Dr. Byeongchan So, Postdoctoral researcher / Ph.D. Department of Nano-Semiconductor Engineering, ​Korea Polytechnic University
Thursday, August 13, 2020, 15:00
- 16:00
https://kaust.zoom.us/j/2377519260
Contact Person
In this seminar, the approaches for improving the efficiency of AlGaN based DUV emitters will be presented. The high-temperature metal organic chemical vapor deposition system has been used to grow high-quality AlGaN based epi-layers and nanostructure on the sapphire substrate.
Thursday, July 30, 2020, 18:00
- 20:00
https://kaust.zoom.us/j/91824151108
Contact Person
Frequency-reconfigurable RF components are highly desired in a wireless system because a single frequency-reconfigurable RF component can replace multiple RF components to reduce the size, cost, and weight. Typically, the reconfigurable RF components are realized using capacitive varactors, PIN diodes, or MEMS switches, which are expensive, require tedious soldering steps, and are rigid and thus non-compatible with futuristic applications of flexible and wearable electronics. In this work, we have demonstrated vanadium dioxide (VO₂) based RF switches that have been realized through additive manufacturing technologies (inkjet printing and screen printing), which dramatically brings the cost down to a few cents. Also, no soldering or additional attachment step is required as the switch can be simply printed on the RF component. The printed VO₂ switches are configured in two types (shunt configuration and series configuration) where both types have been characterized with two activation mechanisms (thermal activation and electrical activation) up to 40 GHz. The measured insertion loss of 1-3 dB, isolation of 20-30 dB, and switching speed of 400 ns is comparable to other non-printed and expensive RF switches. Moreover, as an application for the printed VO₂ switches, a fully printed frequency reconfigurable filter has also been designed in this work.
Thursday, July 30, 2020, 16:00
- 18:00
https://kaust.zoom.us/j/94115666384
Contact Person
Many key problems in machine learning and data science are routinely modeled as optimization problems and solved via optimization algorithms. With the increase of the volume of data and the size and complexity of the statistical models used to formulate these often ill-conditioned optimization tasks, there is a need for new efficient algorithms able to cope with these challenges. In this thesis, we deal with each of these sources of difficulty in a different way. To efficiently address the big data issue, we develop new methods which in each iteration examine a small random subset of the training data only. To handle the big model issue, we develop methods which in each iteration update a random subset of the model parameters only. Finally, to deal with ill-conditioned problems, we devise methods that incorporate either higher-order information or Nesterov's acceleration/momentum. In all cases, randomness is viewed as a powerful algorithmic tool that we tune, both in theory and in experiments, to achieve the best results.
Prof. Katharina Lorenz, Instituto Superior Técnico, University of Lisbon
Thursday, July 23, 2020, 16:00
- 17:30
https://kaust.zoom.us/j/2377519260
Contact Person
Katharina Lorenz's main research interests are the doping of WBS with optically active ions and the study of radiation effects in WBS materials for radiation detectors and radiation resistant electronics.
Prof. Levon Nurbekyan, Department of Mathematics, UCLA
Wednesday, July 15, 2020, 21:00
- 23:00
https://kaust.zoom.us/j/92741593187
Contact Person
In this short course, I will discuss connections between mean-field games (MFG) systems and modern machine-learning (ML) techniques and problems. In the first part of the course, roughly the first two lectures, I will present how various ML techniques can be applied to solve high-dimensional MFG systems that are far out of reach for traditional methods. In the second part of the course, I will discuss the reverse relation, namely, how the MFG framework can be useful for solving specific ML problems.
Prof. Levon Nurbekyan, Department of Mathematics, UCLA
Tuesday, July 14, 2020, 21:00
- 23:00
https://kaust.zoom.us/j/93370710923
Contact Person
In this short course, I will discuss connections between mean-field games (MFG) systems and modern machine-learning (ML) techniques and problems. In the first part of the course, roughly the first two lectures, I will present how various ML techniques can be applied to solve high-dimensional MFG systems that are far out of reach for traditional methods. In the second part of the course, I will discuss the reverse relation, namely, how the MFG framework can be useful for solving specific ML problems.
