Vickie Winston

Friday, Nov. 15, 2024
10:20 – 11:10 a.m. (CST)
ETB 1020       

Dr. Ranveer Chandra
General Manager for M365
Chief Technology Officer of Agri-Food at Microsoft

Title: “Data-Driven Agriculture to Sustainably Nourish the World” 

Abstract
Data-driven techniques help boost agricultural productivity by increasing yields, reducing losses and cutting down input costs. However, these techniques have seen sparse adoption owing to high costs of manual data collection and limited connectivity solutions. In this talk we will describe our innovations that leverage Internet of Things, Artificial Intelligence, and Edge Compute in the Farm and Space, to help make affordable digital agriculture solutions. We will also present our product based on this research, which is in preview, and can be used by partners to build their digital agriculture solutions, and how Generative AI can help transform digital agriculture.

Biography
Dr. Ranveer Chandra is the General Manager for M365 and the CTO of Agri-Food at Microsoft. He was previously the Managing Director of Industry Research and led the Networking Research Group at Microsoft Research, Redmond. He was also the Chief Scientist of Microsoft Azure Global. His research has shipped as part of multiple Microsoft products, including VirtualWiFi in Windows 7 onwards, low power Wi-Fi in Windows 8, Energy Profiler in Visual Studio, Software Defined Batteries in Windows 10, and the Wireless Controller Protocol in XBOX One. His research also led to a new product called Azure Data Manager for Agriculture. Ranveer is active in the networking and systems research community and has served as the Program Committee Chair of IEEE DySPAN 2012, ACM MobiCom 2013, and HotNets 2021.

Ranveer has published more than 100 papers and holds over 125 patents granted by the USPTO. His research has been cited by the popular press – such as the Economist, MIT Technology Review, BBC, Scientific American, New York Times, WSJ, among others. He is an IEEE Fellow and an ACM Fellow, and he has won several awards including best paper awards at ACM CoNext 2008, ACM SIGCOMM 2009, IEEE RTSS 2014, USENIX ATC 2015, Runtime Verification 2016 (RV’16), ACM COMPASS 2019, ACM MobiCom 2019, the Microsoft Research Graduate Fellowship, the Microsoft Gold Star Award, the MIT Technology Review’s Top Innovators Under 35, TR35 (2010) and Fellow in Communications, World Technology Network (2012). He was recognized by “Newsweek” as America’s 50 most Disruptive Innovators (2021). Ranveer has an undergraduate degree from IIT Kharagpur, India and a PhD from Cornell University.

 To learn more about Dr. Chandra, visit our webpage at https://www.microsoft.com/en-us/research/people/ranveer/.

Friday, November 8, 2024
10:20 – 11:10 a.m.  (CST)
ETB 1020           

Dr. I-Hong Hou
Professor of CE in ECE
Texas A&M University

Title: “Distributed No-Regret Learning for Multi-Stage Systems with End-to-End Bandit Feedback” 

Abstract
This talk focuses on multi-stage systems with end-to-end bandit feedback. In such systems, each job needs to go through multiple stages, each managed by a different agent, before generating an outcome. Each agent can only control its own action and learn the outcome of the job. It has neither knowledge nor control on actions taken by agents in the next stage. The goal of this paper is to develop distributed online learning algorithms that achieve sublinear regret in adversarial environments.

The setting of this paper significantly expands the traditional multi-armed bandit problem, which considers only one agent and one stage. In addition to the exploration-exploitation dilemma in the traditional multi-armed bandit problem, we show that the consideration of multiple stages introduces a third component, education, where an agent needs to choose its actions to facilitate the learning of agents in the next stage. To solve this newly introduced exploration-exploitation-education trilemma, we propose a simple distributed online learning algorithm, ϵ−EXP3. We theoretically prove that the ϵ−EXP3 algorithm is a no-regret policy that achieves sublinear regret. Simulation results show that the ϵ−EXP3 algorithm significantly outperforms existing no-regret online learning algorithms for the traditional multi-armed bandit problem.

Biography
Dr. Hou is a Professor in the ECE Department of Texas A&M University. His research interests include cloud/edge computing, wireless networks, and machine learning. Dr. Hou received the B.S. in Electrical Engineering from National Taiwan University in 2004 and his M.S. and Ph.D. in Computer Science from the University of Illinois, Urbana-Champaign in 2008 and 2011, respectively. He received the Best Paper Awards in ACM MobiHoc 2020 and ACM MobiHoc 2017, the Best Student Paper Award in WiOpt 2017, and the C.W. Gear Outstanding Graduate Student Award from the University of Illinois at Urbana-Champaign.

