News & Events

217 MERL Events and MERL Talks found.



Learn about the MERL Seminar Series.



  •  TALK    [MERL Seminar Series 2024] Di Shi presents talk titled AI-assisted Power Grid Dispatch and Control: Optimization, Safety, and Real-world Demonstrations
    Date & Time: Wednesday, November 20, 2024; 1:00 PM
    Speaker: Di Shi, New Mexico State University
    MERL Host: Hongbo Sun
    Research Areas: Artificial Intelligence, Data Analytics, Optimization
    Abstract
    • This presentation delves into the challenges and advancements in optimizing power system operations through Grid Mind, an innovative, data-driven framework designed to enhance the integration of renewable energy sources. Utilizing advanced learning algorithms, Grid Mind excels in strategic resource allocation and control, significantly improving efficiency and reliability in power systems with high renewable energy penetration. The transformative potential of this AI-assisted technology is highlighted through real-world applications, demonstrating its effectiveness in addressing the complexities of modern power systems. In addition, critical safety considerations and practical deployment challenges are explored, emphasizing the need for robust, secure, and adaptable solutions. This talk also discusses the capabilities of Grid Mind as a distributed, learning-based system optimized for edge devices, marking a significant advancement toward sustainable, safe, and efficient power system operations in an era dominated by renewable energy.
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  •  TALK    [MERL Seminar Series 2024] Samuel Clarke presents talk titled Audio for Object and Spatial Awareness
    Date & Time: Wednesday, October 30, 2024; 1:00 PM
    Speaker: Samuel Clarke, Stanford University
    MERL Host: Gordon Wichern
    Research Areas: Artificial Intelligence, Machine Learning, Robotics, Speech & Audio
    Abstract
    • Acoustic perception is invaluable to humans and robots in understanding objects and events in their environments. These sounds are dependent on properties of the source, the environment, and the receiver. Many humans possess remarkable intuition both to infer key properties of each of these three aspects from a sound and to form expectations of how these different aspects would affect the sound they hear. In order to equip robots and AI agents with similar if not stronger capabilities, our research has taken a two-fold path. First, we collect high-fidelity datasets in both controlled and uncontrolled environments which capture real sounds of objects and rooms. Second, we introduce differentiable physics-based models that can estimate acoustic properties of objects and rooms from minimal amounts of real audio data, then can predict new sounds from these objects and rooms under novel, “unseen” conditions.
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  •  EVENT    Prof. Na Li of Harvard University to Deliver Keynote at MERL's Virtual Open House 2024
    Date & Time: Tuesday, November 19, 2024; 1:30-2:10pm
    Location: Virtual Event
    Speaker: Prof. Na Li, Harvard University
    Brief
    • MERL is excited to announce the featured keynote speaker for our Virtual Open House (VOH) 2024: Prof. Na Li from Harvard University.

      Our VOH this year will take place on November 19th, 1:00pm - 4:30pm (EST). Prof. Li’s talk is scheduled for 1:30-2:10pm (EST). For details and agenda of the event, please visit: https://merl.com/events/voh24

      Join us to learn more about who we are, what we do, and discuss our internship, post-doc, and full-time employment opportunities. To register, go to: https://mailchi.mp/merl/voh24

