Seminars

Friday, October 21, 2022
10:20 – 11:10 a.m. (CST)
Virtual via Zoom: https://tamu.zoom.us/j/93347193479 (password in emails or syllabus)

Dr.  Bhuvana Krishnaswamy
Assistant Professor in Electrical and Computer Engineering
University of Wisconsin-Madison

Title: “Scalability in Low Power Wide Area Networks”

Talking Points:
* Long range and low power requirements have contrasting demands on the network.
* Current LPWAN solutions do not satisfy the large-scale needs of practical deployments.

Abstract
Wireless data delivery over long distance is power consuming and challenging for large-scale deployments. Low-power wide area networks (LPWAN) are increasingly in need to develop wireless solutions that satisfy the following requirements (1) Increased battery life, (2) Longer communication range, (3) Large-Scale, and (4) Low-cost. Existing strategies for addressing low-power and long-range do not efficiently address all of these in a large-scale network. In this talk, the fundamental challenges in meeting the above needs of LPWANs will be identified. Collision resolution approaches to meet the demands of large-scale, commercially available LPWANs – LoRa – will be the focus of this talk.

Biography 
Dr. Bhuvana Krishnaswamy is an Assistant Professor in the Department of Electrical and Computer Engineering at University of Wisconsin-Madison. She obtained her MS and Ph.D. in from Georgia Institute of Technology. She is the recipient of NSF CAREER Award, N2Women Rising Star Award, and the Grainger Faculty Scholarship Award. Her research interests are in low-power wireless communication and its challenges in practical deployments. Additional details about her research and team can be found at https://uwconnect.ece.wisc.edu/

More information on Dr. Krishnaswamy can be found HERE.

More info. on past and future CESG Seminars at CESG Seminars (tamu.edu)

* Friday, 10/21/22 at 10:20 a.m. via Zoom *

Friday, October 14, 2022
10:20 – 11:10 a.m. (CST)
Virtual via Zoom: https://tamu.zoom.us/j/93347193479 (password in emails or syllabus)

Dr.  Aditya Mahajan,
Associate Professor in Electrical and Computer Engineering
McGill University – Montreal, Canada

Title: “Approximate Planning and Learning for Partially Observed Systems ”

Talking Points:
* Propose a theoretical framework–based on the fundamental notion of information state–for approximate planning and learning in partially observed systems.
* Define an approximate information state (AIS) and a corresponding approximate dynamic program (ADP).
* Bound the error in using the policy obtained by the solution of the AIS-based ADP.
* Develop RL algorithms based on these bounds and illustrate that they perform well in high-dimensional partially observed grid-world environments.

Abstract
Reinforcement learning (RL) provides a conceptual framework for designing agents which learn to act optimally in unknown environments. RL has been successfully used in various applications ranging from robotics, industrial automation, finance, healthcare, and natural language processing. The success of RL is based on a solid foundation of combining the theory of exact and approximate Markov decision processes (MDPs) with iterative algorithms that are guaranteed to learn an exact or approximate action-value function and/or an approximately optimal policy. However, for the most part, the research on RL theory is focused on systems with full state observations.

In various applications including robotics, finance, and healthcare, the agent only gets a partial observation of the state of the environment. In this talk, I will describe a new framework for approximate planning and learning for partially observed systems based on the notion of approximate information state. The talk will highlight the strong theoretical foundations of this framework, illustrate how many of the existing approximation results can be viewed as a special case of approximate information state, and provide empirical evidence which suggests that this approach works well in practice.

Joint work with Jayakumar Subramanian, Amit Sinha, Raihan Seraj, and Erfan Seyedsalehi

Biography 
Dr. Aditya Mahajan is Associate Professor of Electrical and Computer Engineering at McGill University, Montreal, Canada. He is affiliated with the McGill Center of Intelligent Machines (CIM), Montreal Institute of Learning Algorithms (Mila), and Group for research in decision analysis (GERAD). He received the B.Tech degree in Electrical Engineering from the Indian Institute of Technology, Kanpur, India in 2003 and the MS and PhD degrees in Electrical Engineering and Computer Science from the University of Michigan, Ann Arbor, USA in 2006 and 2008.

He is the recipient of the 2015 George Axelby Outstanding Paper Award, the 2016 NSERC Discovery Accelerator Award, the 2014 CDC Best Student Paper Award (as supervisor), and the 2016 NecSys Best Student Paper Award (as supervisor). His principal research interests are learning and control of decentralized stochastic system.

