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Research Centers & Labs

The UMass Computational Social Science Institute is a cross-disciplinary, collaborative group designed to address the challenges and opportunities presented by collecting, storing, and analyzing large-scale data related to the social world. With core faculty coming from disciplines as diverse as computer science, political science, sociology, and statistics, the group brings together the expertise necessary to create practical solutions to modern problems in quantitative social science.

The Cybersecurity Institute is the intellectual focal point for multi-disciplinary cybersecurity education and research at UMass Amherst. The institute brings together dozens of internationally recognized faculty from across five UMass Amherst schools and colleges to address the critical, cross-industry need for innovative security research and well-trained cybersecurity professionals in the region. Working with partners in government, industry, and academia, the institute seeks to advance scientific and societal understanding of pressing issues related to the field.

The Center for Data Science develops and applies methods to collect, curate, and analyze large-scale data, and to make discoveries and decisions using those analyses.  To accomplish these goals, the Center addresses deep and varied research challenges, including how to design accurate wearable sensors, interpret images and human languages, align information from thousands of databases, and discern cause and effect.

The Center for Intelligent Information Retrieval (CIIR) is one of the leading research groups working in the areas of information retrieval and information extraction. The CIIR studies and develops tools that provide effective and efficient access to large networks of heterogeneous, multimedia information.

The Center for Knowledge Communication (CKC) investigates knowledge-based educational systems, integrating theoretical principles into research systems for empirical evaluation and theoretical analysis.

Please visit CenterforKnowledgeCommunication.org for more details.

The Center for Smart and Connected Society (CS2) is an interdisciplinary center that focuses on enabling communities to improve services, adopt sustainable practices, promote economic growth, and enhance the overall quality of life while cutting across domains.

Founded by Professor Don Towsley in 2022, ACQuIRe focusses on researching advanced topics in classical and quantum information systems and networks, and secure communications. Located at the Manning College of Information and Computer Science, ACQuIRe is also part of the multi-institutional NSF funded Center for Quantum Networks.

The AHHA laboratory aims to understand human movements and the associated health conditions using wearable and ambient sensors. With a primary focus on evolution, the AHHA lab is particularly interested in 1) designing and implementing novel sensors and remote monitoring systems that are motivated by practical medical needs, 2) constructing appropriate clinical trials, and 3) implementing computational models to analyze the obtained data and extract clinically relevant information.

The ALTLab addresses a number of issues including: Personalized Learning; Affect and Motivation; Metacognition; Active Physical Learning; Educational Games; Open-Tutor Platforms; Intelligent Pedagogical Agents; and Learning Technologies for the Developing World.

This group conducts research in all aspects of networked systems including Internet protocols, peer-to-peer systems, wireless and mobile networks, large-scale distributed systems, and network security.

The Architecture and Language Implementation group has the goal of improving the performance of computer systems through the synergistic enhancement of the compiler, run-time environment, and architecture. Efforts include a wide range of optimizations for improving memory subsystem performance, Java virtual machines, garbage collection algorithms, microarchitectural support for advanced compiler and run-time optimizations, and parallel architecture (including optimizations for GPUs).

The Autonomous Learning Laboratory (ALL), formerly the Adaptive NetWorks (ANW) Laboratory, focuses on both machine and biological learning. Areas of study include reinforcement learning, safe machine learning, and biologically inspired machine learning.

The Autonomous Mobile Robotics Laboratory (AMRL) does research in robotics to continually make robots more autonomous, accurate, robust, and efficient, in real-world unstructured environments. We are working on a wide range of problems, including perception for long-term autonomy, high-speed multi-agent planning in adversarial domains, time-optimal control for omnidirectional robots, and autonomous multi-sensor calibration in the wild.

The Biologically Inspired Neural & Dynamical Systems Laboratory aims to apply techniques developed in computer science to problems in biology and to turn insights gained from biological systems to construct better computational algorithms. A specific goal is to employ computational techniques from machine learning, such as clustering and Bayesian network modeling, to solve problems in functional genomics. Another goal of the lab is to build mathematical models of neural circuitries in the brain.

The BioNLP lab conducts research on information retrieval, machine learning, and natural language processing, with a focus on biomedical applications. Our goal is to extract information from the vast amount of unstructured data in the biomedical domain, such as electronic health record (EHR) notes and scientific articles. We have developed and built systems for biomedical question answering, adverse drug event detection, biomedical figure search, EHR note comprehension, and healthcare outcome predictions, among others.

