Automatically Detecting and Repairing Crucial Errors

High-impact errors in programs cause huge money losses, are notoriously expensive for programmers to repair, and affect millions of real-world users. In this talk, I will highlight the need to move beyond our post-mortem, manual error handling to develop tools to automatically prevent, detect, and repair program errors. I will present my methods and findings on misconfiguration detection, merge conflict resolution, and feedback generation for errors in students’ programming assignments. The tools I have developed combine a variety of new techniques including program analysis, machine learning, and large language models. I will present my progress, and discuss the implications for the future of error detection and repair research.

Jialu Zhang is an incoming professor in the ECE department at the University of Waterloo. He obtained his PhD in Computer Science at Yale University advised by Ruzica Piskac. He develops tools to automatically prevent, detect, and repair high-impact errors in systems (misconfigurations), collaborative software development (merge conflicts and continuous integration errors), and most recently, CS Education (generating feedback for intro-level and competitive-level programming assignments). His tool development delivers practical impact. For example, the detected misconfigurations have been confirmed and resolved by the original developers on GitHub. Previously, he spent two summers at Microsoft Research (MSR) working with Shuvendu Lahiri, Sumit Gulwani, and Jose Cambronero.

Routing Our Way to Next-Generation Internet Services

Is the Internet ready to support applications such as VR/AR? Maybe not. The Internet has solidified itself as a critical part of our lives, but it wasn't designed with today's modern use cases in mind. To run today's global-scale applications, clouds and content providers such as Google, Meta, and Akamai have to contend with imperfect protocols to deliver highly performant, reliable services. This talk will discuss methods these large providers use to deliver these applications, and in particular how they route traffic to their global deployments. The talk will touch upon both modern-day routing methods, and emerging routing methods in more critical domains such as enterprise-cloud. The talk will then discuss future research directions that will prepare networks to deliver the next generation of services. 

Thomas Koch is a final-year Electrical Engineering PhD candidate at Columbia University, studying in the systems and networking lab with Ethan Katz-Bassett. He received his bachelors and masters degrees in Electrical Engineering at Cooper Union. Koch is interested in how large networked applications that we use every day are delivered on the Internet, and what we can do to improve their performance. He is also interested in developing measurement methods that indirectly infer properties of the Internet.

AI and Data-Driven Tools that Work for Care Work

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Chronic underinvestment in care economies, such as healthcare and social work, has led to an inability to meet growing care needs globally. In response, governments and nonprofits have turned to AI and data-driven tools to help use limited resources for care more efficiently and at scale. For these tools to alleviate burden on care infrastructures, they must account for complex work practices and existing inequities in access to care among marginalized communities. My research bridges AI and data-driven tools with these realities, contributing ways that design can support the agency and labor of care workers and the communities they serve.

In this talk, I will present three areas of my research focused on bridging AI and data-driven tools with care work in India, Kenya, and the United States. I will first describe my work on how algorithmic decision-making processes in social services can support worker agency and community contestation of decisions. Next, I will discuss my research on how language technologies might augment care workers' facilitation of online health communication for sensitive health concerns. I will then present my work on the appropriation of conversational agents by marginalized communities to cope with structural inequities in health systems. Drawing on this work, I will end by highlighting the need to investigate applications of AI and data-driven tools beyond efficiency and scale, to build communities’ power amidst increasing datafication in care economies. 

Naveena Karusala is a postdoctoral fellow at Harvard University's Center for Research on Computation and Society. Her research in Human-Computer Interaction examines how we can design AI and data-driven tools to address inequities in care economies. Her work has received Best Paper at ACM CHI and Diversity and Inclusion Recognition at ACM CSCW. She holds a PhD in Computer Science from the University of Washington, and a Bachelor's in Computer Science from the Georgia Institute of Technology. Karusala currently serves as Adjunct Chair for Community Support on the ACM SIGCHI Executive Committee.

Computer Security and Privacy for Marginalized Populations

Computer security and privacy is critical for vulnerable and marginalized populations because they can face more determined adversaries or higher consequences for security and privacy issues. At a high level, I work to explore why people are vulnerable--through technical misdesign and societal systems. In this talk, I explore one reason: geopolitical change. First, I will explore how refugees in the US encounter challenges and barriers with authentication because of ill-fitted technology. Next, I will discuss the design and integration of biometric voter verification technologies into the 2019 Afghanistan election, finding a variety of design misalignments. Through these papers and others, I show the importance and difficulty of designing for users' social, political, environmental, and technical contexts.

Lucy Simko is a postdoctoral researcher at the George Washington University in the GW Usable Security lab. Her work focuses on the security and privacy-related needs, practices, and experiences of under-served populations. Her research has been published at IEEE Security & Privacy, CCS, PETS, CHI, CSCW, and IDC. She is the recipient of a National Science Foundation Graduate Research Fellowship and the co-recipient of a Safe and Trustworthy Cybersecurity grant (SaTC). Simko earned her PhD in Computer Science and Engineering in 2022 from the University of Washington.