More information on Dr. Mahajan HERE.

More info. on past and future CESG Seminars at CESG Seminars (tamu.edu)

* Friday, 10/14/22 at 10:20 a.m. via Zoom *

 

 

Friday, September 30, 2022
10:20 – 11:10 a.m. (CST)
Virtual via Zoom: https://tamu.zoom.us/j/93347193479 (password in emails or syllabus)

Dr. Aditya Akella
Regents Chair in Computer Sciences and Professor at UT Austin
Google Software Engineer

Title: “Cloud Velocity Networking: How We Got Here and Where We’re Headed”

Talking Points:

• Cloud services are driving the need for networking velocity
• Programmable network devices are being rapidly developed to serve this need
• We will cover state-of-the-art and open problems in new programmable device architectures and programming models 

Abstract

Over the past few years, networking advances have played a crucial role in enabling high-velocity, agile cloud computing deployments, and use cases. Technologies such as software-defined networking, network virtualization, and high-bisection fabrics have simplified cloud design and operation, brought exciting new workloads to the cloud, and helped lower the bar to cloud adoption. Networking is poised to bring even more interesting and fundamental transformations to the cloud over the next few years, enabling even greater velocity and diversity of use cases, without compromising performance, manageability, and high assurance. In this talk, I will describe some of the key “enabler” networking ideas, spanning high-performance fabrics and network stacks, programmable hardware, abstractions for network automation, and novel inter-domain protocols and services. I will discuss the tantalizing opportunities these ideas offer for future cloud computing, and the fundamental new research and practical challenges they introduce. I will conclude my talk with observations on what it would take for our research community to make rapid and meaningful progress in this space.

Biography

Dr. Aditya Akella is a Regents Chair Professor of Computer Science at UT Austin and a software engineer at Google. Aditya received his B. Tech. from IIT Madras (2000), and PhD from CMU (2005). His research spans computer systems and networking, with a focus on programmable networks, formal methods in systems, and systems for big data and machine learning. His work has influenced the infrastructure of some of the world’s largest online service providers. Aditya has received many awards for his contributions, including ACM SIGCOMM Test of Time Award (2022), selection as a finalist for the US Blavatnik National Award for Young Scientists (2020 and 2021), UW-Madison “Professor of the Year” award (2019 and 2017), IRTF Applied Networking Research Prize (2015), SIGCOMM Rising Star award (2014), NSF CAREER award (2008), and several best paper awards.

More Info on Dr. Akella at: https://www.cs.utexas.edu/~akella/ 

More info. on past and future CESG Seminars at CESG Seminars (tamu.edu)

Please join on Friday, 9/30/22 at 10:20 a.m. via Zoom.

 

 

Friday, September 16, 2022
10:20 – 11:10 a.m. (CST)
Virtual via Zoom: https://tamu.zoom.us/j/93347193479 (password in emails or syllabus)

Dr. Joerg Widmer
Research Professor and Research Director of IMDEA Networks
Madrid, Spain

Title: “Millimeter-Wave Joint Communication and Sensing and Location-based Network Control”

Talking Points:

• Practical challenges of millimeter-wave communications
• Benefits of millimeter-wave frequencies for high-precision sensing and localization

Abstract

The high bandwidth available at millimeter-wave frequencies allows for very high data rates, and the latest wireless technologies already exploit this part of the radio spectrum to achieve rates of several GBit/s per user. Communication at these frequencies typically uses directional antennas which brings about interesting challenges to align antenna beams. Given the high penetration loss, most obstacles (e.g., a person) also completely block the signal. This results in a very dynamic radio environment and channels may appear and disappear over very short time intervals. This seminar highlights some approaches to deal with these networking challenges. The very large bandwidth available at mm-wave frequencies also allows to design highly accurate location systems, and such location information be used to facilitate beam training, optimize access point association, and predict future blockage. We discuss how to implement such millimeter-wave location systems and present network optimization mechanisms based on simultaneous localization and mapping of the environment.

Finally, the information about the wireless channel of millimeter-wave systems can also be used for highly accurate environment sensing. We discuss how to use communication hardware to perform zero-cost monitoring of human movement and activities in indoor spaces (rather than using dedicated radars). We show that access points can be retrofitted to perform radar-like extraction of the micro-Doppler effects caused by the motion of multiple human subjects. We then use this to achieve fine-grained sensing applications such as simultaneous activity recognition and person identification. We will specifically focus on the practical implementation aspects, testbed design and experimental results with such systems.

