BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Penn Engineering Events - ECPv6.15.18//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Penn Engineering Events
X-ORIGINAL-URL:https://seasevents.nmsdev7.com
X-WR-CALDESC:Events for Penn Engineering Events
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20200308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20201101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20210314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20211107T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20220313T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20221106T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211129T120000
DTEND;TZID=America/New_York:20211129T130000
DTSTAMP:20260406T124850
CREATED:20210913T141946Z
LAST-MODIFIED:20210913T141946Z
UID:10006893-1638187200-1638190800@seasevents.nmsdev7.com
SUMMARY:PSOC@Penn Seminar: “Tissues fluidification in Invasive breast cancer” (Giorgio Scita)
DESCRIPTION:Room: Towne 225/Raisler Lounge \nFor zoom link\, contact manu@seas.upenn.edu.
URL:https://seasevents.nmsdev7.com/event/psocpenn-seminar-giorgio-scita/
LOCATION:Raisler Lounge (Room 225)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="PSOC":MAILTO:manu@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211130T100000
DTEND;TZID=America/New_York:20211130T113000
DTSTAMP:20260406T124850
CREATED:20211013T130316Z
LAST-MODIFIED:20211013T130316Z
UID:10006928-1638266400-1638271800@seasevents.nmsdev7.com
SUMMARY:MEAM Seminar: "Predictive Simulations of High-rise Building Peak Wind Pressure Loads"
DESCRIPTION:Computational fluid dynamics (CFD) can inform sustainable design of buildings and cities in terms of optimizing pedestrian wind comfort\, air quality\, thermal comfort\, energy efficiency\, and resiliency to extreme wind events. An important challenge is that the accuracy of CFD results can be compromised by the large natural variability and complex physics that are characteristic of urban flow problems. In this talk\, I will present an investigation of the potential of large-eddy simulations (LES) towards predicting peak wind pressure loads on high rise-buildings. First\, the sensitivity of LES results to the turbulence in the incoming boundary layer wind is quantified and the results are validated against wind tunnel experiments. Next\, high resolution LES are employed to gain a better understanding of the flow physics that govern small-scale extreme suction events observed in specific locations on the façade. Finally\, a multi-fidelity simulation strategy\, aimed at reducing the overall computational cost of LES-based cladding load predictions\, is explored. The talk will conclude with an overview of ongoing work on full-scale wind pressure measurements on high-rise buildings for future validation and uncertainty quantification studies.
URL:https://seasevents.nmsdev7.com/event/meam-seminar-predictive-simulations-of-high-rise-building-peak-wind-pressure-loads/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Mechanical Engineering and Applied Mechanics":MAILTO:meam@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211130T110000
DTEND;TZID=America/New_York:20211130T120000
DTSTAMP:20260406T124850
CREATED:20211124T222730Z
LAST-MODIFIED:20211124T222730Z
UID:10006979-1638270000-1638273600@seasevents.nmsdev7.com
SUMMARY:ESE Fall Colloquium Seminar -  "Bits and Brains: Ultra-low Power\, Neuro-inspired Edge-AI for Autonomous Systems"
DESCRIPTION:As we march towards the age of “ubiquitous intelligence”\, we note that AI and Machine learning are progressively moving from the Cloud to the Edge devices. The success of Edge-AI is pivoted on innovative circuits and hardware that can enable inference and limited learning\, in hardware-constrained ultra-low-power (uW to mW) systems – an area of active research. In this talk\, I will discuss the promises and outlook of Edge-AI and their applications in Autonomous Systems; and elaborate on some of our recent work on enabling such systems in sensor nodes and robotics. While some of these systems extend our understanding of statistical machine learning\, a large class of circuits and systems are inspired by the information representation in the brain. I will talk about the design of such circuits and systems with an emphasis on the impact of mixed-signal circuits\, near-memory and in-memory compute architectures\, non-CMOS (RRAM-based) compute macros\, as well as algorithm-hardware co-design to realize the most energy-efficient Edge-AI ASICs for the next generation of smart and autonomous systems.
