Penn Engineering
The School of Engineering and Applied Science, established in 1852, is composed of six academic departments and numerous interdisciplinary centers, institutes, and laboratories. At Penn Engineering, we are preparing the next generation of innovative engineers, entrepreneurs and leaders. Our unique culture of cooperation and teamwork, emphasis on research, and dedicated faculty advisors who teach as well as mentor, provide the ideal environment for the intellectual growth and development of well-rounded global citizens.
- Department of Bioengineering
- Department of Chemical & Biomolecular Engineering
- Department of Computer & Information Science
- Department of Electrical & Systems Engineering
- Department of Materials Science & Engineering
- Department of Mechanical Engineering & Applied Mechanics
- Energy Research Group
- Engineering Documents
- General Robotics, Automation, Sensing and Perception Laboratory
- Institute for Medicine and Engineering
- Kod*lab
- Laboratory for Research on the Structure of Matter
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Publication MORF: Magnetic Origami Reprogramming and Folding System for Repeatably Reconfigurable Structures with Fold Angle Control(IEEE, 2025-05-19) Unger, Gabriel; Shenoy, Sridhar; Li, Tianyu; Figueroa, Nadia; Sung, CynthiaWe present the Magnetic Origami Reprogramming and Folding System (MORF), a magnetically reprogrammable system capable of precise shape control, repeated transformations, and adaptive functionality for robotic applications. Unlike current self-folding systems, which often lack reprogrammability or lose rigidity after folding, MORF generates stiff structures over multiple folding cycles without degradation in performance. The ability to reconfigure and maintain structural stability is crucial for tasks such as reconfigurable tooling. The system utilizes a thermoplastic layer sandwiched within a thin magnetically responsive laminate sheet, enabling structures to self-fold in response to a combination of external magnetic field and heating. We demonstrate that the resulting folded structures can bear loads over 40 times their own weight and can undergo up to 50 cycles of repeated transformations without losing structural integrity. We showcase these strengths in a reconfigurable tool for unscrewing and screwing bolts and screws of various sizes, allowing the tool to adapt its shape to different bolt sizes while withstanding the mechanical stresses involved. This capability highlights the system’s potential for task-varying, load-bearing applications in robotics, where both versatility and durability are essential.Publication Supplementary Materials: Reparametrization of 3D CSC Dubins Paths Enabling 2D Search(2024-10-08) Ling Xu; Yuliy Baryshnikov; Cynthia Sung; Sung, CynthiaThis paper addresses the Dubins path planning problem for vehicles in 3D space. In particular, we consider the problem of computing CSC paths – paths that consist of a circular arc (C) followed by a straight segment (S) followed by a circular arc (C). These paths are useful for vehicles such as fixed-wing aircraft and underwater submersibles that are subject to lower bounds on turn radius. We present a new parameterization that reduces the 3D CSC planning problem to a search over 2 variables, thus lowering search complexity, while also providing gradients that assist that search. We use these equations with a numerical solver to explore numbers and types of solutions computed for a variety of planar and 3D scenarios. Our method successfully computes CSC paths for the large majority of test cases, indicating that it could be useful for future generation of robust, efficient curvature-constrained trajectories.Publication Deep learning-based generative network modeling for development of brain structural connectivity(2022-06) Shen, Rui Sherry; Osmanlıoğlu, Yusuf; Parker, Drew; Aunapu, Darien; Tunç, Birkan; Yerys, Benjamin E.; Verma, RaginiBrain networks are refined in the transition from childhood to adulthood. Our understanding of wiring mechanisms underlying development, however, remains very limited. Prior work has tested several predefined wiring models and suggested increased connectivity among nodes sharing similar neighbors in addition to cost minimization. While illuminating, this hypothesis-driven approach is prone to missing unforeseen network features and is biased by the researcher’s deductive thinking. Here we present a novel deep learning-based generative network model (Deep GNM) to infer developmental mechanisms in network formation in a data-informed fashionPublication Generative network models for neurodevelopment in infants with and without familial risk for autism(2022-06) Shen, Rui Sherry; Parker, Drew; Tunç, Birkan; Wang, Rongguang; Hernandez, Moises; Estes, Annette; Zwaigenbaum, Lonnie; Styner, Martin; Gerig, Guido; McKinstry, Robert; Dager, Stephen; Botteron, Kelly; Hazlett, Heather; MacIntyre, Leigh; Pandey, Juhi; Yerys, Benjamin E.; Piven, Joseph; Schultz, Robert T.; Verma, RaginiConnectivity abnormality has been widely characterized in autistic children and adolescents, but what mechanisms drive network alterations, and when network divergence arises, are unknown. Here we present a longitudinal study of structural networks over the first two years of life in 369 infants at high and low familial risk for autism. We utilize generative network models (GNMs) to explore possible wiring rules in early development and their associations with behavior scores.Publication Generative Embedding Demonstrates Altered Structural Connectivity in Autism