Prof. Levon Nurbekyan, Department of Mathematics, UCLA
Monday, July 13, 2020, 21:00
- 23:00
https://kaust.zoom.us/j/92773345782
Contact Person
In this short course, I will discuss connections between mean-field games (MFG) systems and modern machine-learning (ML) techniques and problems. In the first part of the course, roughly the first two lectures, I will present how various ML techniques can be applied to solve high-dimensional MFG systems that are far out of reach for traditional methods. In the second part of the course, I will discuss the reverse relation, namely, how the MFG framework can be useful for solving specific ML problems.
Thursday, July 09, 2020, 16:00
- 17:00
https://kaust.zoom.us/j/94054511362
Contact Person
Out-of-Core simulation systems often produce a massive amount of data that cannot fit on the aggregate fast memory of the compute nodes, and they also require to read back these data for computation. As a result, I/O data movement can be a bottleneck in large-scale simulations. Advances in memory architecture have made it feasible to integrate hierarchical storage media on large-scale systems, starting from the traditional Parallel File Systems to intermediate fast disk technologies (e.g., node-local and remote-shared NVMe and SSD-based Burst Buffers) and up to CPU’s main memory and GPU’s High Bandwidth Memory. However, while adding additional and faster storage media increases I/O bandwidth, it pressures the CPU, as it becomes responsible for managing and moving data between these layers of storage. Simulation systems are thus vulnerable to being blocked by I/O operations. The Multilayer Buffer System (MLBS) proposed in this research demonstrates a general method for overlapping I/O with computation that helps to ameliorate the strain on the processors through asynchronous access. The main idea consists in decoupling I/O operations from computational phases using dedicated hardware resources to perform expensive context switches. By continually prefetching up and down across all hardware layers of the memory/storage subsystems, MLBS transforms the original I/O-bound behavior of evaluated applications and shifts it closer to a memory-bound or compute-bound regime.
Thursday, July 09, 2020, 15:00
- 16:00
https://kaust.zoom.us/j/94675922617
Contact Person
In this thesis, we present a pragmatic heterogeneous integration strategy to obtain high-performance 3D electronic systems using existing CMOS technology. Critical challenges addressed during the process are; reliable flexible interconnects, maximum area efficiency, soft-polymeric packaging, and heterogeneous integration compatible with current CMOS technology. First, a modular LEGO approach presents a novel method to obtain flexible electronics in a lock-and-key (plug and play) manner with reliable interconnects. It includes a process to convert standard rigid IC into flexible LEGO without any performance degradation with a high-yield. For the majority of healthcare and environmental monitoring applications, a sensory array is essential for continuous spatiotemporal activity recording. Here we present an ultra- high-density sensory solution (1 million sensors) as an epitome of density and address each of the associated challenges. A generic heterogeneous integration scheme is devised to obtain a physically flexible standalone electronic system using 3D-coin architecture. Lastly, a feather-light non-invasive ‘Marine-Skin’ platform to monitor deep-ocean monitoring is presented using the heterogeneous integration scheme. Electrical and mechanical characterization establish the reliability, integrity, robustness, and ruggedness of the processes, sensors, and multisensory flexible system.
Prof. Qixin Guo, Department of Electrical and Electronic Engineering, Saga University
Thursday, July 09, 2020, 09:00
- 10:30
https://kaust.zoom.us/j/2377519260
Contact Person
Prof. Dr. Guo received B. E., M.E., and Dr. E degrees in electronic engineering from Toyohashi University of Technology in 1990, 1992, and 1996, respectively. He is currently a Professor of Department of Electrical and Electronic Engineering, Saga University as well as Director of Saga University Synchrotron Light Application Center. His research interests include epitaxial growth and characterization of semiconductor materials. Prof. Guo has published more than 300 papers in scientific journals including Nature Communications, Advanced Materials, Physical Review B, and Applied Physics Letters with more than 7200 citations (h-index: 43).
