Research in Machine Learning, Neural Computation,
and Statistical Inference
at the University of Colorado, Boulder
The University of
Colorado at
Boulder
provides an outstanding interdisciplinary
environment for research and graduate training in Machine Learning, Neural Computation, and Statistical Inference
in the fields of Artificial Intelligence, Cognitive Science, Bioinformatics,
and Engineering. Our research spans topics including:
- machine learning
- neural network theory
- reinforcement learning
- network science
- Bayesian statistics
- applications of machine
learning, statistical and optimization methods to engineering
problems
- crowdsourcing
- adaptive control of
complex, nonlinear systems
- computational models of
perception, attention, and cognition
- statistical approaches to
natural language understanding
- speech recognition
- mechanisms of learning in
the brain
- experimental studies using
child and adult behavioral measures,
fMRI, and ERP
- human-machine interaction
- human-centered and humans-in-the-loop machine learning
While these research topics are diverse and involve different subsets
of the faculty, faculty interactions lead to many synergies among the
topics.
Faculty
- Nisar Ahmed
- Collaborative human and
autonomous robot vehicle systems; dynamic state estimation and sensor
fusion; supervisory control and decentralized coordination in networked
systems; statistical system identification, machine learning and
artificial intelligence for aerospace applications
- Stephen
Becker
- Optimization, compressed
sensing, matrix completion, nonnegative
matrix factorization, and physical applications (e.g., medical imaging,
renewable energy)
- Jem
Corcoran
- Markov chain Monte Carlo
methods, recovering Bayes networks from
data, particle filtering and target tracking
- Vanja
Dukic
- Bayesian
modeling, inference, computational statistics, with applications to a
variety of fields ranging from medicine and ecology to risk and
insurance
- Manuel Lladser
-
discrete probability, analytic combinatorics, asymptotic analysis, embedding techniques; computational biology, RNA secondary structure prediction, extrapolation of microbial communities
- Michael
Brandemuehl
- Adaptive control for
building systems; simulation and testing of
energy systems; HVAC systems
- Moncef
Krarti
- Building systems modeling
with neural networks and statistical
techniques
- Gregor
Henze
- Model-based predictive
optimal control and model-free reinforcement learning control of
building energy systems and building thermal mass; time series
prediction and forecasting; building occupancy detection using
distributed belief networks; whole building fault detection and
diagnosis
- Elizabeth
Bradley
- Artificial intelligence
approaches to understanding and modeling
nonlinear dynamics and chaos; nonlinear control
- Aaron
Clauset
- complex networks;
statistical forecasting; stochastic processes;
massive data sets; computational biology and biological computation;
engineering complex systems
- Sidney
D'Mello
- affective computing, attentional computing, intelligent
learning environments, speech and language processing, human-computer
interaction, computational models of cognition
- Rafael
Frongillo
- theoretical machine
learning
and economics, primarily focusing on
domains such as information elicitation and crowdsourcing which involve
the exchange of information for money; research draws on techniques
from convex analysis, game theory, optimization, and statistics.
- Larry Hunter
- Development and
application
of advanced computational techniques
for
biomedicine, particularly the application of machine learning and
statistical
inference techniques to high-throughput molecular assays. Also,
automated
processing of biomedical texts, inference of metabolic and signaling
pathways,
and neurobiologically and evolutionarily informed computational models
of
cognition.
- Dan Larremore
-
Using the methodology of network science, dynamical systems, and statistical
inference to solve problems in social and biological systems
- James
Martin
- Statistical approaches to
machine translation, spoken language
help
systems, and the generation of instructional texts; metaphor
understanding
- Rebecca Morrison
-
High-dimensional Bayesian modeling and model inversion;
Probabilistic graphical models and sparsity of Markov random fields;
Mathematical representations of model inadequacy;
Calibration, validation, and uncertainty quantification for predictive models
- Michael
Mozer
- Improving human learning,
performance, and decision making via
machine learning techniques and computational models of human
perception and cognition
- Martha
Palmer
- Natural language
processing
and knowledge representation; computational linguistics; machine
translation; annotation
- Ben
Shapiro
- Philosophical aspects of machine learning; application of ML methods
to human-computer interfaces
- Chenhao Tan
-
Social aspects of language--how language influences social interaction and how language functions as a lens on social dynamics and human culture; multi-community engagement--how a person interacts with multiple communities and how communities relate to each other
- Wayne
Ward
- Spoken language
understanding, conversational speech systems,
summarization, integrating stochastic and rule-based language models
- Francois
Meyer
- Unsupervised learning;
visualization and interpretation of high dimensional data; signal
processing, applications of machine learning; analysis of fMRI and EEG
data
- Kelvin
Wagner
- Applying organizational
principles of neural networks in the
brain to artificially constructed adaptive systems made from optical
devices. The neurons are implemented using custom integrated circuits
that incorporate photodetectors and light modulators, while the
adaptive synapses are formed as dynamic holographic interconnection
gratings whose strength grows in proportion to the correlated activity
of the source and destination neurons. Architectures, devices, and
simulations have been
developed for self-aligning multilayer holographic optical learning
systems for the implementation of optical back propagation and optical
competitive learning, which are prototypical supervised and
unsupervised learning algorithms.
