Mining and analysis of massive population-based shape data can
result in knowledge of shape variability of the population. such
knowledge can lead to the construction of faithful
subject-specific 3D shape models from sparse measurements,
predict shape-specific functional performance
and
population-wide structural performance variation. Such an ability
brings about unprecedented capabilities and tantalizing
opportunities for mass customization, part-specific failure
prediction and just-in-time part maintenance, and patient-specific
biomedical intervention and treatment. This research proposes a
statistical atlas based approach that incorporates statistical shape
modeling in subject-specific shape reconstruction, finite element
(FE) modeling and analysis. The statistical atlas contains three
parts: the mean shape and the variation modes of the shape
population which span a linear shape space, the FE mesh of the
mean shape (template mesh), and the selected feature points and
sizing dimensions which are obtained by maximizing the total
variance they capture of the shape population. Given a subject
(e.g. a person), the corresponding dimensions are measured and
the3D shape model is synthesized. The template mesh can be
morphed to the subject shape to conduct subject-specific fe
analysis. The FE solution on the template mesh can also be
extrapolated to the subject shape through Taylor expansion. The
shape variances along the variation modes are obtained by the
principal component analysis. These variances tell the amount of
shape variabilities along the variation modes and are combined
with the Taylor expansion of the fE solution to obtain the
structural performance variation across the population. The 2D/3D
numerical examples demonstrate the efficiency and effectiveness
of the proposed approach

Despite the ambitious plans of vehicle manufacturers, vehicletechnologies usually take two to five decades to saturate theirpotential market,and currently, SAE level-2 automation (SAEJ3016 201401) is the state-of-the-art vehicle-automationtechnology. For this level, drivers are still required to monitor theroad and get ready to intervene in a timely manner whennecessary. Thus,visually monitoring the driving environmentand anticipating how a given situation may evolve (anticipatorydriving) may still bring benefits to driving safety. A good designneeds to support drivers' anticipation of both the environmentas well as the automated system that they are interacting withand consider drivers' states when transferring control. Throughdriving simulator experiments, my research aimed to answer thefollowing questions: (1) how automation affects anticipation indriving and (2) what types of feedback can be effective to supportdriver anticipation in automated vehicles. Results show thatdriving experience still matters to driving safety in automatedvehicles, with experienced drivers exhibited more anticipatorydriving behaviors. Distraction engagement may still need to beavoided in vehicles with the current level of driving automation(SAE Level-2), as distraction engagement can impede anticipatorydriving in automated vehicles. Further, this research indicates thatdrivers in automated vehicles should be trained with proper

Rail and urban transit systems often serve a large number of passengerson a daily basis and play an important role in supporting economicactivities. This presentation will discuss how to utilize modeling anddata analysis tools to improve our understanding and performanceof rail or urban transit systems. In particular, this presentation will firstdiscus data mining and modeling techniques for quantifying mobilitypatterns and demand prediction in rail/transit systems. Then, givencertain levels of demand information, pricing and service networkdesign problems in rail/transit systems will be presented. A fewexamples on game-theoretical models and network design models forpassenger-freight integrated transit/rail system will be illustrated. Inaddition, several empirical studies that examine the impacts of raildevelopment on accessibility, economy and equity will be discussedwhere the joint effect of rail development and megalopolis policy willalso be covered. In summary, this presentation illustrates with exampleshow modeling and analysis tools can be applied to rail and urban transitsystems for better policy and decision making.

Additive manufacturing (AM) enables seamless integration of product
design and manufacturing phases, and thus offers significant
advantages over conventional manufacturing. Despite the enormous
progress in recent AM technologies, certain intractable quality issues
persist. These product defects lead to considerable rework and scrap
rates, and thus pose significant impediments for sustainability of am.
Consequently, there is a vital need to advance online methods for
defect detection in AM processes. so that incipient process anomalies
can be identified and possibly prevented at an early stage of
manufacturing. With the above focus, this talk will introduce some of
the ongoing research related to real-time in situ process monitoring
for AM performed in the Sensing and Analytics of Smart Manufacturing
Laboratory at Virginia Tech. The topics cover: applications of machine
learning for real-time process monitoring in AM; 3D point cloud based
dimensional integrity assessment for AM; and real-time sensing based
process monitoring for AM cybersecurity.

The goal of my research is to understand the molecular and geneticregulatory circuits that control cell proliferation and differentiationduring development of multicellular organisms. Using Drosophila as amodel system, my lab focuses on the following two major researchtopics in the past decade: (1) investigating the molecular and cellularmechanisms that control stem cell biology in Drosophila ovary andgut; and (2) creating and optimizing novel genetic tools and resourcesto facilitate sophisticated analyses of Drosophila genome. Thesynergistic effects between two research projects allowed us to gaininsights into the molecular mechanisms that control stem-cell renewain ovary and intestine of Drosophila. I will briefly introduce my labachievements in the past five years.

