning, variational inference, Bayesian deep learning, causal inference, and their application to domains spanning synthetic biology, geospatial analytics, and algorithmic fairness. Education PhD in Computer Vision / Machine Learning (2013) – University of Sydney, Australian Centre for Field Robotics Bachelor of Engineering (Mechatronics, First-Class Honours) – University of Sydney (2008) Bachelor of Commerce (Finance) – University of Sydney (2008) Research Interests Steinberg’s core research agenda revolves around building scalable probabilistic models that can learn efficiently from limited or noisy data and provide principled uncertainty estimates. Key themes include: Variational Inference & Bayesian Deep Learning: developing lightweight yet powerful algorithms for approximate posterior inference in complex models (e.g., Aboleth, Revrand). Active Learning & Experimental Design: creating methods that decide which experiments or measurements will maximise information gain, with recent focus on in-silico protein engineering via Variational Search Distributions (VSD). Causal Inference: leveraging machine-learning tools to perform robust observational causal studies for evidence-based policy, including work on youth well-being and academic outcomes. Algorithmic Fairness: translating normative notions of equity into quantifiable objectives for regression-based decision systems. Large-scale Spatial Analytics: Landshark—an open-source TensorFlow toolkit for supervised learning on massive geospatial raster datasets. Notable Software & Tools Aboleth: A minimal-overhead TensorFlow framework for Bayesian deep learning. Landshark: Command-line tools for large-scale spatial inference. Revrand: Scalable Bayesian generalised linear models with non-conjugate likelihoods. libcluster: Extensible C++ library for hierarchical Bayesian clustering. Scientific Awards Oral Presentation Award – ICML 2025 Workshop on Scaling up Intervention Models (SIMS) Oral Presentation Award – NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty (BDU) Oral Presentation Award – NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning Spotlight Paper Award – NeurIPS 2014 (Extended and Unscented Gaussian Processes) Research Team & Collaborations As Team Leader – Decisions & Statistical Learning at CSIRO Data61, Steinberg directs a multi-disciplinary group that partners with government agencies (e.g., Jobs and Skills Australia, Australian Institute of Health and Welfare) and industry to deploy machine-learning solutions at scale. He has previously held roles as Principal Researcher at Gradient Institute (2019-2023), Senior Research Engineer at CSIRO Data61 (2016-2019), Researcher at NICTA (2013-2016), and Research Associate at the University of Sydney (2012-2013).

Piotr GmytrasiewiczView profile

Associate Professor

• Artificial Intelligence
• Multiagent Systems
• Decision Theory
• +7 more

Piotr Gmytrasiewicz is an Associate Professor at the Department of Computer Science, University of Illinois at Chicago (UIC). He leads the Multiagent Systems Group within the Artificial Intelligence Laboratory at UIC. His research focuses on rationality in artificial agents , particularly in environments with multiple interacting agents. Key areas include Interactive Decision-Making Bayesian Modeling for Agent Communication Recursive Belief Frameworks Time Pressure and Computational Trade-offs Evolution of Agent Communication Languages Dynamic Resource Allocation His recent work examines the rationality of insincere communication and methods to discount potentially insincere information. Past projects include modeling emotions in agent design and developing decision-theoretic approaches to game theory. He has secured funding from prestigious institutions such as the National Science Foundation (NSF) , Office of Naval Research (ONR) , and DARPA . Current and past projects emphasize Automated Linguistic Competence Evolution Emergent Communication Protocols Scalable Multiagent Learning Strategic Coordination under Uncertainty He earned his Ph.D. from the University of Michigan, Ann Arbor (1992) and has previously collaborated with the Department of Computer Science and Engineering (CSE) at the University of Texas at Arlington on DARPA-funded research.

Rafael Pereira PiresView profile

Lecturer

• Distributed Systems
• Machine Learning
• Privacy-Preserving Technologies
• +3 more

Rafael Pereira Pires is a Lecturer and researcher at École polytechnique fédérale de Lausanne (EPFL) , affiliated with the Scalable Computing Systems Laboratory (SACS) and IC-SIN units. His research focuses on systems solutions at the intersection of privacy, efficiency, and machine learning in distributed environments. Education PhD in Computer Science (2019, University of Neuchâtel, Switzerland) Professional Master in Mechatronics (2014, IFSC, Brazil) Master in Computer Science (2009, UFSC, Brazil) His work explores privacy-preserving decentralized learning , trusted execution environments , and resource-efficient distributed systems . Recent publications address techniques like model fragmentation, approximate caching, and secure aggregation in decentralized learning contexts. Key trends in his 2023-2025 publications include: Advancements in federated learning and Mixture-of-Experts (MoE) models Applications of Trusted Execution Environments (SGX) to decentralized systems Optimization techniques for energy-aware and low-cost learning Scientific recognition includes the 2019 Léon Du Pasquier et Louis Perrier award for his PhD thesis. He has contributed to open-source tools like DecentralizePy and served as reviewer/PC member for top conferences including NeurIPS , Middleware , and ICDCS .

