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DTSTART;TZID=America/New_York:20210303T150000
DTEND;TZID=America/New_York:20210303T160000
DTSTAMP:20260511T055549
CREATED:20210225T021013Z
LAST-MODIFIED:20210225T021047Z
UID:4763-1614783600-1614787200@ece.northeastern.edu
SUMMARY:AIX SEMINAR SERIES
DESCRIPTION:We cordially invite you to join the AIX SEMINAR \nZoom Link: https://northeastern.zoom.us/j/96209636039 \n__________________________________________________________________________________ \nLearning Strong Inference Models in Small Data Domains: Enabling AI in Data Extreme ML\nDr. Sarah Ostadabbas  | Assistant Professor\, Northeastern University \nHuman Factors in Artificial Decision Making: Understanding Humans and Helping Humans Understand Learning Agents\nDr. Pedro Sequeira | Advanced Computer Scientist\, SRI International \n__________________________________________________________________________________ \nLearning Strong Inference Models in Small Data Domains: Enabling AI in Data Extreme ML\nSarah Ostadabbas | Assistant Professor\, Electrical and Computer Engineering\, Northeastern University \nAbstract:Recent efforts in machine learning (especially with the new waves of deep learning introduced in the last decade) have obliterated records for regression and classification tasks that have previously seen only incremental accuracy improvements. There are many other fields that would significantly benefit from machine learning (ML)-based inferences where data collection or labeling is expensive. In these domains (i.e. Small Data domains)\, the challenge we now face is how to learn efficiently with the same performance with less data. Many applications will benefit from a strong inference framework with deep structure that will: (i) work with limited labeled training samples; (ii) integrate explicit (structural or data-driven) domain knowledge into the inference model as editable priors to constrain search space; and (iii) maximize the generalization of learning across domains. My research aims to explore a generalized ML approach to solve the small data problem that leverages existing research and fills in key gaps with original work. There are two basic approaches to reduce data needs during model training: (1) decrease inference model learning complexity via data-efficient machine learning\, and (2) incorporate domain knowledge in the learning pipeline through the use of data-driven or simulation-based generative models. In this talk\, I present my recent work on merging the benefits of these two approaches to enable the training of robust and accurate (i.e. strong) inference models that can be applied on real-world problems dealing with data limitation. My plan to achieve this aim is structured in four research thrusts: (i) introduction of physics- and/or data-driven computational models here referred to as weak generator to synthesize enough labeled data in an adjacent domain; (ii) design and analysis of unsupervised domain adaptation techniques to close the gap between the domain adjacent and domain specific data distributions; (iii) combined use of the weak generator\, a weak inference model and an adversarial framework to refine the domain adjacent dataset by employing a set of unlabeled domain specific dataset; and (iv) development and analysis of co-labeling/active learning techniques to select the most informative datasets to refine and adapt the weak inference model into a strong inference model in the target application. \nBio: Professor Ostadabbas is an assistant professor in the Electrical and Computer Engineering Department of Northeastern University (NEU)\, Boston\, Massachusetts\, USA. Professor Ostadabbas joined NEU in 2016 from Georgia Tech\, where she was a post-doctoral researcher following completion of her PhD at the University of Texas at Dallas in 2014. At NEU\, Professor Ostadabbas is the director of the Augmented Cognition Laboratory (ACLab) with the goal of enhancing human information-processing capabilities through the design of adaptive interfaces via physical\, physiological\, and cognitive state estimation. These interfaces are based on rigorous models adaptively parameterized using machine learning and computer vision algorithms. In particular\, she has been integrating domain knowledge with machine learning by using physics-based simulation as generative models for bootstrapping deep learning recognizers. Professor Ostadabbas is the co-author of more than 70 peer-reviewed journal and conference articles and her research has been awarded by the National Science Foundation (NSF)\, Mathworks\, Amazon AWS\, Biogen\, and NVIDIA. ​She co-organized the Multimodal Data Fusion (MMDF2018) workshop\, an NSF PI mini-workshop on Deep Learning in Small Data\, the CVPR workshop on Analysis and Modeling of Faces and Gestures from 2019 and she was the program chair of the Machine Learning in Signal Processing (MLSP2019). Prof. Ostadabbas is an associate editor of the IEEE Transactions on Biomedical Circuits and Systems\, on