URL:https://seasevents.nmsdev7.com/event/ese-fall-colloquium-seminar-bits-and-brains-ultra-low-power-neuro-inspired-edge-ai-for-autonomous-systems/
LOCATION:Zoom – Meeting ID 912 5944 4192
CATEGORIES:Seminar,Colloquium
ORGANIZER;CN="Electrical and Systems Engineering":MAILTO:eseevents@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211130T153000
DTEND;TZID=America/New_York:20211130T163000
DTSTAMP:20260406T124850
CREATED:20211116T184503Z
LAST-MODIFIED:20211116T184503Z
UID:10006972-1638286200-1638289800@seasevents.nmsdev7.com
SUMMARY:CIS Seminar: "Tackling climate change with machine learning"
DESCRIPTION:Machine learning can be a powerful tool in helping society reduce greenhouse gas emissions and adapt to a changing climate. In this talk\, we will explore opportunities and challenges in AI-for-climate\, from optimizing electrical grids to monitoring crop yield\, and how these impactful problems can also drive methodological innovations in machine learning.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-tackling-climate-change-with-machine-learning/
LOCATION:Zoom – Email CIS for link\, cherylh@cis.upenn.edu
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211201T150000
DTEND;TZID=America/New_York:20211201T160000
DTSTAMP:20260406T124850
CREATED:20211129T201637Z
LAST-MODIFIED:20211129T201637Z
UID:10006982-1638370800-1638374400@seasevents.nmsdev7.com
SUMMARY:Fall 2021 GRASP SFI: Lucas Manuelli\, NVIDIA\, “Robot Manipulation with Learned Representations”
DESCRIPTION:We would like to have robots which can perform useful manipulation tasks in real-world environments. This requires robots that can perceive the world with both precision and semantic understanding\, methods for communicating desired tasks to these systems\, and closed loop visual feedback controllers for robustly executing manipulation tasks. This is hard to achieve with previous methods: prior work hasn’t enabled robots to densely understand the visual world with sufficient precision to perform robotic manipulation or endowed them with the semantic understanding needed to perform tasks with novel objects. This limitation arises partly from the object representations that have been used\, the challenge in extracting these representations from the available sensor data in real-world settings\, and the manner in which tasks have been specified. The talk will have two sections. In the first section I will focus on object-centric representations and will present a family of approaches that leverage self-supervision\, both in the visual domain and for learning physical dynamics\, to enable robots to perform manipulation tasks. Specifically we (i) demonstrate the novel application of dense visual object descriptors to robotic manipulation and provide a fully self-supervised robot system to acquire them (ii) introduce the concept of category-level manipulation tasks and develop a novel object representation based on semantic 3D keypoints along with a task specification that uses these keypoints to define the task for all objects of a category\, including novel instances\, (iii) utilize our dense visual object descriptors to quickly learn new manipulation skills through imitation and (iv) use our visual object representations to learn data-driven models that can be used to perform closed loop feedback control in manipulation tasks. The second part of the talk will discuss an alternative action-centric approach that enables the incorporation of language-instructions in our manipulation pipelines.
URL:https://seasevents.nmsdev7.com/event/fall-2021-grasp-sfi-lucas-manuelli-nvidia-robot-manipulation-with-learned-representations/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211201T153000
DTEND;TZID=America/New_York:20211201T163000
DTSTAMP:20260406T124850
CREATED:20210831T004042Z
LAST-MODIFIED:20210831T004042Z
UID:10006871-1638372600-1638376200@seasevents.nmsdev7.com
SUMMARY:CBE Seminar: "From Molecular Simplicity to Supramolecular Complexity: Low Symmetry Packings of Ionic Spherical Micelles"
DESCRIPTION:Abstract \nA delicate balance of noncovalent interactions drives hydrated molecular amphiphiles to self-assemble into lyotropic liquid crystals (LLCs) of varied topologies\, the exquisite nanodomain structures of which suggest applications as separations membranes\, mesoporous materials synthesis templates\, and therapeutic delivery vehicles. Based on hard sphere colloidal crystals\, spherical micelles are intuitively expected to form high symmetry face-centered cubic (FCC) and hexagonally close-packed (HCP) LLCs. However\, we recently discovered that ionic surfactant micelles also form a zoo of low symmetry\, tetrahedrally close-packed Frank-Kasper (FK) phases that mimic the structures of elemental metals and their alloys. The emergence of complex FK s\, A15\, C14\, and C15 LLCs arises from a frustrated non-covalent force balance that minimizes local variations in amphiphile solvation\, while maximizing electrostatic cohesion in the ionic micelle ensemble. We describe how chemical features of the amphiphiles and the path-dependent processing of their aqueous dispersions drive LLC sphere packing symmetry selection\, culminating in our discovery of the ability of oil-swollen ionic micelles to aperiodically order into lyotropic dodecagonal quasicrystals (DDQCs). Thus\, engineering molecular frustration into simple amphiphiles begets high fidelity\, (a)periodic pattern formation at length scales far exceeding those of their constituent molecular and supramolecular building blocks.