Biography

Dr. Joerg Widmer is a Research Professor and Research Director of IMDEA Networks in Madrid, Spain. Before, he held positions at DOCOMO Euro-Labs in Munich, Germany and EPFL, Switzerland. He was a visiting researcher at the International Computer Science Institute in Berkeley, USA, University College London, UK, and TU Darmstadt, Germany. His research focuses on wireless networks, ranging from extremely high frequency millimeter-wave communication and MAC layer design to mobile network architectures. Joerg Widmer authored more than 200 conference and journal papers and three IETF RFCs, and holds 14 patents. He was awarded an ERC consolidator grant, the Friedrich Wilhelm Bessel Research Award of the Alexander von Humboldt Foundation, a Mercator Fellowship of the German Research Foundation, a Spanish Ramon y Cajal grant, as well as nine best paper awards. He is an IEEE Fellow and Distinguished Member of the ACM.

More Info on Dr. Widmer at: https://networks.imdea.org/team/imdea-networks-team/people/joerg-widmer/

More info. on past and future CESG Seminars at CESG Seminars (tamu.edu)

Please join on Friday, 9/16/22 at 10:20 a.m. via Zoom.

 

 

Friday, September 9, 2022
10:20 – 11:10 a.m. (CST)
Virtual via Zoom: https://tamu.zoom.us/j/93347193479 (password in emails or syllabus)

Dr. Subhonmesh Bose
Assistant Professor, University of Illinois Urbana Champaign

Title: “Modeling Risk in Power System Operations and Planning”

Talking Points:

• How you model risk should depend on how you can optimize against it
• Primal-dual algorithms for risk-sensitive optimization

Abstract

Integration of variable renewable and distributed energy resources in the grid makes demand and supply conditions uncertain. In this talk, we will first explore electricity market design for daily operations that explicitly models power delivery risks and system constraint violation risks. Then, we will discuss the distributed solar hosting capacity problem that considers said risks in electricity infrastructure planning. Throughout the talk, we will encode risks via the conditional value at risk (CVaR) measure, properties of which allow efficient, often customized, algorithm design for optimization, and facilitate the definition of economically meaningful prices in market design contexts.

Biography

Dr. Subhonmesh Bose is an Assistant Professor and Stanley Helm Fellow in the Department of Electrical and Computer Engineering at UIUC. His research focuses on facilitating the integration of renewable and distributed energy resources into the grid, leveraging tools from optimization, control and game theory. Before joining UIUC, he was a postdoctoral fellow at the Atkinson Center for Sustainability at Cornell University. Prior to that, he received his MS and Ph.D. degrees from Caltech in 2012 and 2014, respectively. He received the NSF CAREER Award in 2021. His research projects have been supported by grants from NSF, PSERC, Siebel Energy Institute and C3.ai, among others.

More Info: http://boses.ece.illinois.edu/

Please join on Friday, 9/9/22 at 10:20 a.m. via Zoom.

 

 

Friday, September 23, 2022
10:20 – 11:10 a.m. (CST)
ETB 1020 – **In-person** (or Zoom for those receiving emails)

Dr. Biswajit Ray
Associate Professor, Dept. of ECE at The University of Alabama

Title: “Intelligent Data Storage Systems for Cyber-Security, Extreme-Reliability and Edge-Computing”

Talking Points:

• Monolithic 3D NAND Flash technology is the industry’s workhorse for high density data storage
• Data security and user privacy are at stake in Flash storage
• Radiation environment makes Flash storage vulnerable

Abstract

Even though solid-state storage technology has seen unprecedented growth in bit-density over the last few decades, emerging artificial intelligence and edge computing applications present new challenges related to security, resilience, and energy-efficiency. These challenges can only be addressed through innovative system design concepts that aptly utilize the physical properties of storage media. While the traditional storage system mainly relies on technology-agnostic algorithmic functions for the ease of portability, such design underutilizes the rich physical properties of the storage media. Thus, state-of-the art storage solutions are inadequate to ensure resilience, energy efficiency, system security and end-user privacy at the edge nodes and extreme environments.

In this talk, I will present a few innovative techniques that will bridge the gap between device and system design approaches to open-up new opportunities for enhancing resilience, security, and energy efficiency of future edge computing/storage applications. I will illustrate system-level techniques to define hardware security primitives using physical properties of commercial flash memory. I will share my experimental research findings on the radiation effects on the 3D NAND storage and system-level countermeasures. Finally, I will present a conceptual framework for energy-efficient computing and storage using flash memory for error-tolerant applications.

