|Pramod Anantharam||Natalia Diaz Rodriguez||Shiree Hughes|
|Kevin Keys||Sepideh Pourazarm||Jeff Ratzloff|
|Dorian Rosen||Nima Salehi Sadghiani||Susmit Shannigrahi|
Computer Science and Engineering
mHealth Based Approach for Asthma Management
Increasing availability of sensors and mobile devices have created unprecedented opportunities across many domains. Healthcare will have profound implications when doctors and patients have continuous access to physiological, physical, and environmental observations. We address a crucial problem of asthma management in children by utilizing sensors and mobile devices. We pose questions deemed useful by doctors in the context of asthma management. After preliminary data analysis, we propose patient health score and vulnerability score for informed decision making by doctors and patients. With personalized action recommendation, we aspire to reduce asthma attacks in children.
My research interests include Semantic Web, Information Extraction, Internet of Things (IoT), and knowledge representation and reasoning under uncertainty using Bayesian reasoning and Machine Learning techniques. Specifically, I work on algorithms that can leverage existing knowledge of a domain (e.g., ConceptNet, domain ontology) to support probabilistic models. I enjoy applying my research to challenging problems of extracting and understanding events in domains such as Smart Cities and Healthcare.
|Bayesian Reasoning||Machine Learning||Semantic Web, Information Extraction|
Computer Science Department
HYBRID POSSIBILISTIC AND PROBABILISTIC SEMANTIC MODELLING OF UNCERTAINTY FOR SCALABLE HUMAN ACTIVITY RECOGNITION
Human activity recognition in smart environments is a challenging but crucial task in Ambient Intelligence and Ambient Assisted Living. Promising results were obtained using knowledge engineering methods such as semantic modelling with fuzzy description logics. However, we found some issues that still can improve, and further automate, the knowledge representation and learning methodology. We propose the use of Probabilistic Soft Logic (PSL) as an extension with fuzzy ontologies, to deal with problems such as temporal constraints and variations, discovery of new patterns and anomalies, uncertainty treatment in collective inference, as well as scaling the method to large knowledge bases so that they can accommodate for the model's evolution in time.
I graduated on Computer Engineering at the University of Granada in Spain in 2010 and also of a Master on Soft Computing and Intelligent Systems in 2012 at the same university. I defended my PhD last April and I am visiting scholar for the summer at University of California Santa Cruz at the LINQS group (Relational Probabilistic Learning Group) working with Prof. Lise Getoor. I did an internship at Philips Research Eindhoven (Netherlands) last 9 months at the Personal Health Department and worked on modelling lifestyles with wearables. My research interests include Activity Recognition, Artificial Intelligence, Semantic Web, Ontologies, Fuzzy Logic, e-Health, Ambient Intelligence and Ambient Assisted Living. My PhD title is "Semantic and Fuzzy Modelling of Human Activities in Smart Spaces: A case study on Ambient Assisted Living". I am working on a start-up project, AMAPOLA, to help caregivers with reminders and providing non-invasive real-time remote activity monitoring for collaborative elderly care (together with my EIT ICT Labs fellow Gautam R. Moktan). I am very much interested in expanding my knowledge to data science, statistics and more complex machine (and deep) learning methods for everyday problems. I am also searching for an internship position at a company for which I have 6-months funding.
|Artificial Intelligence||Data Science||Deep Learning|
Institute for Sensing and Embedded Network Systems Engineering
Automated Detection of Anomalies in Streaming Sensing Systems
Our society is rapidly moving to large-scale sensor networks for everything from smart buildings to monitoring the environment. As individual systems grow from just a few devices to tens of millions of devices or 100s of millions of devices, not only does the amount of data generated increase, but the probability of transmission error and sensor malfunction also increases. Techniques must be devised to ensure an easy yet efficient method for monitoring such systems for abnormalities, damaged sensors, or other network malfunctions.
I'm a third year PhD student interested in using embedded systems to create smart buildings and monitoring the health of embedded network systems.
|Embedded Network Systems||Software Engineering|
Parsimonious model selection in genome-wide association studies
This white paper sketches an issue with model selection in multiple regression analysis of genome-wide association studies. Based on our current research, we suggest a remedy to perform these large analyses on a desktop machine.
I am interested in numerical methods for model selection and statistical estimation for high-dimensional genomics datasets. As part of my thesis research, I develop software to perform penalized multiple regression on genomic datasets.
|Mathematical optimization||Genomic technology||Computational statistics|
Improving Traffic Management Using Big Data
We study the routing problem for vehicle flows through a road network that includes both battery-powered Electric Vehicles (EVs) and Non-Electric Vehicles (NEVs). We seek to optimize a system-centric (as opposed to user-centric) objective aiming to minimize the total elapsed time for all vehicles to reach their destinations considering both traveling times and recharging times for EVs when the latter do not have adequate energy for the entire journey. We are validating the efficiency of our algorithm using real traffic data in terms of “average speed” on the road segments in Eastern Massachusetts provided by the City of Boston.
