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SUMMARY:Elderly Action Recognition Challenge at WACV2025
DESCRIPTION:Are you ready to make a real-world impact with your AI models? The EAR Challenge\, part of the prestigious Computer Vision for Smalls Workshop at WACV 2025\, is now open for registration! \n💡 Why Join? \nThis challenge is more than just a competition; it’s a mission to advance the recognition of the Activities of Daily Living (ADLs) for the elderly. Your innovations can improve safety and enhance quality of life\, paving the way for groundbreaking advancements in computer vision. \n🎯 Your Objective: \nStart with a general human action recognition benchmark and fine-tune your models on a specialized dataset of elderly-specific activities using transfer learning. Show us your robust\, adaptable\, and scalable solutions in real-world scenarios! \n📅 Important Dates: \n\nSubmission Deadline: February 15\, 2025\n\n🔗 Register Now and learn more\n#️⃣ Discord Channel\n🛠️ Workshop Page \n👥 Who Can Participate? \nEveryone is welcome\, whether you’re from academia\, industry\, or a student passionate about advancing AI for the societal good. \nProudly sponsored by Voxel51\, this is your chance to innovate\, collaborate\, and make a difference. Let’s come together to improve elderly care with cutting-edge AI solutions!
URL:https://coe.northeastern.edu/event/elderly-action-recognition-challenge-at-wacv2025/
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DTSTART;TZID=America/New_York:20250115T120000
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SUMMARY:ChE PhD Dissertation Defense: Chao Xu
DESCRIPTION:Name:\nChao Xu \nTitle:\nTowards Automated Heterogeneous Catalysis Design: Integrating Activation Energy Estimation\, Uncertainty Quantification\, and Coverage-dependent Thermodynamics in Microkinetic Modeling \nDate:\n1/15/2025 \nTime:\n12:00:00 PM \nCommittee Members:\nProf. Richard West (Advisor)\nProf. Qing Zhao\nDr. Franklin Goldsmith\nDr. Zack Ulissi \nLocation:\nEXP 610-A \nAbstract:\nIn the context of climate change\, reducing CO2 emission and advancing sustainable development are global priorities. Developing efficient “green” fuel processes requires overcoming the low productivity of industrial catalysts\, necessitating new catalyst designs. Traditional trial-and-error approaches are costly and time-intensive\, but multi-scale modeling provides a low-cost\, efficient alternative by exploring catalyst design across atomic to reactor scales. \nThis dissertation enhances scientific software such as Reaction Mechanism Generator (RMG) and Cantera etc. for automating heterogeneous catalysis modeling to accelerate catalyst design. An active-learning workflow for calculating coverage-dependent thermodynamics with EquiformerV2\, a graph neural network\, was developed\, complementing the existing RMG coverage-dependent kinetics functionality. The estimated coverage-dependent thermodynamics were validated through a CO/H2 methanation model on Ni. Because reaction activation energies are also affected by species’ coverage\, the dissertation also addresses rapid reaction barrier estimation by implementing the Blowers-Masel Approximation (BMA)\, which relates a reaction barrier to enthalpy using minimal data. Integrated into Cantera\, this method supports catalyst screening with linear scaling relationships (LSRs) and BMA kinetics. A methane partial oxidation study on 81 hypothetical metals demonstrated how BMA affects rate-limiting species and high-selectivity catalysts\, while sensitive reactions remain unaffected. \nLastly\, thermodynamic properties of surface species in catalytic methane partial oxidation models on Rh were estimated using DFT\, LSRs\, and a graph neural network named GEMNET\, combined with Bayesian parameter estimation for process optimization. The three methods provided close thermodynamic data with different prior uncertainties\, validating the feasibility of combining machine learning potentials with RMG for thermodynamic estimation on all kinds of binding facets. Bayesian parameter estimation improved simulation accuracy of the three estimation methods while uncovering active site information inaccessible through conventional means. The workflow effectively integrates experimental and computational uncertainties\, enabling data-informed catalyst design and optimization.
URL:https://coe.northeastern.edu/event/che-phd-dissertation-defense-chao-xu/
LOCATION:610-A EXP\, 360 Huntington Ave\, 610-A EXP\, Boston\, MA\, 02115\, United States
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DTSTART;TZID=America/New_York:20250115T173000
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UID:45599-1736962200-1736965800@coe.northeastern.edu
SUMMARY:Gordon Institute Virtual Information Session
DESCRIPTION:Learn how you can earn a Graduate Certificate in Engineering Leadership as a stand-alone certificate or in combination with one of twenty-three Master of Science degrees offered through Northeastern’s College of Engineering\, College of Science\, or Khoury College of Computer Sciences. \nThe National Academy of Engineering recognized The Gordon Institute of Engineering Leadership (GIEL) for its innovative curriculum\, which combines technical education\, leadership capabilities\, and the “Challenge Project”—an opportunity for students to receive master’ s-level credit while working in industry. \nBy aligning technical proficiency with leadership capabilities\, GIEL accelerates the development of high-potential engineers and prepares them to lead complex projects early in their careers. Upon completing the program\, more than 88% of the 2022 class reported increased leadership responsibility\, while more than 50% of the 2022 class reported being promoted within one year of graduation. \nOur Director of Admissions will answer your application questions for Fall 2025. \nYou will have the opportunity to hear from alumni about how The Gordon Institute propelled their engineering careers. Program professors will also be present to answer curriculum questions.
URL:https://coe.northeastern.edu/event/gordon-institute-virtual-information-session-28/
ORGANIZER;CN="Gordon Engineering Leadership program":MAILTO:gordonleadership@northeastern.edu
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