Bibliographic record
Abstract
Shipra Agrawal (“ Price of Correlations in Stochastic Optimization ”) graduated from Stanford University with a Ph.D. in computer science under the supervision of Yinyu Ye. Her thesis explores the robustness of assuming statistical independence when solving optimization problems under uncertainty. Her research interests include algorithms, online and stochastic optimization, prediction markets, and algorithmic game theory. Augusto Aguayo (“ Optimizing Long-Term Production Plans in Underground and Open-Pit Copper Mines ”) has a degree in mining engineering from the University of Chile. He is the director of engineering for underground projects at Codelco Andina Division. Frédéric Babonneau (“ Design and Operations of Gas Transmission Networks ”) is a scientific consultant in operations research for the company ORDECSYS. He received a Ph.D. in operations research from the University of Geneva and has made contributions in the optimization of nondifferentiable and large-scale models, in the optimization of transportation problems, and in robust optimization. Ning Cai (“ Pricing Asian Options Under a Hyper-Exponential Jump Diffusion Model ”) is an assistant professor in the Department of Industrial Engineering and Logistics Management at the Hong Kong University of Science and Technology. His research interests include financial engineering and applied probability. He received both M.S. and Ph.D. degrees in operations research in the Department of Industrial Engineering and Operations Research at Columbia University and both B.S. and M.S. degrees in probability and statistics in the School of Mathematical Sciences at Peking University. Raúl Cancino (“ Optimizing Long-Term Production Plans in Underground and Open-Pit Copper Mines ”) has a degree in mining engineering from the University of Chile. He is director of engineering at Codelco North Division. Felipe Caro (“ Optimizing Long-Term Production Plans in Underground and Open-Pit Copper Mines ”) is an assistant professor of decisions, operations, and technology management at the UCLA Anderson School of Management. He is interested in decisions made under uncertainty with a strong emphasis on practical applications. He holds a Ph.D. in operations management from Massachusetts Institute of Technology and earned an industrial engineering degree from the University of Chile. He has ongoing projects with his Chilean colleagues, now mostly in the retail sector. Jaime Catalán (“ Optimizing Long-Term Production Plans in Underground and Open-Pit Copper Mines ”) is a computational engineer educated at the University of Chile with an MBA degree from ESADE, Barcelona. Since 1995, he has been working as an engineer in the design and implementation of several mathematical and computational systems, mainly in projects directed by Rafael Epstein. Hong Chen (“ Asymptotic Optimality of Balanced Routing ”) was Alumni Professor in Supply Chain Management at the Sauder School of Business, the University of British Columbia. He joined Shanghai Institute of Finance, Shanghai Jiaotong University, as a professor in August 2011. His research interests include modeling, optimization, and empirical analysis of manufacturing and service operations and supply chain management. Shaojie Deng (“ Sequential Importance Sampling and Resampling for Dynamic Portfolio Credit Risk ”) is an applied researcher at Microsoft. He obtained his Ph.D. in statistics at Stanford University in 2010. His research interests include rare-event simulation, sequential Monte Carlo, hidden Markov models and particle filters, quantitative finance, risk management, probability theory and stochastic processes, controlled experiment, and data mining on Web data. Yichuan Ding (“ Price of Correlations in Stochastic Optimization ”) is a fourth year Ph.D. candidate in the Department of Mangement Science and Engineering at Stanford University. He holds a B.S. from Zhejiang University and an M.Math from the University of Waterloo. His research is focused on operations research methodology and its application in healthcare management. Xuan Vinh Doan (“ On the Complexity of Non-Overlapping Multivariate Marginal Bounds for Probabilistic Combinatorial Optimization Problems ”) was a postdoctoral fellow in the Combinatorics and Optimization Department of the University of Waterloo, Canada. He joined the Operational Research and Management Sciences Group in Warwick Business School, UK, in September 2011 as an assistant professor. His research interests include optimization under uncertainty and sparse optimization. James S. Dyer (“ A Copulas-Based Approach to Modeling Dependence in Decision Trees ”) holds the Fondren Centennial Chair in Business in the McCombs School of Business at the University of Texas at Austin. He served as chair of the Department of