Enhancing Youths' Agency with Culturally Responsive AI Literacy and Engagement Opportunities

What does agency with AI look like in an increasingly computational world? In this talk, I spotlight children from marginalized backgrounds. Regularly impacted by algorithms, youth are stakeholders in the future of responsible AI. I present findings from work exploring culturally responsive AI literacy and engagement opportunities with the design and upkeep of AI. In particular, the talk will yield insight about knowledge gaps and culturally responsive ways of supporting AI literacy, such as through fostering critical consciousness and emphasizing design thinking. The talk will then address common notions that are often barriers to youth engaging in responsible AI practices. Findings from this work lay the foundation to focus on not whether but how diverse youth can be included in creating a future of fairer AI. The talk then lends insight for centering marginalized participants in design research workshops. Lastly, I discuss future directions for supporting diverse communities’ agency with AI.

Jaemarie Solyst is a PhD candidate at Carnegie Mellon University in the Human-Computer Interaction Institute. Her research is at the intersection of human-computer interaction, responsible AI, and learning science. With a commitment to social justice, she focuses on supporting stakeholders' agency with AI by (co-)designing empowering, playful, and informative experiences around computing technology. She is a CERES scholar with the Jacobs Foundation, and her work has received awards at ACM CHI and ACM CSCW.

Design Tools for Creative Empowerment

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Design tools can play a transformative role in supporting creators by enabling new forms of expression. My research integrates new technologies (particularly technologies traditionally requiring deep technical expertise to use) into creative design tools, with the goal of empowering users with a broad range of interests and skill levels to engage in designing with computing.  

In this talk, I will share two threads of work: 1) supporting beginners building creative applications powered by machine learning, and 2) enabling new forms of design documentation for young people to share their design process. These ideas manifest in three different research projects: Co-ML, a collaborative ML modeling tablet app for building machine learning models; PlushPal, a tool for supporting storytelling with interactive toys using gesture recognition; and Spin, an open-hardware platform for capturing playful animated documentation of physical design projects. I will share how these tools were used by young people in makerspaces, museums, and schools to support computing education and creative expression.

I will then end with a discussion of how these two threads might combine to enable new generative AI-powered design tools sensitive to the needs and workflows of creatives, from ideation to iteration and refinement. Through these tools, my work contributes new design principles for enabling learners to realize their creative potential by learning to use and create with new technologies.

Tiffany Tseng is a senior research scientist at Apple in the Human Centered Machine Learning group. Her research contributes design tools that empower young people to realize their creative potential through learning to create with new technologies – including collaborative ML modeling tools, 3D CAD software, and online code editors. She has partnered with a range of learning institutions, including the Exploratorium, New York Hall of Science, and Chicago Public Library to develop new creative computing tools. Prior to Apple, she was a Project Assistant Professor at the University of Tokyo’s Graduate School of Engineering and a lead UX Designer at Autodesk and Design Engineer at Glitch, developing free design software like Tinkercad, a popular 3D design tool with millions of users.

Her work has been recognized with Best Paper and Honorable Mention Awards from ACM Interaction Design and Children and a Disney Research Learning Challenge Innovation Award. She received her PhD from the MIT Media Lab in 2016, sponsored by an NSF Graduate Research Fellowship and holds an MS in Mechanical Engineering from Stanford University and a BS in Mechanical Engineering from MIT.

Solving Global Grand Challenges with High Performance Data Analytics

Emerging real-world graph problems include: detecting and preventing disease in human populations; revealing community structure in large social networks; protecting our elections from cyber-threats; and improving the resilience of the electric power grid. Unlike traditional applications in computational science and engineering, solving these problems at scale often raises new challenges because of the sparsity and lack of locality in the data, the need for additional research on scalable algorithms and development of frameworks for solving these problems on high performance computers, and the need for improved models that also capture the noise and bias inherent in the torrential data streams. In this talk, Bader will discuss the opportunities and challenges in massive data-intensive computing for applications in computational science and engineering.