URL:https://seasevents.nmsdev7.com/event/cbe-seminar-from-molecular-simplicity-to-supramolecular-complexity-low-symmetry-packings-of-ionic-spherical-micelles/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Seminar
ORGANIZER;CN="Chemical and Biomolecular Engineering":MAILTO:cbemail@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211202T123000
DTEND;TZID=America/New_York:20211202T133000
DTSTAMP:20260406T124850
CREATED:20211118T211608Z
LAST-MODIFIED:20211118T211608Z
UID:10006974-1638448200-1638451800@seasevents.nmsdev7.com
SUMMARY:MSE Seminar: "Data-driven materials design in the quantum regime: motif-centric learning framework and local-symmetry-guided material discovery"
DESCRIPTION:Materials design in the quantum regime call for the integration of multi-tier materials information that go beyond atomic structures. Especially\, many quantum behaviors are greatly controlled by local symmetries and local bonding environments. In this talk\, motivated by Pauling’s rules\, Dr. Yan will show that local bonding environments (motifs) can be incorporated in a graph-based machine learning architecture to make reliable property predictions for solid-state quantum materials including complex metal oxides. The proposed atom-motif dual network model demonstrates the feasibility to incorporate beyond-atom materials information in a graph network framework and achieves the state-of-the-art performance in predicting the electronic structure properties of complex metal oxides. As an example of quantum material design by local bonding symmetry\, he will discuss how data-driven material science can be combined with symmetry-based physical principles to guide the search for quantum defects in two-dimensional (2D) materials for quantum information processing and quantum computing. The use of local bonding symmetry (irreducible representations) as a material design hypothesis enables the identification of anion antisite defects as promising spin qubits and quantum emitters in six monolayer transition metal dichalcogenides. The work creates a technically accessible 2D platform for the fabrication of defect-based multi-qubit systems for quantum computing. At the end of the talk\, he will discuss the continued development of machine learning models that embrace symmetries and symmetry-based interactions and the discovery of quantum defects in a vast space of 2D material systems.
URL:https://seasevents.nmsdev7.com/event/mse-seminar-data-driven-materials-design-in-the-quantum-regime-motif-centric-learning-framework-and-local-symmetry-guided-material-discovery/
LOCATION:Auditorium\, LRSM Building\, 3231 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Materials Science and Engineering":MAILTO:johnruss@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211202T140000
DTEND;TZID=America/New_York:20211202T150000
DTSTAMP:20260406T124850
CREATED:20211124T170430Z
LAST-MODIFIED:20211124T170430Z
UID:10006978-1638453600-1638457200@seasevents.nmsdev7.com
SUMMARY:Fall 2021 GRASP Seminar: Gregory S. Chirikjian\, "Robot Imagination: Affordance-Based Reasoning about Unknown Objects"
DESCRIPTION:Today’s robots are very brittle in their intelligence. This follows from a legacy of industrial robotics where robots pick and place known parts repetitively. For humanoid robots to function as servants in the home and in hospitals they will need to demonstrate higher intelligence\, and must be able to function in ways that go beyond the stiff prescribed programming of their industrial counterparts. A new approach to service robotics is discussed here. The affordances of common objects such as chairs\, cups\, etc.\, are defined in advance. When a new object is encountered\, it is scanned and a virtual version is put into a simulation wherein the robot “imagines’’ how the object can be used. In this way\, robots can reason about objects that they have not encountered before\, and for which they have no training using. Videos of physical demonstrations will illustrate this paradigm\, which the presenter has developed with his students Hongtao Wu\, Meng Xin\, Sipu Ruan\, and others.
URL:https://seasevents.nmsdev7.com/event/fall-2021-grasp-seminar-gregory-s-chirikjian-robot-imagination-affordance-based-reasoning-about-unknown-objects/
LOCATION:Levine 307\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211202T153000
DTEND;TZID=America/New_York:20211202T163000
DTSTAMP:20260406T124850
CREATED:20211202T132732Z
LAST-MODIFIED:20211202T132732Z
UID:10006985-1638459000-1638462600@seasevents.nmsdev7.com
SUMMARY:CIs Seminar: "Modeling cancer progression with neural networks"
DESCRIPTION:The exponential growth of biological datasets in recent years has increased the dependency of cancer research on AI and computational analysis. Cancer evolves through the accumulation of genetic events over time\, which are represented through different types of data. Integration of machine learning methods with temporal modeling and genomic methods has the potential to improve our understanding and treatment of cancer. In this talk\, we will discuss emerging data science challenges for cancer research and opportunities to address these challenges using integrated AI frameworks.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-modeling-cancer-progression-with-neural-networks/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211203T103000
DTEND;TZID=America/New_York:20211203T114500
DTSTAMP:20260406T124850
CREATED:20211110T144715Z
LAST-MODIFIED:20211110T144715Z
UID:10006964-1638527400-1638531900@seasevents.nmsdev7.com
SUMMARY:GRASP on Robotics: "Toward Object Manipulation Without Explicit Models"
DESCRIPTION:*This will be a HYBRID Event with in-person attendance in Wu & Chen Auditorium and Virtual attendance via Zoom Webinar here.  \nThe prevalent approach to object manipulation is based on the availability of explicit 3D object models. By estimating the pose of such object models in a scene\, a robot can readily reason about how to pick up an object\, place it in a stable position\, or avoid collisions. Unfortunately\, assuming the availability of object models constrains the settings in which a robot can operate\, and noise in estimating a model’s pose can result in brittle manipulation performance. In this talk\, I will discuss our work on learning to manipulate unknown objects directly from visual (depth) data. Without any explicit 3D object models\, these approaches are able to segment unknown object instances\, pickup objects in cluttered scenes\, and re-arrange them into desired configurations. I will also present recent work on combining pre-trained language and vision models to efficiently teach a robot to perform a variety of manipulation tasks. I’ll conclude with our initial work toward learning implicit representations for objects.
