[ad_1]
Ruomeng Cui won Management Science best paper award
Ruomeng Cui, an Amazon Visiting Academic with Amazon’s Supply Chain Optimization Technologies (SCOT) team, won the 2023 Management Science Best Paper Award in Operations Management.
Cui, who is on leave from her role as an associate professor in the department of Information System and Operations Management at the Goizueta Business School, Emory University, won the award along with her co-authors Jun Li and Dennis Zhang for their 2020 paper, “Reducing discrimination with reviews in the sharing economy: Evidence from field experiments on Airbnb.”
Their paper explored ways to reduce “widespread discrimination by hosts against guests of certain races in online marketplaces” by using peer-generated online reviews. Their work has influenced sharing platforms’ strategies to fight discrimination.
The award is given “to the manuscript judged to be most deserving for its contribution to the theory and practice of operations management among all operations papers published in the past 3 years at Management Science.”
Cui earned her PhD in operations management from the Kellogg School of Management, Northwestern University in 2014. In June 2022, she joined Amazon as a Visiting Academic. In that role, she is building and implementing cutting-edge causal inference, machine learning, optimization, and economic models to make supply chain decisions.
Christos Faloutsos won Donald G. Fink Overview Paper Award
Christos Faloutsos, an Amazon Scholar and the Fredkin Professor of Computer Science at Carnegie Mellon University, was part of a team that received the 2023 IEEE Signal Processing Society Donald G. Fink Overview Paper Award by the IEE Signal Processing Society for “Tensor Decomposition for Signal Processing and Machine Learning.”
In their 2016 overview paper, Faloutsos and his coauthors — Nicholas D. Sidiropoulos, Lieven De Lathauwer, Xiao Fu, Kejun Huang, and Evangelos E. Papalexakis — noted that while tensors, which are a higher-dimensional analogue of a matrix, already had “a rich history, stretching over almost a century, and touching upon numerous disciplines” their usage had only then “become ubiquitous in signal and data analytics at the confluence of signal processing, statistics, data mining and machine learning.” Their overview aimed “to provide a good starting point for researchers and practitioners interested in learning about and working with tensors.”
The IEEE Signal Processing Society Overview Paper Award honors the authors “of a journal article of broad interest that has had substantial impact over several years on a subject related to the Society’s technical scope.”
Faloutsos said he believes the paper’s impact can be attributed to the fact that tensors are powerful tools. “They can handle static graphs, time evolving graphs, knowledge graphs which consist of triplets such as subject, verb, object, e.g., who plays in what team, who lives in, what city, who is friends with whom.”
Faloutsos, who joined Amazon as a Scholar in 2018, researches large-scale data mining with emphasis on graphs and time sequences, anomaly detection, tensors, and fractals.
Nicholas Kullman won 2023 Transportation Science Journal Paper of the Year
Nicholas Kullman, a senior research scientist with Amazon Line Haul, won the 2023 Transportation Science Journal Paper of the Year. Kullman and his coauthors — Martin Cousineau, Justin C. Goodson, and Jorge E. Mendoza — were awarded for their 2021 paper, “Dynamic Ride-Hailing with Electric Vehicles”.
In the paper, the authors “consider the problem of an operator controlling a fleet of electric vehicles for use in a ride-hailing service. The operator, seeking to maximize profit, must assign vehicles to requests as they arise as well as recharge and reposition vehicles in anticipation of future requests.”
“As autonomous vehicles become more common, fleets of taxis may become more centrally coordinated,” Kullman explained. “We wanted to consider this case where there’s a central authority that controls whether or not requests are accepted or rejected.
“We wanted to look at good policies for figuring out which vehicles should serve which requests and what do you do with your vehicles when they’re not serving requests so that they are better positioned to be able to serve future requests — a sort of dynamic stochastic vehicle routing problem.”
The team utilized deep reinforcement learning to develop new policies. Those policies were compared “against some more classical operations research approaches” and “and against dual bounds on the value of an optimal policy.”
“I think one of the other reasons why the paper was well received was that we had dual bounds,” Kullman explained. “We built out a benchmark where we knew we could not have done better than that standard. Basically, if you’re the taxi authority and you know exactly where and when these requests are going to pop up, what would you do?”
The team found its “best policy trained with deep reinforcement learning outperforms the reoptimization approach.” Kullman, who joined Amazon in 2021, earned a PhD in operations research from Université de Tours. At Amazon, he researches optimization of middle-mile linehaul operations.