Protein posttranslational modifications (PTMs) play essential roles inthe regulation of almost all biological processes, ranging from DNAreplication and gene expression to metabolism. The dysregulation ofproteins PTMs often leads to human diseases such as cancer andneurodegenerative diseases. Therefore, it is of great importance toreveal the biological significance of protein PTMs and their regulatorymechanisms. Benefiting from the rapid development of massspectrometry technology, the number of known PTMs has increasedrapidly. However, only a handful of protein PTMs have beenextensively studied, while the cellular mechanisms and functions ofmany other PTMs, particularly those newly identified ones, remainspoorly understood. In this talk, I will present the new strategies anddiscoveries on the biological significance and regulation mechanismof several novel protein PTMs.

The immune response is a hallmark of cancer playingcritical roles in both pathogen defense and tumorprogression, operating through intracellular protein-proteininteraction networks organized in space and time. Abnormalprotein-protein interactions and signaling activations lead todysregulated cell-cell interactions and diseases. By usingproximity labeling mediated by engineered ascorbic acidperoxidase (APEX) and genetic mouse models, we uncoveredprotein complexes that are essential for antiviralimmuneactivation,cancer metastasis, immune cell-state transitionand cell-cell interactions in the tumor microenvironment. Inlight of the unsurpassable flexibility and spatiotemporalprecision to manipulate cell physiology, optogenetics hasbeen gaining rapid momentum in both neuronal cells andnon-excitable cells. To modulate immune cell function at ahigh spatiotemporal resolution in vivo, we devised a series ofnano-optogenetic tools for the optical control of effectorfunctions of immune cells and light-inducible tumor killing invivo. To continue my interests in immune-related diseases, lpropose to apply pooled screen, single cell and spatialtechnologies to dissect cell circuits and gene programsgoverning the progression of immune-related diseases.

Building intelligent visual systems is essential for the nextgeneration of artificial intelligence systems. It is a fundamentaltool for many disciplines and beneficial to various potentialapplicationssuch asautonomousdriving,robotics.surveillance,augmented reality, to name a few. An accurateand efficient intelligent visual system has a deep understandingof the scene, objects, and humans. lt can automaticallyunderstand the surrounding scenes. In general, 2D images and3D point clouds are the two most common data representationsin our daily life. Designing powerful image understanding andpoint cloud processing systems are two pillars of visualintelligence,enabling the artificial intelligence systems toand interact with the current status of theunderstandenvironment automatically. In this talk, I will first present ourefforts in designing modern neural systems for 2D imageunderstanding,including high-accuracy and high-efficiencysemantic parsing structures, and unified panoptic parsingarchitecture. Then, we go one step further to design neuralsystems for processing complex 3D scenes, includingsemantic-level and instance-level understanding. Further, weshow our latest works for unified 2D-3D reasoning frameworkswhich are fully based on self-attention mechanisms. In the end.the challenges, up-to-date progress, and promising futuredirections for building advanced intelligent visual systems will bediscussed.

Rail and urban transit systems often serve a large number of passengerson a daily basis and play an important role in supporting economicactivities. This presentation will discuss how to utilize modeling anddata analysis tools to improve our understanding and performanceof rail or urban transit systems. In particular, this presentation will firstdiscus data mining and modeling techniques for quantifying mobilitypatterns and demand prediction in rail/transit systems. Then, givencertain levels of demand information, pricing and service networkdesign problems in rail/transit systems will be presented. A fewexamples on game-theoretical models and network design models forpassenger-freight integrated transit/rail system will be illustrated. Inaddition, several empirical studies that examine the impacts of raildevelopment on accessibility, economy and equity will be discussedwhere the joint effect of rail development and megalopolis policy willalso be covered. In summary, this presentation illustrates with exampleshow modeling and analysis tools can be applied to rail and urban transitsystems for better policy and decision making.

Rapid advances in artificial intelligence (Al) have made itincreasingly applicable to support human work (eg. healthcare)However, the achievement of only accurate predictions of Alsystems is not sufficient for their deployment in practice. lf notcarefully designed with stakeholders, Al systems can exacerbateuser experience, and easily be abandoned. Instead, it is criticalthat these systems are designed to leverage the best of humanability, but also assist to overcome human limitations.In this talk, will introduce my work on creating two interactivesystems that augments a machine learning model with humanfeedback in the context of physical stroke rehabilitation therapy1) human-Al collaborative decision making on rehabilitationassessment for therapists and 2) human-robot collaborativerehabilitation therapy for post-stroke survivors. Then, I will shareinsights from the design, development, and evaluation ofcollaborative systems on rehabilitation with therapists andpost-stroke survivors. Finally, I will discuss emerging and futuredirections for my research, exploring core challenges of creatingeffective human-Al/robot collaborative systems.