Q&A with Clemson University’s Bart Knijnenburg, research award recipient for improving ad experiences

by Facebook AI

In this monthly interview series, we turn the spotlight on members of the academic community and the important research they do — as partners, collaborators, consultants, or independent contributors.

For February, we nominated Bart Knijnenburg, assistant professor at Clemson University. Knijnenburg is a 2019 UX-sponsored research award recipient in improving ad experiences, whose resulting research was nominated for Best Paper at the 54th Hawaii International Conference on System Sciences (HICSS). Knijnenburg has also been involved in the Facebook Fellowship Program as the adviser of two program alumni, Moses Namara and Daricia Wilkinson.

In this Q&A, Knijnenburg describes the work he does at Clemson, including his recently nominated research in improving ad experiences. He also tells us what inspired this research, what the results were, and where people can learn more.

Q: Tell us about your role at Clemson and the type of research you and your department specialize in.

Bart Knijnenburg: I am an assistant professor in the Human-Centered Computing division of the Clemson University School of Computing. Our division studies the human aspects of computing through user-centered design and user experiments, with faculty members who study virtual environments, online communities, adaptive user experiences, etc. My personal interest lies in helping people make better decisions online through adaptive consumer decision support. Within this broad area, I have specialized in usable recommender systems and privacy decision-making.

In the area of recommender systems, I focus on usable mechanisms for users of such systems to input their preferences, and novel means to display and explain the resulting recommendations to users. An important goal I have in this area is to build systems that don’t just show users items that reflect their preferences, but help users better understand what their preferences are to begin with — systems I call “recommender systems for self-actualization.”

In the area of privacy decision-making, I focus on systems that actively assist consumers in their privacy decision-making practices — a concept I have dubbed “user-tailored privacy.” These systems should help users translate their privacy preferences into settings, thereby reducing the users’ burden of control while at the same time respecting their inherent privacy preferences.

Q: What inspired you to pursue your recent research project in improving ad experiences?

BK: Despite recent efforts to improve the user experience around online ads, there is a rise of distrust and skepticism around the collection and use of personal data for advertising purposes. There are a number of reasons for this distrust, including a lack of transparency and control. This lack of transparency and control not only generates mistrust, but also makes it more likely that the user models created by ad personalization algorithms reflect users’ immediate desires rather than their longer-term goals. The presented ads, in turn, tend to reflect these short-term likes, ignoring users’ ambitions and their better selves.

As someone who has worked extensively on transparency and control in both the field of recommender systems and the field of privacy, I am excited to apply this work to the area of ad experiences. In this project, my team therefore aims to design, build, and evaluate intuitive explanations of the ad recommendation process and interaction mechanisms that allow users to control this process. We will build these mechanisms in line with the nascent concepts of recommender systems for self-actualization and user-tailored privacy. The ultimate goal of this effort is to make advertisements more aligned with users’ long-term goals and ambitions.

Q: What were the results of this research?

BK: The work on this project is still very much ongoing. Our first step has been to conduct a systematic literature review on ad explanations, covering existing research on how they are generated, presented, and perceived by users. Based on this review, we developed a classification scheme that categorizes the existing literature on ad explanations offering insights into the reasoning behind the ad recommendation, the objective of the explanation, the content of the explanation, and how this content should be presented. This classification scheme offers a useful tool for researchers and practitioners to synthesize existing research on ad explanations and to identify paths for future research.

Our second step involves the development of a measurement instrument to evaluate ad experiences. The validation of this measurement instrument is still ongoing, but the end result will entail a carefully constructed set of questionnaires that can be used to users’ reactions toward online ads, including aspects of targeting accuracy, accountability, transparency, control, reliability, persuasiveness, and creepiness.

A third step involves a fundamental redesign of the ad experience on social networks, reimagining the very concept of advertising as a means to an end that serves the longer-term goals of the user. We are still in the very early stages of this activity, but we aim to explore the paradigm of recommendations, insights, and/or personal goals as a vehicle for this transformation of the ad experience.

Q: How has this research been received so far?

BK: Our paper on the literature review and the classification scheme of ad explanations was accepted to HICSS and was nominated as the Best Paper in the Social Media and e-Business Transformation minitrack. We are working on an interactive version of the classification scheme that provides a convenient overview of and direct access to the most relevant research in the area of ad explanations.