URL:https://seasevents.nmsdev7.com/event/grasp-on-robotics-toward-object-manipulation-without-explicit-models/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="General Robotics%2C Automation%2C Sensing and Perception (GRASP) Lab":MAILTO:grasplab@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211203T140000
DTEND;TZID=America/New_York:20211203T150000
DTSTAMP:20260406T124850
CREATED:20210920T160029Z
LAST-MODIFIED:20210920T160029Z
UID:10006912-1638540000-1638543600@seasevents.nmsdev7.com
SUMMARY:PICS Colloquium: "Computational and Experimental Modeling of Membrane Distillation"
DESCRIPTION:Abstract: \nComputational fluid dynamics simulations and experiments were conducted to study the performance of distillation membrane process for water treatment and desalination applications. Polarizations and fouling/scaling in a direct contact membrane distillation module were examined. Laminar and turbulent flow regimes in the feed and permeate channels were considered. The hydrophobic membrane separates feed and permeate channels. The membrane is treated as a functional surface where the permeate flux is modeled as a function of local temperature\, concentration using the Dusty-Gas diffusion model. The combination of Knudsen and viscous diffusion governs the vapor transport through the membrane. Vaporization occurs in the feed channel at the entrance of pores of the membrane due to the well-known confinement (surface tension) effect. Vapor is pushed through the membrane by the partial pressure difference created by the temperature concentration difference across the membrane. The permeability (resistance) of the membrane depends strongly on the membrane thickness\, porosity\, pore size\, and tortuosity. Mitigation of polarizations and fouling/scaling and the optimization of module design will be presented and discussed.
URL:https://seasevents.nmsdev7.com/event/pics-colloquium-computational-and-experimental-modeling-of-membrane-distillation/
LOCATION:PICS Conference Room 534 – A Wing \, 5th Floor\, 3401 Walnut Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Colloquium
ORGANIZER;CN="Penn Institute for Computational Science (PICS)":MAILTO:dkparks@seas.upenn.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211203T150000
DTEND;TZID=America/New_York:20211203T170000
DTSTAMP:20260406T124850
CREATED:20211115T161436Z
LAST-MODIFIED:20211115T161436Z
UID:10006970-1638543600-1638550800@seasevents.nmsdev7.com
SUMMARY:Keeping Up the Good Work: Honoring 40 Years of Excellence in Diversity
DESCRIPTION:This year marks the 40th anniversary of Penn Engineering’s diversity efforts and we invite you to join us on Friday\, December 3\, at 3:00 p.m. EST to celebrate Penn Engineering’s new Office of Diversity\, Equity\, and Inclusion (ODEI)\, formerly known as the Office of Minority Programs.\n\nWe plan to meet in Heilmeier Hall (Towne 100) from 3:00-3:30 p.m. for remarks and we will then move upstairs to 211 Towne to officially unveil and tour the brand new ODEI Suite.\n\nThe new suite that will be unveiled honors and builds upon the remarkable work of Ms. Cora Ingrum\, ODEI’s former Director and her Administrative Assistant\, Ms. Donna Hampton. Over the years\, the office has been instrumental in identifying and retaining diverse engineering talent and helping students successfully transition into graduate and professional programs as well as productive professional careers. ODEI has a legacy of rich programs and partnerships with academia and industry that has continued to be strengthened under the leadership of Dr. Laura Stubbs\, the current Director of ODEI.\n\n\nThe recording of this event is posted here for your viewing.\nhttps://youtu.be/y7-cTd03e3Y\n\n\n  \n\n\n 
URL:https://seasevents.nmsdev7.com/event/keeping-up-the-good-work-honoring-40-years-of-excellence-in-diversity/
LOCATION:Heilmeier Hall (Room 100)\, Towne Building\, 220 South 33rd Street\, Philadelphia\, PA\, 19104\, United States
CATEGORIES:Faculty,Graduate,Student,Master's,Postdoctoral,Alumni,Undergraduate,Diversity, Equity and Inclusion,Staff
END:VEVENT
END:VCALENDAR