Biography

Dr. Biswajit Ray is an Associate Professor of Electrical and Computer Engineering with the University of Alabama in Huntsville (UAH), AL, USA, where he leads Hardware Security and Reliability Laboratory. Dr. Ray received Ph.D. from Purdue University, West Lafayette, IN and then he worked in SanDisk Corporation, Milpitas, California developing 3D NAND Flash memory technology. Dr. Ray holds 17 U.S. issued patents on non-volatile memory systems, published more than 50 research papers in international journals and conferences. Dr. Ray is a recipient of NSF CAREER Award (2022), NSF EPSCoR Research Fellow (2020) and Outstanding Faculty Award (2022) at UAH.

References:
[1] S. Sakib, A. Milenkovic, and B. Ray, “Flash Watermark: An Anti-Counterfeiting Technique for NAND Flash Memories,” IEEE Transaction on Electron Devices, vol. 67, no. 10, pp. 4172–4177, 2020.
[2] P. Kumari, S. Huang, M. Wasiolek, K. Hattar, and B. Ray, “Layer Dependent Bit Error Variation in 3-D NAND Flash Under Ionizing Radiation,” IEEE Transactions on Nuclear Science, vol. 67, no. 9, pp. 2021-2027, 2020.
[3] M. Hasan and B. Ray, “Data Recovery from “Scrubbed” NAND Flash Storage: Need for Analog Sanitization,” in Proc. of the 29^th USENIX Security Symposium, Boston, MA, Aug. 2020.

Google Page: https://sites.google.com/a/uah.edu/ray_biswajit 

Please join on Friday, 9/23/22 at 10:20 a.m. in ETB 1020.

Host: Dr. Sunil Khatri

 

Friday, September 2, 2022
10:20 – 11:10 a.m. (CST)
ETB 1020 – **In-person** (or by Zoom for those receiving emails)

Dr. Joshua Peeples
ACES Faculty Fellow & Visiting Assistant Professor, Texas A&M University, Electrical & Computer Engineering

Title: “Statistical Texture Feature Learning for Image Analysis”

Talking Points:

• Convolutional neural networks are biased towards structural textures
• Histogram layer(s) provide statistical context within deep learning models to improve performance

Abstract

Feature engineering often plays a vital role in the fields of computer vision and machine learning. A few common examples of engineered features include histogram of oriented gradients (HOG), local binary patterns (LBP), and edge histogram descriptors (EHD). Features such as pixel gradient directions and magnitudes for HOG, encoded pixel differences for LBP, and edge orientations for EHD are aggregated through histograms to extract texture information. However, the process of designing handcrafted features can be difficult and time consuming. Artificial neural networks (ANNs) such as convolutional neural networks (CNNs) have performed well in various applications such as facial recognition, semantic segmentation, object detection, and image classification through automated feature learning.

A new histogram layer is proposed to learn features and maximize the performance of ANNs for statistical texture analysis. Current approaches using ANNs or handcrafted features do not perform well for some texture applications due to inherent problems within texture datasets (e.g., high intrinsic dimensionality, large intra-class variations) and limitations in methods that use handcrafted and/or deep learning features. The proposed approach is a novel method to synthesize both neural and traditional features into a single pipeline. The histogram layer can estimate bin centers and widths through the backpropagation of errors to aggregate the features from the data while also maintaining spatial information. The improved performance of each network with the addition of histogram layer(s) demonstrates the potential for the use of this new element within ANNs.

Biography

Dr. Joshua Peeples is an ACES Faculty Fellow and Visiting Assistant Professor in the Department of Electrical and Computer Engineering at Texas A&M University. Dr. Peeples received his Bachelor of Science degree in electrical engineering with a minor in mathematics from the University of Alabama at Birmingham. He earned his Ph.D. in the Department of Electrical and Computer Engineering at the University of Florida with Dr. Alina Zare. During his Ph.D. studies, Dr. Peeples developed and refined novel deep learning methods for texture characterization, segmentation, and classification. Dr. Peeples’ current research seeks to extend his dissertation work and explore new aspects such as developing algorithms for explainable AI and various real-world applications in other domains (e.g., biomedical, agriculture). These methods can then be applied toward automated image understanding, object detection, and classification. Dr. Peeples has been recognized with several awards, including the Florida Education Fund’s McKnight Doctoral Fellowship and National Science Foundation Graduate Research Fellowship. In addition to research and teaching, Dr. Peeples is dedicated to service and advocacy for students at the university and in the community.