David A. Bader is a Distinguished Professor and founder of the Department of Data Science and inaugural Director of the Institute for Data Science at New Jersey Institute of Technology. Prior to this, he served as founding Professor and Chair of the School of Computational Science and Engineering, College of Computing, at Georgia Institute of Technology. Dr. Bader is a Fellow of the IEEE, ACM, AAAS, and SIAM; a recipient of the IEEE Sidney Fernbach Award; and the 2022 Innovation Hall of Fame inductee of the University of Maryland’s A. James Clark School of Engineering. He advises the White House, most recently on the National Strategic Computing Initiative (NSCI) and Future Advanced Computing Ecosystem (FACE). Bader is a leading expert in solving global grand challenges in science, engineering, computing, and data science. His interests are at the intersection of high-performance computing and real-world applications, including cybersecurity, massive-scale analytics, and computational genomics, and he has co-authored over 300 scholarly papers and has best paper awards from ISC, IEEE HPEC, and IEEE/ACM SC. Dr. Bader is Editor-in-Chief of the ACM Transactions on Parallel Computing and previously served as Editor-in-Chief of the IEEE Transactions on Parallel and Distributed Systems. He serves on the leadership team of Northeast Big Data Innovation Hub as the inaugural chair of the Seed Fund Steering Committee.  In 2012, Bader was the inaugural recipient of University of Maryland’s Electrical and Computer Engineering Distinguished Alumni Award. In 2014, Bader received the Outstanding Senior Faculty Research Award from Georgia Tech.  Bader has also served as Director of the Sony-Toshiba-IBM Center of Competence for the Cell Broadband Engine Processor and Director of an NVIDIA GPU Center of Excellence. In 1998, Bader built the first Linux supercomputer that led to a high-performance computing (HPC) revolution, and Hyperion Research estimates that the total economic value of Linux supercomputing pioneered by Bader has been over $100 trillion over the past 25 years. Bader is a cofounder of the Graph500 List for benchmarking “Big Data” computing platforms. He is recognized as a “RockStar” of High Performance Computing by InsideHPC and as HPCwire’s People to Watch in 2012 and 2014.

Forgotten Promise, Current Peril, & Future Potential of the Internet Trust Architecture

The Public Key Infrastructure (PKI) determines the code our computers install, the web sites we recognize as trustworthy, and what apps our phones will accept.   The reliability of the PKI ecosystem depends on the trustworthiness of the Certificate Authorities (CAs), the code, the cryptography, and the selection of keys.  It also depends on the governance structure and human factors. Who decides what roots of trust are shipped as part of browsers and phones, and in the future automobiles, toys, appliances, and airplane components?  How do certificates fail?  Beginning with a machine learning approach to identify failures, then moving to qualitative analyses. I argue for a more nuanced understanding of trust in the Internet ecosystem. The talk includes an overview of emerging standards, current state, and past practice in PKI.

Jean Camp is a Professor of Informatics and Computer Science. Her research focuses on the intersection of human and technical  trust, with the goal of building for end to end empowerment. She was a member of the 2022 class of Fellows of the ACM. She was selected as a Fellow of the Institute of Electronic and Electrical Engineers in 2018. She was elected a Fellow of the American Association for the  Advancement of Science in 2017.  She was inducted into Sigma Xi – the national research honor society - in 2017. She is currently employed as a Professor at the Luddy School with appointments in Informatics and Computing Science at Indiana University. She joined Indiana after eight years at Harvard’s Kennedy School where her courses were  also listed in Harvard Law, Harvard Business, and the Engineering Systems Division of MIT. She spent the year after earning my doctorate from Carnegie Mellon as a Senior Member of the Technical Staff at Sandia National Laboratories. She began her career as an engineer at Catawba Nuclear Station after a double major in electrical engineering and mathematics, followed by a MSEE in optoelectronics at University of North Carolina at Charlotte.

Threshold Cryptography: From Private Federated Learning to Protecting Your Cryptocurrency

We will present the notion of Threshold Cryptography which aims to secure cryptographic keying materials.  The keys in a cryptographic system are the most critical part, and losing them can cause considerable damage. We will explain how to enhance the means for storing them and how to apply these techniques to specific applications. 

The talk is for a general audience and will be self contained.

Tal Rabin is a Rachleff Family Professor of Computer Science at the University of Pennsylvania. Prior to joining UPenn she has been the head of research and Algorand Foundation and prior to that she had been at IBM Research for 23 years as a Distinguished Research Staff Member and the manager of the Cryptographic Research Group.  She received her PhD from the Hebrew University in 1995. 

Tal’s research focuses on cryptography and, more specifically, on secure multiparty computation, threshold cryptography, and proactive security and recently adapting these technologies to the blockchain environment. Her works have been instrumental in forming these areas.  She has served as the Program and General Chair of the leading cryptography conferences and as an editor of the Journal of Cryptology.   She has initiated and organizes the Women in Theory Workshop, a biennial event for graduate students in Theory of Computer Science. Tal currently serves as the chair of the ACM SIGACT Executive Board.

Tal is an ACM Fellow, an IACR (International Association of Cryptologic Research) Fellow and member of the American Academy of Arts and Sciences. Tal’s work won the 30-year test of time award at STOC. She is the 2019 recipient of the RSA Award for Excellence in the Field of Mathematics. She was named by Forbes in 2018 as one of the Top 50 Women in Tech in the world. In 2014 Tal won the Anita Borg Women of Vision Award winner for Innovation and was ranked by Business Insider as the #4 on the 22 Most Powerful Women Engineers.  

