Categories
Programs

DSL and SIG Lab Renovations: Out with the Old, In with the New

As the School of Engineering and Applied Science grows, so do the rooms that house all of the creative minds within our departments. Just in time for the Fall 2022 semester, two labs that are a part of the C.I.S. Department and are located in the Moore Building have gotten a makeover this past summer and the faculty and students are thrilled. The Distributed Systems Laboratory (DSL) in Moore 100 and The SIG Center for Computer Graphics in Moore 103 have both undergone renovations for more student space to collaborate, create and liven up the space!

Cheryl Hickey, the Administrative/Event Coordinator in C.I.S., collaborated with the directors of the labs and a design team. She was tasked with choosing the furniture, color scheme, and layout that would best suit the space and the students. The Distributed Systems Lab received all new desks as well as a redone kitchen area for faculty and students. Jonathan M. Smith, Olga and Alberico Pompa Professor of C.I.S. said, “The DSL renovation allow for maximum flexibility in placement of student desks and experiments. Moving the network drops into the DSL machine room freed up space in the main areas for scholarly interaction and increased physical security.” The lab now encourages students to work together within the space better than ever before.

The Distributed Systems Laboratory (Moore Hall 100)

“The DSL renovation is nicely done and timely. I know many of our students really appreciate the new space. The new desks are superb. These days, when I walk past DSL, I’m pleased to see many students interacting with each other. The vibrancy and bustle are back in DSL! Our DSL seminars have also been packed with faculty and students listening to each other’s research. Nothing beats in-person interactions, and the best ideas happen when students talk and iterate through ideas in close proximity.” (Boon Thau Loo, Director of DSL)

Students have noticed the changes to the DSL lab as well since they began the semester just over a month ago. Jess Woods, a C.I.S. PhD being advised by Sebastian Angel, stated that, “It’s nice having our own functional cabinets/lockers. There are more desks and less clutter, which seems to encourage more people to come to campus to work, which makes the environment more collaborative and productive”. Having a space post-Covid that is inviting and open for all students to take advantage of is incredibly positive for the department.

While a part of the SIG Center for Computer Graphics is fully renovated as of this past summer, the lab is still continuing to grow. New technologies are in the works in addition to a couple of new faculty members who will be officially joining the department this coming Spring 2023 semester. Incoming assistant professor in Computer Graphics, Lingjie Liu, stated that there will be many upgrades to the space and equipment including GPU clusters and data storage space in support of research development.

Upon her arrival to Penn Engineering, Lingjie will “we will also purchase and set up a multi-view volumetric camera capture stage for capturing human motions and human-object interactions with multiple synchronized cameras (about 40 cameras).” This set of equipment will be an addition to the labs’ Vicon 16-camera motion capture system.

“The research focus of the new SIG Lab will be developing Artificial Intelligence (AI) technologies for perceiving, understanding, and interacting with 3D objects, people, and environments. Specifically, we will pursue three research goals: (1) High-quality reconstruction of real-world scenes from sparse RGB (camera) images; (2) Photo-realistic image synthesis of real-world scenes with 3D control; and (3) Large-scale 3D scene generation for 3D machine learning tasks.

Lingjie states that the SIG Lab’s new research focus will be “developing Artificial Intelligence (AI) technologies for perceiving, understanding, and interacting with 3D objects, people, and environments.” There are three research goals that the lab will pursue which include:

  1. High-quality reconstruction of real-world scenes from sparse RGB (camera) images
  2. Photo-realistic image synthesis of real-world scenes with 3D control
  3. Large-scale 3D scene generation for 3D machine learning tasks.

“We will approach these goals by designing new algorithms that incorporate AI advances into classical computer graphics methods. We believe that with the new research focus and the facilities upgrade, we will create new research achievements and continue the success of our SIG Lab.”

