Monday, February 06, 2023, 12:00
- 13:00
Building 9, Level 2, Room 2325, Hall 2
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In this work we focus our attention on distributed optimization problems in the context where the communication time between the server and the workers is non-negligible. We obtain novel methods supporting bidirectional compression (both from the server to the workers and vice versa) that enjoy new state-of-the-art theoretical communication complexity for convex and nonconvex problems.
Prof. David Bromberg, Distributed computing systems, University of Rennes (IRISA)
Thursday, February 02, 2023, 15:30
- 16:30
Building 4, Level 5, Room 5220.
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In this talk we will explore how research in systems and distributed systems may improve the resilience to cyber attacks following 3 axes targeting mobile systems, distributed systems, and operating systems
Monday, January 30, 2023, 12:00
- 13:00
Building 9, Level 2, Room 2325, Hall 2
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In this talk, we will define prototypical random walks, a mechanism we introduced to improve visual classification with limited data (few-shot learning), and then developed the mechanism in a conceptually different way to facilitate novel image generation and unseen class recognition tasks. More specifically, in the few-shot learning setting, we will show how we can develop a random walk semi-supervised loss that enables the network to learn representations that are compact and well-separated.
Prof. Mohammad Mannan, Concordia Institute for Information Systems Engineering, Concordia University, Montreal
Thursday, January 26, 2023, 15:30
- 16:30
Building 4, Level 5, Room 5220.
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I will discuss four related proposals: Gracewipe (coercion-resistant disk data deletion), Hypnoguard (cold-boot protection for RAM data in sleep), SafeKeeper (protecting web credentials from rougue IT admins), and Blindfold (protecting PKI private keys from human admins). While our solutions are possibly a step forward, more importantly, we highlight pitfalls of such solutions against a strong adversary.
Monday, January 23, 2023, 18:30
- 20:30
Building 2, Level 5, Room 5209
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With video data dominating the internet traffic, it is crucial to develop automated models that can analyze and understand what humans do in videos. Such models must solve tasks such as action classification, temporal activity localization, spatiotemporal action detection, and video captioning. This dissertation aims to identify the challenges hindering the progress in human action understanding and propose novel solutions to overcome these challenges.
Prof.Rodrigo Rodrigues, Instituto Superior Tecnico (ULisboa)
Monday, January 23, 2023, 12:00
- 13:00
Building 9, Level 2, Room 2322, Hall 1
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Trusted Execution Environments (TEEs) ensure the confidentiality and integrity of computations in hardware. Subject to the TEE's threat model, the hardware shields a computation from most externally induced fault behavior except crashes. As a result, a crash-fault tolerant (CFT) replication protocol should be sufficient when replicating trusted code inside TEEs.  However, TEEs do not provide efficient and general means of ensuring the freshness of the external, persistent state. Therefore, CFT replication is insufficient for TEE computations with an external state, as this state could be rolled back to an earlier version when a TEE restarts.  Furthermore, using BFT protocols in this setting is too conservative, because these protocols are designed to tolerate arbitrary behavior, not just rollback during a restart.
Prof.Patrick Farrell, University of Oxford
Monday, December 05, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322, Hall 1
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Building on the work of Schöberl, Olshanskii, and Benzi, in this talk we present the first preconditioner for the Newton linearization of the stationary Navier--Stokes equations in three dimensions that achieve both optimal complexity in of count and Reynolds-robustness. The exact details of the preconditioner varies with discretization, but the general theme is to combine augmented Lagrangian stabilisation, a custom multigrid prolongation operator involving local solves on coarse cells, and an additive patchwise relaxation on each level that captures the kernel of the divergence operator.
Dr.Syed Adnan Yusuf
Monday, November 28, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322, Hall 1
This seminar focuses on providing the audience with the context and scope of our internship program. The program is for the young and talented graduate students with an active interest in solving real-world problems. Some of the projects that will be presented in the seminar are actively developed in Elm and include domains such as computer vision, robotics and automation, healthcare, IoT, video analytics, and NLP. The seminar will serve as a launch pad to allow students to discuss their future interests and aspirations with the speaker. It will also enable them to develop a better awareness of domains more relevant to their future research aspirations.