 To learn more about Dr. Hou, visit our webpage at https://cesg.tamu.edu/faculty/i-hong-hou/.

Friday, November 1, 2024
10:20 – 11:10 a.m.  (CST)
ETB 1020           

Dr. Saurabh Kadekodi
Senior Research Scientist in Storage Analytics
Google

Title: “Data-Driven IO Modeling and Optimizations in Cluster Storage Systems” 

Abstract
In this talk, I will cover two recent data-driven research projects in cluster storage systems — Thesios and Morph. Thesios is a methodology to accurately synthesize full-resolution representative IO traces, and counterfactual “what-if” traces by carefully combining down-sampled IO traces collected from multiple disks attached to multiple storage servers. Representative traces help inform the design and configuration of storage systems on real-world workloads, and counterfactual traces help assess the impact of anticipated or hypothetical new storage policies or hardware prior to deployment. I will also discuss the usefulness of Thesios for academia in order to obtain real-world traces, and the experience in open-sourcing synthesized traces comprising 2.5 billion IO requests.

Morph is the data-driven redundancy adaptation of files stored in cluster storage systems, over their lifetimes to address changes in data temperature and latency requirements. For newly ingested data, commonly stored via 3-way replication, Morph introduces a hybrid redundancy scheme that combines a replica with an erasure coded (EC) stripe, reducing both ingest IO and capacity overheads while enabling free transcode to EC by deleting replicas. For subsequent transcodes to wider, more space-efficient EC configs, Morph exploits Convertible Codes, which minimize data read for EC transcode, and introduces new block placement policies to maximize their effectiveness. Morph is thus designed to optimize redundancy by taking a file-lifetime view and minimizing IO overheads without affecting performance.

Biography
Saurabh Kadekodi obtained his PhD in the Computer Science Department at Carnegie Mellon University (CMU) in 2020 as part of the Parallel Data Laboratory (PDL) under the guidance of Prof. Gregory Ganger and Prof. Rashmi Vinayak. After graduation, Saurabh joined Google as a Visiting Faculty Researcher and is currently a Senior Research Scientist in the Storage Analytics team. Saurabh is broadly interested in designing distributed systems with special focus on the performance and reliability of storage systems.

To learn more about Dr. Kadekodi, visit his homepage at https://www.cs.cmu.edu/~saukad.

Friday, October 18, 2024
10:20 – 11:10 a.m.  (CST)
ETB 1020           

Dr. Prabhakar R. Pagilla
Professor, Mechanical Engineering Department
Texas A&M University

Title: “Planning and Control Problems in Robotics for Manufacturing Operations” 

Abstract
There has been a significant growth in the use of articulated robots for automation of manufacturing operations across many industrial sectors, including aerospace, transportation, construction, electronics, etc., with applications ranging from assembly, surface finishing, to material handling and delivery. For example, mechanical surface finishing operations, such as grinding, sanding, polishing, chamfering, etc., are widely employed in many industrial sectors to remove part anomalies and achieve a desired surface finish. Surface finishing has predominantly been a manual operation that is highly labor intensive and requires skilled operators. The key benefits of integrating robots into these environments include consistent surface quality, improved productivity, preventing hazardous exposure to vibration and particulate, significant flexibility for small as well as large batch manufacturing of finished parts, and the potential for re-purposing the robot quickly to adapt to a new part.

One can find many recent research and technology development activities focusing on the technical challenges of integrating robots into these operations in various environments. This talk will provide an overview of some of the essential elements, challenges, and recent advances in integrating robots to improve manufacturing operations, including registration of the workpiece in the robot workspace, path planning and control. A portion of the talk will also discuss some recent work on the benefits and challenges of V2V communication in connected and autonomous vehicles.

Biography
Prabhakar R. Pagilla is a Professor in the Mechanical Engineering Department at Texas A&M University. His formal background and research interests are in dynamic systems and control with applications in robotics, manufacturing, and autonomy. His current research focuses on robot motion planning and control problems in manufacturing, modeling and control of transport behavior of flexible materials in roll-to-roll manufacturing, and cooperative adaptive cruise control systems in connected and autonomous vehicles. He teaches undergraduate and graduate courses in dynamic systems, control, and robotics.

Please join us on Friday, 10/18/24 at 10:20 a.m. in ETB 1020 to learn more and meet Dr. Pagilla.

For more on Dr. Pagilla visit his website at https://pagilla.engr.tamu.edu.