      Title: Representation-based Learning and Control for Dynamical Systems
      Abstract: The explosive growth of machine learning and data-driven methodologies have revolutionized numerous fields. Yet, the translation of these successes to the domain of dynamical physical systems remains a significant challenge. Closing the loop from data to actions in these systems faces many difficulties, stemming from the need for sample efficiency and computational feasibility, along with many other requirements such as verifiability, robustness, and safety. In this talk, we bridge this gap by introducing innovative representations to develop nonlinear stochastic control and reinforcement learning methods. Key in the representation is to represent the stochastic, nonlinear dynamics linearly onto a nonlinear feature space. We present a comprehensive framework to develop control and learning strategies which achieve efficiency, safety, robustness, and scalability with provable performance. We also show how the representation could be used to close the sim-to-real gap. Lastly, we will briefly present some concrete real-world applications, discussing how domain knowledge is applied in practice to further close the loop from data to actions.
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  •  EVENT    MERL Virtual Open House 2024
    Date & Time: Tuesday, November 19, 2024; 1:00 - 4:30 EST
    Location: Virtual Event
    Brief
    • Join us for MERL's Virtual Open House (VOH) 2024 on November 19th. Live sessions will be held from 1:00-4:30pm EST, including an overview of recent activities by our research groups, a featured guest speaker and live interaction with our research staff through the Gather platform. Registered attendees will be able to browse our virtual booths at their convenience and connect with our research staff to learn about employment opportunities, including internship/post-doc openings as well as visiting faculty positions.

      For agenda and details of the event, please visit: https://www.merl.com/events/voh24

      To register for the VOH, please go to:
      https://mailchi.mp/merl/voh24
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  •  TALK    [MERL Seminar Series 2024] Zhaojian Li presents talk titled A Multi-Arm Robotic System for Robotic Apple Harvesting
    Date & Time: Wednesday, October 2, 2024; 1:00 PM
    Speaker: Zhaojian Li, Mivchigan State University
    MERL Host: Yebin Wang
    Research Areas: Artificial Intelligence, Computer Vision, Control, Robotics
    Abstract
    • Harvesting labor is the single largest cost in apple production in the U.S. Surging cost and growing shortage of labor has forced the apple industry to seek automated harvesting solutions. Despite considerable progress in recent years, the existing robotic harvesting systems still fall short of performance expectations, lacking robustness and proving inefficient or overly complex for practical commercial deployment. In this talk, I will present the development and evaluation of a new dual-arm robotic apple harvesting system. This work is a result of a continuous collaboration between Michigan State University and U.S. Department of Agriculture.
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  •  TALK    [MERL Seminar Series 2024] Tom Griffiths presents talk titled Tools from cognitive science to understand the behavior of large language models
    Date & Time: Wednesday, September 18, 2024; 1:00 PM
    Speaker: Tom Griffiths, Princeton University
    Research Areas: Artificial Intelligence, Data Analytics, Machine Learning, Human-Computer Interaction
    Abstract
    • Large language models have been found to have surprising capabilities, even what have been called “sparks of artificial general intelligence.” However, understanding these models involves some significant challenges: their internal structure is extremely complicated, their training data is often opaque, and getting access to the underlying mechanisms is becoming increasingly difficult. As a consequence, researchers often have to resort to studying these systems based on their behavior. This situation is, of course, one that cognitive scientists are very familiar with — human brains are complicated systems trained on opaque data and typically difficult to study mechanistically. In this talk I will summarize some of the tools of cognitive science that are useful for understanding the behavior of large language models. Specifically, I will talk about how thinking about different levels of analysis (and Bayesian inference) can help us understand some behaviors that don’t seem particularly intelligent, how tasks like similarity judgment can be used to probe internal representations, how axiom violations can reveal interesting mechanisms, and how associations can reveal biases in systems that have been trained to be unbiased.
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  •  EVENT    MERL Hosts its Annual Women In Science Luncheon
    Date: Friday, July 26, 2024
    Location: MERL Offices
    Speaker: Dr. Na Li, Harvard University
    MERL Contact: Elizabeth Phillips
    Brief
    • On July 26th, MERL hosted its annual Women in Science luncheon. This event brings together MERL's female interns and employees to hear about the experiences and careers of women working in research and engineering fields. This year, we were honored to have Dr. Na Li as our guest speaker. Dr. Li, who joined MERL as a visiting faculty this summer, is a Winokur Family Professor of Electrical Engineering and Applied Mathematics at Harvard University. Dr. Li's talk highlighted her personal journey from rural China to Harvard Professor. Her insights and experiences provided invaluable inspiration to everyone in attendance. We appreciate both Dr. Li and all who participated in making this event a success. MERL remains committed to fostering an inclusive environment that supports the growth and development of women in STEM.