Pursuing Transparency and Accountability in Data and Decision Processes

Algorithmic systems and data processes are being deployed to aid a wide range of high-impact decisions: from school applications or job interviews to gathering, storing, and analyzing personal data, or even performing critical tasks in the electoral process. These systems can have momentous consequences for the people they affect but their internal behaviors are often inadequately communicated to stakeholders, leaving them frustrated and distrusting of the outcomes of the decisions. Transparency and accountability are critical prerequisites for building trust in the results of decisions and guaranteeing fair and equitable outcomes.

In this talk, I will present my work on making these opaque processes more transparent and accountable to the public in several real-world applications.  In particular, I will discuss how ranking aggregation functions traditionally used in decision systems inadequately reflect the intention of the decision-makers. Providing transparent metrics to clarify the ranking process, by assessing the contribution of each parameter used in the ranking function in the creation of the final ranked outcome, more accurately captures the true impact of each parameter in the ranking decision. Furthermore,  ranking functions that are used in resource allocation systems often produce disparate results because of bias in the underlying data.  I will show how a sample-based mechanism based on the use of compensatory bonus points can transparently address disparity in ranking applications.

Organizations and agencies do not have strong incentives to explain and clarify their decision processes; however, stakeholders are not powerless and can strategically combine their efforts to push for more transparency. I will discuss the results and lessons learned from such an effort: a parent-led crowdsourcing campaign to increase transparency in the New York City school admission process. This work highlights the need for oversight and AI governance to improve the trust of stakeholders who have no choice but to interact with automated decision systems.

Amélie Marian is an Associate Professor in the Computer Science Department at Rutgers University, where she was Director of the Undergraduate program from 2014 to 2020. Her research interests are in Explainable Rankings, Accountability of Decision-making Systems, Personal Digital Traces, and Data Integration. Her recent public scholarship work on explaining the NYC School Admission lottery process to families, in collaboration with elected parent representatives, was instrumental in increasing transparency and accountability in the NYC high school application system. Amélie received her Ph.D. in Computer Science from Columbia University in 2005. She is the recipient of a Microsoft Live Labs Award, three Google Research Awards, and an NSF CAREER award.

Bringing Time and Social Space into Natural Language Processing

Human languages have extremely large vocabularies, and by assembling words into sequences, humans can express complex and novel ideas to each other. The likelihood of selecting any given word at any point in time varies greatly as a function of the context. Understanding and formalizing these contextual influences is essential for building robust and adaptable NLP systems.  This talk will focus on two sources of variability: variation over time, and variation across speakers. I will first consider the how individual words behave as a function of who is speaking and what the topic of discussion is.  I will explain how vector space representations of words (also known as word embeddings) make it possible to investigate the abstract concepts that underpin the use of groups of semantically related words. Finally, using social networks defined from social media posts, I will illustrate how graph neural networks can be used to investigate the structure and dynamics of opinions in the social space.

Professor Janet Pierrehumbert has an interdisciplinary background from Harvard and MIT in linguistics, mathematics, and electrical engineering and computer science. Her PhD dissertation developed a model of English intonation that was applied to generate pitch contours in synthetic speech. She began her career as a Member of Technical Staff at AT&T Bell Laboratories in Linguistics and Artificial Intelligence Research. From there, Pierrehumbert moved to Northwestern University, where she headed a research group that used experimental and computational methods to understand lexical systems in English and many other languages.  Pierrehumbert joined the University of Oxford faculty in 2015 as Professor of Language Modelling in the Oxford e-Research Centre,  Her current focusses on robust and interpretable natural language processing methods, in particular ones that can handling variation across different topics, topics, and social contexts. She has held visiting appointments at Stanford, the Royal Institute of Technology, the École Normale Superieure, and the University of Canterbury.

Pierrehumbert is a Member of the National Academy of Sciences, a Fellow of the American Academy of Arts and Sciences, a Fellow of the Cognitive Science Society and a Fellow of the Linguistic Society of America. She won the Medal for Scientific Achievement of the International Speech Communication Association (ISCA) in 2020.

Human-centric Natural Language Processing for Social Good and Responsible Computing

Large language models (LLMs) constitute a paradigm shift in Natural Language Processing and its applications across all domains. To move towards human-centric NLP designed for social good and responsible computing, I argue we need knowledge-aware NLP systems and human-AI collaboration frameworks.  NLP systems that interact with humans need to be knowledge aware (e.g., linguistic, commonsense, sociocultural norms) and context aware (e.g., social, perceptual) so that they communicate better and in a safer and more responsible fashion with humans. Moreover, NLP systems should be able to collaborate with humans to create high-quality datasets for training and/or evaluating NLP models, to help humans solve tasks, and ultimately to align better with human values. In this talk, I will give a brief overview of my lab’s research around NLP for social good and responsible computing (e.g., misinformation detection, NLP for education and public health, building NLP technologies with language and culture diversity in mind). I will highlight key innovations on theory-guided and knowledge-aware models that allow us to address two important challenges: lack of training data, and the need to model commonsense knowledge. I will also present some of our recent work on human-AI collaboration frameworks for building high-quality datasets for various tasks such as generating visual metaphors or modeling cross-cultural norms similarities and differences. 