We are also working with Facebook researchers to make sure that our ad experience measurement instrument optimally serves their goal of creating a user-friendly ad experience.

Q: Where can people learn more about your research?

BK: You can find a project page about this research at www.usabart.nl/FBads. We will keep this page updated when new results become available!

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Improving attitudes about mask wearing via Facebook ad campaigns

by Facebook AI

Wearing masks is an important part of the COVID-19 response, but the adoption of mask-wearing varies by geography and demographics. We know from the literature that social norms and attitudes around mask-wearing are among the factors that determine whether people actually wear masks. To help meet this urgent need, we recently evaluated two campaigns leveraging social norms and attitudes to improve mask-wearing behavior. These campaigns were run on our ads platform and measured using Brand Lift.

The first campaign used interest-based targeting of posts by public figures posting with the #wearamask hashtag. Within two days of people’s seeing the ad, we asked them via survey: “When you think of most people whose opinions you value, how much would they approve of you wearing a mask to help slow the spread of COVID-19?” Of those in the control group, 69.4 percent selected “A great deal” or “Quite a bit,” and 77.4 percent of those in the test group selected these desired options (other responses were “Somewhat,” “A little,” and “Not at all”). Thus, this campaign resulted in an eight-point increase at 99 percent confidence in the percentage of those reporting in-group approval for personal mask-wearing. That represents over 2 million people out of the 26 million who were reached during the campaign.

The second was the “You Will See Me” ad campaign developed by the Ad Council in partnership with the CDC and CDC Foundation. It was designed for Black Americans, considering the disproportionate impact COVID-19 has had on the Black community. We then asked the following via survey: “In the last 2 days, how often did you wear a mask in public to slow the spread of the coronavirus (COVID-19)?” 79.4 percent who were exposed to the campaign answered “Often” or “Always” versus 75.5 percent in the control group (other responses were “Sometimes,” “Rarely,” and “Never”). Thus, this campaign resulted in more than a three-point increase at 99 percent confidence in those reporting wearing masks in public frequently. That represents over 200,000 people out of six million who were reached during the campaign.

The results demonstrate that interventions like these can have significant impact, and we’re now working with public health partners to scale similar projects as part of our COVID-19 response. For more information about what Facebook is doing to keep people safe and informed about the coronavirus, read the latest updates on Newsroom.

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Accelerating rural connectivity research: How Facebook helps bring connectivity to hard-to-reach areas

by Facebook AI

All deployment site photos from Peru were taken by our partners at Mayu Telecomunicaciones are used here with permission. To request permission to use the photos, contact servicios@mayutel.com.

Facebook Connectivity’s mission is to enable better, broader global connectivity to bring more people online to a faster internet. We collaborate with others in the industry — including telecom operators, community leaders, technology developers, and researchers — in order to find solutions that are scalable and sustainable. One major research area of interest is rural connectivity, as many rural areas around the world still don’t have access to mobile connectivity and technology innovations are needed. An important element of rural connectivity is backhaul, the links that connect remote sites to the core network of the internet. Wireless backhaul using microwave radio provides low-cost, fast deployment in comparison with other options.

Today, the design of microwave backhaul relies on clear line-of-sight (LOS) requirements. Unfortunately, for rural areas, lack of LOS between settlements means that a repeater or reflector has to be built, which leads to cost constraints. In this project, we explore the use of diffraction, a physics phenomenon through which some wireless signal energy is bent into the geometric shadow of the obstacle. If diffraction can be predicted reliably, it could be used to design and build wireless backhaul links in challenging environments, reducing the need to build repeaters and making network design more efficient.

Example physics-based signal propagation modeling result showing that some signal energy is diffracted into the shadow region

Illustration of how diffractive NLOS wireless links can reduce the need to build repeaters

To address this challenge, Facebook Connectivity developed a research partnership with university and industry partners. We recognized that we need field data that can be used to validate and calibrate signal prediction algorithms, improved network design methodologies, and an assessment of real-world cost-coverage impact. To facilitate knowledge sharing and collaboration, Facebook Connectivity organized a number of meetings, including a workshop in 2019. In this workshop, Omar Tupayachi Calderon (CEO of Mayu Telecomunicaciones, a rural mobile infrastructure operator in Peru) shared that “Peru has an incredible diversity of challenges, and 60,000 rural settlements still do not have broadband connectivity. We need your help.”