The SIG Center for Computer Graphics (Moore Hall 103)

The finished and fully furnished side of the SIG lab will now be filled with another incoming assistant professor of C.I.S. and ESE, Mingmin Zhao‘s students. “Working with the renovation and designer team was a pleasure, and the new lab is absolutely gorgeous. I am excited to attract and recruit more students and spend time with them in the new lab.”

While touring SIG lab earlier this month, there were several students hard at work and raved about how much they enjoy spending time in the space. One PhD student who is being advised by Mingmin, Gaoxiang Luo, said, “The renovation of Moore 103B is way beyond my expectation. The workspace shelves allow us to store electronic components, while the wall pegboard allows us to hang tools. While the space is large enough for us to conduct wireless experiments using radar with a certain distance, the ESD flooring further ensures our safety. It’s also worth mentioning that I love the ergonomic height-adjustable desk and chair, which is particularly useful for our health as we work with our computer frequently nowadays.”

It is incredible to see the impact that this renovation has had on the students and faculty. The department is looking forward to seeing the work and successes that come out of these two labs for this new generation of students in Penn Engineering!

Categories
Programs

The ASSET Center: Enabling Trust Between AI and its User

Picture this: you’re getting ready to watch a movie on Netflix, popcorn in hand, and several films pop up that have been curated just for you. What are you going to do: choose one from the list recommended by the underlying AI algorithm, or worry about how this list was generated and whether you should trust it? Now, think about when you are at the doctors’ office and the physician decides to consult an online system to figure out what dosage of medicine you as the patient should take. Would you feel comfortable having a course of treatment chosen for you by artificial intelligence? What will the future of medicine look like where the doctor is not involved at all in making the decision?

This is where the ASSET Center comes into play. This initiative, led by the C.I.S. Department in Penn Engineering, to focuses on the trustworthiness, explainability, and safety of AI-systems. The faculty members and students who are a part of this Center have tasked themselves with finding ways to achieve this trust between an AI-system and a user. Through new collaborations between departments all throughout Penn, innovative research projects, and student engagement, ASSET will be able to unlock AI abilities that have never been achieved before.

Rajeev Alur, Zisman Family Professor and inaugural director of ASSET

I recently spoke with Rajeev Alur, Zisman Family Professor in the C.I.S. Department and inaugural director of ASSET. He elaborated on our Netflix example to explain the trust between an AI-system and a user and when it is absolutely critical for the adoption of AI by society. Based on movies and shows that the user watches, Netflix is able to give several recommendations, and it is the user’s choice as to whether they will go for something new. While the recommendations may be decent picks to the user “there is no guarantee or assurance that what they are recommending is foolproof or safe”, says Rajeev. Although AI is found to be useful in the case of choosing what to watch, the user needs a higher level of assurance with the system in more critical applications. An example of this could be when a patient is receiving treatment from a doctor. This high assurance can become important in two cases. One is when the system is completely autonomous, or what is called a “closed loop system,” and the other case is when the system is making a recommendation to a physician who decides what course of action to take. For this latter case, the AI does not make the decision directly, but its recommendation may still be highly influential. In many clinical settings, there are AI-systems already in place that dole out courses of treatment that best suits the patient, and a physician consults and tweaks these choices. What ASSET is looking to implement in the medical field are autonomous AI-systems that are trustworthy and safe in their decision making for the users.

“The ultimate goal is to create trust between AI and its users. One way to do this is to have an explanation and the other one is to have higher guarantees that this decision the AI-system is making is going to be correct,” Rajeev explains.

Collaborations

For ASSET to succeed, the center must nurture connections throughout Penn Engineering and beyond its walls. Within C.I.S., machine learning and AI experts are working together with faculty members in formal methods and programming languages to come up with tools and ideas for AI safety. Outside of C.I.S., Rajeev explains that robotics faculty in ESE and MEAM are interested in designing control systems and software that uses AI techniques in the Center. Going beyond Penn Engineering, ASSET is dedicated to making connections with Penn’s Perelman School of Medicine. “There is a great opportunity because Penn Medicine is right here and there are lots of exciting projects going on. They all want to use AI for a variety of applications and we have started a dialogue with them…This will all be a catalyst to having new research collaborations”, says Rajeev.

