Bayesian Methods | Methodologies

Mar 16

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Liang Zhao

By jadong |

I am working on analyzing the solar wind plasma measurements obtained by multiple space missions. Especially, I am interested in the solar wind heavy ion elemental abundance and charge states measured by SWICS on Ulysses and ACE and HIS onboard Solar Orbiter mission. Applications of machine learning and artificial intelligence algorithms on solar wind plasma classification and heliophysics parameter prediction are a brand new research area that we have been working on.

Additional Information

What are some of your most interesting projects?

How did you end up where you are today?

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

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

What are 1-3 interesting facts about yourself?

Mar 16

0

Maria Masotti

By jadong |

Dr. Masotti develops statistical methods for data derived from medical imaging processes. This data presents unique challenges such as spatial and temporal autocorrelation and high dimensionality.

Dr. Masotti developed a 3D boundary detection algorithm for discovering prostate cancer lesions non-invasively via multi-parametric MRI. This image displays the different theoretical tumor volumes in blue and the estimated boundary surface in red.

Image: Dr. Masotti developed a 3D boundary detection algorithm for discovering prostate cancer lesions non-invasively via multi-parametric MRI. This image displays the different theoretical tumor volumes in blue and the estimated boundary surface in red.

Mar 07

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Abdon Pena-Francesch

By prisharp |

The BIOINSPIRED MATERIALS LAB is an interdisciplinary research group working on biomaterials science, polymer chemistry, soft matter physics, and nanotechnology, with focus on exploring biological and bioinspired functional materials to develop solutions for healthcare, robotics, and sustainability. Particularly, we are interested in multiscale modeling of soft biomaterials (from molecular dynamics to continuum models) and learning/control methods in microrobotics.

Feb 23

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Eric Swanson

By jadong |

Most of my current work is in social and political philosophy of language and metaethics. It pays special attention to relationships between language, context, and ideology, to the particularities of speech situations, and to the norms governing language use. I’m especially interested in how the force of language can go beyond the information that is apparent to or shared amongst discourse participants.

Recently I’ve been thinking about how the above makes trouble (a.k.a. interesting but sometimes scary new things to think about) for work on artificial intelligence, artificial general intelligence, and automated content moderation and promotion.

I have continuing interests in the interfaces between language and epistemology (epistemic modals and conditionals), language and metaphysics (causal talk and the logic of causation), language and ethics (deontic modals), and on two frameworks for linguistic theorizing that bear on the above—‘constraint semantics’ and ‘ordering supervaluationism.’

Additional Information

What are some of your most interesting projects?

One of my recent papers argues that not saying a particular thing can generate a conversational implicature — a means of conveying a message without explicitly committing oneself to that message. Debates about such ‘omissive implicatures’ are especially common in online language use.

How did you end up where you are today?

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

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

Contemporary work in AI interacts with countless interesting and pressing philosophical questions.

What are 1-3 interesting facts about yourself?

Feb 23

0

Anne Draelos

By jadong |

My lab’s research focuses on understanding computation in large-scale neural circuits through adaptive perturbations and real-time inference. We develop statistical machine learning algorithms to adaptively build models of neural and behavioral data online, and use them for understanding the mapping between multidimensional neural stimulations and complex behavioral outcomes. We emphasize data-driven Bayesian approaches suited for real-time prediction of latent neural and behavioral dynamics.

Additional Information

How did you end up where you are today?

My academic training began in both physics and computer science. My masters work in electrical and computer engineering focused on modeling interconnected resistive systems, and I did my doctoral work in experimental condensed matter physics, focused on the interplay between various quantum phenomena in two-dimensional materials. I am most interested in methods for dissecting complex networked systems, and I ultimately decided to make the switch to neuroscience to study arguably the most complex network around: the brain.v

My postdoctoral work focused on automated neural circuit dissection in larval zebrafish, where I designed both mathematical and software tools for experimental integration of machine learning algorithms. As an Assistant Professor, my lab is focused on leveraging increasingly rich behavioral data (in e.g., mice, monkeys) alongside neural data in streaming contexts to causally relate perturbations of neural function and resultant outcomes in latent behavior spaces. My ultimate goal is to bridge the gap between algorithm and implementation, using adaptive methods as a new design language for neuroscience experiments.

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

One of the reasons I decided to move into neuroscience as my application domain was the relatively recent data explosion in the field. We can now record from tens of thousands up to a million neurons simultaneously, at quite high-resolution in time. The field is also increasingly focused on naturalistic and effectively non-repeatable events as behavioral metrics. Both of these items make for incredibly complex and rich problems for neuroscience, which is why I think data science and AI have amazingly interesting roles to play there.

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.