Bringing rural connectivity to Peru

Universidad Politécnica de Madrid (UPM), The Ohio State University (OSU), Air Electronics, and Plexus Controls developed instruments to measure signal propagation over difficult terrain and conducted systematic experiments in southern Ohio, in areas near Madrid, Spain, and in southern Ontario, Canada. University of Michigan, George Mason University, OSU, and MIT developed propagation models, resulting in a number of publications and open source software.

 

 

 

 

Experimental data and setups used by OSU and UPM (click to enlarge)

The complete solution set that Facebook developed included an end-to-end workflow for link design, network planning, and site deployment, which we are sharing as a white paper in the Telecom Infra Project Network as a Service Solution project group.

 

 

 

 

Rural Peru deployment pictures taken by Mayutel (click to enlarge)

Scaling the solution

To make the solution usable in as many parts of the world as possible, Facebook took several next steps:

First, we collaborated with OSU and George Mason University to make a MATLAB and Python version of the Irregular Terrain Model and Longley-Rice algorithm available as free, open source software.

Second, we broadened the collaboration to include Contract Telecommunications LTD — the makers of Pathloss, the most widely used microwave link planning software in the world — to implement the outputs of this project into their platform.

Third, we developed a field-grade drone-mounted measurement kit with Plexus Controls to enable experimentalists to gather field data economically, and for connectivity infrastructure developers to validate signal strength in the field prior to building their sites. Further, we developed a software for data visualization and basic processing. The drone and the software are designed to enable faster, simpler field experiments and validation than ever before.

Fourth, we are contributing our learnings to the Telecom Infra Project Network as a Service Solution Group.

Finally, we have expanded our partnership through collaborations with TeleworX, Internet para Todos (IpT) de Peru, and Mayu Telecomunicaciones (Mayutel). IpT de Peru is a major network operator that is significantly expanding broadband access in rural parts of the country. Founded in 2019, IpT has deployed hundreds of broadband sites in rural areas of Peru to date. IpT has successfully deployed dozens of NLOS links in their network, providing both end point and backbone transport connectivity. Mayutel works with the local communities in rural Peru to build the telecom sites, deploy 4G radio systems, and provide broadband connectivity for the first time to many of the community.

Learn more

As we look forward to bringing this solution to other parts of the world, please learn more about the technology behind this project through our publications:

To learn about NLOS in the Telecom Infra Project Network-as-a-Service Solutions project group, please see our recently published white paper on the subject. For more about the Telecom Infra Project, visit their website. You can also learn about other initiatives on the Facebook Connectivity website.

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Q&A with Ayesha Ali, two-time award winner of Facebook request for research proposals in misinformation

by Facebook AI

Facebook is a place where bright minds in computer science come to work on some of the world’s most complex and challenging research problems. In addition to recruiting top talent, we maintain close ties with academia and the research community to collaborate on difficult challenges and find solutions together. In this new monthly interview series, we turn the spotlight on members of the academic community and the important research they do — as partners, collaborators, consultants, or independent contributors.

This month, we reached out to Ayesha Ali, professor at Lahore University of Management Sciences (LUMS) in Pakistan. Ali is a two-time winner of the Facebook Foundational Integrity Research request for proposals (RFP) in misinformation and polarization (2019 and 2020). In this Q&A, Ali shares the results of her research, its impact, and advice for university faculty looking to follow a similar path.

Q: Tell us about your role at LUMS and the type of research you and your department specialize in.

Ayesha Ali: I joined the Department of Economics at LUMS in 2016 as an assistant professor, after completing my PhD in economics at the University of Toronto. I am trained as an applied development economist, and my research focuses on understanding and addressing policy challenges facing developing countries, such as increasing human development, managing energy and environment, and leveraging technology for societal benefit. Among the themes that I am working on is how individuals with low levels of digital literacy perceive and react to content on social media, and how that affects their beliefs and behavior.

Q: How did you decide to pursue research projects in misinformation?

AA: Before writing the first proposal back in 2018, I had been thinking about the phenomenon of misinformation and fabricated content for quite some time. On multiple occasions, I had the opportunity to interact with colleagues in the computer science department on this issue, and we had some great discussions about it.

We quickly realized that we cannot combat misinformation with technology alone. It is a multifaceted issue. To address this problem, we need the following: user education, technology for filtering false news, and context-specific policies for deterring false news generation and dissemination. We were particularly interested in thinking about the different ways we could educate people who have low levels of digital literacy to recognize misinformation.