- Michael Paul
-
Develops methods for analyzing and understanding data using machine
learning, statistical
modeling, and natural language processing.
Focus on health informatics and epidemiology, using new and
transformative sources of data.
- Danielle
Albers Szafir
- Develops interactive
visualization systems and techniques for exploring large and
complex data in domains ranging from biology to the humanities.
Research
focuses on increasing the scalability and comprehensibility of
information visualization by quantifying perception and cognition for
design.
- Alaa Ahmed
- Biomechanical and
sensorimotor processes underlying human movement control and
decision making. Investigates the effects of instability and
uncertainty on the control of movement and posture using
a combination of methodologies including virtual reality, robotic
interfaces, kinetic and kinematic analyses,
and computational modeling.
- Mans
Hulden
- Computational linguistics;
formal language theory, machine learning and natural language
processing for less-resourced languages
- Lise
Menn
- Child language
acquisition;
neurolinguistics; aphasia
- Marie
Banich
- cognitive neuroscience of
attention, memory, and executive
function;
human neuropsychology
- Eliana
Colunga
- Language development,
concept acquisition, statistical learning; improving child vocabulary
learning;
methodologies include developmental studies and computational modeling
- Tim
Curran
- Human learning and memory
from a cognitive neuroscience
perspective, using EEG and ERP measures to predict and improve human
learning
- Matt Jones
- human learning and
knowledge
representation, with emphases on
categorization, similarity, generalization, relational representations,
and sequential decision making
- Walter
Kintsch
- Psychological theories of
comprehension, discourse processing,
and higher reasoning based on statistical approaches, including
parallel
relaxation search and latent semantic analysis, a technique that
derives estimates of relatedness from very large text corpora.
- Yuko
Munakata
- Memory, attention, and
controlled processing, assessed through
computational models and studies of behavioral dissociations in
children and adults
- Randy
O'Reilly
- Biologically constrained
computational modeling of cognitive
phenomena. Currently focusing on interactions between hippocampus,
prefrontal cortex, and posterior association cortex in episodic
memory phenomena, and on developing a biologically plausible yet
computationally powerful model of long-term learning in the neocortex.
- Tor Wager
- Application of machine
learning methods to develop fMRI-based
biomarkers for clinical outcomes (e.g., pain); multivariate pattern
analysis, multivariate brain connectivity, neural regulation of pain
and emotion.
Graduate Study
Applications for graduate study and further information about graduate
programs can be obtained from the home page of the relevant
department. Those with an interest in cognitive science and cognitive
neuroscience should visit the Institute
of
Cognitive Science home page.
Support is generally
available
for Ph.D. students in the form of
teaching
and research assistantships.
In addition to providing an
exciting research environment,
the
Boulder area offers
an exceptional quality of life. Spectacularly situated at the foot of
the
Rockies, Boulder provides a wide variety of extraordinary outdoor
activities
and an average of 330 sunny days per year. Together with Denver,
Boulder
also
affords a broad range of cultural opportunities.
Courses
See
Machine Learning Courses at the University of Colorado for more information on current
offerings. Here is a sample of
of recent course offerings
relating to neural and statistical computation. Consult department home
pages for more information about course topics.
- Artificial intelligence
- Neural networks and deep
learning
- Machine learning
- Bayesian statistics
- Unsupervised learning and
dimensionality reduction methods
- Reinforcement learning
- Network analysis and
modeling
- Statistical pattern
recognition
- Time series analysis and
prediction
- Natural language
processing
- Knowledge-based systems
- Robot control
- Speech processing
- Issues and methodologies
in
cognitive science
- Introduction to cognitive
simulation
- Psychology of thinking and
problem solving
- Judgement and decision
making
- The scientific
investigation
of consciousness
- Language acquisition
- Brains, minds, and
computers
- Neural systems
This page
is
maintained by Mike Mozer