Monday, November 21, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322, Hall 1
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In this talk, I will first give a convergence analysis of gradient descent (GD) method for training neural networks by relating them with finite element method. I will then present some acceleration techniques for GD method and also give some alternative training algorithms
Francesco Orabona, Associate Professor of Electrical and Computer Engineering, Boston University
Monday, November 14, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322
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Parameter-free online optimization is a class of algorithms that does not require tuning hyperparameters, yet they achieve the theoretical optimal performance. Moreover, they often achieve state-of-the-art performance too. An example would be gradient descent algorithms completely without learning rates. In this talk, I review my past and present contributions to this field. Building upon a fundamental idea connecting optimization, gambling, and information theory, I discuss selected applications of parameter-free algorithms to machine learning and statistics. Finally, we conclude with an overview of the future directions of this field.
Prof. Michal Mankowski, Assistant Professor of Operations Research, Erasmus University Rotterdam, Netherlands
Thursday, November 10, 2022, 10:00
- 11:30
Building 1, Level 3, Room 3119
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The aim of this course is to familiarize the students with the usage of Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation and variance reduction. Later in the course, the concept of simulation-based optimization and output analysis will be discussed. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed.
Prof. Michal Mankowski, Assistant Professor of Operations Research, Erasmus University Rotterdam, Netherlands
Wednesday, November 09, 2022, 10:00
- 11:30
Building 1, Level 3, Room 3119
Contact Person
The aim of this course is to familiarize the students with the usage of Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation and variance reduction. Later in the course, the concept of simulation-based optimization and output analysis will be discussed. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed.
Prof. Michal Mankowski, Assistant Professor of Operations Research, Erasmus University Rotterdam, Netherlands
Tuesday, November 08, 2022, 10:00
- 11:30
Building 1, Level 3, Room 3119
Contact Person
The aim of this course is to familiarize the students with the usage of Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation and variance reduction. Later in the course, the concept of simulation-based optimization and output analysis will be discussed. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed.
Tobias Isenberg, Senior Research Scientist, Inria
Monday, November 07, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322, Hall 1
Contact Person
In this talk I will report on various research projects that I carried out with my students to better understand the interaction landscape and will report on lessons we learned. I will focus mostly on AR-based setups with application examples from physical flow visualization, molecular visualization, visualization of particle collisions, biomolecular dynamics in cells, and oceanography. I will show interaction techniques that rely on purely gestural interaction, phones or tablets as input and control devices, and hybrid setups that combine traditional workstations with AR views. I will discuss navigation, data selection, and visualization system control as different interaction tasks. With this overview I aim to provide an understanding of typical challenges in immersive visualization environments and how to address some of these challenges.
Monday, October 31, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322, Hall 1
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From my experience, I will try to answer doubts and dilemmas PhD students are often faced with, in their path towards a degree. Namely, I'll discuss how advisors, colleagues, peers, reviewers and so forth, fit in the universe of a PhD student, and I will end sharing my own definition of 'excellence', as an objective to pursue.
Prof.Evgeny Burnaev, Applied AI Center, Skolkovo Institute of Science and Technology
Monday, October 24, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322, Hall 1
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Skoltech Applied AI center’s mission is to create AI models and frameworks for solving the problems of sustainable development of industry and economy. In my presentation, I will overview the current center's activities, applied and fundamental problem statements, and corresponding recent results.
Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University
Wednesday, October 19, 2022, 16:00
- 17:00
Building 9, Level 2, Room 2325
Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in Digital Behavioral Health. However, after a reinforcement learning algorithm has been run in a clinical study, how do we assess whether personalization occurred? We might find users for whom it appears that the algorithm has indeed learned in which contexts the user is more responsive to a particular intervention. But could this have happened completely by chance? We discuss some first approaches to addressing these questions.