Research Projects

F1Tenth Racing Car that is used in competitions
F1Tenth Racing Car

In keeping with the idea of autonomous AI that was discussed earlier, one of ASSET’s flagship projects is called Verisig. The goal of this project involving the collaborative efforts of Rajeev Alur, Insup Lee, and George Pappas, is to “give verified guarantees regarding correct behaviors of AI-based systems” (Rajeev Alur). In a case study being performed by the Center, researchers have verified the controller of an autonomous F1/10 racing car to check the design and safety of the controller so that the car is guaranteed to avoid collisions. The purpose of this project is to further understand assured autonomy; if a controller of a small car can be found trustworthy and safe, these methods may eventually be generalized and used in AI applications within the medical field.

How to get involved

The best way for students to get involved with ASSET is engaging in the center’s Seminar Series. They happen every Wednesday in Levine 307 from noon to 1pm, and the great thing about them is that any Penn student can join. There are incredible speakers lined up through the Fall and Spring semesters this school year, so instead of turning on Netflix and letting the system choose your next bingeworthy show, join ASSET every Wednesday for exciting talks about creating safe and trustworthy AI!

Categories
Faculty

In the Spotlight: Eric Wong and Developing Debuggable AI-Systems

What happens when AI goes wrong? Probably not the Terminator or the Matrix – despite what Hollywood suggests – but rather, something that could still harm a human, such as a self-driving car that gets into an accident, or an algorithm that discriminates against certain people. Fortunately Penn has innovative researchers like Eric Wong, who build tools to make sure AI works correctly!

You may have already seen Eric on campus or perhaps teaching his advanced graduate class. Just like the Class of 2026 who are quickly learning their way around Levine Hall, Eric is one of the C.I.S. Department’s newest faculty members. An Assistant Professor who works in Machine Learning, Eric is a Carnegie Mellon Ph.D. graduate and a former MIT post-doctoral researcher in the Computer Science and Artificial Intelligence Lab.

As this semester is in full swing, Eric Wong is busy at work teaching course 7000-05: Debugging Data and Models. When asked what he is looking forward to most about teaching in Penn Engineering, Eric stated,

“One of the key skills that students will learn is how to tinker with AI systems in order to debug and identify their failure modes. I’m excited to see the new ways in which Penn Engineering students will break AI systems, as well as the innovations they come up with to repair them!”

The initiatives that Penn Engineering has launched in recent times are what drew Eric to the C.I.S. Department, specifically the ASSET Center. “Penn Engineering is well-situated to ensure that the tools and systems we develop as computer scientists actually satisfy the needs and requirements of those that want to use them.”, said Eric. He will be one of many faculty members working with ASSET to develop reliable and trustworthy AI-systems which coincides with his own research.

Some of Eric’s specialized interests in this field include “verifying safety properties of an AI-system, designing interpretable systems, and debugging the entire AI pipeline (i.e. the data, models, and algorithms).” His research goals are working towards debugging AI-systems so that the user is able to understand the decision process of a system and learn how to inspect its defects. Eric is also interested by the interdisciplinary work of connecting these methods to other fields outside of engineering. Collaborators in medicine, security, autonomous driving, and energy would “ensure that the fundamental methods we develop are guided by real-world issues with AI reliability.”

As AI is being developed and deployed at a rapid rate, Eric worries that, “it is only a matter of time before the ‘perfect storm’ induces a catastrophic accident for a deployed AI system.” In teaching methods of debugging AI-systems, he strives to give his students the tools and knowledge toward building safer and more trustworthy AI for the future. He hopes that with his research and teachings in the classroom, students take the time to “critically examine their own system” before sending them out into the world.