      #WomenInScience #WomenInSTEM #MERL #Inspiration #Engineering #Research
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  •  TALK    [MERL Seminar Series 2024] Chuchu Fan presents talk titled Neural Certificates and LLMs in Large-Scale Autonomy Design
    Date & Time: Wednesday, May 29, 2024; 12:00 PM
    Speaker: Chuchu Fan, MIT
    MERL Host: Abraham P. Vinod
    Research Areas: Artificial Intelligence, Control, Machine Learning
    Abstract
    • Learning-enabled control systems have demonstrated impressive empirical performance on challenging control problems in robotics. However, this performance often arrives with the trade-off of diminished transparency and the absence of guarantees regarding the safety and stability of the learned controllers. In recent years, new techniques have emerged to provide these guarantees by learning certificates alongside control policies — these certificates provide concise, data-driven proofs that guarantee the safety and stability of the learned control system. These methods not only allow the user to verify the safety of a learned controller but also provide supervision during training, allowing safety and stability requirements to influence the training process itself. In this talk, we present two exciting updates on neural certificates. In the first work, we explore the use of graph neural networks to learn collision-avoidance certificates that can generalize to unseen and very crowded environments. The second work presents a novel reinforcement learning approach that can produce certificate functions with the policies while addressing the instability issues in the optimization process. Finally, if time permits, I will also talk about my group's recent work using LLM and domain-specific task and motion planners to allow natural language as input for robot planning.
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  •  TALK    [MERL Seminar Series 2024] Na Li presents talk titled Close the Loop: From Data to Actions in Complex Systems
    Date & Time: Wednesday, April 10, 2024; 12:00 PM
    Speaker: Na Li, Harvard University
    MERL Host: Yebin Wang
    Research Areas: Control, Dynamical Systems, Machine Learning
    Abstract
    • The explosive growth of machine learning and data-driven methodologies have revolutionized numerous fields. Yet, translating these successes to the domain of dynamical, physical systems remains a significant challenge, hindered by the complex and often unpredictable nature of such environments. Closing the loop from data to actions in these systems faces many difficulties, stemming from the need for sample efficiency and computational feasibility amidst intricate dynamics, along with many other requirements such as verifiability, robustness, and safety. In this talk, we bridge this gap by introducing innovative approaches that harness representation-based methods, domain knowledge, and the physical structures of systems. We present a comprehensive framework that integrates these components to develop reinforcement learning and control strategies that are not only tailored for the complexities of physical systems but also achieve efficiency, safety, and robustness with provable performance.
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  •  TALK    [MERL Seminar Series 2024] Fadel Adib presents talk titled Decoding Hidden Worlds: Unprecedented Sensing and Connectivity for Climate, Robotics, & Smart Environments
    Date & Time: Wednesday, April 3, 2024; 12:00 PM
    Speaker: Fadel Adib, MIT & Cartesian
    MERL Host: Wael Hajj Ali
    Research Areas: Computational Sensing, Dynamical Systems, Signal Processing
    Abstract
    • This talk will cover a new generation of technologies that can sense, connect, and perceive the physical world in unprecedented ways. These technologies can uncover hidden worlds around us, promising transformative impact on areas spanning climate change monitoring, ocean mapping, healthcare, food security, supply chain, and even extraterrestrial exploration.

      The talk will cover four core technologies invented by Prof. Adib and his team. The first is an ocean internet-of-things (IoT) that uses battery-free sensors for climate change monitoring, marine life discovery, and seafood production (aquaculture). The second is a new perception technology that enables robots to sense and manipulate hidden objects. The third is a new augmented reality headset with ``X-ray vision”, which extends human perception beyond line-of-sight. The fourth is a wireless sensing technology that can “see through walls” and monitor people’s vital signs (including their breathing, heart rate, and emotions), enabling smart environments that sense humans requiring any contact with the human body.

