Seminars

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

Friday, April 8, 2022
4:10 – 5:00 p.m.
 
ETB 1020 – **In-person**
 
Carmen Mota
Licensed Professional Counselor-Supervisor, Texas State Board of Examiners of Professional Counselors
Engineering Counselor for Texas A&M University
 
Talking Points:

• Recognizing Unhealthy Anxiety,
• Challenging Anxious Thinking, and
• Plan for Managing Anxiety

Title: “Your Anxiety Toolbox”

Abstract
The focus of this presentation is to increase our knowledge of anxiety, including anxiety symptoms and underlying thinking patterns. Attendees make an individualized Plan for Managing Anxiety.

Biography
Carmen Mota: I have been a counselor since 2011 and worked in the areas of developmental delays and mental health since 2002. Throughout this time, I have worked with people from diverse backgrounds and in every age group. I continue to learn from and be inspired by every person that I meet in the counseling relationship. Both the counselor and the client/student bring so much to the counseling relationship and form a therapeutic and collaborative relationship. I am trained in CBT (Cognitive Behavior Therapy), CPT (Cognitive Processing Therapy for trauma work), Sand tray, Play therapy, Filial therapy for parents, and have some limited training in Equine therapy. The approach I use with each client/student begins with Rogerian theory, as I believe “unconditional positive regard” is essential in creating a safe space to begin the counseling relationship. As a counselor, my goal is to help clients/students find their hope and self-compassion. I strive to assist clients/students in achieving their goals, both academic and personal.

I’m a Licensed Professional Counselor-Supervisor, Texas State Board of Examiners of Professional Counselors, No.65959. My education was at M.A., Counseling, Sam Houston State University and have a B.S., Psychology, Minor in Sociology, Texas A&M University

A large portion of my career has been educating people on mental health awareness. I believe I have a responsibility to challenge the stigmas that still exist in society and within families. And in my work, challenging the stigmas against mental health and seeking treatment, begins with individuals. When we accept ourselves, differences, struggles, and all, we can show the world that differences are okay. That diversity is not only okay, but important. Our uniqueness should be celebrated. I am a trained family facilitator with NAMI, (National Alliance on Mental Illness) and highly encourage their support groups and educational workshops as well. We cannot ignore the effects that social injustice and stigma has on us all. I began this work with educating myself and challenging my own biases and misconceptions. I am a life-long learner, of others, the world, and myself.

As a supervisor, I get to help shape future counselors. I believe it’s necessary for the interns I work with to have a well-rounded experience, including individual, couples, and group therapy, and crisis management. It is essential that counseling interns gain insight into themselves, including their own experiences and backgrounds and particular biases that may impact helping others. When beneficial, I encourage interns to begin working with a therapist themselves. Compassion for self and others is a necessary component in supervision.

Department Website: https://caps.tamu.edu/

In-Person @ ETB 1020 @ 4:10 p.m. on Friday, 4/8/22

Friday, April 1, 2022
4:10 – 5:00 p.m.
Zoom: https://tamu.zoom.us/j/96343481647
 
Dr. Siva Theja Maguluri
Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering, Georgia Tech

Talking Points

• Lyapunov methods for Stochastic Approximation and Reinforcement learning
• Generalized Moreau Envelop based on infimal convolution smoothing as a Lyapunov function for Stochastic Approximation of contractive operators
• Unified framework to obtain sample complexity of a large class of RL algorithms
• Linear speedup in the number of agents for Federated Reinforcement learning

Title
“A Lyapunov Theory of Finite-Sample Guarantees of Stochastic Approximation and Reinforcement Learning”

Abstract
The focus of our work is to obtain finite-sample and/or finite-time convergence bounds of various model-free Reinforcement Learning (RL) algorithms. Many RL algorithms involve solving the Bellman fixed point equation, which is done using Stochastic Approximation (SA). SA is a popular approach for solving fixed point equations when the information is corrupted by noise. We develop a Lyapunov framework and obtain mean square error bounds on the convergence of a general class of SA algorithms for contractive operators under general norms and Markovian noise. The key tool we use is generalized Moreau envelope as a smooth potential/ Lyapunov function. These powerful results immediately provide sample complexity results of a large class of RL algorithms including TD learning, Q-learning, actor-critic algorithms, their off-policy variants, and their distributed variants. The talk will present a couple of these applications in off-policy RL and/or Federated RL.

Biography
Siva Theja Maguluri is Fouts Family Early Career Professor and Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He obtained his Ph.D. and MS in ECE as well as MS in Applied Math from UIUC, and B.Tech in Electrical Engineering from IIT Madras. His research interests span the areas of Control, Optimization, Algorithms and Applied Probability. In particular, he works on Reinforcement Learning theory, scheduling, resource allocation and revenue optimization problems that arise in a variety of systems including Data Centers, Cloud Computing, Wireless Networks, Block Chains, Ride hailing systems, etc. His research and teaching are recognized through several awards including the  “Best Publication in Applied Probability” award, NSF CAREER award, second place award at INFORMS JFIG best paper competition, Student best paper award at IFIP Performance, “CTL/BP Junior Faculty Teaching Excellence Award,” and “Student Recognition of Excellence in Teaching: Class of 1934 CIOS Award.”

Dr. Maguluri: https://sites.google.com/site/sivatheja/

On Zoom @ 4:10 p.m. on Friday, 4/1/22

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

Friday, March 4, 2022
4:10 – 5:00 p.m.
Zoom: https://tamu.zoom.us/j/96343481647
 
Dr. Tommaso Melodia
William L. Smith Professor, Northeastern University

Talking Points
– Open, Programmable, and Virtualized Wireless Architectures
– O-RAN architecture: Research Challenges
– Deep Reinforcement Learning for sequential decision making in the Open RAN
– Resources available for at scale prototyping, testing, data collection: PAWR, Colosseum

Title
“Toward AI-based Control and Orchestration in the Open RAN: Architectures, Algorithms, Testbeds”

Abstract
This talk will present an overview of our work on laying the basic principles to design open, programmable, AI-driven, and virtualized next-generation wireless networks. We will cover in detail challenges and opportunities associated with the evolution of cellular systems into cloud-native softwarized architectures enabling fine grained AI-based control of end-to-end functionalities on mobile devices, in the radio access network, and at the edge. We will also discuss existing testbeds and platforms available to the community for prototyping, experimentation, and data collection in virtualized and softwarized wireless systems.

Biography
Tommaso Melodia is the William Lincoln Smith Professor with the Department of Electrical and Computer Engineering at Northeastern University in Boston. He is also the Founding Director of the Institute for the Wireless Internet of Things and the Director of Research for the PAWR Project Office. He received his Laurea (integrated BS and MS) from the University of Rome – La Sapienza and his Ph.D. in Electrical and Computer Engineering from the Georgia Institute of Technology in 2007. He is an IEEE Fellow and recipient of the National Science Foundation CAREER award. Prof. Melodia is serving as Editor in Chief for Computer Networks, and has served as Associate Editor for IEEE Transactions on Wireless Communications, IEEE Transactions on Mobile Computing, IEEE Transactions on Multimedia, among others. He was the Technical Program Committee Chair for IEEE Infocom 2018, and General Chair for ACM MobiHoc 2020, IEEE SECON 2019, ACM Nanocom 2019, and ACM WUWNet 2014. Prof. Melodia’s research on modeling, optimization, and experimental evaluation of wireless networked systems has been funded by US federal agencies and industry.

Dr. Melodia: https://ece.northeastern.edu/wineslab/tmelodia.php

On Zoom @ 4:10 p.m. on Friday, 3/4/22

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