Q: What were the results of your first research project, and what are your plans for the second one?

AA: In our first project, we studied the effect of two types of user education programs in helping people recognize false news using a randomized field experiment. Using a list of actual news stories circulated on social media, we create a test to measure the extent to which people are likely to believe misinformation. Contrary to their perceived effectiveness, we found no significant effect of video-based general educational messages about misinformation.

However, when video-based educational messages were augmented with personalized feedback based on individuals’ past engagement with false news, there was a significant improvement in their ability to recognize false news. Our results show that, when appropriately designed, educational programs can be effective in making people more discerning consumers of information on social media.

Our second project aims to build on this research agenda. We plan to focus on nontextual misinformation, such as audio deepfakes. Audio messages are a popular form of communication among people with low levels of literacy and digital literacy. Using surveys and experiments, we will examine how people perceive, consume, and engage with information received via audio deepfakes, and what is the role of prior beliefs and analytical ability in forming perceptions about the accuracy of such information. We also plan to design and experimentally evaluate an educational intervention to increase people’s ability to identify audio deepfakes.

Q: What is the impact of your research in your region and globally?

AA: I think there are at least three ways in which our work is having an impact:

  1. Our work raises awareness about the importance of digital literacy campaigns in combating misinformation. It shows that such interventions hold promise in making users more discerning consumers of information if they are tailored to the target population (e.g., low literacy populations).
  2. Our work can affect policy about media literacy campaigns and how to structure them, especially for low digital literacy populations. We are already in touch with various organizations in Pakistan to see how our findings can be put to use in various digital literacy campaigns. For example, the COVID-19 vaccination is likely to be made available in the coming months, and there is a need to raise awareness about its importance and to proactively dispel any conspiracy theories and misinformation about them. Past experiences with polio vaccination campaigns have shown that conspiracy theories can take strong root and even endanger human lives.
  3. We hope that work will motivate others to work on such global societal challenges, especially in developing countries.

Q: What advice would you give to academics looking to get their research funded?

AA: I think there are three ingredients in a good research proposal:

  1. It tackles an important problem that ideally has contextual/local relevance.
  2. It demonstrates a well-motivated solution or a plan that has contextual/local relevance.
  3. It shows or at least makes the case for why you are uniquely placed to solve it well.

Q: Where can people learn more about your research?

AA: They can learn about my research on my webpage.

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Sample-efficient exploration of trade-offs with parallel expected hypervolume improvement

Sample-efficient exploration of trade-offs with parallel expected hypervolume improvement

by Facebook AI

What the research is:

q-Expected Hypervolume Improvement (qEHVI) is a new sample-efficient method for optimizing multiple competing expensive-to-evaluate black-box functions. Traditional methods for multiobjective black-box optimization include evolutionary strategies that are robust and can efficiently generate a large batch of candidate designs to evaluate on the true functions in parallel, but they require many evaluations to converge to the set of Pareto optimal trade-offs.

However, in the case when the objectives are expensive to evaluate, sample efficiency is critical. In this case, Bayesian optimization is commonly used to evaluate designs. Typically, candidates are generated sequentially (e.g., using Expected Hypervolume Improvement). In addition, candidate generation usually involves numerically optimizing an acquisition function, which in existing approaches often does not provide gradients.

In this work, we propose a new acquisition function for multiobjective Bayesian optimization that 1) enables generating multiple candidates in parallel or asynchronously with proper uncertainty propagation over the pending candidate points, 2) generates candidates quickly using exact gradients, 3) yields state-of-the-art optimization performance, and 4) has desirable theoretical convergence guarantees.

qEHVI has several use cases across Facebook. For example, it is being used to tune parameters in Instagram’s recommendation systems, where it enables product teams to understand the optimal trade-offs between user engagement and CPU utilization, and has identified policies that yielded simultaneous improvement in both objectives. qEHVI has also been used to optimize the reward functions for the contextual bandit algorithms to determine video compression rates at upload time for Facebook and Instagram. This allows us to identify the set of optimal trade-offs between video upload quality and reliability, which has led to improved quality of service.