Smaranda Muresan is a Research Scientist at the Data Science Institute at Columbia University and an Amazon Scholar. Before joining Columbia, she was a faculty member in the School of Communication and Information at Rutgers University where she co-founded the Laboratory for the Study of Applied Language Technologies and Society. At Rutgers, she was the recipient of the Distinguished Achievements in Research Award. Her research focuses on human-centric Natural Language Processing for social good and responsible computing. She develops theory-guided and knowledge-aware computational models for understanding and generating language in context (e.g., visual, social, multilingual, multicultural) with applications to computational social science, education, and public health. Research topics that she worked on over the years include: argument mining and generation, fact-checking and misinformation detection, figurative language understanding and generation (e.g., sarcasm, metaphor, idioms), and multilingual language processing for low-resource and endangered languages. Recently, her research interests include explainable models and human-AI collaboration frameworks for high-quality datasets creation. She received best papers awards at SIGDIAL 2017 and ACL 2018 (short paper). She served as a board member for the North American Chapter of the Association for Computational Linguistics (NAACL) 2020-2021, as a co-founder and co-chair of the New York Academy of Sciences’ Annual Symposium on NLP/Dialog/Speech (2019-2020) and as a Program Co-Chair for SIGDIAL 2020 and ACL 2022.  

Misinformation, Harassment, and Violence through a Cybersecurity and Privacy Lens

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Technology companies play a central role in mediating online discourse and monitoring people's actions. Unfortunately, these products are spreading misinformation, harassment, and enabling violence. Currently, technology companies have struggled to mitigate these problems. In this talk, I will discuss how we use robust cybersecurity, data science, and independent data collection techniques to better understand these issues. I will show how this approach can illuminate the systemic incentives and design choices that likely contribute to unsafe technology products that are vulnerable to attacks. In addition, I will show how we can leverage those insights to design safer technology systems and improve resources for those targeted by these attacks. In cases where companies' interests are not aligned with their users, effecting changes that result in safer technology products often requires independent data collection and engaging with civil society, journalists, regulators, and policymakers.

Damon McCoy (he/she) is an Associate Professor of Computer Science and Engineering at New York University's Tandon School of Engineering and the co-director of Cyber Security for Democracy. Her research focuses on empirically understanding the security and privacy of technology systems and their intersection with society. In particular, he investigates problems through the lens of cybersecurity and privacy, such as hate, harassment, misinformation, and violence, that are more traditionally explored by social scientists. She is normally a down to earth person and only talks about herself in the third person when requested.

Multispectral Analysis and Deep Learning for Life Science and Biomedical Research

Several plant and animal species are more comprehensively understood by multispectral analysis. For example, ultraviolet fluorescence reveals original color patterns on colorless fossils for species classification, while Infrared imaging permits study of subsurface materials hidden under pigments. However, faded color, and material layers that exhibit subsurface scattering and spatially varying surface reflectance make it difficult to reconstruct the shape and appearance of biological materials. This talk presents a texture transfer framework that reconstructs invisible (or faded) appearance properties in organic materials with complex color patterns. I will motivate the project with a study that computes surface orientation (normals) at different material layers as a function of emission wavelength for effective scientific analysis in life science. Key contributions include a novel ultraviolet illumination system that records changing material property distributions, and a color reconstruction algorithm that uses spherical harmonics and principles from chemistry and biology to learn relationships between color appearance and material composition and concentration. Finally, I will explain a novel algorithm that extends the effective receptive field of a convolutional neural network for multi-scale detection of cancerous tumors in high resolution slide scans. The results permit efficient real-time analysis of medical images in pathology and biomedical research fields.

Corey Toler-Franklin is an Assistant Professor of Computer Science at the University of Florida where she directs the Graphics, Imaging & Light Measurement Laboratory. Dr. Toler-Franklin obtained a Ph.D. in Computer Science from Princeton University. She earned an M.S. degree from the Cornell University Program of Computer Graphics, and a B. Arch. degree from Cornell. Before joining UF faculty, Dr. Toler Franklin was a UC President's Postdoctoral Fellow at UC Davis, and a researcher at the UC Berkeley CITRIS Banatao Institute. She also held positions at Autodesk, Adobe, and Google.

Dr. Toler-Franklin’s research in computer graphics and vision includes Machine Learning, Data Acquisition, Appearance Modeling, Imaging Spectroscopy and Non-Photorealistic Rendering, with real-world applications in Life Science, Bio-Medical Research and Archaeology. Her algorithms use mathematical principles in optics to capture and analyze the shape and appearance of complex materials. Her recent work develops AI algorithms for biomedical research. Collaborating with the UF College of Medicine Oncology and Pathology Departments, and the UF Neuroscience Department, Dr. Toler-Franklin developed deep learning algorithms for diagnosing metastatic cancers and studying behaviors associated with neurological disorders (Alzheimer's and autism).