      The talk will touch on the journey of these technologies from their inception at MIT to international collaborations and startups that are translating them to real-world impact in areas spanning healthcare, climate change, and supply chain.
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  •  TALK    [MERL Seminar Series 2024] Sanmi Koyejo presents talk titled Are Emergent Abilities of Large Language Models a Mirage?
    Date & Time: Wednesday, March 20, 2024; 1:00 PM
    Speaker: Sanmi Koyejo, Stanford University
    MERL Host: Jing Liu
    Research Areas: Artificial Intelligence, Machine Learning
    Abstract
    • Recent work claims that large language models display emergent abilities, abilities not present in smaller-scale models that are present in larger-scale models. What makes emergent abilities intriguing is two-fold: their sharpness, transitioning seemingly instantaneously from not present to present, and their unpredictability, appearing at seemingly unforeseeable model scales. Here, we present an alternative explanation for emergent abilities: that for a particular task and model family, when analyzing fixed model outputs, emergent abilities appear due to the researcher's choice of metric rather than due to fundamental changes in model behavior with scale. Specifically, nonlinear or discontinuous metrics produce apparent emergent abilities, whereas linear or continuous metrics produce smooth, continuous predictable changes in model performance. We present our alternative explanation in a simple mathematical model. Via the presented analyses, we provide evidence that alleged emergent abilities evaporate with different metrics or with better statistics, and may not be a fundamental property of scaling AI models.
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  •  EVENT    MERL Contributes to ICASSP 2024
    Date: Sunday, April 14, 2024 - Friday, April 19, 2024
    Location: Seoul, South Korea
    MERL Contacts: Petros T. Boufounos; François Germain; Chiori Hori; Sameer Khurana; Toshiaki Koike-Akino; Jonathan Le Roux; Hassan Mansour; Kieran Parsons; Joshua Rapp; Anthony Vetro; Pu (Perry) Wang; Gordon Wichern
    Research Areas: Artificial Intelligence, Computational Sensing, Machine Learning, Robotics, Signal Processing, Speech & Audio
    Brief
    • MERL has made numerous contributions to both the organization and technical program of ICASSP 2024, which is being held in Seoul, Korea from April 14-19, 2024.

      Sponsorship and Awards

      MERL is proud to be a Bronze Patron of the conference and will participate in the student job fair on Thursday, April 18. Please join this session to learn more about employment opportunities at MERL, including openings for research scientists, post-docs, and interns.

      MERL is pleased to be the sponsor of two IEEE Awards that will be presented at the conference. We congratulate Prof. Stéphane G. Mallat, the recipient of the 2024 IEEE Fourier Award for Signal Processing, and Prof. Keiichi Tokuda, the recipient of the 2024 IEEE James L. Flanagan Speech and Audio Processing Award.

      Jonathan Le Roux, MERL Speech and Audio Senior Team Leader, will also be recognized during the Awards Ceremony for his recent elevation to IEEE Fellow.

      Technical Program

      MERL will present 13 papers in the main conference on a wide range of topics including automated audio captioning, speech separation, audio generative models, speech and sound synthesis, spatial audio reproduction, multimodal indoor monitoring, radar imaging, depth estimation, physics-informed machine learning, and integrated sensing and communications (ISAC). Three workshop papers have also been accepted for presentation on audio-visual speaker diarization, music source separation, and music generative models.

      Perry Wang is the co-organizer of the Workshop on Signal Processing and Machine Learning Advances in Automotive Radars (SPLAR), held on Sunday, April 14. It features keynote talks from leaders in both academia and industry, peer-reviewed workshop papers, and lightning talks from ICASSP regular tracks on signal processing and machine learning for automotive radar and, more generally, radar perception.

