Data Integration | Methodologies

Apr 25

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Nishil Talati

By jordomck |

I am a research faculty at the Computer Science and Engineering department at University of Michigan. I work with a group of talented PhD students on computer architecture, compiler techniques, and software engineering. My group focuses on developing novel software and hardware solutions to optimize large-scale data intensive worklods (e.g., graph traversals).

I earned my PhD degree in CSE from University of Michigan, Ann Arbor, USA, master’s degree in EE from Technion – Israel Institute of Technology, Haifa, Israel, and an undergraduate degree in EEE from BITS Pilani, Goa Campus, Goa, India.

Apr 19

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Aditi Verma

By jordomck |

I am interested in how nuclear technologies specifically and complex technologies broadly—and their institutional infrastructures—can be designed in more creative, and participatory ways that are epistemically inclusive of both lay and expert perspectives. To this end, my work focuses on developing a more fundamental understanding of the early stages of the design process to improve design practice and pedagogy, and also improve the tools with which designers of complex systems work. I am particularly interested in developing novel approaches for gathering both qualitative and quantitative data generated throughout the design processes of complex systems and using this data to improve design outcomes. In my work, I focus on three main research questions:

How can a fundamental understanding of design be used to improve design practice, design tools, and engineering pedagogy?
How can design processes be made more open and participatory such that epistemic plurality and inclusivity are achieved as part of the design process?
How can insights from design research be applied to the designs of policies and institutions for the governance — both innovation and regulation — of nuclear technologies?

The picture depicts the Sociotechnical Readiness Level which is a framework which critiques and reimagines the traditional Technology Readiness Level Framework. As part of the new SRL framework, we are developing new metrics and indicators to measure both social and technical aspects of a technology's readiness throughout its design and development process Image: The Sociotechnical Readiness Level, which is a framework which critiques and reimagines the traditional Technology Readiness Level Framework. As part of the new SRL framework, we are developing new metrics and indicators to measure both social and technical aspects of a technology’s readiness throughout its design and development process.

What are some of your most interesting projects?

Two new projects I am most excited about are (1) developing a sociotechnical readiness level framework for the assessment of novel technologies and (2) diagnosing the causes of cost overruns in the early stages of technology design.
In the first project, we are critiquing and reimagining the traditional Technology Readiness Level framework to capture both societal and engineering aspects of a technology’s readiness and integrate them into a single framework. As part of this work, we will develop metrics and indicators for assessing a technology’s readiness across the nine levels of sociotechnical readiness.
In the second project, we are studying how decisions made in the early stages of design impact overall system cost. This work is especially crucial in a nuclear energy context because recent nuclear plant construction projects have been severely impacted by cost overruns as well as project delays. It is vitally important to diagnose the causes of these cost overruns and address them in the early stages of the design if nuclear reactors are to play a significant role in our low-carbon energy systems.

How did you end up where you are today?

I was drawn to the field of nuclear engineering because of the enormous potential nuclear technologies have for solving one of the grandest challenges of our time: access to reliable, clean, and abundant energy.

What is the most significant scientific contribution you would like to make?

I hope to develop a deeper and more fundamental understanding of the design processes of complex systems that leads to more creative, robust, societally, and environmentally beneficial design outcomes.

What makes you excited about your data science and AI research?

There is an enormous opportunity to use data science and AI to study the early stages of the design processes of complex systems and develop new tools to stimulate a broader exploration of the design space and to ensure better design outcomes.

What are 1-3 interesting facts about yourself?

I love learning new languages. Depending on how you count, I speak (with varying levels of ability) six (or seven).

Apr 07

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Minji Kim

By qingshus |

Communication mechanisms exist inside our cells to regulate gene expression and function. In cells, communication between a gene and sequences that regulate the gene occurs through physical contact. These physical contacts between regulatory element enhancers and the gene promoters may activate gene expression. Our goal is to understand how this communication occurs within the nucleus. In particular, we leverage 3D genome mapping technologies to capture the dynamic chromatin interactions and study gene regulation in the context of developmental biology. Algorithms on graphs allow us to interpret these data generated from high-throughput sequencing assays.

How did you end up where you are today?

I studied electrical engineering, hoping to advance the field of information and coding theory. Instead, I aim to understand the communication mechanisms governing gene transcription.

What makes you excited about your data science and AI research?

Interpreting biological data is often a creative process. I enjoy browsing through the data, connecting pieces, and finding patterns.

Apr 05

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Todd Allen

By jordomck |

My research is focussed on nuclear energy systems and public policy. My research has included data analysis to predict equipment failures and materials degradation.