When Eric is not spending time making sure AI-systems are at the top tier of trustworthiness and reliability, he enjoys trying to recreate the recipes of meals that he orders at restaurants. Trying to “reverse engineer its creation process” is harder than it might seem. Eric mentioned that, “It does not always look the same as the original, nor does it always taste as good, but sometimes it works!”. Maybe someday that too will be something an AI can do (correctly)!

Categories
Students

Welcome Back C.I.S. Students: Let’s See What’s New!

Melvin J. and Claire Levine Hall

The 2022-2023 academic year has kicked off last week and summer has officially come to a close. We would like to welcome back returning Computer and Information Science students as well as the Class of 2026! Penn Engineering is excited to have you on board.

With a new school year comes new changes for Penn Engineering and the CIS department. In the past year, we have hired an exceptional number of faculty, brought in new research initiatives, renovated our spaces, and broke ground on a state-of-the-art facility. We are also incredibly thankful to be able to see so many faces in person this year as the circumstances surrounding Covid-19 continues to improve and in-person activities can commence.

Faculty

Several brand new assistant professors have joined the department this semester, while the rest will arrive in January and next Fall. Rejoining the CIS department, in a new role as an Assistant Professor, is Osbert Bastani who develops innovative techniques for programming and building software that incorporate machine learning components. Two new faces to Penn Engineering features Danaë Metaxa, who works in areas of human-computer interaction and communications, and Eric Wong, who works on robust and reliable machine learning.

“We are delighted to welcome an unprecedented 10 new assistant professors arriving over the next year.  Each brings new innovations to the curriculum and more opportunities to get involved in undergraduate research projects.”, says Zachary Ives, Chair of the CIS Department.

Each new faculty member entering into Computer and Information Science has arrived to break barriers and help our students grow.

Space

As our department is growing, our spaces have been transforming as well. This summer, the Distributed Systems Lab (DSL) and the SIG Lab for Computer Graphics on the first floor of Moore have both undergone major renovations. On the second floor of Levine Hall, the brand new Penn Human-Computer Interaction Lab, led by Andrew Head and Danaë Metaxa, just opened.

“We couldn’t be more excited for the start of this year and the official launch of our group. We’re looking forward to teaching Penn’s first Human-Computer Interaction courses at all levels, welcoming our first cohort of PhD students and opening our physical space- the HCI Lab- in Levine 255. We welcome interested students to reach out to us!” -Danaë Metaxa, Assistant Professor, CIS Dept, University of Pennsylvania.

In addition to our growth in space, Penn Engineering is not stopping there. The construction of Amy Gutmann Hall has begun during the summer. The creation of new space, study rooms, and research labs is anticipated to be opening in September 2024.

Research Initiatives

Since the launch of this past year’s interdisciplinary research initiative, Innovation in Data Engineering and Science (IDEAS), the CIS Department announced the ASSET Center directed by Rajeev Alur. ASSET (AI-enabled Systems: Safe, Explainable, Trustworthy) focuses on implementing tools and science to guarantee AI systems do exactly what they are designed to do. Getting students involved in this new initiative is a top priority for the Center.

“The best way to get involved is to join our seminars. It’s every Wednesday at noon and we have a great line-up of speakers. There is a number of faculty from our department, other Penn faculty, and also outside speakers. Some of the topics will include applications to healthcare, explainablility, and safety.” -Rajeev Alur, Director of ASSET, University of Pennsylvania.

With every new faculty member, space renovation, and research initiative; all of these things are implemented to give students the best opportunities for success. We have another exciting year just beginning at Penn Engineering. Let’s make it count.

Categories
Programs

Computational Social Science Lab’s PennMAP to Expand Thanks to Leadership Gift

Stevens University Professor Duncan Watts, founder of the CSS Lab

The Wharton School and the University of Pennsylvania are delighted to announce the expansion of the Penn Media Accountability Project (PennMAP), an interdisciplinary, nonpartisan research project dedicated to enhancing media transparency and accountability. Its growth is made possible by a new leadership gift from Richard Jay Mack, W’89.