      Gordon Wichern will present an invited keynote talk on analyzing and interpreting audio deep learning models at the Workshop on Explainable Machine Learning for Speech and Audio (XAI-SA), held on Monday, April 15. He will also appear in a panel discussion on interpretable audio AI at the workshop.

      Perry Wang also co-organizes a two-part special session on Next-Generation Wi-Fi Sensing (SS-L9 and SS-L13) which will be held on Thursday afternoon, April 18. The special session includes papers on PHY-layer oriented signal processing and data-driven deep learning advances, and supports upcoming 802.11bf WLAN Sensing Standardization activities.

      Petros Boufounos is participating as a mentor in ICASSP’s Micro-Mentoring Experience Program (MiME).

      About ICASSP

      ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing. The event attracts more than 3000 participants.
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  •  TALK    [MERL Seminar Series 2024] Stefanos Nikolaidis presents talk titled Enhancing the Efficiency and Robustness of Human-Robot Interactions
    Date & Time: Friday, March 8, 2024; 1:00 PM
    Speaker: Stefanos Nikolaidis, University of Southern California
    MERL Host: Siddarth Jain
    Research Areas: Machine Learning, Robotics, Human-Computer Interaction
    Abstract
    • While robots have been successfully deployed in factory floors and warehouses, there has been limited progress in having them perform physical tasks with people at home and in the workplace. I aim to bridge the gap between their current performance in human environments and what robots are capable of doing, by making human-robot interactions efficient and robust.

      In the first part of my talk, I discuss enhancing the efficiency of human-robot interactions by enabling robot manipulators to infer the preference of a human teammate and proactively assist them in a collaborative task. I show how we can leverage similarities between different users and tasks to learn compact representations of user preferences and use these representations as priors for efficient inference.

      In the second part, I talk about enhancing the robustness of human-robot interactions by algorithmically generating diverse and realistic scenarios in simulation that reveal system failures. I propose formulating the problem of algorithmic scenario generation as a quality diversity problem and show how standard quality diversity algorithms can discover surprising and unexpected failure cases. I then discuss the development of a new class of quality diversity algorithms that significantly improve the search of the scenario space and the integration of these algorithms with generative models, which enables the generation of complex and realistic scenarios.

      Finally, I conclude the talk with applications in mining operations, collaborative manufacturing and assistive care.
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  •  TALK    [MERL Seminar Series 2024] Melanie Mitchell presents talk titled "The Debate Over 'Understanding' in AI's Large Language Models"
    Date & Time: Tuesday, February 13, 2024; 1:00 PM
    Speaker: Melanie Mitchell, Santa Fe Institute
    MERL Host: Suhas Lohit
    Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Human-Computer Interaction
    Abstract
    • I will survey a current, heated debate in the AI research community on whether large pre-trained language models can be said to "understand" language -- and the physical and social situations language encodes -- in any important sense. I will describe arguments that have been made for and against such understanding, and, more generally, will discuss what methods can be used to fairly evaluate understanding and intelligence in AI systems. I will conclude with key questions for the broader sciences of intelligence that have arisen in light of these discussions.
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  •  TALK    [MERL Seminar Series 2024] Greta Tuckute presents talk titled Computational models of human auditory and language processing
    Date & Time: Wednesday, January 31, 2024; 12:00 PM
    Speaker: Greta Tuckute, MIT
    MERL Host: Sameer Khurana
    Research Areas: Artificial Intelligence, Machine Learning, Speech & Audio
    Abstract
    • Advances in machine learning have led to powerful models for audio and language, proficient in tasks like speech recognition and fluent language generation. Beyond their immense utility in engineering applications, these models offer valuable tools for cognitive science and neuroscience. In this talk, I will demonstrate how these artificial neural network models can be used to understand how the human brain processes language. The first part of the talk will cover how audio neural networks serve as computational accounts for brain activity in the auditory cortex. The second part will focus on the use of large language models, such as those in the GPT family, to non-invasively control brain activity in the human language system.
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  •  TALK    [MERL Seminar Series 2023] Dr. Kristina Monakhova presents talk titled Robust and Physics-informed machine learning for low light imaging
    Date & Time: Tuesday, November 28, 2023; 12:00 PM
    Speaker: Kristina Monakhova, MIT and Cornell
    MERL Host: Joshua Rapp
    Research Areas: Computational Sensing, Computer Vision, Machine Learning, Signal Processing
    Abstract
    • Imaging in low light settings is extremely challenging due to low photon counts, both in photography and in microscopy. In photography, imaging under low light, high gain settings often results in highly structured, non-Gaussian sensor noise that’s hard to characterize or denoise. In this talk, we address this by developing a GAN-tuned physics-based noise model to more accurately represent camera noise at the lowest light, and highest gain settings. Using this noise model, we train a video denoiser using synthetic data and demonstrate photorealistic videography at starlight (submillilux levels of illumination) for the first time.

