Title
Topic
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Dong receives NSF funding for ‘Enzyme-Mimicking Catalysts for Cellulose Processing’
“Lignocellulosic biomass from plants is a renewable, carbon-neutral material produced at a scale of 170-200 billion tons per year. The depolymerization of cellulose is a key step in biomass conversion, but it is challenged by the stability and crystalline nature of the cellulose fibers. We will develop synthetic catalysts based on molecularly imprinted nanoparticles that mimic endocellulase, exocellulase and beta-glucosidase for the efficient hydrolysis of cellulose.” The title of the project is “Collaborative Research: Enzyme-Mimicking Catalysts for Cellulose Processing.”
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Brown receives DFCI funding for drug delivery in ‘metastatic, resistant breast cancer’ cases
“The objective of this work is to generate clinically relevant data to support the use of PARPi in combination with local and systemic drug delivery platforms of STING agonists in order to treat metastatic, resistant breast cancer.”
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NIH supports Deming’s research into negative mood and its relation to the brain
“Negative mood is a common feature of anxiety, depression, bipolar disorder, and schizophrenia, [inflicting] immeasurable human suffering. … There is no mechanistic explanation for how negative affect is caused in the brain. A solution to this barrier can be found in predictive processing, an emerging paradigm for unifying brain mechanisms across emotion, cognition, perception, movement and other psychological domains. … I will take advantage of a conceptual innovation from our lab and thirty years of tract-tracing studies in mammals to test the hypothesis that prediction signals and prediction error signals can be traced across specific layers of cerebral cortex and…
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Center for Research Innovation awards $300K to ‘commercially valuable inventions’
“The Spark Fund supports commercially valuable inventions from university researchers in earlier stages of development,” the Center for Research Innovation wrote, “from any field. The goal of the award is to advance a technology or suite of technologies from academia toward commercialization.” Each cycle, award recipients receive a grant up to $50,000. In Fall 2023, those recipients were professors Rebecca Carrier, Eno Ebong, Randall Erb, Leigh Plant, Dori Woods and Yi Zheng. Follow the link to read more about their individual projects.
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‘Why Look at Dead Animals?’ asks Coughlin in provocative taxidermy study
“Lion Attacking a Dromedary was a sensational object for its first viewers at the Paris Universal Exposition in 1867. … As we now know, the Verreaux brothers embedded human remains in the figure of the rider that had formerly been assumed to be just a clothed mannequin. … This essay suggests that theoretical tools derived from Material Ecocriticism and Monster Theory that may help us to think about, or alongside, the affective power of this disturbing taxidermy assemblage, ever aware that this piece draws its power from the theatrical, colonial violence of extraction and extinction.”
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‘Deep Bayesian Active Learning for Accelerating Stochastic Simulation’
“Stochastic simulations such as large-scale, spatiotemporal, age-structured epidemic models are computationally expensive at fine-grained resolution. While deep surrogate models can speed up the simulations, doing so for stochastic simulations and with active learning approaches is an underexplored area. We propose Interactive Neural Process (INP), a deep Bayesian active learning framework for learning deep surrogate models to accelerate stochastic simulations. INP consists of two components, a spatiotemporal surrogate model built upon Neural Process (NP) family and an acquisition function for active learning.” Find the paper and full list of authors in the SIGKDD Conference on Knowledge Discovery and Data Mining proceedings.
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‘Disentangling Node Attributes From Graph Topology for Improved Generalizability in Link Prediction’
“Link prediction is a crucial task in graph machine learning with diverse applications. We explore the interplay between node attributes and graph topology and demonstrate that incorporating pre-trained node attributes improves the generalization power of link prediction models. Our proposed method, UPNA (Unsupervised Pre-training of Node Attributes), solves the inductive link prediction problem by learning a function that takes a pair of node attributes and predicts the probability of an edge, as opposed to Graph Neural Networks, … which can be prone to topological shortcuts in graphs with power-law degree distribution.” Find the paper and full list of authors at…
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‘Can Euclidean Symmetry Be Leveraged in Reinforcement Learning and Planning?’
“In robotic tasks, changes in reference frames typically do not influence the underlying physical properties of the system, which has been known as invariance of physical laws.These changes, which preserve distance, encompass isometric transformations such as translations, rotations, and reflections, collectively known as the Euclidean group. In this work, we delve into the design of improved learning algorithms for reinforcement learning and planning tasks that possess Euclidean group symmetry.” Find the paper and full list of authors at ArXiv.
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‘Leveraging Structure for Improved Classification of Grouped Biased Data’
“We consider semi-supervised binary classification for applications in which data points are naturally grouped … and the labeled data is biased. … The groups overlap in the feature space and consequently the input-output patterns are related across the groups. To model the inherent structure in such data, we assume the partition-projected class-conditional invariance across groups. … We demonstrate that under this assumption, the group carries additional information about the class, over the group-agnostic features, with provably improved area under the ROC curve.” Find the paper and full list of authors in the AAAI Conference on Artificial Intelligence proceedings.