“Our goal at PennMAP is to detect and expose biased, misleading, and otherwise problematic content in media from across the political spectrum and spanning television, radio, social media, and the broader web,” says Duncan Watts, the Stevens University Professor and a Wharton professor of Operations, Information and Decisions who leads PennMAP. Watts also holds faculty appointments in the Annenberg School of Communication and in the Department of Computer and Information Science in the School of Engineering and Applied Science. He is a Faculty Fellow of Analytics at Wharton, the preeminent and first business school center focused on research, teaching, and corporate partnerships around analytics and their application in business, non-profits, and society.

“Clearly this is an ambitious goal that requires a substantial investment in research infrastructure as well as building collaborations with a diverse set of partners,” says Watts. “Richard Mack’s generous gift will allow us to significantly accelerate our efforts and increase our impact both in terms of research and the public conversation on these important issues. We are tremendously grateful for his support.”

PennMAP is a product of the University’s Computational Social Science Lab (CSSLab), a joint venture of the School of Engineering and Applied Science, the Annenberg School for Communication, and the Wharton School. The Lab seeks novel, replicable insights into societally relevant problems by applying computational methods to large-scale data.

This article originally appears on The Wharton School site. Click here to read it in full.
Categories
Featured Students

Happy Halloween! From CIS and Penn Engineering 👻🎃🦇

On Friday, October 29, each department of the School of Engineering and Applied Science gathered together on the West Towne Lawn in the spirit of Halloween celebration!

Students, staff and faculty were able to stop by each department’s station for delicious treats and candy, Penn Engineering swag, and fun Halloween stickers and toys.

Not only were costumes encouraged, but the Penn Engineering community is hosting a costume contest, with entries accepted until November 3!

Scroll down for some amazing photos of the day, which also included a photo backdrop and Halloween tunes!

Other CIS-affiliated Halloween events include:

  • The Penn Society of Women Engineers Meet and Greet – Levine Lobby, October 29, 4-5pm
    Come take a break from studying, meet other students and enjoy some arts and crafts and insomnia cookies.
  • CIS Faculty and Postdoctoral Fellow Halloween TGIF – Quain Courtyard, October 29, 5-8pm
    There will be a Halloween Costume Contest with gift card prizes for winners! There will also be a pumpkin carving event, food, and an expanded selection of alcoholic and non-alcoholic drinks.
Categories
Faculty

Secure Imprecision: Professor Andreas Haeberlen speaks on the importance of Differential Privacy

Left: Andreas Haeberlen
Right: “Informal Definition of Differential Privacy,” courtesy of the National Institute of Standards and Technology
October is Cybersecurity Awareness Month. This article is one of a cybersecurity-focused series.

Last year during the peak of the COVID-19 pandemic in the US, testing and contact tracing failed to quell the spread. Many circumstances — including a decades-old underfunding of state health departments, and slow workforce build – have contributed to this outcome.  

However, according to Department of Computer and Information Science Professor Andreas Haeberlen, one of the main reasons contact tracing wasn’t relatively more successful is simple: people don’t’ feel comfortable sharing their information. 

“It’s really scary to think of people knowing all the things that you type in your phone,” said Haeberlen. “Like what you’ve had for breakfast, or your medical information, or where you’ve been all day or who you’ve met. All of that data is super super sensitive.” 

Haeberlen, whose research centers distributed systems, networking, security, and privacy, believes that differential privacy could be the solution. 

“Differential privacy is a way to purpose private information so that you can really guarantee that somebody can’t later learn something sensitive from this information,” said Haeberlen. “[It] has a very solid mathematical foundation.” 

The National Institute of Science and Technology defines differential privacy in terms of mathematical qualification. “It is not a specific process, but a property that a process can have,” said NIST on their website. “For example, it is possible to prove that a specific algorithm ‘satisfies’ differential privacy.” 

And so we might assert that, if an analysis of a database without Joe Citizen’s individual data and an analysis of a database with Joe Citizen’s individual data yield indistinguishable results, then differential privacy is satisfied. “This implies that whoever sees the output won’t be able to tell whether or not Joe’s data was used, or what Joe’s data contained,” said NIST on their site. 