More information on Dr. Peeples at https://engineering.tamu.edu/electrical/profiles/peeples-joshua.html 

Please join on Friday, 9/2/22 at 10:20 a.m. in ETB 1020.

 

Thursday, June 2, 2022
10:00 – 10:50 a.m. (CST)
ETB 1035 – **In-person**

Dr. Muhammad Awais Bin Altaf
Assistant Professor, Electrical Engineering
Lahore University of Management Sciences (LUMS), Pakistan

Title: “On-Chip Energy-Efficient Neural Diagnostics: Advancing Neuroscience through Wearable Devices”

Talking Points:

• Wearable Healthcare
• On-Chip Energy Efficient Digital Processing Techniques for ML algorithms
• Early Detection of Negative Emotion Outburst

Abstract
Artificial intelligence (AI) has influenced all aspects of human life and neurology is no exception to this growing trend. Today, neurology faces multiple challenges in the field of diagnostic and management modalities. This ranges from simple issues like identification of healthy sleep patterns to more complicated issues like early detection and reduction in the duration of rehabilitation of acute ischemic stroke diagnosis of rare subtypes of epilepsy. The increasing availability and progress of analytical techniques are opening new doors in health care. Machine learning, neural networks and other AI tools are used to classify the patient’s electroencephalogram (EEG) data to help neurologists in making an early diagnosis and improving care. Hence, the development of ultra-low-power System-on-Chip (SoC) for the next generation of the neuro-wearables, in the realms of detecting, diagnosing, and even preventing irreversible outcomes due to neurological disorders is essential. The uptake in the use of neuro-wearable technology by both patients and clinicians will have a huge impact on the future of healthcare.

This talk will cover the design strategies of energy-efficient patient-specific SoC biomedical devices. I will first explore the challenges, limitations and potential pitfalls in wearable interface circuit design, and strategies to overcome such issues. Moreover, I will describe on-chip energy-efficient digital processing techniques for the implementation of machine-learning algorithms for disease detection focusing on the negative emotion outburst early detection in Autistic patients. The talk will conclude with interesting aspects and opportunities that lie ahead.

Biography
Muhammad Awais Bin Altaf  (S’11–M’16) received a B.S. degree from the University of Engineering and Technology, Lahore, Pakistan, in 2008, and the M.Sc. and Ph.D. degrees in microsystems engineering and interdisciplinary engineering from the Masdar Institute of Science and Technology (MIST), Abu Dhabi, United Arab Emirates, in 2012 and 2016, respectively. From 2012 to 2013, he was a Digital Design Engineer Intern at Design Solutions, Global Foundries, Dresden, Germany, where he was involved in the implementation of digital test chips in support of 20 and 14 nm technologies. In 2015, he was an exchange-Ph.D. a student with the Massachusetts Institute of Technology, Cambridge, MA, USA.

During his stay at MIST, he developed an energy-efficient machine-learning-based feature extraction and classification processor as well as a SoC for epileptic seizure detection. He is the recipient of the IEEE Solid-State Circuits Society Predoctoral award for his work on efficient machine learning hardware implementation for wearable healthcare in 2016. Since 2016, he has been with the Electrical Engineering Department, Lahore University of Management Sciences (LUMS), Lahore, Pakistan where he is currently an Assistant Professor. His current research interests include analog and digital IC design, energy-efficient applied AI and the development of ultra-low-power circuits and systems for wearable bio-medical applications.

Google Scholar: https://scholar.google.ae/citations?user=XVyrDmgAAAAJ&hl=en

In-Person @ ETB 1035 @ 10:00 a.m. on Thursday, 6/2/22

Friday, April 29, 2022
4:10 – 5:00 p.m.
Zoom: https://tamu.zoom.us/j/96343481647
 
Dr. Vijay Subramanian
Associate Professor in the EECS at the University of Michigan, Ann Arbor, MI

Talking Points

• Multi-agent dynamic games with asymmetric information
• Information states
• Compression based equilibria
• Sequential decomposition

Title
“Games in Multi-Agent Dynamic Systems: Decision-Making with Compressed Information”