Spectrum Disorder(2021-09) Shen, Rui Sherry; Osmanlıoğlu, Yusuf; Parker, Drew; Aunapu, Darien; Yerys, Benjamin E.; Tunç, Birkan; Verma, RaginiStudies on neurobiology of autism predominantly focus on describing topological properties of brain connectivity, offering little insight into mechanisms that give rise to associated alterations. Recently, generative network modeling has been proposed to uncover those mechanisms with parameterized wiring rules. However, most models utilize predefined wiring rules, which may not be the best characterization. Here we propose a novel deep learning based generative modeling to infer underlying mechanisms that drive atypical network growth. We demonstrate high similarity of generated network to observed structural networks, with their latent features predicting autism diagnosis. Given the neurobiological interpretability of embedding, our approach shows potential in providing connectomic biomarkers for autism.Publication Big Data, Small Bias: Harmonizing Structural Connectomes to Mitigate Site Bias in Data Integration(2025) Shen, Rui Sherry; Paker, Drew; Chen, Andrew An; Yerys, Benjamin E.; Tunç, Birkan; Roberts, Timothy P.L.; Shinohara, Russell T.; Verma, RaginiStructural connectomes are commonly used to investigate connectivity changes related to various disorders. However, small sample sizes in individual studies and highly heterogeneous disorder-related manifestations underscore the need to pool datasets across multiple studies to identify coherent and generalizable patterns linked to disorders. Yet, combining datasets introduces site bias due to variations in scanner hardware or acquisitions. This highlights the necessity for data harmonization to mitigate site bias while preserving the biological integrity associated with participant demographics and the disorders. While several paradigms exist for harmonizing normally distributed imaging data, this study represents the first effort to establish a harmonization framework specifically for structural connectomes.Publication Deriving subject-specific imaging markers for autism spectrum disorder using normative modeling on large-scale diffusion MRI data(2024-05) Shen, Rui Sherry; Parker, Drew; Wingert, Isabel A.; Reckner, Erin; Thourani, Naveen; Tunç, Birkan; Yerys, Benjamin E.; Verma, RaginiThe quest for precision medicine in autism spectrum disorder (ASD) necessitates quantitative imaging markers that account for individual variability. Traditional case-control studies offer only group-level insights, lacking subject-specific ASD information. A promising alternative is normative modeling, which defines normative developmental trajectory using large neurotypical (NT) data, and neurodevelopmental conditions are measured as individual deviations from this norm. This approach allows for the identification of personalized imaging patterns, which can be precursors to biomarkers. In this study, we applied normative modeling on large-scale diffusion MRI (dMRI) data pooled from 10 sites, aiming to 1) derive imaging markers to characterize each ASD subject, and 2) explore their relationship with autistic traits.Publication Combatting site effects to investigate autism spectrum disorder in a large multi-site diffusion tensor imaging sample(2024-05) Shen, Rui Sherry; Parker, Drew; Reckner, Erin; Thourani, Naveen; Shen, Rui Sherry; Tunç, Birkan; Yerys, Benjamin E.; Verma, RaginiDiffusion tensor imaging (DTI) is a valuable medical imaging technique for characterizing white matter (WM) in the brain. Numerous DTI studies have demonstrated reduced WM integrity in samples with autism spectrum disorder (ASD) diagnoses, as evidenced by lower fractional anisotropy (FA) values, especially in the corpus callosum (CC) (Hrdlicka, 2019), suggesting the potential of developing imaging biomarkers. This requires large sample sizes that can be achieved by aggregating data across multiple studies. This is hampered by variations in both equipment and scanner parameters, leading to confounding between site-specific effects and biological covariates. It is common to attempt to mitigate site effects by including a site term in analysis. Alternatively, data harmonization with ComBat (Fortin et al, 2018) obviates the need to control for site during analysis, allowing integration of patient data from different sources. The goal of this project is to demonstrate the importance of ComBat harmonization in mitigating non-biological site-specific sources of variation (equipment, scan parameters). Another goal was to determine if clinical group differences are enhanced after data harmonization.Publication Uncovering Altered Developmental Patterns in Structural Connectivity for Autism Spectrum Disorder through Deep Generative Modeling of Normative Development(2023-11) Shen, Rui Sherry; Tunç, Birkan; Osmanlıoğlu, Yusuf; Parker, Parker; Aunapu, Aunapu; Wingert, Isabel A.; Yerys, Benjamin E.; Verma, RaginiThe evolution of structural connectivity during development involves intricate biological processes, disruptions in which are the hallmark of several disorders, including autism spectrum disorder (ASD). To characterize the normative developmental patterns and detect deviations therein, we propose a novel deep generative model which employs a variational auto-encoder with biologically meaningful wiring constraints to infer the latent embedding of structural connectivity at different ages, as shown in Fig.1. This approach allows us to 1) obtain latent features and map normative variation for typical developing controls (TDCs), 2) estimate brain age for each subject, 3) generate brain networks