      For multiphoton microscopy, which is a form a scanning microscopy, there’s a trade-off between field of view, phototoxicity, acquisition time, and image quality, often resulting in noisy measurements. While deep learning-based methods have shown compelling denoising performance, can we trust these methods enough for critical scientific and medical applications? In the second part of this talk, I’ll introduce a learned, distribution-free uncertainty quantification technique that can both denoise and predict pixel-wise uncertainty to gauge how much we can trust our denoiser’s performance. Furthermore, we propose to leverage this learned, pixel-wise uncertainty to drive an adaptive acquisition technique that rescans only the most uncertain regions of a sample. With our sample and algorithm-informed adaptive acquisition, we demonstrate a 120X improvement in total scanning time and total light dose for multiphoton microscopy, while successfully recovering fine structures within the sample.
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  •  TALK    [MERL Seminar Series 2023] Gioele Zardini presents talk titled Co-Design of Complex Systems: From Autonomy to Future Mobility
    Date & Time: Tuesday, November 21, 2023; 11:00 AM
    Speaker: Gioele Zardini, ETH Zürich and MIT
    Research Areas: Control, Dynamical Systems
    Abstract
    • When designing complex systems, we need to consider multiple trade-offs at various abstraction levels and scales, and choices of single components need to be studied jointly. For instance, the design of future mobility solutions (e.g., autonomous vehicles, micromobility) and the design of the mobility systems they enable are closely coupled. Indeed, knowledge about the intended service of novel mobility solutions would impact their design and deployment process, whilst insights about their technological development could significantly affect transportation management policies. Optimally co-designing sociotechnical systems is a complex task for at least two reasons. On one hand, the co-design of interconnected systems (e.g., large networks of cyber-physical systems) involves the simultaneous choice of components arising from heterogeneous natures (e.g., hardware vs. software parts) and fields, while satisfying systemic constraints and accounting for multiple objectives. On the other hand, components are connected via collaborative and conflicting interactions between different stakeholders (e.g., within an intermodal mobility system). In this talk, I will present a framework to co-design complex systems, leveraging a monotone theory of co-design and tools from game theory. The framework will be instantiated in the task of designing future mobility systems, all the way from the policies that a city can design, to the autonomy of vehicles part of an autonomous mobility-on-demand service. Through various case studies, I will show how the proposed approaches allow one to efficiently answer heterogeneous questions, unifying different modeling techniques and promoting interdisciplinarity, modularity, and compositionality. I will then discuss open challenges for compositional systems design optimization, and present my agenda to tackle them.
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  •  TALK    [MERL Seminar Series 2023] Prof. Flavio Calmon presents talk titled Multiplicity in Machine Learning
    Date & Time: Tuesday, November 7, 2023; 12:00 PM
    Speaker: Flavio Calmon, Harvard University
    MERL Host: Ye Wang
    Research Areas: Artificial Intelligence, Machine Learning
    Abstract
    • This talk reviews the concept of predictive multiplicity in machine learning. Predictive multiplicity arises when different classifiers achieve similar average performance for a specific learning task yet produce conflicting predictions for individual samples. We discuss a metric called “Rashomon Capacity” for quantifying predictive multiplicity in multi-class classification. We also present recent findings on the multiplicity cost of differentially private training methods and group fairness interventions in machine learning.