the Editorial Board of the IEEE Sensors Letters and Digital Biomarkers Journal\, and has been serving in several signal processing and machine learning conferences as a technical chair or session chair. She is a member of IEEE\, IEEE Computer Society\, IEEE Women in Engineering\, IEEE Signal Processing Society\, IEEE EMBS\, IEEE Young Professionals\, International Society for Virtual Rehabilitation (ISVR)\, and ACM SIGCHI​. \n__________________________________________________________________________________ \n  \nHuman Factors in Artificial Decision Making: Understanding Humans and Helping Humans Understand Learning Agents\nDr. Pedro Sequeira | Advanced Computer Scientist\, SRI International\, Artificial Intelligence Center \nAbstract: In this talk I will overview some of my research in the broad topic of human factors in artificial decision-making. I will start by showing how reinforcement learning (RL) agents\, equipped with intrinsic motivation provided by emotion appraisal-like rewards\, can learn more efficiently and overcome perceptual limitations. In the second part of the talk\, I will present a program synthesis approach for automated cognitive behavior analysis (ACBA)\, where genetic programming (GP) is used to search for programs that are able to reproduce observed human decisions and thereby help understand their underlying strategies and goals. I will show the results of an experiment where we used ACBA-GP to analyze human negotiation behavior\, which generated programs resulting in strategies consistent with the way people with different personality traits address negotiation tasks. Finally\, I will overview our work in the area of explainable RL (XRL)\, where a framework based on interestingness elements identifies relevant decision points given an RL policy that can help understand an agent’s behaviors in a task. I will show the results of a user study where we presented people short video clips of RL agents\, selected using our XRL framework\, allowing the subjects to correctly identify the capabilities and limitations of different agents in a task. \nBio: Dr. Pedro Sequeira is an advanced computer scientist at SRI International in the Artificial Intelligence Center (AIC). His research interests are mainly in the field of Machine Learning (ML) and involve the creation of autonomous and adaptive systems that learn and reason under uncertainty. His approach is based on creating ML mechanisms inspired by human learning and decision-making and use ML to better understand how humans learn and make decisions in complex tasks. Prior to joining SRI\, Dr. Sequeira was an associate research scientist at Northeastern University (NU) working in the Cognitive Embodied Social Agents Research (CESAR) lab\, led by Prof. Stacy Marsella\, on the topics of modeling human decision-making from observation and multiagent systems in the context of pharmaceutical supply-chains. Dr. Sequeira completed the Ph.D. Program in Information Systems and Computer Engineering in 2013 at Instituto Superior Técnico (IST)\, Universidade de Lisboa in Portugal\, under the supervision of Prof. Ana Paiva and Prof. Francisco S. Melo. His thesis focused on building more flexible and robust reward mechanisms for intrinsically-motivated reinforcement learning agents\, based on appraisal theories of emotions. \n_________________________________________ \nTo Receive Further AIX Seminar Notifications \nSign up to receive further AIX seminar notifications \nPresented by the Institute for Experiential Robotics at Northeastern University \n 
URL:https://ece.northeastern.edu/event/aix-seminar-series-2/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210318T123000
DTEND;TZID=America/New_York:20210318T133000
DTSTAMP:20260511T055549
CREATED:20210316T213855Z
LAST-MODIFIED:20210317T003641Z
UID:4786-1616070600-1616074200@ece.northeastern.edu
SUMMARY:IEEE Magnetics Society Distinguished Seminar: Dr. Mathias Kläui
DESCRIPTION:IEEE Magnetics Society Seminar for 2021 \nDistinguished Lecture: Dr. Mathias Kläui \nJointly hosted by: Richmond Section Jt. Chapter\, Washington/Northern Virginia Jt Chapter\, and the Boston Section Chapter \n\nPlease join us online on Thursday\, March 18th\, at 12:30 pm for the first installment of out IEEE Distinguished Lecture Seminar Series.