Prof. Francisco J. Silva, Applied Mathematics, University of Limoges, France
Wednesday, July 08, 2020, 14:00
- 17:00
https://kaust.zoom.us/j/95449443799
Contact Person
In this course, we will consider the so-called Lagrangian approach to mean-field games. We will introduce the problem by recalling some basic results for nonatomic games in a static framework. Next, based on recent results in collaboration with Markus Fischer (University of Padua), I will introduce the analogous setting in the deterministic and dynamic framework. In the second part of the course, I will present the details of a convergence result, obtained in collaboration with Saeed Hadikhanloo, of equilibria of a suitable sequence of discrete-time and finite mean-field games, as introduced by Gomes, Mohr, and Souza in 2010, to an equilibrium of the first-order mean-field game system. The convergence proof relies importantly on the Lagrangian formulation of equilibria.
Prof. Francisco J. Silva, Applied Mathematics, University of Limoges, France
Monday, July 06, 2020, 14:00
- 17:00
https://kaust.zoom.us/j/99470417690
Contact Person
In this course, we will consider the so-called Lagrangian approach to mean-field games. We will introduce the problem by recalling some basic results for nonatomic games in a static framework. Next, based on recent results in collaboration with Markus Fischer (University of Padua), I will introduce the analogous setting in the deterministic and dynamic framework. In the second part of the course, I will present the details of a convergence result, obtained in collaboration with Saeed Hadikhanloo, of equilibria of a suitable sequence of discrete-time and finite mean-field games, as introduced by Gomes, Mohr, and Souza in 2010, to an equilibrium of the first-order mean-field game system. The convergence proof relies importantly on the Lagrangian formulation of equilibria.
Thursday, July 02, 2020, 14:00
- 16:00
https://kaust.zoom.us/j/7625776125
Contact Person
In biochemically reactive systems with small copy numbers of one or more reactant molecules, stochastic effects dominate the dynamics. In the first part of this thesis, we design novel efficient simulation techniques, based on multilevel Monte Carlo methods and importance sampling, for a reliable and fast estimation of various statistical quantities for stochastic biological and chemical systems under the framework of Stochastic Reaction Networks (SRNs). In the second part of this thesis, we design novel numerical methods for pricing financial derivatives. Option pricing is usually challenging due to a combination of two complications: 1) The high dimensionality of the input space, and 2) The low regularity of the integrand on the input parameters. We address these challenges by using different techniques for smoothing the integrand to uncover the available regularity and improve quadrature methods' convergence behavior. We develop different ways of smoothing that depend on the characteristics of the problem at hand. Then, we approximate the resulting integrals using hierarchical quadrature methods combined with Brownian bridge construction and Richardson extrapolation.
Prof. Marco Cirant, Department of Mathematics, University of Padova, Italy
Thursday, July 02, 2020, 09:00
- 12:00
https://kaust.zoom.us/j/95230210819
Contact Person
In this short course, I will address some regularity aspects in the theory of Mean-Field Games systems, with special emphasis on stationary and uniformly elliptic problems. I will first describe some regularity results for linear uniformly elliptic PDEs and semi-linear PDEs of Hamilton-Jacobi type. Then, I will show how to use these tools to prove the existence (and in some cases uniqueness) of solutions to MFG systems.