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‘Accelerating Neural MCTS Algorithms Using Neural Sub-Net Structures’
“Neural MCTS algorithms are a combination of Deep Neural Networks and Monte Carlo Tree Search (MCTS) and have successfully trained Reinforcement Learning agents in a tabula-rasa way. … However, these algorithms … take a long time to converge, which requires high computational power and electrical energy. It also becomes difficult for researchers without cutting-edge hardware to pursue Neural MCTS research. We propose Step-MCTS, a novel algorithm that uses subnet structures, each of which simulates a tree that provides a lookahead for exploration.” Find the paper and full list of authors in the International Conference on Autonomous Agents and Multiagent Systems…
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‘Sustainable HCI Under Water: Opportunities for Research with Oceans, Coastal Communities and Marine Systems’
“Although the world’s oceans play a critical role in human well-being, they have not been a primary focus of the sustainable HCI (SHCI) community to date. In this paper, we present a scoping review to show how concerns with the oceans are threaded throughout the broader SHCI literature and to find new research opportunities. We identify several themes that could benefit from focused SHCI research, including marine food sources, culture and coastal communities, ocean conservation, and marine climate change impacts and adaptation strategies.” Find the paper and full list of authors at the Conference on Human Factors in Computing Systems.
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‘Rapid Convergence: The Outcomes of Making PPE During a Healthcare Crisis’
“The U.S. National Institute of Health (NIH) 3D Print Exchange is a public, open-source repository for 3D printable medical device designs with contributions from clinicians, expert-amateur makers, and people from industry and academia. In response to the COVID-19 pandemic, the NIH formed a collection to foster submissions of low-cost, locally manufacturable personal protective equipment (PPE). We evaluated the 623 submissions in this collection … [and] found an immediate design convergence to manufacturing-focused remixes of a few initial designs affiliated with NIH partners and major for-profit groups.” Find the paper and full list of authors at ACM Transactions on Computer-Human Interaction.
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‘OPTIMISM: Enabling Collaborative Implementation of Domain Specific Metaheuristic Optimization’
“For non-technical domain experts and designers it can be a substantial challenge to create designs that meet domain specific goals. This presents an opportunity to create specialized tools that produce optimized designs in the domain. However, implementing domain-specific optimization methods requires a rare combination of programming and domain expertise. … We present OPTIMISM, a toolkit which enables programmers and domain experts to collaboratively implement an optimization component of design tools.” Find the paper and full list of authors in the Conference on Human Factors in Computing Systems proceedings.
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How a deep dive into the internet’s vital protocols earned a ‘Best Paper’ honor
Milton Posner, for the Khoury College of Computer Science, details how “A Formal Analysis of Karn’s Algorithm,” a paper written by professor Cristina Nita-Rotaru, PhD. student Max von Hippel, and “Lenore Zuck at the University of Illinois Chicago, and Ken McMillan at the University of Texas at Austin,” has won a best paper award for its exploration of a protocol important to the basic functioning of the internet.
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Ozone Tattoo project empowers citizen scientists to track pollution in their communities
The revolutionary Ozone Tattoo project, created by professor Dietmar Offenhuber, teaches observers how to identify the specific damage patterns of ground-level ozone on plant leaves. The project is now a Falling Walls 2023 award winner.
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Understanding human decision-making during supply chains shortages
“Research conducted by mechanical and industrial engineering associate professor Jacqueline Griffin, professor Ozlem Ergun, and professor Stacy Marsella [in the Khoury College of Computer science, titled] ‘Agent-Based Modeling of Human Decision-Makers Under Uncertain Information During Supply Chain Shortages’ was published in the proceedings from the 2023 International Conference on Autonomous Agents and Multiagent Systems.”
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Hajjar receives leadership award for contributions to civil engineering education
“The American Society of Civil Engineers (ASCE) has awarded … Jerome F. Hajjar, CDM Smith Professor and chair of the department of civil and environmental engineering at Northeastern University, the 2023 Thomas A. Lenox Excellence in Civil Engineering Education (ExCEEd) Leadership Award for extraordinary leadership in civil engineering education. The award was presented at the Civil Engineering Division Banquet at the 2023 Annual Conference of the American Society for Engineering Education in Baltimore, Maryland.”
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Developing new generation of intelligent tutoring systems for advanced manufacturing
“Mechanical and industrial engineering assistant professor Mohsen Moghaddam is leading a $850K NSF grant for ‘Accelerating Skill Acquisition in Complex Psychomotor Tasks via an Intelligent Extended Reality Tutoring System.’ Project collaborators include Northeastern University co-principal investigators Kemi Jona, assistant vice chancellor for digital innovation and enterprise learning, Casper Harteveld, associate professor of game design and associate dean of the College of Arts, Media and Design and Mehmet Kosa, postdoctoral research associate working with Casper. This project builds upon the ongoing research of the PI at the intersection of AI and augmented, virtual, and mixed reality, sponsored by NSF, DARPA, and…
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Air Force Office of Scientific Research provides $540K grant for video anomaly detection
“Electrical and computer engineering and Khoury College of Computer Science professor Yun Raymond Fu received a $540,000 Air Force Office of Scientific Research grant to address video anomaly detection through deep learning and perturbation techniques.”
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Wanunu receives $2M grant for ‘single-molecule’ protein identification
“Meni Wanunu, associate professor of physics and bioengineering affiliated faculty member, received a $2,000,000 R01 grant from the National Institutes of Health (NIH)/National Human Genome Research Institute (NHGRI) for ‘Asymmetric Single-Chain MspA Nanopores for Electroosmotic Stretching and Sequencing Proteins.’ … [Wanunu and collaborators] will develop a next-generation single-molecule protein sequencer based on engineered high-resolution nanopores. Protein identification and/or single-molecule protein sequencing from minute amounts could revolutionize our understanding of health by providing a picture of the molecular state of the cell at the level of its most functional molecules.”