The Computer Graphics (GFX) Laboratory focuses on modeling of the real world, and simulation of physically based illumination phenomena. Research topics include global illumination algorithms, real-time rendering, graphics hardware based rendering, and geometric acquisition of the real-world.

The Computer Vision Research Laboratory was established with the goal of investigating the scientific principles underlying the construction of integrated vision systems and the application of vision to problems of real-world importance.

CCSL research is aimed at supporting the creation and use of systems that incorporate components from different sources (e.g., written in different languages or imported from different Internet sites) into a synergistic whole. Current projects include JavaSPIN, a persistence extension for Java, and PolySPIN, an approach to automating seamless interoperability among Java, C++ and CLOS software modules.

CSForAll Springfield Research Practice Partnership

CSforAll Springfield is a research practice partnership between: teachers; school and district leaders; specialists from Springfield Public Schools (SPS); a partnership expert from the Five College Consortium; education, social science and computing researchers from University of Massachusetts Amherst and Rutgers University; and evaluators from SageFox Consulting Group. The partnership is supporting teacher-led, dyadic design teams to engage in collaborative Design-Based Implementation Research, focused on the examination, interpretation, and integration of the concepts and practices defined in the Massachusetts DLCS standards to develop student learning progressions, design instructional approaches, and produce equity-based lesson plans ready for full-scale diffusion in K-5 classrooms across SPS.

The Digital Forensics Laboratory is a unique partnership between UMass Amherst and the Massachusetts State Police.  The Lab's work advances digital forensics science and technology, addressing the challenge of the protean nature of computer systems and the Internet. Our core mission is to develop and apply novel research and technology in forensics and privacy to address the interests of government, law, and society.

Data Management focuses on building systems for efficiently and securely managing data. Data has been playing an increasingly central role, and so data management now encompasses diverse topics, including probabilistic databases, privacy-preserving data analysis, mining and analysis of social networks and graph data, secure database architectures, database support for machine learning, machine learning for data management, database auditing, data stream processing, sensor data management, flash-based database management, provenance, causality, reverse data management, diversity and fairness, among others.

The DARoS laboratory aims to make robots to be practical tools for human life by advancing the systems faster, smarter, and robust. A primary focus of the DARoS lab is developing dexterous legged robots that can explore anywhere whlie performing important tasks. 

The NSF Engineering Research Center (ERC) for Collaborative Adaptive Sensing of the Atmosphere (CASA) seeks to revolutionize the way we observe, detect, and predict atmospheric phenomena by creating distributed collaborative adaptive sensor networks that sample the atmosphere where and when end-users needs are greatest.

The HCI-VIS Lab conducts both fundamental and applied research at the intersection of HCI and visualization. The mission of the lab is to augment the abilities of individuals and groups to understand data and solve complex problems. The lab develops novel visualization and interaction techniques to facilitate data analysis, communication, and exploration. Additionally, they build community-centered tools to address real-world sociotechnical problems in domains such as civics and healthcare.

The Human-Centered Robotics Lab is dedicated to cutting-edge research on lifelong collaborative autonomy, with the goal of enabling robots to operate and adapt over long periods of time (e.g., across days and seasons, and eventually over the robots' lifetimes), and to naturally collaborate with humans and adaptively work with other robots as teammates, in order to take over tasks where our current society has shortcomings and where intelligent robots can be a promising solution or a critical component. Our research lies at the intersection of robotics, artificial intelligence, and machine learning, with specific research topics focusing on robot adaptation and human-robot/swarm teaming in dynamic, unstructured, novel, and potentially adversarial environments.

The Information Extraction and Synthesis Laboratory (IESL) specializes in the theoretical development and implementation of systems for extracting databases from unstructured text on the Web, and mining those databases to find patterns, predict the future, and provide decision support.

The Information Fusion Lab focuses on machine learning for multimodal, heterogeneous data, particularly from the health domain. Specifically, the lab researches techniques that can combine images, time series and structured data, and can introduce domain-specific saliency into the models. The Information Fusion lab encompasses expertise on a wide range of ML methods, including deep learning, ensemble learning, object recognition, transfer of causal models and normalizing flows. The target applications include modeling trajectories for chronic conditions such as Alzheimer’s disease and osteoarthritis, medical imaging, clinical outcome prediction for critical care applications and assurance of fairness in health across multiple populations.