Wednesday, July 01, 2020, 16:00
- 18:00
https://kaust.zoom.us/j/97077508273
Contact Person
Mean-field games (MFGs) study the behavior of rational and indistinguishable agents in a large population. Agents seek to minimize their cost based upon statistical information on the population's distribution. In this dissertation, we study the homogenization of a stationary first-order MFG and seek to find a numerical method to solve the homogenized problem. More precisely, we characterize the asymptotic behavior of a first-order stationary MFG with a periodically oscillating potential. Our main tool is the two-scale convergence. Using this convergence, we rigorously derive the two-scale homogenized and the homogenized MFG problems. Moreover, we prove the existence and uniqueness of the solution to these limit problems. Next, we notice that the homogenized problem resembles the problem involving effective Hamiltonians and Mather measures, which arise in several problems, including homogenization of Hamilton--Jacobi equations, nonlinear control systems, and Aubry--Mather theory. Thus, we develop algorithms to solve the homogenized problem, effective Hamiltonians, and Mather measures.
Prof. Marco Cirant, Department of Mathematics, University of Padova, Italy
Tuesday, June 30, 2020, 09:00
- 12:00
https://kaust.zoom.us/j/98958901323
Contact Person
In this short course, I will address some regularity aspects in the theory of Mean-Field Games systems, with special emphasis on stationary and uniformly elliptic problems. I will first describe some regularity results for linear uniformly elliptic PDEs and semi-linear PDEs of Hamilton-Jacobi type. Then, I will show how to use these tools to prove the existence (and in some cases uniqueness) of solutions to MFG systems.
Jun Chen, Department of Bioengineering, University of California Los Angeles
Monday, June 29, 2020, 19:00
- 20:30
https://kaust.zoom.us/j/2377519260
Contact Person
Dr. Jun Chen is currently an assistant professor in the Department of Bioengineering, University of California, Los Angeles. His current research focuses on nanotechnology and bioelectronics for energy, sensing, environment and therapy applications in the form of smart textiles, wearables, and body area sensor networks.
Prof. Alessio Porretta, Mathematical Analysis, University of Rome Tor Vergata, Italy
Thursday, June 25, 2020, 14:00
- 17:00
https://kaust.zoom.us/j/99286703478
Contact Person
We introduce several PDE tools which are useful in the study of mean field game systems with local couplings. Due to the lack of regularity of solutions, refined compactness and renormalization arguments are needed for a general approach leading to existence and uniqueness results. If time is enough, congestion models will be treated by similar techniques.
Prof. Alessio Porretta, Mathematical Analysis, University of Rome Tor Vergata
Monday, June 22, 2020, 14:00
- 17:00
https://kaust.zoom.us/j/91767652234
Contact Person
We introduce several PDE tools which are useful in the study of mean field game systems with local couplings. Due to the lack of regularity of solutions, refined compactness and renormalization arguments are needed for a general approach leading to existence and uniqueness results. If time is enough, congestion models will be treated by similar techniques.
Dr. Emad Felemban, Associate Professor in Computer engineering of Umm Al-Qura University
Wednesday, June 17, 2020, 13:00
- 14:00
https://kaust.zoom.us/j/92493419513
Contact Person
Recent real disastrous crowd incidents have shown that crowded places can be exposed to significant safety dangers and that the presence of many pedestrians can potentially result in injuries and fatalities at large scales if not planned and managed reasonably. This fact has resulted in significant challenges for managing the safety of large volumes of pedestrians in dense areas. In retrospect, many such real crowd disasters could have been avoided with better crowd management. Better tools and methodologies to predict crowd behavior during planning for potential emergencies would enable authorities to plan and prepare for improved public safety in crowded environments. Better still, real-time management of crowds might avert disasters if live event data could be used to make rapid predictions of crowd dynamics over the immediate future, allowing management to be optimized as an event unfolds. Such tools do not yet exist, and the technical demands of creating them are not trivial; they will require innovative approaches to both empirical research and modeling.
Prof Ping Chen, Institute of Semiconductor, Chinese Academy of Sciences
Tuesday, June 16, 2020, 16:00
- 17:00
https://kaust.zoom.us/j/93243111120
Contact Person
Dr. Ping Chen now works as a full Professor in the Institute of Semiconductors, Chinese Academy of Sciences (Beijing China). He received his bachelor’s degree of Physics from the University of Science and Technology of China (USTC) in 2003, and doctor’s degree of Microelectronics and Solid State Electronics from the Graduate School in University of Chinese Academy of Sciences in 2008. He worked in Georgia Institute of Technology (Atlanta, GA) as a Visiting Scholar from 2017 to 2019.