Mar 16

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Max Li

By jadong |

Max’s research interests lies in the design, management, and optimization of large-scale infrastructure systems, focusing on the air transportation system and emerging aerial mobility systems. He is interested in the application of methods applicable to networked systems, especially with resource constraints (e.g., airspace and airport capacity), diverse stakeholders (e.g., passenger-centric, airline-centric), and complex dynamics (e.g., changing temporal behaviors). Max has worked on a variety of data-driven problems related to analyzing flight delays across airport networks, strategic/tactical air traffic management and delay assignments, privacy and routing in drone-based applications, and uncertainty-aware traffic management. He is interested in methods such as graph signal processing and signal processing over non-Euclidean domains, data-driven optimization, mixed-integer/integer/combinatorial programs, resilient network design, and stochastic optimization. Broadly, Max hopes to contribute to a safe, resilient, and efficient air transportation system (inclusive of intra- and inter-city modalities) within the context of a passenger’s (or cargo’s) door-to-door journey.

A flight delay assignment mechanism that does not rely on knowing the exact value of an airline's private per-flight valuation of flight delays. The mechanism requires the system capacities/demand per round, and computes an initial solution. This solution is adjusted by a Coordinating Airline using privatized information from Participating Airlines. The resultant solution is proposed, and any negative public delays incurred is recorded to a ledger. The role of Coordianting/Participating Airlines then rotate depending on the current ledger balance.

Image: A flight delay assignment mechanism that does not rely on knowing the exact value of an airline’s private per-flight valuation of flight delays. The mechanism requires the system capacities/demand per round, and computes an initial solution. This solution is adjusted by a Coordinating Airline using privatized information from Participating Airlines. The resultant solution is proposed, and any negative public delays incurred is recorded to a ledger. The role of Coordinating/Participating Airlines then rotate depending on the current ledger balance.

What is the most significant scientific contribution you would like to make?

Working with air traffic controllers and traffic flow managers to deploy/prototype a congestion management solution/model!

What makes you excited about your data science and AI research?

The potential positive impact on the air transportation system, and helping to make it more efficient and equitable in terms of access.

Feb 23

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Talia Moore

By jadong |

My research involves discovery, modeling, and design for comparative biomechanics and bio-inspired robotics. I develop new information-based methods to analyze the behavior and locomotion of animals in natural environments and use laboratory-based experiments to build detailed models of how animal motion is generated by muscles, tendons, and bones. I’ve developed machine learning methods to automatically segment non-standard animals in photographs for taxonomically-broad phylogenetic comparative analyses of color pattern and behavior. I also use Finite Element Analysis to understand how the evolution of 3-dimensional shape in animal bones and teeth have adapted to a variety of ecological situations, such as novel substrates or diet.

Because FEA and other biomechanical methods are so computationally intensive, I’ve adopted a surrogate model approach that uses Bayesian Optimization to perform stimulus selection and make accurate predictions of sample performance with the minimum number of model datapoints. I’ve also begun applying this surrogate model approach to explore the design space of bio-inspired grippers made from dielectric fluid actuators. Data-driven modeling also informs the design rigid legged robots in my lab, which we will use to test hypotheses regarding how limb shape affects overall locomotion.

Finite element modeling of snake fangs helps us understand how fang shape can be adapted for different types of loading conditions, and therefore prey types. We use Bayesian Optimization to select which species to analyze for our surrogate model, which minimizes the computation time while maximizing prediction accuracy.

Additional Information

What are some of your most interesting projects?

Jerboas are bipedal desert rodents that hop erratically with zig-zag trajectories when they are escaping from predators (some describe them as “ricochetal”). These animals are about the size of your fist, but they can jump over 3 feet straight up in the air or forwards. Little was known about their locomotion, and the majority of biomechanical locomotion research was performed in lab environments on treadmills. However, training an animal to run on a treadmill reduces the variability in direction and speed that the animals would need to survive in the wild. So I went out into the desert and filmed these animals jumping and running on their natural substrates.

To understand how these trajectories might confound predators, I measured the unpredictability (or entropy) and found that bipedal desert rodents are much less predictable than quadrupedal desert rodents. Then, taking a closer look using high-speed video cameras, I determined that they have at least 5 distinct bipedal gaits. Using both kinematic and dynamic data, I built a modified Spring-Loaded Inverted Pendulum model with a torsional spring to control the neutral leg swing angle. I then performed a numerical search using a continuing approach to test neighboring parameter values for viability to discover bifurcations in gait structure. I found that by decoupling the neutral leg swing angle between left and right legs, the model was capable of transitioning between gaits across the entire speed range, just as the real jerboas do. This research will be used to inform the design of controllers for legged robots to switch gaits smoothly across a wide range of speeds.

How did you end up where you are today?

I started off not knowing at all what I wanted to do, but enjoying martial arts. A friend told me to check out the biomechanics class in my last year in undergrad at UC Berkeley, and I was hooked! I joined the lab immediately and worked there for two years after graduating. I wanted to learn everything, so I worked on a different project with a different graduate student every day of the week. During that time, I worked with cockroaches, geckos, iguanas, and agama lizards, learning about how they generate and control their motions. I was also lucky to get hands-on experience with designing and building bio-inspired robots and using them to test biological hypotheses to reveal fundamental principles of animal locomotion.