KDL investigates how to find useful patterns in large and complex databases. We study the underlying principles of data mining algorithms, develop innovative techniques for knowledge discovery, and apply those techniques to practical tasks in areas such as fraud detection, scientific data analysis, and web mining.

The Laboratory for Advance Software Engineering Research (LASER) is investigating the issues surrounding the development of complex software, and the construction of software environments. LASER research emphasizes software analysis and software process.

The Laboratory for Advanced System Software (LASS) conducts research in the areas of file systems, operating systems, computer networks, and large-scale distributed systems, all with an emphasis on multimedia.

Research at LIDS spans all aspects of  Internet-scale distributed systems and cloud services, including algorithms, architectures, performance, security, economics, and user behavior.  We study foundational scientific principles that enable Internet-based services to be reliable, scalable, secure, high-performance, energy-efficient, and low-cost.

The Laboratory for Perceptual Robotics investigates planning and control methodologies for complex, multi-objective robotic systems, geometric reasoning for automated assembly planning, and robot learning. Research platforms include integrated hand/arm systems, mobile robots, legged systems, and articulated stereo heads.

The Laboratory in Kine(ma)tics and Geometry's research belongs to computational geometry: the investigation of algorithmic problems with geometric content. Its focus is on rigidity, flexibility and motion for constrained structures like linkages or frameworks in mechanics or robotics. In an interdisciplinary spirit, LinKaGe also considers applications to computational biology, and investigates computational methods for motion generation in molecules (in particular, proteins).

The natural language processing group at UMass Amherst is made up of faculty and students from the College of Information & Computer Sciences, as well as affiliated members in linguistics, social sciences, and humanities. Our research spans a diverse range of topics, including computational social science, information extraction, neural language modeling and representation learning, question answering, and digital humanities.

The PLASMA group (Programming Languages And Systems at MAssachusetts) investigates issues spanning the areas of programming languages, run-time systems (especially memory management) and operating systems. The focus of the group is on cooperative system support for robust and high-performing computing in the context of modern programming languages.

Research in Presentation Production for Learning Electronically

The Research in Presentation Production for Learning Electronically (RIPPLES) project is investigating how to most effectively use learning technologies to deliver lectures and course materials inside--and outside of--the classroom.

The Resource-Bounded Reasoning Research Group studies the construction of intelligent systems that can operate in real-time environments under uncertainty and limited computational resources. The group conducts research in decision theory, real-time planning, autonomous agent architectures and reasoning under uncertainty.

The Secure, Private Internet (SPIN) Research Group aims at making Internet communications secure and private. Towards this, we analyze the security and privacy provided by existing network protocols, tools, and services, based on which we propose design adjustments to regain users' security and privacy, or devise clean-slate Internet communication tools. Our work combines the development of practical systems with rigorous theoretical analysis and incorporates techniques from various disciplines such as computer networking, cryptography, and statistics. Particular problems we have explored in the past include Internet censorship resistance, network traffic analysis, network situational awareness, social network malware, mobile security, and multimedia information hiding. 

The wireless sensor networks research group conducts research on a variety of systems, networking and data managment issues in data-centric sensor networks. The group's focus is on building scalable energy-efficient sensor networks through the use of heterogeneous sensor modalities, sensor platforms and processors.

The Statistical Social LANGuage Analysis Lab (SLANG), develops natural language processing and machine learning methods to help scientific investigation about political and social trends.  For example, we analyze Congresisonal bills, news about political events, and sentiment in social media.  We are affiliated with research groups for data science, information retrieval, and computational social science here at UMass.

The SOLAR Lab investigates topics centered around carbon-intelligent computing (application) and data-driven online optimization (theory). In the SOLAR Lab, we develop rigorous algorithms using data-driven online optimization and learning tools that are applicable in several domains, such as data center energy optimization, electricity market, electric vehicles, smart energy systems, and networking applications such as multimedia networking and edge/cloud networking.

Theoretical Computer Science is the quantitative and formal study of computing: which problems can be solved? what resources (for example, time or memory space) are required to solve them? The group's faculty specialize in a variety of areas, including the complexity of algebraic computations, the complexity of parallel computation, the descriptive complexity of computation, and the theory of parallel and distributed processing.

The College of Information and Computer Sciences includes over 30 research centers, laboratories, and groups.