Feb 15

0

Stacy Rosenbaum

By jordomck |

I am a biological anthropologist who studies the ecological causes and evolutionary consequences of social behavior. Humans and many other animals develop myriad social relationships across the course of their lifetimes. My lab members and I study how these relationships develop and are maintained, what their consequences are for participants, and how individual relationships impact evolutionary dynamics. I also study how relationships and physiology impact one another, and how their interactions affect health, longevity, and reproductive success. My primary field and laboratory research focuses on a wild population of endangered mountain gorillas in Rwanda that has been monitored for more than 50 years. In addition to studying gorillas, I also work on complementary research questions about human and non-human primate health, evolution, and sociality using the Cebu Longitudinal Health and Nutrition Survey (a long-term study of humans in Cebu, the Philippines), and data collected for the Amboseli Baboon Research Project, in southern Kenya. My research program integrates behavioral, demographic, genetic, ecological, and physiological data to gain a richer understanding of the evolution of mammals generally, and the hominid lineage specifically.

Preparing samples at a field lab in Rwanda

Additional Information

What are some of your most interesting projects?

The things that happen to us early in our lives often have major impacts on our health and wellness many years later. Interestingly, this phenomenon does not appear to be unique to humans; similar effects are observed in many species. My colleagues and I are working to understand why and how this happens. Using data from long-term primate field sites, we are examining the connections between negative experiences during the nonhuman primate equivalent of childhood, and the animals’ longevity, health, and social relationships as adults. One of the interesting challenges for this project is figuring out how to define and measure ‘health’ in other animals–and in particular, in animals that we can’t touch due to ethical, safety, and conservation concerns. Advances in non-invasive physiological research have improved our ability to determine what is happening inside their bodies without the aid of (e.g.) blood or tissue.

How did you end up where you are today?

As a child, I was endlessly fascinated with other animals and the way they behaved. Everyone, including me, assumed that I would be a veterinarian when I grew up, but I was never excited about the idea of practicing medicine. In college, I accidentally stumbled across a class on primate behavior, and fell completely in love with the subject. I was fortunate to have faculty mentors who encouraged my interest in research, and 20+ years later, now I am working to help others establish their own research careers. Having a job that involves asking and answering questions about what we do (and don’t!) have in common with other species is a dream come true.

Jan 19

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Mariel Lavieri

By jordomck |

Dr. Lavieri’s group is focused on creating novel modeling frameworks that utilize the rich datasets available in healthcare to personalize screening, monitoring, and treatment decisions of chronic disease patients. Her group has also created models for health workforce and capacity planning.

Jan 04

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Majdi Radaideh

By jordomck |

Prof. Majdi Radaideh leads the Artificial Intelligence and Multiphysics Simulations lab (AIMS), which focuses on the intersection between nuclear reactor design, nuclear multiphysics modeling and simulation, advanced computational methods, and machine learning algorithms to drive advanced reactor research and improve the sustainability of the current reactor fleet. AIMS extensively employs data science and machine learning methods for various goals including but not limited to:
1- Development of surrogate models for expensive nuclear reactor simulations in steady-state and time-dependent modes using convolutional and recurrent neural networks.
2- Large-scale combinatorial optimization to improve the performance of the nuclear fuel inside nuclear power plants using physics-informed reinforcement learning and neuroevolution algorithms.
3- Long-short term memory and ensemble methods for anomaly detection and fault prognosis to monitor the health of the nuclear power plant components.
4- Uncertainty quantification of data-driven models with Bayesian inference and Gaussian processes.
5- Natural language processing methods to process nuclear plant maintenance and burnup records.

AIMS lab aims on bridging the gap between nuclear reactor design, nuclear multiphysics modeling and simulation, advanced computational methods, and machine learning algorithms to drive advanced nuclear reactor research and improve the sustainability of the current reactor fleet to promote nuclear power as a carbon-free energy source in order to achieve net-zero carbon emission.

Jan 04

0

Tanya Rosenblat

By jordomck |

My main research interest lies in experimental economics, social networks and social learning. I am particularly interested in how people aggregate information from social networks and news sources and form posterior beliefs. I use regression techniques to uncover causal relationships as well as classification to reduce the dimensionality of data.

Some of my recent research looks at how people update beliefs when they derive direct utility from beliefs. This occurs, for example, when people receive feedback on their ability. They often seem to weigh positive information more strongly than negative information. I am also interested in understanding differences between statistical and anecdotal reasoning. Under statistical reasoning, people have known objectives and they update beliefs through Bayes’ rule. Under anecdotal reasoning, people recall anecdotes that are relevant for forming a belief about a new objective that has not been encountered before. In these situations, memory recall and recognition are important to understand the formation of beliefs.

Mean absolute belief revisions by prior belief in response to positive/negative information. Prior deciles are ordered in increasing (decreasing) order for positive (negative) information. Bayesian should have equal responses.