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1. Virtually Even: Status Equalizing in Distributed Organizations

   https://doi.org/10.1287/orsc.2021.15846  

   Hinds, Rebecca; Valentine, Melissa; DeCelles, Katherine; Berg, Justin M (July 2025, Organization Science)

   In distributed organizations, perceived status differences between workers are ubiquitous and harmful. Yet research suggests that once they are formed, status beliefs in organizations become entrenched in hierarchies and are hard to dismantle. In an inductive qualitative study, we observed how established status differences between remote and in-person workers in distributed organizations dissolved during the initial stages of the COVID-19 pandemic when everyone began working remotely. We use these data to theorize a novel status-equalizing process through which remote workers came to see themselves on an “equal playing field” with their in-person peers. We theorize how this status equalizing occurred through workers’ changing their “in-person default” use of technology—that is, their new behavior challenged embedded cultural practices that had treated the in-person workplace experience as the standard, normal, and valued perspective, implicitly guiding how employees used technology. Workers adopted new and more inclusive technology practices—including the use of asynchronous communication, greater codification of work, and virtual socializing—which resulted in remote workers perceiving new and more equal communication standards, access to information, and opportunity for social connection. As a result, these workers reported feeling less negatively stereotyped and treated more fairly in their virtual interactions with colleagues, fostering feelings of inclusion and deepening relationships across the previously established status divide. At a time when many organizations are grappling with the challenges of distributed, remote, and hybrid work, our research illuminates how inclusive technology practices can help nullify entrenched status imbalances. 

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2. Service Function Chain Placement in Cloud Data Center Networks: a Cooperative Multi-Agent Reinforcement Learning Approach

   Gao Lynn; Chen Yutian; Tang Bin (January 2021, 11th EAI International Conference on Game Theory for Networks (GameNets 2021))

   Service function chaining (SFC), consisting of a sequence of virtual network functions (VNFs) (i.e., firewalls and load balancers), is an effective service provision technique in modern data center networks. By requiring cloud user traffic to traverse the VNFs in order, SFC im- proves the security and performance of the cloud user applications. In this paper, we study how to place an SFC inside a data center to mini- mize the network traffic of the virtual machine (VM) communication. We take a cooperative multi-agent reinforcement learning approach, wherein multiple agents collaboratively figure out the traffic-efficient route for the VM communication. Underlying the SFC placement is a fundamental graph-theoretical prob- lem called the k-stroll problem. Given a weighted graph G(V, E), two nodes s, t ∈ V , and an integer k, the k-stroll problem is to find the shortest path from s to t that visits at least k other nodes in the graph. Our work is the first to take a multi-agent learning approach to solve k- stroll problem. We compare our learning algorithm with an optimal and exhaustive algorithm and an existing dynamic programming(DP)-based heuristic algorithm. We show that our learning algorithm, although lack- ing the complete knowledge of the network assumed by existing research, delivers comparable or even better VM communication time while taking two orders of magnitude of less execution time. 

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3. Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits

   Li, Chuanhao; Wang, Hongning (March 2022, Proceedings of The 25th International Conference on Artificial Intelligence and Statistics)

   Camps-Valls, Gustau; Ruiz, Francisco J.; Valera, Isabel (Ed.)

   Linear contextual bandit is a popular online learning problem. It has been mostly studied in centralized learning settings. With the surging demand of large-scale decentralized model learning, e.g., federated learning, how to retain regret minimization while reducing communication cost becomes an open challenge. In this paper, we study linear contextual bandit in a federated learning setting. We propose a general framework with asynchronous model update and communication for a collection of homogeneous clients and heterogeneous clients, respectively. Rigorous theoretical analysis is provided about the regret and communication cost under this distributed learning framework; and extensive empirical evaluations demonstrate the effectiveness of our solution. 

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4. Extended Reality Trans Technologies: Bridging Digital and Physical Worlds to Support Transgender People

   Haimson, Oliver L; DeGuia, Aloe; Saber, Rana; Brewster, Kat (November 2024, Proceedings of the ACM on humancomputer interaction)

   Extended reality (XR) technologies are becoming increasingly pervasive, and may have capacity to help marginalized groups such as transgender people. Drawing from interviews with n = 18 creators of trans technology, we examined how XR technologies do and can support trans people. We uncovered a number of creative ways that XR technologies support trans experiences. Trans technology creators are designing augmented reality (AR) and virtual reality (VR) systems that help people explore trans identity, experience new types of bodies, educate about and display trans stories and curated trans content, manipulate the physical world, and innovate gender-affirming surgical techniques. Additionally, we show how considering XR as an analogy for trans identity helps us to think about the fluidity and fluctuation inherent in trans identity in new ways, which in turn enables envisioning technologies that can better support complex and changing identities. Despite XR’s potential for supporting trans people, current AR and VR systems face limitations that restrict their large-scale use, but as access to XR systems increase, so will their capacity to improve trans lives. 

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5. Students’ Computational Thinking in Two Mathematics Block-based Programming Environments: Research During Covid-19

   Hernández-Zavaleta, J. E.; Becker, S.; Clark, D.; Brady, C.; Major, N. (July 2021, International STEM in Education Conference (STEM 2021))

   null; null; null; null (Ed.)

   This paper analyzes the computational practices that four 7th and 8th grade students engaged in when learning geometric transformations in two different online block-based programming environments. The data sources include video footage of students’ interviews in Zoom where they shared their screens and cameras. The findings determined that students utilized in particular, decomposition and pattern recognition as important computational thinking practices required for learning in STEM disciplines. The paper also describes the changes made in how research method, data collection, and analysis configured opportunities to study computational thinking in remote locations due to the restrictions brought on by COVID-19. We identified three main challenges in the transition to online research: (a) recruiting research participants which included instituting necessary revisions to ethics protocols; (b) rethinking data gathering and analysis techniques along with interactions with participants in virtual settings; (c) dealing with glitches associated with technologies and virtual communication media in just-in-time ways. We conclude that even given the challenges with researching during COVID-19, there are still opportunities for rich, robust research in online settings. 

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