Haeberlen insists that, with widespread application of differential privacy, user trust is not only no longer a barrier, but that it is not necessarily required. Surrendering our sensitive information to large corporations such as Apple would no longer require a leap of faith. 

Building the tools

A popular industry standard of cybersecurity involves adding imprecision into results to purposefully skew them, and thus protect individual user data. Challenges to this application, according to Haeberlen, include the ongoing debate among experts about whether it satisfies differential privacy specifications, and its lack of scalability. 

“Fuzzi: A Three-Level Logic for Differential Privacy,” a paper by Haeberlen and fellow researchers Edo Roth, Hengchu Zhang, Benjamin C. Pierce and Aaron Roth, is one of many of Haeberlen’s oeuvre that focuses on developing tools that can do the work for us. The paper presents a prototype called Fuzzi, whose top level of operational logic “is a novel sensitivity logic adapted from the linear-logic-inspired type system of Fuzz, a differentially private functional language,” according to the abstract. 

Essentially, a researcher would input data into the tool, define what that data means, and specify what data output they’re searching for. The tool would be able to state if that output satisfies differential privacy specifications, and, if not, what amount of imprecision would need to be added in order to meet specifications.  

“The way that we did that was by baking differential privacy into a programming language,” said Haeberlen. “As a practitioner you don’t have to understand what differential privacy is, you also don’t have to be able to prove it.” 

In the world of science, imprecision usually means error and gross miscalculation. However, in the more specific realm of differential privacy, imprecision equals security. 

“Imprecision is good because it causes the adversary to make mistakes,” said Haeberlen. In this case, the “adversary” is any person or system trying to gain access to sensitive information. 

All tools developed by Haeberlen and his team have been made available under open-source license, and companies such as Uber and Facebook are currently releasing data sets using differential privacy.  

Visit Professor Andreas Haeberlen’s page to learn more about his current projects and recent publications.  
Categories
Featured

New Penn Engineering Data Science Building named Amy Gutmann Hall


School of Engineering and Applied Science Dean Vijay Kumar, President Amy Gutmann, Trustee and naming donor Harlan M. Stone, and Penn Engineering Board Chair Rob Stavis at the October 1, 2021 groundbreaking for Amy Gutmann Hall to be located on the northeast corner of 34th and Chestnut Streets.
Courtesy of University of Penn Almanac site

On Friday, October 1, 2021, the University of Pennsylvania’s School of Engineering and Applied Science held a groundbreaking ceremony for its new data science building and unveiled the building’s official name, Amy Gutmann Hall, honoring Penn’s President. Amy Gutmann is the eighth and longest-serving President in Penn’s history, leading the University since 2004; her term will conclude at the end of this academic year.

Amy Gutmann Hall will serve as a hub for cross-disciplinary collaborations that harness expertise, research, and data across Penn’s 12 schools and numerous academic centers. Upon completion, it will centralize resources that will advance the work of scholars across a wide variety of fields while making the tools and concepts of data analysis more accessible to the entire Penn community.

“I am thrilled Penn Engineering’s new data science building will honor Dr. Gutmann’s remarkable legacy at Penn,” said Vijay Kumar, the Nemirovsky Family Dean of Penn Engineering. “Her Penn Compact and the principles of inclusion, innovation, and impact influenced the school’s strategic priorities from which the plan for a data science building emerged. This revolutionary new facility will create a centralized home for data science research and provide collaborative and accessible space for our faculty and students, as well as the Philadelphia community.”

The 116,000-square-foot, six-floor building will be located at the northeast corner of 34th and Chestnut Streets. Planned academic features include a data science hub, the translational and outreach arm of Penn Engineering in the area of data science and artificial intelligence; research centers for new socially aware data science methodologies and novel, bio-inspired paradigms for computing; and laboratories that will develop data-driven, innovative approaches for safer and more cost-effective health care. 