Actual Causality in Reactive Systems

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Counterfactual reasoning is an approach to infer the cause of an observed effect by comparing a given scenario in which the suspected cause and the effect are present, to the hypothetical scenarios where the suspected cause is not present. The seminal works of Halpern and Pearl have provided a definition of counterfactual causality for finite settings. In this talk, we propose an approach to check causality for reactive systems, i.e., systems that interact with their environment over a possibly infinite duration. First, we focus on finding causes for violations of hyperproperties. Hyperproperties, unlike trace properties, can relate multiple traces and thus express complex security properties. Here, the suspected cause is represented by a finite set of events occurring on the set of traces. Then, we lift Halpern and Pearl's definition to the case where the causes themselves (as well as effects) are omega-regular properties, not just sets of events. Given the causality algorithms, our tool HyperVis generates interactive visualizations of the given model, specification, and cause of the counterexample.

Niklas Metzger is a PhD student at CISPA Helmholtz Center for Information Security in Germany. He is advised by Bernd Finkbeiner and a member of the Reactive Systems Group. Before joining CISPA, he received his BSc and MSc at Saarland University in Germany. Niklas’ research focuses on compositional reactive synthesis guided by the principles of knowledge, actual causality in reactive systems, and building machine learning models as heuristics in complex formal method tasks.

Music Information Retrieval and Hit Songs

Music technology is a wonderful combination of the technical and the artistic. In this talk, I'll be discussing my computational approaches to studying, specifically, popular music. Popular music, as measured by the Billboard charts, is widely listened-to and contributes broadly to culture and society. I'll be discussing my work using machine learning to predict Billboard hits based on past data, and my current work specifically studying the vocal lines of popular songs from 1922-2010. Finally, I'll broadly talk about the field of Music Information Retrieval, current research topics in the field, and some industry career paths in music and engineering.Music technology is a wonderful combination of the technical and the artistic. In this talk, I'll be discussing my computational approaches to studying, specifically, popular music. Popular music, as measured by the Billboard charts, is widely listened-to and contributes broadly to culture and society. I'll be discussing my work using machine learning to predict Billboard hits based on past data, and my current work specifically studying the vocal lines of popular songs from 1922-2010. Finally, I'll broadly talk about the field of Music Information Retrieval, current research topics in the field, and some industry career paths in music and engineering.

Elena is a PhD student at NYU's Music and Audio Research Lab where she studies music information retrieval, sound recording, and music perception/cognition. Before coming to NYU, Elena taught sound recording at Stanford University's Center for Computer Research in Music and Acoustics (CCRMA), where she received her master’s degree in 2019. So far, Elena has worked on projects related to hit song science, sound recording, vocal expression, computer music, and neuroimaging. Elena has presented her work at several international conferences, universities, and Silicon Valley tech companies. In her music life, Elena performs with several vocal groups in New York City, enjoys recording and mixing music, and is learning the drums. She also holds a Bachelor of Science degree from UCLA in Cognitive Science.

Game Audio in Industry

Game audio is a unique field that combines cutting edge technology and creativity to make compelling entertainment experiences. Audio programmers work closely with Sound Designers to develop tools for better audio integration, new playback mechanisms, acoustical analysis, and more - all within the constraints of current processing power and memory. I'll share a bit about my experiences in game development, and some of what my day to day work looks like as an Audio SE.

Dylan Sundberg is an Audio Software Engineer at Electronic Arts working remotely from Grand Rapids, Michigan. Dylan graduated with a B.S. in Sound Engineering from the University of Michigan in 2015, and has been at EA for the last 7 years.  Dylan has worked on several EA titles including Battlefield and the upcoming Skate, covering different roles including Sound Designer, Technical Sound Designer, and Audio Programmer.

 Cognitive Design Principles for Programming Tools

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Programming tools designed for people ought to derive from human-centered principles: how we think and how we learn. My research is about translating concepts from psychology into practical insights for designing programming tools. In this talk, I will present two kinds of cognitive design principles: 

The first principle is about how limitations of cognitive resources, namely working memory, influence the practice of programming. I will discuss my previous work about how cognitive support tools like program slicing can reduce the cognitive load of tasks like program comprehension.

The second principle is about how people build mental models of programming concepts. I will discuss my ongoing work about making the Rust programming language easier to learn by characterizing the space of (mis)conceptions about Rust features like ownership and traits, and designing learning materials to facilitate the acquisition of correct mental models.