Francisco CastroView profile

Assistant Professor

• Auctions
• Market Design
• Online Display Advertising
• +3 more

Francisco Castro is an Assistant Professor at the UCLA Anderson School of Management since 2020. His research bridges operations management and market design , focusing on spatial and incentive dynamics in digital marketplaces. Prior to UCLA, he served as a Postdoctoral Research Scientist at Uber , where he developed models for ride-hailing platforms and supply chain resilience during crises. Ph.D., Decision, Risk and Operations (2020, Columbia Business School) M.S., Mathematical Engineering (2014, University of Chile) B.S., Engineering Sciences (2013, University of Chile) Castro’s research explores optimal pricing policies and incentive-compatible contracts in platforms like Uber. His work on surge pricing frameworks (Management Science, 2021) and spatial capacity planning (Operations Research, 2021) has implications for ride-hailing, delivery logistics, and high-dimensional market design challenges. Recent projects address autonomous vehicle integration and spatial inequality risks in platform economies. His publications span journals like Operations Research , Games and Economic Behavior , and Management Science , with working papers examining matching queues , two-sided marketplace dynamics , and AI-driven market design . Castro emphasizes translating these insights into MBA curriculum , preparing students for post-pandemic economic shifts through creative problem-solving and data-driven decision frameworks .

Bhaswar B. BhattacharyaView profile

Associate Professor

• nonparametric statistics
• distribution-free inference
• networks analysis
• +4 more

Bhaswar B. Bhattacharya is an Associate Professor of Statistics and Data Science at The Wharton School of the University of Pennsylvania, with a secondary appointment in the Department of Mathematics. His research spans several interconnected areas at the intersection of statistics, probability, and computational geometry. Dr. Bhattacharya received his Ph.D. in Statistics from Stanford University in 2016 under the supervision of Persi Diaconis. Prior to that, he earned both his Bachelor of Statistics (2009) and Master of Statistics (2011) from the Indian Statistical Institute in Kolkata. His research interests focus on three main pillars: nonparametric statistics (including distribution-free inference, nearest-neighbor methods, and inference on networks), combinatorial probability (covering counting problems in random graphs, random colorings, and graph limit theories), and discrete and computational geometry (including facility location problems, Voronoi games, and geometric Ramsey problems). His work often bridges theoretical developments with practical applications in network analysis, statistical learning, and geometric optimization. Recent publications demonstrate a strong trajectory in developing distribution-free methods for network analysis, with significant contributions to understanding fluctuations in graphon-based random graphs and developing optimal tests for inhomogeneous random graph models. His work also shows increasing focus on higher-order network structures through hypergraph models and applications to real-world problems like vaccination site optimization. NSF Career Award (2021-2026) Alfred P. Sloan Research Fellowship (2021) Probability Dissertation Award, Stanford University (2016) Sabyasachi Roy Memorial Gold Medal for best master's thesis, Indian Statistical Institute (2009-2011) Dr. Bhattacharya teaches advanced courses in mathematical statistics at both undergraduate and graduate levels at Wharton. His research program involves collaborations across multiple institutions and disciplines, with recent work applying statistical methods to public health challenges such as optimizing vaccination site locations. He maintains active research collaborations with colleagues in statistics, computer science, and applied mathematics departments.

• Astrophysics
• Particle Physics
• Neutrino Astronomy
• +5 more

Irene Tamborra is a Professor at the Niels Bohr Institute (University of Copenhagen) and leads the Particle Astrophysics group . She holds the Mercator Fellow visiting professorship at the Max Planck Institutes for Physics and Astrophysics in Garching, Germany. Her research bridges astrophysics and particle physics, focusing on multi-messenger astronomy through neutrinos , gravitational waves , and photons . Current research themes include: Stellar explosions (supernovae, gamma-ray bursts) Neutrino flavor evolution in extreme environments Physics beyond the Standard Model using astrophysical probes Nucleosynthesis of heavy elements Recent work (2024-2025) explores neutrino production mechanisms in compact transients, fast flavor instabilities , and multi-messenger signatures of stellar collapses. She collaborates extensively with institutions in Germany, Spain, and the US. Scientific awards: MERAC Prize (European Astronomical Society) Duggal Award (IUPAP) ERC Consolidator Award She leads the Particle Astrophysics group at the Niels Bohr Institute and contributes to supernova neutrino theory , neutrino quantum kinetics , and cosmic accelerators analysis.