      This talk is based on work published at ICML'20, NeurIPS'22, ACM FAccT'23, and NeurIPS'23.
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  •  TALK    [MERL Seminar Series 2023] Dr. Tanmay Gupta presents talk titled Visual Programming - A compositional approach to building General Purpose Vision Systems
    Date & Time: Tuesday, October 31, 2023; 2:00 PM
    Speaker: Tanmay Gupta, Allen Institute for Artificial Intelligence
    MERL Host: Moitreya Chatterjee
    Research Areas: Artificial Intelligence, Computer Vision, Machine Learning
    Abstract
    • Building General Purpose Vision Systems (GPVs) that can perform a huge variety of tasks has been a long-standing goal for the computer vision community. However, end-to-end training of these systems to handle different modalities and tasks has proven to be extremely challenging. In this talk, I will describe a lucrative neuro-symbolic alternative to the common end-to-end learning paradigm called Visual Programming. Visual Programming is a general framework that leverages the code-generation abilities of LLMs, existing neural models, and non-differentiable programs to enable powerful applications. Some of these applications continue to remain elusive for the current generation of end-to-end trained GPVs.
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  •  EVENT    Prof. Yuejie Chi of Carnegie Mellon University to Deliver Keynote at MERL's Virtual Open House
    Date & Time: Wednesday, November 15, 2023; 3:00-3:40pm (EST)
    Location: Virtual Event
    Speaker: Prof. Yuejie Chi, Carnegie Mellon University
    MERL Contact: Bingnan Wang
    Brief
    • MERL is excited to announce the featured keynote speaker for our Virtual Open House 2023: Prof. Yuejie Chi from Carnegie Mellon University.

      Our virtual open house this year will take place on November 15, 2023, 1:00pm - 5:30pm (EST). Prof. Chi’s talk is scheduled for 3:00-3:40pm (EST). For details and agenda of the event, please visit: https://merl.com/events/voh23

      Join us to learn more about who we are, what we do, and discuss our internship, post-doc, and full-time employment opportunities. To register, go to: https://mailchi.mp/merl/voh23


      Title: Sample Complexity of Q-learning: from Single-agent to Federated Learning

      Abstract: Q-learning, which seeks to learn the optimal Q-function of a Markov decision process (MDP) in a model-free fashion, lies at the heart of reinforcement learning practices. However, theoretical understandings on its non-asymptotic sample complexity remain unsatisfactory, despite significant recent efforts. In this talk, we first show a tight sample complexity bound of Q-learning in the single-agent setting, together with a matching lower bound to establish its minimax sub-optimality. We then show how federated versions of Q-learning allow collaborative learning using data collected by multiple agents without central sharing, where an importance averaging scheme is introduced to unveil the blessing of heterogeneity.
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  •  EVENT    MERL's Virtual Open House 2023
    Date & Time: Wednesday, November 15, 2023; 1:00 - 5:30 EST
    Location: Virtual Event
    MERL Contact: Bingnan Wang
    Brief
    • Join us for MERL's Virtual Open House (VOH) 2023 on November 15th. Live sessions will be held from 1:00-5:30pm EST, including an overview of recent activities by our research groups, a featured guest speaker and live interaction with our research staff through the Gather platform. Registered attendees will be able to browse our virtual booths at their convenience and connect with our research staff to learn about engagement opportunities, including internship/post-doc openings as well as visiting faculty positions.