Joseph Jean Boutros
TAMU Qatar
Professor of Electrical & Computer Engineering

Thursday, October 10, 2024
&
Tuesday, October 15, 2024

11:10 a.m. – 12:25 p.m.
@ the WEB 333’s Fishbowl

Title: “Algebra of Codes, Goppa Codes, and Applications to Post-Quantum Cryptography”

Abstract
We introduce linear codes over finite fields. Then, we study Generalized Reed-Solomon (RS) codes. Goppa codes are defined as subfield subcodes of RS codes. Finally, we describe the McEliece cryptosystem based on Goppa codes for asymmetric post-quantum cryptography.

Biography
Joseph Jean Boutros received the M.S. degree in electrical engineering in 1992 and the Ph.D. degree in 1996, both from Ecole Nationale Superieure des Telecommunications (ENST, Telecom ParisTech), Paris, France. From 1996 to 2006, he was with the Communications and Electronics Department at ENST as an Associate Professor. Also, Dr Boutros was a member of the research unit UMR-5141 of the French National Scientific Research Center (CNRS) in Paris. In July 2007, Doctor Boutros joined Texas A&M University at Qatar (TAMUQ) as a full Professor in the electrical engineering program.

Prof. Boutros’ fields of research are codes on graphs, lattice sphere packings, iterative decoding, joint source-channel coding, compressive sensing, space-time coding, physical-layer security, and physical-layer network coding.

Friday, October 11, 2024
10:20 – 11:10 a.m.  (CST)
ETB 1020           

Dr. Nina Taft
Principal Scientist/Director at Google

Title: “Leveraging Deep Learning to Understand Users’ Views about Privacy” 

Abstract
Privacy nudges can offer developers suggestions to improve the privacy of their apps.  We design a multi-stage methodology that leverages recent advances in NLP and LLMs to automatically extract privacy insights from smartphone app reviews.  Our analysis pipeline includes a privacy classifier, automated issue tagging for thematic clusters, a classifier to attach emotions to reviews, and extracts temporal and geographic trends.  We apply this methodology to publicly visible app reviews on the Google Play store that span a 10-year period and uncover 12 million instances of privacy-relevant reviews.  We’ll summarize users’ perspectives about smartphone app privacy along multiple dimensions – across a decade of time, from over 200 countries, and across a diversity of app types and privacy topics.  This approach complements traditional user studies by providing developers with actionable feedback from a vast and diverse user base.

Biography
Nina Taft is a Principal Scientist/Director at Google where she leads the Applied Privacy Research group. Nina received her PhD from UC Berkeley and has worked in industrial research labs since then – at SRI, Sprint Labs, Intel Berkeley Labs, and Technicolor Research before joining Google.  For many years, Nina worked in the field of networking, focused on Internet traffic modeling, traffic matrix estimation, and intrusion detection. In 2017, she received the top-10 women in networking IEEE N2Women award. In the last decade, she has been working on privacy enhancing technologies with a focus on applications of machine learning for privacy. She has been chair of the SIGCOMM, IMC and PAM conferences, has published over 100 papers, and holds 10 patents.

Please join us on Friday, 10/11/24 at 10:20 a.m. in ETB 1020!

Friday, October 25, 2024
10:20 – 11:10 a.m.  (CST)
ETB 1020           

 

Dr. Vijay Narayanan
Pennsylvania State University
Professor of Computer Science & Engineering and Electrical Engineering
Associate Dean of Innovation
Director of the Penn State Center for AI Intelligence Foundations and Engineering Systems

Title: “Designing Emerging Computing Systems with Ferroelectric Devices” 

Abstract
This talk will present a brief overview of advances in ferroelectric devices and their integration into computing systems to provide novel functionality and energy efficiency in various data intensive applications. The talk will emphasize on cross-stack design opportunities in designing stacked intelligent 3D memory systems.

Biography
Vijaykrishnan Narayanan is an Evan Pugh University Professor and Robert Noll A. Chair Professor of Computer Science and Engineering and Electrical Engineering at Pennsylvania State University.
He is a Fellow of ACM, IEEE, AAAS and the National Academy of Inventors. He serves as associate director of DoE 3DFeM center, thrust lead for DARPA/SRC PRISM center, associate Editor-in-Chief of IEEE Micro, the academic coordinator for the India-US Defense Acceleration Ecosystem and Associate Executive Director of AI for the GeoEd Foundation.

To learn more about Dr. Narayanan go to https://sites.psu.edu/vijaykrishnannarayanan.

Join us on Friday, 10/25/24 at 10:20 a.m. in ETB 1020 to hear Dr. Narayanan present in-person.