Developing efficient software and hardware has never been harder whether it is for a tiny IoT device or an Exascale supercomputer. Apart from the ever growing design and optimization complexity, there exist even more fundamental problems such as lack of interdisciplinary knowledge required for effective software/hardware co-design, and a growing technology transfer gap between academia and industry.
We introduce our new educational initiative to tackle these problems by developing Collective Knowledge (CK), a unified experimental framework for computer systems research and development. We use CK to teach the community how to make their research artifacts and experimental workflows portable, reproducible, customizable and reusable while enabling sustainable R&D and facilitating technology transfer. We also demonstrate how to redesign multi-objective autotuning and machine learning as a portable and extensible CK workflow. Such workflows enable researchers to experiment with different applications, data sets and tools; crowdsource experimentation across diverse platforms; share experimental results, models, visualizations; gradually expose more design and optimization choices using a simple JSON API; and ultimately build upon each other's findings.
As the first practical step, we have implemented customizable compiler autotuning, crowdsourced optimization of diverse workloads across Raspberry Pi 3 devices, reduced the execution time and code size by up to 40%, and applied machine learning to predict optimizations. We hope such approach will help teach students how to build upon each others' work to enable efficient and self-optimizing software/hardware/model stack for emerging workloads.
CATVis: A Collaborative Multi-Agent Workflow for Turbomachinery Simulation Data Visualization
Beijing China 2 Computer Network Information Center Chinese Academy of Sciences（中国科学院计算机网络信息中心）；Hangzhou Institute for Advanced Study UCAS Hangzhou China（杭州高等研究院（UCAS））；Michigan Technological University Houghton MI USA（密歇根理工大学）
Collaborative writing is essential for teams that create documents together. Creating documents in large-scale collaborations is a challenging task that requires an efficient workflow. The design of such a workflow has received comparatively little attention. Conventional solutions such as working on a single Microsoft Word document or a shared online document are still widely used. In this paper, we propose a new workflow consisting of a combination of the lightweight markup language AsciiDoc together with the state-of-the-art version control system Git. The proposed process makes use of well…
A Human-AI Collaborative Workflow for Mathematical Discovery: A Case Study in Grover-Compatible Riemannian Optimization
Beijing International Center for Mathematical Research, Peking University（北京大学北京国际数学研究中心）；Yau Mathematical Sciences Center, Tsinghua University（清华大学尤伊数学科学中心）
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Amazon AI（亚马逊人工智能）
We propose a novel three-stage FIND-RESOLVE-LABEL workflow for crowdsourced annotation to reduce ambiguity in task instructions and thus improve annotation quality. Stage 1 (FIND) asks the crowd to find examples whose correct label seems ambiguous given task instructions. Workers are also asked to provide a short tag which describes the ambiguous concept embodied by the specific instance found. We compare collaborative vs. non-collaborative designs for this stage. In Stage 2 (RESOLVE), the requester selects one or more of these ambiguous examples to label (resolving ambiguity). The new label(s…
University of Washington（华盛顿大学）；MIT（麻省理工学院）；IBM Research（IBM研究院）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）
Today, the prominence of data science within organizations has given rise to teams of data science workers collaborating on extracting insights from data, as opposed to individual data scientists working alone. However, we still lack a deep understanding of how data science workers collaborate in practice. In this work, we conducted an online survey with 183 participants who work in various aspects of data science. We focused on their reported interactions with each other (e.g., managers with engineers) and with different tools (e.g., Jupyter Notebook). We found that data science teams are ext…
From Data to Decision: Data-Centric Infrastructure for Reproducible ML in Collaborative eScience
Dept. of Industrial and Systems Engineering（工业与系统工程系）；University of Southern California（南加州大学）；Information Sciences Institute（信息科学研究所）；Dept. of Computer Science（计算机科学系）；Dept. of Ophthalmology（眼科学系）
Graphical history mechanisms have been widely utilized in many domains to support humans' limited working memory, error recovery, collaboration, and presentation in visual analysis. Yet, there are aspects that remain under-explored in designing graphical history systems for visual analytics systems to help analysts who have complicated workflows. In this paper we report on our design study performed with domain experts, where we characterize domain tasks and designed a visual graphical workflow management environment. Our environment allows analysts to efficiently review, edit, navigate, and e…
Artificial Intelligence (AI) brings advancements to support pathologists in navigating high-resolution tumor images to search for pathology patterns of interest. However, existing AI-assisted tools have not realized this promised potential due to a lack of insight into pathology and HCI considerations for pathologists' navigation workflows in practice. We first conducted a formative study with six medical professionals in pathology to capture their navigation strategies. By incorporating our observations along with the pathologists' domain knowledge, we designed NaviPath -- a human-AI collabor…
"We'll have to see how it works": An interview study to understand collaborative practices in interdisciplinary artificial intelligence and healthcare research
William Harvey Research Institute（威廉·哈维研究所）；Queen Mary University of London（伦敦玛丽女王大学）；Wolfson Institute of Population Health（沃尔夫森人口健康研究所）
In the past few years, augmented reality (AR) and virtual reality (VR) technologies have experienced terrific improvements in both accessibility and hardware capabilities, encouraging the application of these devices across various domains. While researchers have demonstrated the possible advantages of AR and VR for certain data science tasks, it is still unclear how these technologies would perform in the context of exploratory data analysis (EDA) at large. In particular, we believe it is important to better understand which level of immersion EDA would concretely benefit from, and to quantif…
Recent developments in AI have provided assisting tools to support pathologists' diagnoses. However, it remains challenging to incorporate such tools into pathologists' practice; one main concern is AI's insufficient workflow integration with medical decisions. We observed pathologists' examination and discovered that the main hindering factor to integrate AI is its incompatibility with pathologists' workflow. To bridge the gap between pathologists and AI, we developed a human-AI collaborative diagnosis tool -- xPath -- that shares a similar examination process to that of pathologists, which c…
MAIA: A Collaborative Medical AI Platform for Integrated Healthcare Innovation
Department of Biomedical Engineering and Health Systems, KTH, Royal Institute of Technology（生物医学工程与健康系统系，皇家理工学院）；Department of Clinical Sciences, Intervention and Engineering, Karolinska Institutet（临床科学、干预与工程系，卡罗林斯卡研究所）；Division of Medical Radiation Physics, Department of Physics, Stockholm University（医学放射物理 division，物理系，斯德哥尔摩大学）；Department of Oncology-Pathology, Karolinska Institutet（肿瘤学-病理学系，卡罗林斯卡研究所）；Department of Radiology, Karolinska University Hospital（放射学系，卡罗林斯卡大学医院）；Department of Molecular Medicine and Surgery, Karolinska Institutet（分子医学与外科系，卡罗林斯卡研究所）；Department of Neurobiology, Care Sciences and Society, Karolinska Institutet（神经生物学、护理科学与社会系，卡罗林斯卡研究所）
本文提出开源协作平台 MAIA，基于 Kubernetes 集成数据、建模、标注、部署与临床反馈工具，以推动医学影像 AI 从研究到临床的可复现转化。