Dr. Naresh Chand, Life Fellow of IEEE, Associate Vice President, Chapter Relations of the IEEE Photonics Society
Tuesday, June 09, 2020, 16:00
- 17:30
https://kaust.zoom.us/j/2377519260
Contact Person
Dr. Naresh Chand is a Life Fellow of IEEE, Associate Vice President, Chapter Relations of the IEEE Photonics Society, and the Chair, Photonics Society, North Jersey Chapter. Dr. Naresh Chand was previously with US R&D Center of Huawei Technologies in NJ in 2011-2019 where he was working on developing low-cost advanced technologies for Ultra Broadband Optical Access Networks. Prior to this, he worked for BAE Systems (2003-11), Agere Systems and AT&T/Lucent Bell Laboratories (1986-2003), and Dept of Electronics, Government of India (1974-79).
Sunday, June 07, 2020, 16:00
- 18:00
https://kaust.zoom.us/j/99434336745
Contact Person
In this work, we develop a new framework of trajectory planning for AUVs in realistic ocean scenarios. We divide this work into three parts. In the first part, we provide a new approach for deterministic trajectory planning in steady current, described using Ocean General Circulation Model (OGCM) data. The latter are used to specify both the ocean current and the bathymetry. We apply a NLP to the optimal-time trajectory planning problem. To demonstrate the effectivity of the resulting model, we consider the optimal time trajectory planning of an AUV operating in the Red Sea and the Gulf of Aden. In the second part, we generalize our 3D trajectory planning framework to time-dependent ocean currents. We also extend the framework to accommodate multi-objective criteria, focusing specifically on the Pareto front curve between time and energy. The scheme is demonstrated for time-energy trajectory planning problems in the Gulf of Aden. In the last part, we address uncertainty in the ocean current field. The uncertainty in the current is described in terms of a finite ensemble of flow realizations. The proposed approach is based on a non-linear stochastic programming methodology that uses a risk-aware objective function, accounting for the full variability of the flow ensemble. Advanced visualization tools are used to amplify simulation results.
Prof. Rajendra Singh, Indian Institute of Technology Delhi
Friday, June 05, 2020, 16:00
- 17:30
https://kaust.zoom.us/j/2377519260
Contact Person
Dr. Rajendra Singh is presently a Professor at the Department of Physics, IIT Delhi. He did M.Sc. (Physics) from D.B.S. College, Dehra Dun (affiliated to H.N.B. Garhwal University) in 1995. After that he joined Inter University Accelerator Centre (formerly Nuclear Science Centre), New Delhi for Ph.D. His Ph.D. work was related to the study of the effect of swift heavy ion irradiation on electrical properties of Si and GaAs. He completed his Ph.D. in 2001 with degree from Jawaharlal Nehru University, New Delhi. He then joined Walter Schottky Institute (WSI), Technical University of Munich (TUM), Germany as a post doctoral fellow. There he worked on the design, fabrication and characterization of InP-based heterojunction bipolar transistors (HBTs). He extensively used Class 100 Cleanroom facilities at WSI working on various processing tools such as photolithography, wet etching, reactive ion etching, UHV metallization and rapid thermal annealing. In January 2004 he joined the Max Planck Institute of Microstructure Physics, Halle, Germany as a post doctoral fellow. There he worked in the area of direct wafer bonding and layer splitting of semiconductors for the fabrication of silicon-on-insulator (SOI) and strained silicon-on-insulator (sSOI). He worked in a Class 10 Cleanroom facility at MPI Halle using processing tools such as wet benches, wafer bonding system, plasma enhanced chemical vapour deposition (PECVD) and annealing furnaces.