that mimic observed ones, and 4) detect individual deviation from the normative model to derive an overall deviation score. We assessed the model’s performance in two datasets: PNC (TDC=968, age 8-12) and CAR (TDC=196, ASD=229, age 6-19). We compared our model to 13 classic generative models and 5 machine/deep learning models. Our model consistently achieved the best performance with smallest discrepancy between generated and observed data, and the lowest MAE & RMSE between estimated brain age and chronological age. The gap between brain and chronological age is significantly correlated with autistic traits (Spearman R = −0.29, p < 0.01) and IQ (Pearson R = 0.20, p < 0.01). We observe substantial variability in regional differences among autistics, indicating notable heterogeneity in our sample. Generally, youth with more autistic traits exhibit larger brain deviations across more brain regions. The visual (right medial visual cortex), default mode (bilateral prefrontal cortex), dorsal attention (bilateral frontal eye fields), somatomotor (right pericentral cortex), and executive control networks (bilateral lateral prefrontal cortex) are most frequently associated with autism. Overall, our model is effective in characterizing normative and altered developmental brain trajectories, and brain deviation is a promising subject-specific marker of autism.Publication Detecting Divergent Structural Connectivity in Autism Via Multisite Harmonization(2025) Shen, Rui Sherry; Paker, Drew; Chen, Andrew An; Roberts, Timothy P.L.; Tunç, Birkan; Shinohara, Russell T.; Yerys, Benjamin E.; Verma, RaginiBackground: Investigation of structural connectivity in autism using diffusion MRI (dMRI) based structural connectomes has not produced consistent findings, primarily due to small sample sizes of individual studies and high heterogeneity in autism. To maximize information gleaned from existing studies, it is essential to integrate data retrospectively while overcoming extensive site-related differences. Solving this need requires specialized harmonization paradigms to address statistical complexity of structural connectomes while preserving biological variability. We anticipate that harmonized data will facilitate robust detection of autism-related patterns by increasing pooled sample size. Objectives: To propose a harmonization paradigm for pooling structural connectomes from retrospective studies, and to investigate divergence in structural connectivity associated with autism in a large, pooled cohort. Methods: We analyzed dMRI of 1503 participants (1194 neurotypicals and 309 autism, aged 6-21 years) from 6 sites (Table1). Structural connectomes were created by parcellating the brain into 86 regions defined by Desikan-Killiany atlas and performing probabilistic tractography, with streamline counts used as connectivity weights, yielding 86x86 connectivity matrices. Since connectivity weights were non-gaussian distributed, we modeled edgewise weights using a gamma-distributed generalized linear model (gamma-GLM) with a log link, incorporating site, sex, age, and age² as covariates. Site-related coefficients were estimated edgewise from neurotypicals alone and applied to harmonize structural connectomes for autism and neurotypical groups. Removal of site effects was validated using ANOVA tests on harmonized edgewise weights and 6 graph topological measures, controlling for sex, age, and age². We assessed group-level differences and group-by-age interactions in graph measures in harmonized data between autistic and neurotypicals. Only males (n=890) were included in this analysis. We further tested Spearman correlations between graph measures and autism clinical assessments, controlling for age. We compared harmonized structural connectomes to unharmonized data where site covariate was regressed out at the level of graph measures. Results: Among 1480 edges after consistency-based thresholding, 1377 (93%) exhibited significant site effects pre-harmonization (p<0.05). Site effects at all edges were non-significant following gamma-GLM harmonization (Fig.1A). Additionally, all derived graph measures showed significant site effects before harmonization (p<0.05), while none were significant after harmonization (Fig.1B). Unharmonized data revealed no group-level differences in graph measures. A weak group-by-age interaction in clustering coefficients (standardized β=0.16, p=0.043) and a marginally significant interaction for global efficiency (standardized β=0.16, p=0.052) were noted (Fig.1C top), but no measures were linked to clinical scores. Post harmonization, males with autism showed significantly higher intra-hemispheric strength compared to neurotypicals (Cohen's d=0.37, p=0.041). Significant group-by-age interactions were observed for clustering coefficient (standardized β=0.27, p<0.001) and global efficiency (standardized β=0.26, p=0.002), with both measures increasing with age in autism but decreasing in neurotypicals (Fig.1C bottom). Intra-hemispheric strength positively correlated with SRS-2 t-score (R=0.12, p=0.012), while clustering coefficient (R=0.15, p=0.006) and global efficiency (R=0.14, p=0.013) correlated positively with VABS communication subscale. Conclusions: Our gamma-GLM harmonization can effectively remove site-related differences in structural connectomes for retrospective data integration. With gamma-GLM harmonized structural connectomes from 6 sites, we observed hyper intra-hemispheric connectivity and divergent age-related patterns in clustering coefficient and global efficiency in autistic children compared to neurotypicals.