      For agenda and details of the event: https://www.merl.com/events/voh23

      To register for the VOH, go to:
      https://mailchi.mp/merl/voh23
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  •  TALK    [MERL Seminar Series 2023] Prof. Shaoshuai Mou presents talk titled Inverse Optimal Control for Autonomous Systems
    Date & Time: Tuesday, October 10, 2023; 1:00 PM
    Speaker: Shaoshuai Mou, Purdue University
    MERL Host: Yebin Wang
    Research Areas: Control, Dynamical Systems, Robotics
    Abstract
    • Inverse Optimal Control (IOC) aims to achieve an objective function corresponding to a certain task from an expert robot driven by optimal control, which has become a powerful tool in many applications in robotics. We will present our recent solutions to IOC based on incomplete observations of systems' trajectories, which enables an autonomous system to “sense-and-adapt", i.e., incrementally improving the learning of objective functions as new data arrives. This also leads to a distributed algorithm to solve IOC in multi-agent systems, in which each agent can only access part of the overall trajectory of an optimal control system and cannot solve IOC by itself. This is perhaps the first distributed method to IOC. Applications of IOC into human prediction will also be given.
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  •  TALK    [MERL Seminar Series 2023] Prof. Komei Sugiura presents talk titled The Confluence of Vision, Language, and Robotics
    Date & Time: Thursday, September 28, 2023; 12:00 PM
    Speaker: Komei Sugiura, Keio University
    MERL Host: Chiori Hori
    Research Areas: Artificial Intelligence, Machine Learning, Robotics, Speech & Audio
    Abstract
    • Recent advances in multimodal models that fuse vision and language are revolutionizing robotics. In this lecture, I will begin by introducing recent multimodal foundational models and their applications in robotics. The second topic of this talk will address our recent work on multimodal language processing in robotics. The shortage of home care workers has become a pressing societal issue, and the use of domestic service robots (DSRs) to assist individuals with disabilities is seen as a possible solution. I will present our work on DSRs that are capable of open-vocabulary mobile manipulation, referring expression comprehension and segmentation models for everyday objects, and future captioning methods for cooking videos and DSRs.
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  •  TALK    [MERL Seminar Series 2023] Prof. Zac Manchester presents talk titled Composable Optimization for Robotic Simulation, Planning, and Control
    Date & Time: Wednesday, September 27, 2023; 1:00 PM
    Speaker: Zac Manchester, Carnegie Mellon University
    MERL Host: Devesh K. Jha
    Research Areas: Optimization, Robotics
    Abstract
    • Contact interactions are pervasive in key real-world robotic tasks like manipulation and walking. However, the non-smooth dynamics associated with impacts and friction remain challenging to model, and motion planning and control algorithms that can fluently and efficiently reason about contact remain elusive. In this talk, I will share recent work from my research group that takes an “optimization-first” approach to these challenges: collision detection, physics, motion planning, and control are all posed as constrained optimization problems. We then build a set of algorithmic and numerical tools that allow us to flexibly compose these optimization sub-problems to solve complex robotics problems involving discontinuous, unplanned, and uncertain contact mechanics.
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  •  TALK    [MERL Seminar Series 2023] Prof. Faruque Hasan presents talk titled A Process Systems Engineering Perspective on Carbon Capture: Key Challenges and Opportunities
    Date & Time: Tuesday, September 19, 2023; 1:00 PM
    Speaker: Faruque Hasan, Texas A&M University
    MERL Host: Scott A. Bortoff
    Research Areas: Applied Physics, Machine Learning, Multi-Physical Modeling, Optimization
    Abstract
    • Carbon capture, utilization, and storage (CCUS) is a promising pathway to decarbonize fossil-based power and industrial sectors and is a bridging technology for a sustainable transition to a net-zero emission energy future. This talk aims to provide an overview of design and optimization of CCUS systems. I will also attempt to give a brief perspective on emerging interests in process systems engineering research (e.g., systems integration, multiscale modeling, strategic planning, and optimization under uncertainty). The purpose is not to cover all aspects of PSE research for CCUS but rather to foster discussion by presenting some plausible future directions and ideas.
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