Lin Ma

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My research interests lie in the intersection of database management systems (DBMSs) and machine learning (ML), especially using ML/AI techniques to automate database administration/tuning to remove human impediments.

X. Jessie Yang

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Dr. X. Jessie Yang is an Associate Professor in the Department of Industrial and Operations Engineering, with courtesy appointments at the School of Information. She is an expert in human-autonomy/robot interaction, particularly in modeling trust in human-autonomy teams. She and her team use machine learning tools to model human behaviors when interacting with autonomous and robotic agents.

Srijita Das

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My research is about building sample-efficient machine learning models. My long term goal is to develop collaborative systems that can actively seek advice from humans and make faster decisions, resulting in reliable and practical systems. I specifically focus on design of sequential decision-making models to make them learn faster. We leverage advice from humans in various forms (implicit and explicit) to encourage favorable decisions and avoid decisions having catastrophic consequences. We also focus on minimizing the cost of seeking advice by building suitable machine learning models from historical advice data and reusing them when required. Our research also develops ways to solve complex tasks in Reinforcement Learning by leveraging various kinds of knowledge transfer mechanisms, curriculum learning, teacher-student framework etc. Advances in these directions would make decision-making models sample-efficient and better suited for solving real-world problems. Along the supervised machine learning spectrum, we also focus on problems related to learning with less data, traditionally known as Active Learning, semi-supervised learning, and learning from multiple experts.

Scott Peltier

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My research deals with functional MRI data acquisition and analysis. My areas of interest include brain network connectivity; multimodal imaging; real-time fMRI neurofeedback; and the use of multivariate and data-driven analysis techniques, including machine learning.

Benjamin Goldstein

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Benjamin Goldstein is Assistant Professor of Environment and Sustainability and head of the Sustainable Urban-Rural Futures (SURF) lab. The SURF Lab (www.surf-lab.ca) studies and emphasizes urban sustainability at multiple scales. Through his work at the SURF Lab, Benjamin helps understand how urban processes and urban form drive the consumption of materials and energy in cities and produce environmental change inside and outside cities. He develops methods and tools to quantify the scale of these changes and the locations where they occur using life cycle assessment, input-output analysis, geospatial data, and approaches from data science. Benjamin is particularly interested in combining quantitative methods with theory rooted in social science to explore multiple dimensions of sustainability and address issues of distributive justice. His topical foci include urban food systems (esp. urban agriculture), agri-commodities, residual resource engineering, global supply chains, sustainable production and consumption, and energy systems.

Mosharaf Chowdhury

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I am a computer scientist and an associate professor at CSE Michigan, where I lead the SymbioticLab (https://symbioticlab.org/). My research improves application performance and system efficiency of AI/ML and Big Data workloads with a recent focus on optimizing energy consumption and data privacy. I lead the ML Energy initiative (https://ml.energy/), a consortium of researchers focusing on understanding, controlling, and reducing AI/ML energy consumption. Over the course of my career, I have worked on a variety of networked and distributed systems. Recent major projects include Infiniswap, the first scalable memory disaggregation solution; Salus, the first software-only GPU sharing system for deep learning; FedScale, a scalable federated learning and analytics platform; and Zeus, the first GPU energy optimizer for AI. In the past, I invented the coflow abstraction for efficient distributed communication, and I am one of the original creators of Apache Spark. Thanks to my excellent collaborators, I have received many individual awards, fellowships, and paper awards from top venues like NSDI, OSDI, ATC, and MICRO.

Mohamed Abouelenien

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Mohamed Abouelenien’s areas of interest broadly cover data science topics, including applied machine learning, computer vision, and natural language processing. He established the Affective Computing and Multimodal Systems Lab (ACMS) which focuses on modeling human behavior and developing multimodal approaches for different applications. He has worked on a number of projects in these areas, including multimodal deception detection, multimodal sensing of drivers’ alertness levels and thermal discomfort, distraction detection, circadian rhythm modeling, emotion and stress analysis, automated scoring of students’ progression, sentiment analysis, ensemble learning, and image processing, among others. His research is funded by Ford Motor Company (Ford), Educational Testing Service (ETS), Toyota Research institute (TRI), and Procter & Gamble (P&G). Abouelenien has published in several top venues in IEEE, ACM, Springer, and SPIE. He also served as a reviewer for IEEE transactions and Elsevier journals and served as a program committee member for multiple international conferences.

Hao-Wen Dong

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My research aims to empower music and audio creation with machine learning. My long-term goal is to lower the barrier of entry for music composition and democratize audio content creation. I am broadly interested in music generation, audio synthesis, generative AI, multimodal learning, and music information retrieval. I study a wide range of topics centered around Generative AI for Music and Audio, including multitrack music generation, automatic instrumentation, automatic arrangement, automatic harmonization, music performance synthesis, text-queried sound separation, text-to-audio synthesis and symbolic music processing software.

Grant Schoenebeck

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My current research combines machine learning tools and economic approaches (e.g game theory, mechanism design, and information design) to develop and analyze systems for eliciting and aggregating information from of diverse group of agents with varying information, interests, and abilities.
This work applies to scenarios where a collective decision-making process is required, such as peer grading, peer review, crowd-sourcing, content moderation, misinformation detection, surveys, and employment hiring/evaluation.
More broadly, I am interested in multi-agent systems, a subfield of AI; data economics; and algorithmic game theory.

Carol Menassa

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My group’s research focuses on understanding and modeling the interconnections between human experience and the built environment. We design autonomous systems that support wellbeing, safety and productivity of office and construction workers, and provides them opportunities for lifelong learning and upskilling. In all research projects, we work hard to ensure that the results are inclusive and benefit people of different abilities in their daily activities and empower them for nontraditional careers.