Information, Risk, and Operations Management for nine years (1988–1997). He is the former Chair of the Decision Analysis Society of the Operations Research Society of America (now INFORMS). He received the Frank P. Ramsey Award for outstanding career achievements from the Decision Analysis Society of INFORMS in 2002. He was named a Fellow of INFORMS in 2006 and also received the MCDM Society's Edgeworth-Pareto Award in 2006. His research interests include the valuation of risky investment decisions and risk management. Faramroze G. Engineer (“ A Branch-Price-and-Cut Algorithm for Single-Product Maritime Inventory Routing ”) is a lecturer at the University of Newcastle, Australia. His research interests include the development and application of optimization methods to solve problems in logistics and supply chain management, transportation, network design, and healthcare. He participated in this research project while he was a Ph.D. student in the School of Industrial and Systems Engineering at Georgia Institute of Technology. Rafael Epstein (“ Optimizing Long-Term Production Plans in Underground and Open-Pit Copper Mines ”) is an associate professor at the Industrial Engineering Department of the University of Chile. His research interests include applications of operations research in the areas of forestry, mining, logistics, and combinatorial auctions. He is a former winner of the INFORMS Edelman Award for Achievement in Operations Research and the Management Sciences for work with the forest industries and the IFORS OR for Development Prize for the improvement of the auction of school meals in Chile. Peter I. Frazier (“ The Knowledge Gradient Algorithm for a General Class of Online Learning Problems ”) is an assistant professor in the School of Operations Research and Information Engineering at Cornell University. He received a Ph.D. in operations research and financial engineering from Princeton University in 2009. In 2010 he received the AFOSR Young Investigator Award. His research interest is in the optimal acquisition of information with applications in simulation, medicine, and operations management. Kevin C. Furman (“ A Branch-Price-and-Cut Algorithm for Single-Product Maritime Inventory Routing ”) has led programs and teams related to optimization and logistics research and software application development across multiple ExxonMobil affiliates. He received his Ph.D. in chemical engineering from the University of Illinois at Urbana–Champaign. His nine years at ExxonMobil have been focused on research, development, and leadership in the areas of operations research and process systems engineering. Sergio Gaete (“ Optimizing Long-Term Production Plans in Underground and Open-Pit Copper Mines ”) has a degree in mathematical engineering from the University of Chile. He is director of business plan development at El Teniente, Codelco. Kay Giesecke (“ Sequential Importance Sampling and Resampling for Dynamic Portfolio Credit Risk ”) is assistant professor of management science and engineering at Stanford University. His research interests include stochastic simulation, stochastic modeling, approximation algorithms, stochastic processes, inference and hypothesis testing for stochastic processes and applications in financial engineering, including derivatives pricing and hedging, and risk management. Peter W. Glynn (“ Consistency of Multidimensional Convex Regression ”) is the Thomas Ford Professor of Engineering in the Department of Management Science and Engineering at Stanford University, and also holds a courtesy appointment in the Department of Electrical Engineering. He was Director of Stanford's Institute for Computational and Mathematical Engineering from 2006 until 2010. He is a Fellow of INFORMS, a Fellow of the Institute of Mathematical Statistics, has been cowinner of Best Publication Awards from the INFORMS Simulation Society in 1993 and 2008, was a cowinner of the Best (Biannual) Publication Award from the INFORMS Applied Probability Society in 2009, and was the cowinner of the John von Neumann Theory Prize from INFORMS in 2010. His research interests lie in computational probability, queuing theory, statistical inference for stochastic processes, and stochastic modeling. Marcel Goic (“ Optimizing Long-Term Production Plans in Underground and Open-Pit Copper Mines ”) is an assistant professor at the Industrial Engineering Department of the University of Chile. He received a Ph.D. in industrial administration from the Tepper School of Business, Carnegie Mellon University. His research interests include database marketing, decision support systems, and retail management, with a focus on pricing, assortment, and promotion decisions. Boaz Golany (“ Network Optimization Models for Resource Allocation in Developin
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".