How it works:

In objective optimization, there typically is no single best solution; rather, the goal is to identify the set of Pareto optimal solutions such that improving any objective means deteriorating another.A natural measure of the quality of a Pareto frontier in the outcome space is the hypervolume that is dominated by the Pareto frontier and bounded from below by a reference point. Without loss of generality, we assume that the goal is to maximize all objectives. The utility of a new candidate is its hypervolume improvement, which is the volume that is exclusively dominated by the new point in the outcome space corresponding to the candidate (and not by the preexisting Pareto frontier). The hypervolume improvement is typically nonrectangular, but it can be computed efficiently by partitioning the nondominated space into disjoint hyperrectangles.

To generate candidates in parallel, we compute the joint hypervolume improvement across multiple new points by using the inclusion-exclusion principle to compute the volume of the union of the overlapping hyperrectangles. Since we do not know the objective values for a new candidate point a priori, we integrate over our uncertainty around the unobserved objective values provided by our probabilistic surrogate model (typically a Gaussian process), and use the expected hypervolume improvement over the new candidate points as our acquisition function.

Why it matters:

Generating and evaluating designs in parallel is important for fast end-to-end optimization time. For example, when tuning the hyperparameters of machine learning models, one can often evaluate many hyperparameter settings in parallel by distributing evaluations across a cluster of machines. In addition, due to the high evaluation costs, generating high-quality candidates is critical. In many existing methods, the numerical optimization to find the maximizers of the acquisition function is very slow due to the lack of gradient information. Our acquisition function is differentiable, enabling gradient-based optimization and thus faster convergence and better candidates. Moreover, computation can be extremely parallelized: The acquisition function has constant time complexity given infinite cores and can be efficiently computed in many practical scenarios by exploiting GPU acceleration. We empirically show that our acquisition function achieves state-of-the-art optimization performance on a variety of benchmark problems.

In addition, we provide theoretical convergence guarantees on optimizing the acquisition function. Improving sample efficiency is important for speeding up current initiatives spanning from ranking systems, AutoML, materials design to robotics, and opening the door to new optimization problems that require expensive and/or time-consuming evaluations of black-box functions.

Read the full paper:

Differentiable expected hypervolume improvement for parallel multi-objective Bayesian optimization

Check out our open source implementations:

qEHVI is available as part of Ax, our open source library for adaptive experimentation. The underlying algorithm is implemented in BoTorch, and researchers in the area of Bayesian optimization can find implementation details there.

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How Facebook Core Systems’ Shruti Padmanabha transitioned from PhD candidate to Research Scientist

by Facebook AI

Our expert teams of Facebook scientists and engineers work quickly and collaboratively to solve some of the world’s most complex technology challenges. Many researchers join Facebook from top research institutions around the world, often maintaining their academic connections through industry collaborations, partnerships, workshops, and other programs.

While some researchers come to work at Facebook after extensive careers in academia, others make the transition toward the beginning of their career. Shruti Padmanabha, who joined Facebook after receiving her PhD in computer science and engineering, is one such example.

Padmanabha is a Research Scientist on the Disaster Recovery team within Facebook Core Systems. Her focus is on building distributed systems that are reliable and tolerant to failures in their hardware and service dependency stack. Padmanabha’s journey with Facebook Core Systems began with a PhD internship, an experience that encouraged her to switch fields from computer architecture to distributed systems.

We sat down with Padmanabha to learn more about her experience joining Facebook full-time directly after earning her PhD, as well as her current research projects, the differences between industry and PhD research, and her academic community engagement efforts. Padmanabha also offers advice for PhDs looking to transition to industry after graduation.

Tell us about your experience in academia before joining Facebook.

After earning an undergraduate degree in electrical engineering in India, I moved to Michigan to pursue my master’s and PhD in computer science and engineering at the University of Michigan, in Ann Arbor. I was interested in how low-level circuits came together to form computers, and started working research problems in computer architecture. Specifically, I focused on energy-efficient general-purpose architectures (like those that drive desktops).

What excited me about grad school was the opportunity to continuously extend state-of-the-art technology. There, I learned the importance of communication, both written and in person, and to navigate myself in a male-dominated environment (I was the first and only woman in my adviser’s lab!) by leaning on and volunteering in underrepresented groups for grad students. I also found passion in teaching and mentorship activities.

But foremost, I valued the company of the brightest peers and network that being in grad school brought me. It was a proud feeling to be treated as a peer by professors that I greatly respected!

What has your journey with Facebook been like so far?