Will Crichton is a postdoctoral researcher at Brown University advised by Shriram Krishnamurthi, working on making Rust easier to learn. Will recently completed his Ph.D. at Stanford University advised by Pat Hanrahan and Maneesh Agrawala. His research combines programming language theory and cognitive psychology to design principled and practical tools for programmers. His goal is to enable people to build the computational infrastructure of today that can tackle the problems of tomorrow.

Variational Bayesian Methodologies for the Life Sciences

Bayesian statistics has experienced a rapid growth in popularity due to advances in approximate inference techniques coinciding with increases in computational resources. As transistor counts across microprocessors have skyrocketed, what were once theoretically appealing methodologies applicable only to textbook problems are now the predominant approach to modern machine learning. Computational statistics and Bayesian machine learning play a central role within the natural sciences, however the life sciences and the field of biology is uniquely positioned to undergo a historical period of discovery analogous to that of the early 20th century for the physical sciences. We will discuss the use of approximate Bayesian inference to address open problems in the life sciences and highlight several recent contributions to variational Bayesian inference in phylogenetics. 

Antonio Moretti is currently Vice President of Data Science at Roc Capital, a digital financial services platform. Before this, he worked on the search algorithm team at Walmart Global Tech using machine learning to improve customer experience. Antonio completed a PhD in the Computer Science Department at Columbia University. He has developed a number of Bayesian inference methodologies for open problems in computational biology. To address these questions, his research has focused on the development of expressive statistical methodologies along with tractable inference algorithms for fast approximate inference on structured sequential data.

Evolutionary Creativity and Artificial Life

Biological evolution has displayed immense creativity, resulting in a diverse richness of form in the natural world. This talk will cover innovations in the fields of evolutionary computation and artificial life, which seek to capture the unrivaled generative potential of evolution in computational processes and simulations. A brief introduction to evolutionary algorithms will be given, followed by a discussion about what kinds of artificial environments foster the kind of richness necessary for innovation, with a focus on video games as a testbed for experiments on phenomena related to open-ended evolution.

Lisa Soros completed her PhD in 2018 at the University of Central Florida, where she was a member of the Evolutionary Complexity Research Group. She then spent two years as an Assistant Professor at Champlain College in Burlington, Vermont, before completing a postdoc at the Game Innovation Lab at New York University. She is currently is a postdoctoral research fellow at Cross Labs, which is an institute in Kyoto, Japan dedicated to understanding intelligent processes through computational means.

Optimization Opportunities in Human-in-the-loop Systems

An emerging trend is to leverage an under-explored and richly heterogeneous pool of human knowledge inside machine algorithms, a practice popularly termed as human-in-the-loop (HIL) process. A wide variety of applications, starting from query processing to text translation, feature engineering, or even human decision making in complex uncertain environments stand to benefit from such synergistic man-machine collaboration. This talk will discuss our ongoing projects, recent research results, and impacts that investigate a variety of optimization opportunities inside such HIL systems, considering the roles and responsibilities of three key stakeholders – humans (workers), machines (algorithms), and platforms (online infrastructure where the work takes place). Following that, the talk will briefly discuss how this ongoing research is well aligned in the context of the future-of-work.

Senjuti Basu Roy is the Panasonic Chair in Sustainability and an Associate Professor in the Department of Computer Science at the New Jersey Institute of Technology. Her research focus lies on the intersection of data management, data exploration, and AI, especially enabling human-machine analytics in scale. Senjuti has published more than 70 research papers in high impact data management and data mining conferences and journals. She is the tutorial co-chair of VLDB 2023, The Web Conference 2022, has served as the Mentorship co-chair of SIGMOD 2018, PhD workshop co-chair of VLDB 2018, and has been involved in organizing several international workshops and meetings. She is a recipient of the NSF CAREER Award, a PECASE nominee, and one of the 100 invited early career engineers to attend the National Academy of Engineering’s 2021 US Frontiers of Engineering Symposium.

Tackling Diabetic Retinopathy in a Safety Net Healthcare Setting with Telehealth and Machine Learning

Diabetic retinopathy is the leading cause of blindness in working age adults in the United States. It is challenging to address in both rural and urban underserved settings, which suffer from shortages of eye specialists. This talk will describe the approach taken to address this condition in a medically underserved area (South Los Angeles) by researchers in the Center for Biomedical Informatics at Charles R. Drew University of Medicine and Science, using telehealth and machine learning on data from patient electronic health records.

Dr. Omolola Ogunyemi is a computer scientist and biomedical informatics researcher.  She is a Professor in the Department of Preventive and Social Medicine at Charles R. Drew University of Medicine and Science (CDU) in Los Angeles.  She is also Director of the University's Center for Biomedical Informatics, which focuses on providing biomedical informatics solutions that benefit medically underserved communities.

Her research interests include computerized medical decision support, reasoning under uncertainty, 3D graphics and visualization, machine learning, and telehealth. Her recent work includes a National Library of Medicine-funded R01 grant to explore diabetic retinopathy prediction from safety net healthcare system clinical data, using different machine learning strategies.