After that, I went on to study biomechanics and evolutionary biology at Harvard, where I was introduced to jerboas for the first time. They are such strange and wonderful creatures that I knew I wanted to study them for the rest of my life. I came to UM in 2015 and worked as a postdoc in Ecology and Evolutionary Biology, where I developed a new modular ethogram system to analyze snake anti-predator behaviors and design snake-mimicking soft robots. Then I became a Research Scientist in the Robotics Institute, followed by being hired as an Assistant Professor in Mechanical Engineering in January of 2021. Now I am appointed in both Robotics and Mechanical Engineering and have affiliations with Ecology and Evolutionary Biology and the Museum of Zoology.

What is the most significant scientific contribution you would like to make?

I really want to build tools to bridge the gap between biology and engineering. So many biological questions are constrained by the technology we have available. By forming connections between these fields, I have already facilitated more quantitative study of non-steady-state locomotion in natural environments. There is also a big gap between what motions animals and robots are capable of performing. I hope to learn strategies from animals and design robots to succeed in unstructured and complex environments.

What makes you excited about your data science and AI research?

I’m extremely excited that data science is making it possible to analyze the types of large datasets that we can collect from animals. There is no limit to the amount of data you can collect about animal locomotion, behavior, appearance, or structure, and the types of studies that used to take decades can now be done in a semester thanks to data science and AI. This makes it possible to integrate information from multiple different data streams and understand more complex relationships between animals, their environments, and how these relationships change through space and time.

What are 1-3 interesting facts about yourself?

I’ve done fieldwork in Malaysia, China, Australia, California, the Bahamas and Peru. I think it’s incredibly important to examine animals in their natural habitats, because our assumptions about their behaviors might be totally wrong if we only see them in zoos or labs.
The first sentence I try to learn in every language is “Where is the bathroom?”
My two rules for fieldwork are: 1) Never stop moving and 2) Never sit down or lean on anything.

Jan 23

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Vineet Kamat

By jordomck |

My group conducts research in automation and robotics to improve work processes in the construction, operation, and maintenance of civil infrastructure and the built environment. Our research has developed several licensable technologies that include visualization, perception, and modeling techniques to help on-site construction robots with autonomous decision making. We are particularly interested in exploring new methods for enabling collaborative work strategies for human-robot teams jointly performing field construction work. In addition, we are also interested in exploring methods to integrate data to support semi-autonomous mobility for people with physical disabilities in the urban built environment.

Data-Driven Co-Robotic Field Construction Work

Jan 23

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Peter Song

By jordomck |

My research interests lie in two major fields: In the field of statistical methodology, my interests include data integration, distributed inference, federated learning and meta learning, high-dimensional statistics, mixed integer optimization, statistical machine learning, and spatiotemporal modeling. In the field of empirical study, my interests include bioinformatics, biological aging, epigenetics, environmental health sciences, nephrology, nutritional sciences, obesity, and statistical genetics.

Jan 11

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Ryan Stidham

By jordomck |

Dr. Stidham is an academic gastroenterologist specializing in medical image analysis in Crohn’s disease, ulcerative colitis, inflammatory bowel diseases (IBD), and gastroenterology conditions at large. His research is focused on developing new measures of disease activity to power automated care models and clinical decision support systems in IBD with a focus on medical image analysis and new technology development. His work has focused on automation of existing IBD disease measures that relying on colonoscopy, CT, MRI, and ultrasound using neural networks and novel image analysis approaches. Dr. Stidham is also developing new measures of disease activity, inflammation, and fibrosis that leverage advances in image segmentation, transfer learning, signals analysis, and fuzzy network approaches as well as collaborating for development of new image acquisition modalities. Finally, his team has active projects in collaboration with the Department of Learning Health Sciences for merging data from clinical office notes with imaging data using computational linguistics approaches. His work has been supported by the NIH, DOD, NSF, and several large investigator-initiated industry collaborations.

Jan 04

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Sabine Loos

By jordomck |

My research focuses on natural hazards and disaster information, everything from understanding where disaster data comes from, how it’s used, and its implications to design improved disaster information systems that prioritize the human experience and lead to more effective and equitable outcomes.

My lab takes a user-centered and data-driven approach. We aim to understand user needs and the effect of data on users’ decisions through qualitative research, such as focus groups or workshops. We then design new information systems through geospatial/GIS analysis, risk analysis, and statistical modeling techniques. We often work with earth observation, sensor, and survey data. We consider various aspects of disaster information, whether it be the hazard, its physical impacts, its social impacts, or a combination of the three.

I also focus on the communication of information, through data visualization techniques, and host a Risk and Resilience DAT/Artathon to build data visualization capacity for early career professionals.

Geospatial model for predicting inequities in recovery from the 2015 Nepal earthquake