Computational Biology Group (CBG)Director(s): Nurit Haspel
The computational biology group focuses on the development of novel algorithms and the application of state-of-the-art existing methodologies to solve various key problems in molecular biology, nanobiology and biochemistry. We combine biophysical and biochemical principles with algorithmic techniques, aiming to better understand protein structure and dynamics. Current research topics include modeling conformational changes in proteins, design of novel nano-structures, modeling protein-protein interactions and understanding protein folding and docking
Data Mining Group (DMG)Director(s): Dan Simovici
Vast amounts of data are being gathered at a very rapid rate in various fields, such as, high energy particle physics, astronomy, astrophysics, and bioinformatics. The need for efficient and high quality algorithms to mine these datasets for hidden patterns has pushed the frontiers of research in machine learning and data mining.
The Data Mining Group (DMG) at UMass Boston is investigating novel ways of exploring large datasets using diverse techniques, such as, mathematical programming, information theoretic methods, Bayesian models, and statistical and neural net based learning.
Database Applied Research Group (DARG)Director(s): Elizabeth O'Neil , Patrick O'Neil
DARG seeks to develop advances in database technology that support commercially viable database internal and application improvements. Recent research topics include devising benchmarks and improving designs for data warehousing, isolation testing to provide guarantees of correctness at lower isolation levels, concurrency control algorithms, and bit-sliced indexes and arithmetic. A student from our group was involved with us in a prototype product named C-Store that has since been used as the basis of an industry startup named Vertica.
Distributed Software Systems Group (DSSG)Director(s): Jun Suzuki
DSSG carries out fundamental and applied research in the area of distributed software systems. Its long-term research goal is to make distributed software systems more autonomous, scalable, adaptive, survivable and easier to develop. DSSG's research efforts address the research issues that cross the boundaries among distributed computing, software engineering and artificial intelligence.
High Performance Computing Group (HPCG)Director(s): Ming Ouyang
The focus is to parallelize data analysis algorithms, statistical computation, and bioinformatics applications using Nvidia GPUs, AVX instructions, and Intel Xeon Phi processors.
Knowledge Discovery Laboratory aims at the development of data analysis and data management techniques with applications to challenging problems in geosciences, astronomy, environmental sciences. Areas of research include mining discriminating patterns, discovering interesting regions of arbitrary shape and granularity, designing new classification algorithms, and developing scalable algorithms to cope with large real-world datasets.
Machine learning methods are inspired by neuroscientific discoveries-but current methods are fragile, do not generalize well, and require massive amounts of data. We use visual computing methods to better understand the similarities and differences between human and machine intelligence.
Mobile Security and Privacy Research Lab (MobSP)Director(s): Xiaohui Liang
Our research interests include security and privacy for mobile devices, smartphones, and IoT devices. We are currently working on security and privacy issues related to health care, smartphones, and personal voice assistance.
NCLab's primary mission is to perform cutting-edge research in emerging areas of networks and data management, particularly in support of smart social, sensing, storage, and search applications. NCLab also offers research opportunities to students outside the classroom and engages in multidisciplinary R&D collaborations with other academic units at UMass as well as other institutions in and beyond the Boston area.
Services Computing Laboratory (SCLab)Director(s): Kenneth Fletcher
Services Computing Laboratory (SCLab) focuses on developing methods and algorithms services selection and recommendation. Specifically, SCLab focuses on capturing and including users’ evolving preferences in machine/deep learning algorithms to improve accuracy of services selection and recommendation. Areas of research include exploring user temporal and contextual preferences from sequential actions, detecting fake likers in social media network, and improving recommendation accuracy with Knowledge Graphs.
Software Verification Laboratory (SVL)Director(s): Tiago Cogumbreiro
The aim of the Software Verification Laboratory is improve the programmer’s productivity, by developing tools and techniques that localize bugs, and enforce the correctness of systems. Our main focus is on verifying scientific codes, in the domain of High Performance Computing. We employ the fields of programming languages, formal methods, concurrency theory, and statistical machine learning to improve the correctness of software.
VALab studies the attentional mechanisms underlying human vision and is particularly interested in investigating how attention is controlled for efficient performance of visual tasks. The main research paradigms are eye-movement recording and computational modeling. The findings resulting from this research are applied to the construction of computer vision systems and human-computer interfaces. More specifically, there are three main areas of research that the lab focuses on: Studying Human Eye Movements, Computational Modeling of Cognitive and Perceptual Processes, and Developing Gaze-Controlled Human-Computer Interfaces.