I received the B.S degree in Electrical Engineering-Electronics and M.S degree in Electrical Engineering-Control Systems from K.N.Toosi University of Technology, Tehran, Iran in 2004 and 2007 respectively. From 2007 to 2011 I worked as an Instrumentation and Control Engineer in the oil and gas industry in Iran. I am currently working toward the Ph.D. degree in the Division of Systems Engineering at Boston University. My research interests include Optimal Routing and Resource Allocation in Wireless Sensor Networks, Optimal Motion Control of Electric Vehicles and Optimal Routing and Recharging policy of Energy-aware Vehicles.
|Optimization||Traffic Networks||Data Analysis|
Physics and Astronomy
The Data Challenge of the First Gigapixel Full-sky Telescope
We have built and recently deployed a new class of telescope that solves the challenges of rare, short timescale objects. In doing so, a significant data set is created that presents new processing, storage, and computational challenges.
Our group specializes in astronomical instrumentation aimed at finding exoplanets around rare, short time objects. Our primary instruments feature unique, large field all sky survey hardware.
|Astrophyscis - Exoplanets||Astrophyscis - Instrumentation|
Materials Science Engineering
Data Mining and Machine Learning to Guide Novel Thermoelectric Development
This white paper describes the possible uses of thermoelectric materials, and addresses the problems associated with conducting high-risk studies to synthesize novel compounds from chemical white space. By data mining the ever-increasing number of materials science publications, a comprehensive database is being constructed. Newly-developed machine-learning systems are being used to predict the thermoelectic properties of hypothetical materials, and bridge the gap between computational tools and experimental needs.
My name is Dorian Rosen and I've joined Dr. Spark's research group at the University of Utah studying thermoelectric materials.
Department of Industrial and Operations Engineering
Retail Chain Network Design under Mixed Uncertainties
Retailing is one of the main business sectors in urban areas whose business continuity is very crucial especially in emergencies. Unexpected disruptions such as disruptions in supplies’ incoming flows to stores due to natural disasters may impose ever-lasting detrimental effects on the continuity of retail networks. In these situations, it is critical for retailing managers to be able to distribute supplies rapidly from their unaffected supply facilities to undisrupted retail stores especially those in affected areas in an efficient and effective manner. Moreover, uncertainties in parameters (demands, costs, and time) and possible partial or complete disruption of network’s facilities, vehicles, etc. are always threatening the optimality and feasibility of the developed plans. Retail Chain Network (RCN) designing involves several strategic decisions such as the number, location, and capacity of required facilities to provide requested supplies to given customer zones in a timely and efficient manner. For designing a RCN, anticipation of production, warehousing, distribution, transportation and demand management decisions, associated with costs, revenues and service levels are required.
The focus of my research is on the development of models of supply chain structures and response mechanisms to increase the resilience of the systems. In particular, I focus on uncertain threats and the design of supply chains that are capable of withstanding disruptions as well as uncertainty in the model inputs (e.g., demand, travel time, and exchange rate uncertainty).
|Supply Chain Network Design under Mixed Uncertainties||Data Analytics in Supply Chains||Global Sourcing and Supply Chain Optimization|
Computer Sc. Dept
Named Data Networking for Large Scientific Data Management
This paper discusses how using Named Data Networking (NDN) reduces the complexities of large scientific data management. Scientific data collections require safe archiving and easy retrieval while maintaining data provenance and integrity. The large size and distributed nature of these datasets complicates already challenging data management task. NDN (Named Data Networking), a NSF project for investigating future Internet architectures, replaces IP endpoints by hierarchical content names. NDN implicitly overcomes many of the challenges associated with managing scientific data. We describe a framework developed with NDN to reduce such challenges.
Susmit is a graduate student at Colorado State University working on applying NDN, a future Internet architecture, to large scientific data management.
|Future Internet Architectures||Scientific Data Management|
Electrical Engineering and Computer Science
Learning Tailored Risk Scores from Large-Scale Datasets
Risk scores are simple models that let users assess risk by adding, subtracting and multiplying a few small numbers. These models are widely used in medicine and crime prediction but difficult to learn from data because they need to be accurate, sparse, and use integer coefficients. We formulate the risk score problem as a mixed integer non-linear programming problem, and present a cutting-plane algorithm to solve it for datasets with large sample sizes.
I am a PhD student in the Electrical Engineering and Computer Science Department at MIT, where I work with Prof. Cynthia Rudin on topics that involve statistics and optimization. I am interested in developing new methods for data-driven decision-making with applications in climate change, crime prediction, healthcare and revenue management.