\nJoin with Google Meet: http://meet.google.com/xft-dvqw-pac\nJoin by phone: (US) +1 413-359-0778; PIN: 184921174#\n(US) +1 413-359-0778 PIN: 184 921 174# \n\nMathias Kläui is professor of physics at Johannes Gutenberg-University Mainz and adjunct professor at the Norwegian University of Science and Technology. He received his PhD at the University of Cambridge\, after which he joined the IBM Research Labs in Zürich. He was a junior group leader at the University of Konstanz and then became associate professor in a joint appointment between the EPFL and the PSI in Switzerland before moving to Mainz. His research focuses on nanomagnetism and spin dynamics on the nanoscale in new materials. His research covers from blue sky fundamental science to applied projects with major industrial partners. He has published more than 300 articles and given more than 200 invited talks. He is a Senior member of the IEEE and\, a Fellow of the IOP and has been awarded a number of prizes and scholarships. He is one of the 2020/2021 IEEE Magnetics Society Distinguished Lecturers. Contact details and more information at www.klaeui-lab.de. \n\nAntiferromagnetic Insulatronics: Spintronics without Magnetic fields \nWhile known for a long time\, antiferromagnetically ordered\nsystems have previously been considered\, as expressed by Louis Néel in his Nobel Prize Lecture\, to be “interesting but useless”. However\, since antiferromagnets potentially promises faster operation\, enhanced stability with respect to interfering magnetic fields and higher integration due to the absence of dipolar coupling\, they could potentially become a game changer for new spintronic devices. The zero net moment makes manipulation using conventional magnetic fields challenging. However recently\, these materials have received renewed attention due to possible manipulation based on new approaches such as photons or spin-orbit torques. In this talk\, we will present an overview of the key features of antiferromagnets to potentially functionalize their unique properties. This includes writing\, reading and transporting information using antiferromagnetic. \nWe recently realized switching in the metallic antiferromagnet Mn2Au by intrinsic staggered spin-orbit torques and characterize the switching properties by direct imaging. While switching by staggered intrinsic spin-orbit torques in metallic AFMs requires special structural asymmetry\, interfacial non-staggered spin-orbit torques can switch multilayers of many insulating AFMs capped with heavy metal layers. We probe switching and spin transport in selected collinear insulating antiferromagnets\, such as NiO\, CoO and hematite. In NiO and CoO we find that there are multiple switching mechanisms that result in the reorientation of the Néel vector and additionally effects related to electromigration of the heavy metal layer can obscure the magnetic switching. For the spin transport\, spin currents are generated by heating as resulting from the spin Seebeck effect and by spin pumping measurements and we find in vertical transport short (few nm) spin diffusion lengths. For hematite\, however\, we find in a non-local geometry that spin transport of tens of micrometers is possible. We detect a first harmonic signal\, related to the spin conductance\, that exhibits a maximum at the spin-flop reorientation\, while the second harmonic signal\, related to the Spin Seebeck conductance\, is linear in the amplitude of the applied magnetic field. The first signal is dependent on the direction of the Néel vector and the second one depends on the induced magnetic moment due to the field. We identify the domain structure as the limiting factor for the spin transport. We recently also achieved transport in the easy plane phase\, which allows us to obtain long distance spin transport in hematite even at room temperature. From the power and distance dependence\, we unambiguously distinguish long-distance transport based on diffusion from predicted spin superfluidity that can potentially be used for logic. A number of excellent reviews are available for further information on recent developments in the field.
URL:https://ece.northeastern.edu/event/ieee-magnetics-society-distinguished-seminar-dr-mathias-klaui/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210322T100000
DTEND;TZID=America/New_York:20210322T110000
DTSTAMP:20260511T055549
CREATED:20210226T235630Z
LAST-MODIFIED:20210315T180259Z
UID:4767-1616407200-1616410800@ece.northeastern.edu
SUMMARY:ECE MS Thesis Defense: Ibrahim Farah
DESCRIPTION:MS Thesis Defense: Multipath Beam Steering for OFDM Systems \nIbrahim Farah \nLocation: Zoom \nAbstract: OFDM systems prove effective in combatting the frequency-selective fading\, dispersive response of underwater acoustic channels. Coherent symbol detection requires estimation of this channel response at each receiving element\, typically done through tap-based models (Least Squares) or path-based models (Path Identification). This talk presents a spatial processing design which uses a multi-channel receiver to isolate individual multi-path returns\, both easing the channel estimation requirements and increasing the SNR for symbol detection. The beam steering algorithms are considered in both a coherent and differential OFDM system context and include narrowband and broadband beamforming to the principal\, stable path\, as well as narrowband and broadband null-steering. These spatial processing algorithms are then extended to an iterative implementation\, which approaches the theoretical performance for a beamformer with full multipath channel knowledge. The performance of these algorithms is compared to their single-channel equivalents using both the LS and PI algorithms for coherent detection.