Monday, October 10, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322, Hall 1
Contact Person
In the big data era, it is necessary to rely on distributed computing. For distributed optimization and learning tasks, in particular in the modern paradigm of federated learning, specific challenges arise, such as decentralized data storage. Communication between the parallel machines and the orchestrating distant server is necessary but slow. To address this main bottleneck, a natural strategy is to compress the communicated vectors. I will present EF-BV, a new algorithm which converges linearly to an exact solution, with a large class of deterministic or random, biased or unbiased compressors.
Monday, October 03, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322, Hall 1
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Random fields are popular models in statistics and machine learning for spatially dependent data on Euclidian domains. However, in many applications, data is observed on non-Euclidian domains such as street networks. In this case, it is much more difficult to construct valid random field models. In this talk, we discuss some recent approaches to modeling data in this setting, and in particular define a new class of Gaussian processes on compact metric graphs.
Prof. Dhabaleswar K. (DK) Panda, Professor, Computer Science and Engineering, The Ohio State University
Monday, September 26, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322, Hall 1
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This talk will focus on challenges and opportunities in designing middleware for HPC, AI (Deep/Machine Learning), and Data Science. We will start with the challenges in designing runtime environments for MPI+X programming models by considering support for multi-core systems, high-performance networks (InfiniBand and RoCE), GPUs, and emerging BlueField-2 DPUs. Features and sample performance numbers of using the MVAPICH2 libraries will be presented. For the Deep/Machine Learning domain, we will focus on MPI-driven solutions to extract performance and scalability for popular Deep Learning frameworks (TensorFlow and PyTorch), large out-of-core models, and Bluefield-2 DPUs.
Fahad Khan, Associate Professor at MBZUAI and Linköping University
Monday, September 19, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322
Contact Person
Machine perception that corresponds to the ability to understand the visual world based on the input from sensors, such as cameras is one of the central problems in Artificial Intelligence. To this end, recent years have witnessed tremendous progress in various instance-level recognition tasks having real-world applications in e.g., robotics, autonomous driving and surveillance. In this talk, I will first present our recent results towards understanding state-of-the-art deep learning-based visual recognition networks in terms of their robustness and generalizability. Next, I will present our results on learning visual recognition models with limited human supervision. Finally, I will discuss moving one step further from instance-level recognition to understand visual relationships between object pairs.
Wednesday, September 14, 2022, 16:00
- 18:30
Building 5, Room 5220
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In this thesis, we discuss a few fundamental and well-studied optimization problem classes: decentralized distributed optimization (Chapters 2 to 4), distributed optimization under similarity (Chapter 5), affinely constrained optimization (Chapter 6), minimax optimization (Chapter 7), and high-order optimization (Chapter 8). For each problem class, we develop the first provably optimal algorithm: the complexity of such an algorithm cannot be improved for the problem class given. The proposed algorithms show state-of-the-art performance in practical applications, which makes them highly attractive for potential generalizations and extensions in the future.
Tuesday, September 13, 2022, 14:00
- 15:30
B9, L2, R2325
In this talk, I will start by providing our vision for next-generation networks. Throughout the talk, I will highlight several challenges in existing communication technologies that could have the potential of shaping new research and deployment directions of future wireless networks, including, (i) review our recent advances in non-terrestrial networks, which includes both UAVs and satellite (ii) show satellite systems are essential for today’s traffic-intensive applications while maintaining an accepted end-to-end latency for delay-sensitive applications and (iii) show how we integrated both existing Wi-Fi technology with optics to extend the Internet as we use it today to the underwater environments via Aqua-fi.
Monday, September 12, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322, Hall 1
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This talks presents a very serious emerging threat: the bots scraping web sites and hiding their IPs thanks to residential IP providers. The problem, state of the art and a new solution will be explained.
Monday, September 05, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322, Hall 1
Contact Person
In this talk, I will discuss communication compression and aggregation mechanisms for curvature information in order to reduce these costs while preserving theoretically superior local convergence guarantees.