The impressive building is the design of executive architects Lake/Flato, with KSS Architects serving as associate architects. The building’s architecture will signify the future and the dynamic shift from the traditional to the digital. The facility is planned to be the first mass timber building in Philadelphia and will be designed sustainably.

Construction will begin in spring 2022 and is slated for completion in 2024.

This article originally appears on the University of Pennsylvania Almanac site. To read the full article, click here.
Categories
Programs

The new Computational Social Science Lab aims to thrive on mass collaboration and open science

The School of Engineering and Applied Science, the Annenberg School for Communication and the Wharton School have joined together to form the Computational Social Science (CSS) Lab. Established in March 2021, the CSS Lab brings together a team of undergraduate, graduate and postdoc students from all three schools, as well as a dedicated staff, and researchers from other esteemed universities. 

“Our central mission is to integrate methods and ways of thinking from the computational and social sciences in the service of real-world applications,” said Lab Director and Stevens University Professor in the Department of Computer Science Duncan Watts. “We are also dedicated to building research infrastructure to support mass collaboration around shared data, and to facilitate open, transparent, and replicable science. We have a great team and a great set of initial projects. I’m really looking forward to seeing what we can do.” 

Some of those projects include a set of interactive data dashboards that utilize demographic and mobility data around COVID-19 to help inform decisions, as well as a project “dedicated to enhancing media transparency and accountability at the scale of the entire information ecosystem,” according to the Lab site.  

Among the team who will help bring the mission to fruition are Associate Research Scientist Homa Hosseinmardi, Research Data Engineer Yingquan Li, and the Lab’s Executive Director, Valery Yakubovich. 

As a former professor at ESSEC Business School, Yakubovich is excited to bring his management expertise to the Lab. 

“Creating a hub for cutting-edge research at the intersection of social science and high-tech requires genuine intrapreneurship, open and secure digital organization, and a functionally diverse team of staff speaking in one language,” said Yakubovich. “Under the auspices of three professional schools—each as renowned as it is independently-minded—this task is especially challenging but equally rewarding.”  

CIS looks forward to the exciting work the Lab will produce. Visit the CSS Lab site, and be sure to check our blog for important updates and research findings.  

Categories
Students

Shirin Saeedi Bidokhti receives 2021 NSF CAREER Award

Shirin Saeedi Bidokhti (Illustration by Melissa Pappas, Courtesy of Penn Engineering Today)
This article originally appeared in Penn Engineering Today, written by Melissa Poppas.

Humans have never been more connected to one another, though the speed with which we can share with one another has its drawbacks. For example, the spread of COVID-19, as well as misinformation about it, have both been facilitated by our highly connected online and in-person networks. Fortunately, the branches of mathematics known as information theory and network theory can help us to understand how both systems work and how to control them.

NSF CAREER Award recipient Shirin Saeedi Bidokhti, Assistant Professor in Electrical and Systems Engineering, will use the grant to conduct research on both online social networks and COVID-19 contact tracing networks. As case studies, these real-word examples will inform networked systems’ theoretical foundations, as well as the design of learning and decision-making algorithms that help us to make sense of them. She will also use the funding to develop a new course module that brings information and network theory into practice for undergraduate students at Penn.

Using a combination of tools from information theory, network theory and machine learning, Saeedi Bidokhti aims to narrow the gap between theory and practice through algorithm-informed real-time data sampling, estimation and inference in networked systems. Her work aims to produce smarter algorithms that can extract information, infer about these systems, and ultimately provide more precise control of them.

While such algorithms are already improving our ability to understand complex networks, there is always a tradeoff that needs to be considered when it comes time to use that information.

“In information extraction, knowing when to sample with real-time data makes a difference, says Saeedi Bidokhti. “It helps us to know if we should act now or wait to sample, facing the tradeoff of gathering the most information while minimizing costs to most efficiently control the system.”

To read the full article, visit Penn Engineering Today.