My first experience at Facebook was a 2015 summer internship in Core Systems. This internship was a leap for me since my expertise was in computer architecture, not in distributed systems. I worked on a proposal to improve energy efficiency of Facebook data centers by dynamically scaling machine tiers based on traffic patterns. The insights we gained from this early experimental work influenced the design of some of our production capacity management systems today.

During my internship, I had the opportunity to work on challenging problems for state-of-the-art technologies and to work with smart, driven peers. I liked that I could innovate on problems with more immediate, real-world applications than in academia. This is why I decided to pursue a full-time position at Facebook after 27 years in school.

When I joined, I was given the choice of being a computer architect on another Facebook team or to switch fields and rejoin Core Systems. I had a very positive internship experience on the Core Systems team, and I liked that they maintained strong academic collaborations, so I decided to permanently switch fields and move to distributed systems.

My first position was on an exploratory team that was trying to think of novel ways to distribute Facebook systems in data centers of different sizes and in different geological locations, which gave me a crash course in real-world distributed systems of all kinds. It also provided me with the unique opportunity to learn about the physical side of designing data centers at Facebook’s scale, from power transformers to submarine network backbones to CDNs.

I’ve learned that there’s always room to learn new things and grow within Core Systems. To keep a healthy flow of fresh information flowing, I lead biweekly brown bag sessions where teams present technical achievements and design challenges, as well as help present introductory infrastructure classes to Facebook n00bs.

What are you currently working on?

My current focus is on improving fault tolerance and reliability at Facebook. Facebook’s service infrastructure consists of a complex web of hundreds of interconnected microservices that change dynamically throughout the day. These services run on 11 data centers, which are designed in house and located across the globe.

At this scale, failures are bound to happen — like a single host losing power, a power transformer getting hit by lightning, a hurricane posing a risk to an entire data center, a single code change leading to a cascading failure, and so on. Our team’s focus is to help design Facebook’s services in a way that such failures are tolerated gracefully and transparently to the user.

Our OSDI paper from 2018 talks about one mitigation approach of draining traffic away from failing data centers. Justin Meza and I also gave a talk at Systems@Scale 2019 where we described the problem of handling power outages at a sub–data center scale. The solution required building solutions that span the stack — from respreading hardware resources across the data center floor to balancing services across them.

What are some of the ways you’ve shown up in the academic community?

I’ve stayed in touch with the academic community by attending conferences, participating in program committees, and mentoring interns. I served on the program committees for a few academic conferences (ISCA, HPCA), as well as for Grace Hopper. I enjoy participating in events where I can reach out to PhD candidates directly, especially those from underrepresented groups. For instance, I moderated a panel at MICRO 2020 on Tips and Strategies for PhD candidates looking for jobs in industry, and participated in the Research@ panel in GHC 2017.

At Facebook Core Systems, we maintain and encourage a strong tie to academia, in terms of both publishing at top systems conferences and awarding research grants to academic researchers, for which I also had the opportunity to help out.

What are some main differences between doing PhD research and doing industry research?

In a PhD candidacy, one’s work tends to be fairly independent and self-driven. In industry, on the other hand, projects are distributed across team members who are equally invested in its success. I had to deliberately change my academic mindset of being solely responsible for the delivery of a project to working in a collaborative environment as an individual contributor.

Industry research also might have the advantage of mature infrastructure and access to real-world data. To me, not having to build/hack simulators and micro-benchmarks was a breath of fresh air. At Core Systems, development progresses from a proof of concept to being tested and rolled out in production relatively quickly.

I’ve also observed a difference in how projects are prioritized in industry and how success is measured. Within Core Systems, we have the freedom to choose the most important problems that need to be worked on within the scope of company-wide goals and build long-term visions for their solutions. Half-yearly reviews are the checkpoints for measuring success toward this vision.

For computer science PhDs curious about transitioning to industry after completing their dissertation, where would you recommend they start?

Explore different sides of the industry through internships, and leverage your professional networks. We’re fortunate that, in our field, internships are usually plentiful. Experiment with different kinds of internships at research labs and product groups if you can. These opportunities will not only give you a window into the kind of career you could build in the industry but also help you build connections with industry researchers. Many industry jobs and internships have rather generic-sounding descriptions that make it hard to envision the work involved, so it’s best to get hands-on experience.

Also, leverage connections through your adviser and alumni network, and engage with industry researchers at conferences. Be sure to discuss the breadth of research at their company and their job interview processes. Lastly, don’t be afraid to step outside of your research comfort zone to try something new!

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