Niklas Karlsson named IEEE Fellow
Niklas Karlsson, a senior principal research scientist in Amazon Advertising Engineering, was recently named an IEEE Fellow for “technical leadership to vSLAM and online advertising.” The designation took effect on Jan. 1. Karlsson leads a team within Amazon DSP (ADSP) engineering, where he oversees research pertaining to ADSP bidding and optimization.
Karlsson earned a master’s in engineering physics from Lund University and then earned both a master’s in statistics and applied probability and a PhD in control, dynamic systems, and robotics, from UC Santa Barbara. After graduating he joined Evolution Robotics as senior navigation and control engineer. While there, he and his colleagues developed and patented vSLAM (visual simultaneous localization and mapping), an odometry- and vision-based SLAM system.
In 2005, Karlsson transitioned to a role as principal control engineer with Advertising.com. There he leveraged his expertise in feedback control and systems engineering to develop a next generation of scalable and adaptive bidding solutions for ad campaign optimization. By way of acquisitions and mergers, he ended up with Yahoo, where, after 17 years in online advertising, he departed as the chief scientist and vice president of research and development for Yahoo’s Demand Side Platform.
The IEEE Fellow designation is conferred by the IEEE board of directors upon individuals with outstanding records of accomplishment in any of the IEEE fields of interest. The total number selected in any one year cannot exceed 0.1% of the total voting membership. IEEE Fellow is the highest grade of membership and is recognized by the technical community as a prestigious honor and an important career achievement.
Joan Feigenbaum named IEEE Fellow
Joan Feigenbaum, an Amazon Scholar and the Grace Murray Hopper professor of computer science at Yale University, will be elevated to IEEE Fellow grade in 2024. The grade of IEEE Fellow “recognizes exceptional distinction in the engineering profession.”
Feigenbaum, who works in the AWS Cryptographic Algorithms group on privacy-preserving computation, was awarded “for contributions to trust-management systems and Internet algorithmics.”
Hugo Krawczyk named IACR Distinguished speaker
Hugo Krawczyk, senior principal scientist, Amazon Web Services, was selected to present the 2023 IACR Distinguished Lecture.
The International Association for Cryptologic Research (IACR) Distinguished Lectures are awarded “to people who have made important contributions to cryptology research.”
Krawczyk, who is also an IACR Fellow, has made fundamental contributions to the cryptographic design of Internet standards like IPsec, IKE, and TLS. He also co-invented numerous cryptographic algorithms including the HMAC message authentication algorithm.
Prior to joining Amazon in July 2023, he was a principal researcher at the Algorand Foundation and part of its founding team. Prior to that, he was an IBM Fellow and Distinguished Research Staff Member at the IBM T.J. Watson Research Center as a member of the Cryptography Research group from 1992 to 1997, and again from 2004 to 2019. He was an associate professor at the Department of Electrical Engineering at the Technion in Israel from 1997 until 2004.
Aaditya Ramdas won Peter Gavin Hall IMS Early Career Prize
Aaditya Ramdas, an Amazon Visiting Academic who is also an assistant professor of statistics and machine learning at Carnegie Mellon University (CMU), won the Peter Gavin Hall Institute of Mathematical Statistics (IMS) Early Career Prize. Ramdas was recognized “for significant contributions in the areas of reproducibility in science and technology; active, sequential decision-making; and assumption-light uncertainty quantification.”
The prize “recognizes one researcher annually who is within the first eight years of completing their doctoral degree.” Ramdas has a bacehlor’s degree in computer science and engineering from IIT-Bombay and earned both a master’s and a PhD in statistics and machine learning from CMU.
Ramdas researches selective and simultaneous inference, game-theoretic statistics, and black-box predictive inference. His areas of applied interest include neuroscience, genetics and auditing.
Aaron Roth named CyLab’s 2023 Distinguished Alumni Award winner
Aaron Roth, an Amazon Scholar who is the Henry Salvatori Professor of Computer and Cognitive Science at the University of Pennsylvania, was named Distinguished Alumni Award winner by CyLab, Carnegie Mellon University’s security and privacy research institute. The award recognizes “Roth’s excellence in algorithms and machine learning, leadership in the field, and commitment to his students.”
Roth, who joined Amazon as a Scholar in 2020, researches the algorithmic foundations of data privacy, algorithmic fairness, game theory, learning theory, and machine learning.
[ad_2]
Source link