Abstract
The model of multi-agent dynamic systems has a wide range of applications in numerous socioeconomic and engineering settings: spectrum markets, e-commerce, transportation networks, power systems, etc. In this model, each agent takes  actions over time to interact with the underlying system as well as each other to achieve their respective objectives. In many applications of this model, agents have access to a huge amount of information that increases over time. Determining solutions of such multi-agent dynamic games can be complicated due to the huge domains of strategies. Meanwhile, agents have restrictions on their computational power and communication capability as well as latency limitations, which prevent them from implementing complicated strategies. Therefore, it is important to identify suitable compression schemes so that at equilibrium each agent can make decisions based on a compressed version of their information instead of the full information. However, compression of information could result in loss of some or all equilibrium outcomes. This talk  presents results on this issue for a general class of multi-agent dynamic games, and designs and analyzes appropriate  information compression schemes. Our results highlight the tension among information compression, preservation of  equilibrium outcomes, and applicability of sequential decomposition algorithms to find compression-based equilibria. This is joint work with Dengwang Tang (University of California, Berkeley) and Demos Teneketzis (University of Michigan).

Biography
Vijay Subramanian received the Ph.D. degree in electrical engineering from the University of Illinois at Urbana-Champaign,  Champaign, IL, USA, in 1999. He worked at Motorola Inc., at the Hamilton Institute, Maynooth, Ireland, for many years, and in the EECS Department, Northwestern University, Evanston, IL, USA. In Fall 2014, he started in his current position as Associate Professor with the  EECS Department at the University of Michigan, Ann Arbor. His research interests are in stochastic analysis, random  graphs, multi-agent systems, and game theory (mechanism and information design) with applications to social, economic and technological networks.

Dr. Subramanian: https://subramanian.engin.umich.edu

On Zoom @ 4:10 p.m. on Friday, 4/29/22

Join Zoom Meeting
https://tamu.zoom.us/j/96343481647
Meeting ID: 963 4348 1647

Friday, April 22, 2022
4:10 – 5:00 p.m.
 
ETB 1020 – **In-person**
 
Takashi Tanaka
Assistant Professor, University of Texas at Austin
Department of Aerospace Engineering and Engineering Mechanics
 
Title: “Minimum-Information Kalman-Bucy Filtering and Fundamental Limitation of Continuous-Time Data Compression”

Talking Points:

• Event-based vs. frame-based cameras
• Information theory for networked control systems
• Causal source coding

Abstract
Motivated by a practical scenario where a continuous-time source signal is encoded, compressed, and transmitted to a remote user where the signal is reproduced in a real-time manner (e.g., streaming of neuromorphic camera data), we study the fundamental trade-off between the encoding data rate and the best achievable data quality (distortion). After briefly reviewing the “causal” rate-distortion theory in discrete-time, in this talk, we consider the problem of estimating a continuous-time Gauss-Markov source process observed through a vector Gaussian channel with an adjustable channel gain matrix. For a given (generally time-varying) channel gain matrix, we provide formulas to compute (i) the mean-square estimation error attainable by the classical Kalman-Bucy filter, and (ii) the mutual information between the source process and its Kalman-Bucy estimate. We then formulate a novel “optimal channel gain control problem” where the objective is to control the channel gain matrix strategically to minimize the weighted sum of these two performance metrics. To develop insights into the optimal solution, we first consider the problem of controlling a time-varying channel gain over a finite time interval. A necessary optimality condition is derived based on Pontryagin’s minimum principle. For a scalar system, we show that the optimal channel gain is a piece-wise constant signal with at most two discontinuities. We also consider the problem of designing the optimal time-invariant gain to minimize the average cost over an infinite time horizon. A novel semidefinite programming (SDP) heuristic is proposed to compute the optimal solution.

Biography
Takashi Tanaka is an Assistant Professor in the Department of Aerospace Engineering and Engineering Mechanics at the University of Texas at Austin since 2017. He received his B.S. degree from the University of Tokyo in 2006, M.S. and Ph.D. degrees from UIUC in 2009 and 2012, all in Aerospace Engineering. Prior to joining UT Austin, he held postdoctoral researcher positions at MIT and KTH Royal Institute of Technology. His research interest is broad in control, optimization, games, and information theory; most recently their applications to networked control systems, real-time data sharing, and strategic perception. He is the recipient of the DARPA Young Faculty Award, the AFOSR Young Investigator Program award, and the NSF Career award.

Personal Website: http://sites.utexas.edu/tanaka/

In-Person @ ETB 1020 @ 4:10 p.m. on Friday, 4/22/22