URL:https://ece.northeastern.edu/event/ms-thesis-defense-ibrahim-farah/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210326T120000
DTEND;TZID=America/New_York:20210326T130000
DTSTAMP:20260511T055549
CREATED:20210323T180009Z
LAST-MODIFIED:20210323T180009Z
UID:4808-1616760000-1616763600@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Mo Han
DESCRIPTION:PhD Dissertation Defense: Human Grasp Intent Inference and Multimodal Control in Prosthetic Hands \nMo Han \nLocation: Zoom Link \nAbstract: Upper limb and hand functionality is critical to many activities of daily living and the amputation of one can lead to significant functionality loss for individuals. From this perspective\, advanced prosthetic hands of the future are anticipated to benefit from improved shared control between a robotic hand and its human user\, but more importantly from the improved capability to infer human intent from multimodal sensor data to provide the robotic hand perception abilities regarding the operational context. Such multimodal data may be collected from various environment sensors such as camera providing visual information\, as well as easily-accessed human physiologic sensors including electromyographic (EMG) sensors. A fusion methodology for environmental state and human intent estimation can combine these sources of evidence in order to help prosthetic hand motion planning and control. \nAs part of a multi-disciplinary project\, i.e. HANDS project\, which aims at designing a robotic hand as an upper limb prosthetic device\, we developed two independent prosthetic control systems (HANDS V1 and HANDS V2) integrating multimodal sources of EMG and visual evidences into the control loop. Multiple grasps required for activities of daily living can be performed by both robotic systems which were developed in a lighter and cheaper semi-autonomous manner. The HANDS V1 system was first developed to provide an easy and convenient prosthesis with a portable EMG armband and a built-in palm camera\, and hereafter the HANDS V2 was constructed as an upgraded solution of HANDS V1 to achieve more difficult tasks with more identified grasp types\, more EMG channels and more complicated visual information involved. Both systems depend on multimodal signals from EMG and vision\, where the EMG could reflect the physiologic features related to user intents\, while the robustness and adaptability to different users could be retained by the visual information relying more on surrounding environments. We collected two datasets for the initialization of each system\, and the developments of the EMG-control\, visual-control\, and joint-control algorithms were conducted for both systems. We exploited efficient computer vision and physiological signal processing methodologies to decrease the system complexity as well as improve the user comfort\, in order to provide smarter and cheaper prosthetic hands to the audience. Online experiments were executed and evaluated on both HANDS V1 and HANDS V2 systems\, implemented by the Robot Operating System (ROS) system.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-mo-han/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210326T130000
DTEND;TZID=America/New_York:20210326T140000
DTSTAMP:20260511T055549
CREATED:20210322T180141Z
LAST-MODIFIED:20210322T180141Z
UID:4805-1616763600-1616767200@ece.northeastern.edu
SUMMARY:ECE Seminar: Sara Dean
DESCRIPTION:ECE Seminar: Reliable Machine Learning in Feedback Systems \nSara Dean \nLocation: Zoom Link \nAbstract: Machine learning techniques have been successful for processing complex information\, and thus they have the potential to play an important role in data-driven decision-making and control. However\, ensuring the reliability of these methods in feedback systems remains a challenge\, since classic statistical and algorithmic guarantees do not always hold. In this talk\, I will provide rigorous guarantees of safety and discovery in dynamical settings relevant to robotics and recommendation systems. I take a perspective based on reachability\, to specify which parts of the state space the system avoids (safety) or can be driven to (discovery). For data-driven control\, we show finite-sample performance and safety guarantees which highlight relevant properties of the system to be controlled. For recommendation systems\, we introduce a novel metric of discovery and show that it can be efficiently computed. In closing\, I discuss how the reachability perspective can be used to design social-digital systems with a variety of important values in mind. \nBio: Sarah is a PhD candidate in the Department of Electrical Engineering and Computer Science at UC Berkeley\, advised by Ben Recht. She received her MS in EECS from Berkeley and BSE in Electrical Engineering and Math from the University of Pennsylvania. Sarah is interested in the interplay between optimization\, machine learning\, and dynamics in real-world systems. Her research focuses on developing principled data-driven methods for control and decision-making\, inspired by applications in robotics\, recommendation systems\, and developmental economics. She is a co-founder of a transdisciplinary student group\, Graduates for Engaged and Extended Scholarship in computing and Engineering\, and the recipient of a Berkeley Fellowship and a NSF Graduate Research Fellowship.
URL:https://ece.northeastern.edu/event/ece-seminar-sara-dean/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20210330T093000
DTEND;TZID=America/New_York:20210330T103000
DTSTAMP:20260511T055549
CREATED:20210322T180259Z
LAST-MODIFIED:20210322T180259Z
UID:4807-1617096600-1617100200@ece.northeastern.edu
SUMMARY:Electrical and Computer Engineering Graduate Programs Webinar
DESCRIPTION:Please join faculty\, staff\, and current students to learn more about graduate programs in the Electrical and Computer Engineering Department on March 30 at 9:30 EST. \nRegistration may be found at:  https://us02web.zoom.us/webinar/register/WN_cKfKDbSOQQu63xcwc9y4WA \nA recording will be available for those who are unable to attend.
URL:https://ece.northeastern.edu/event/electrical-and-computer-engineering-graduate-programs-webinar/
ORGANIZER;CN="Graduate School of Engineering":MAILTO:coe-gradadmissions@northeastern.edu
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