Before moving to CDU, Dr. Ogunyemi was a biomedical informatics faculty member in the Department of Radiology at Brigham and Women's Hospital and Harvard Medical School from 1999 until 2007. She was also a member of the affiliated faculty in the Harvard-MIT Division of Health Sciences and Technology from 2003 until 2007. She has taught graduate level biomedical informatics courses in the Harvard-MIT Division of Health Sciences and Technology, at UCLA, at CDU, and short courses on informatics at the University of Natal (now KwaZulu-Natal), Durban, South Africa.

Dr. Ogunyemi is an elected fellow of the American College of Medical Informatics. She holds an undergraduate degree in Computer Science from Barnard College, New York, and an M.S.E, and Ph.D. in Computer and Information Science from the University of Pennsylvania. 

Hardware Acceleration for Realtime Robotics

In order for robots to achieve their full potential to assist humans, they need to be able to reliably operate in dynamic and unstructured real-world environments. Opportunities exist for roboticists to leverage hardware acceleration to overcome many of the computational bottlenecks that confine robots to the lab. In this talk I will show how Graphics Processing Units (GPUs) can be used to improve the performance of robotics algorithms through a case study of rigid body dynamics, and preview the potential for further acceleration through the use of custom Application Specific Integrated Circuits (ASICs).

Brian Plancher is a Ph.D. Candidate studying Robotics at Harvard University working with Vijay Janapa Reddi and Scott Kuindersma. His research is focused on developing and implementing open-source algorithms for dynamic motion planning and control of robots by exploiting both the mathematical structure of algorithms and the design of computational platforms (e.g., CPUs, GPUs, FPGAs). As such, his research sits at the intersection of Robotics and both Computer Architecture and Numerical Optimization. Brian also wants to improve the accessibility of STEM education and co-chairs TinyMLedu. He enjoys teaching and designing new interdisciplinary, project-based, open-access courses that lower the barrier to entry of cutting edge topics like robotics and TinyML. Brian enjoys spending his free time with his wife, daughter, and puppy, and ski racing in the winters.

Empowering People with Disabilities through Accessible User Interfaces

Computer science education has become widely popular as a gateway to higher education and lucrative careers. In learning computer science, students can develop life skills like problem-solving, critical thinking, and logical thinking. Computer science education can also empower people with disabilities to gain technical knowledge to solve accessibility barriers that they may face in the future, enhance their communication skills, and lead to more self-determination and independence.

This talk will provide an overview of accessibility and Human-Computer Interaction research exploring multimodal tools for inclusive learning and supporting independent living. The projects discussed in this talk will describe creating novel research prototypes that utilize computer vision and augmented reality and analyze them through the lens of three key perspectives in Human-Computer Interaction: user-centered design, participatory design, and ability-based design.  The talk will conclude with a discussion on future research areas around designing shareable displays for multi-user contexts and designing educational technologies to expand awareness around privacy and security. This research can lead to accessible adaptive assistive technologies for people with disabilities in the future. 

Varsha Koushik is a Ph.D. Candidate in Computer science at the University of Colorado Boulder. She is advised by Shaun Kane, and her research interests lie at the intersection of Human-Computer interaction and accessibility. Varsha’s research focuses on creating accessible smart technologies that empower people with disabilities in their everyday activities. Currently, she is exploring how Augmented Reality(AR) can support people with cognitive disabilities to customize supportive prompts in their daily routines. Varsha’s research has been published at premier international HCI conferences such as CHI, ASSETS, IDC, and VL/HCC, including a Best Paper Honorable Mention Award at CHI 2019. Her work has been recognized by the University of Colorado Boulder through the Hope Schutz Jozsa Award, the PhD Summer Research Fellowship, and the Carol Lynch Memorial Fellowship. She has also been a Coleman Fellow at the Coleman Institute for Cognitive Disabilities. Varsha is also the computer science department’s lead teaching assistant (2021-22). Previously, she received her M.S in Computer Science from CU Boulder.

Considering Process in Algorithmic Bias Detection and Mitigation

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Artificial Intelligence (AI) systems now affect important decisions in people's lives, from the news articles they read, to whether or not they receive a loan. While the use of AI may lead to great accuracy and efficiency in the making of important decisions, recent news and research reports have shown that AI models can act unfairly: from exhibiting gender bias in hiring models, to racial bias in recidivism prediction systems. 

In this talk, I’ll discuss methods for understanding fairness issues in AI through considering the process by which models arrive at their decisions. This technique contrasts with a large portion of AI fairness literature, which focuses on studying model outcomes alone. Specifically, I will show how considering a model’s end-to-end decision process allows us to expand our understanding of unfair behavior---such as in my work demonstrating how model instability can lead to unfairness by having important decisions rely on arbitrary modeling choices (e.g. whether or not a person is granted a loan from a decision-making model may depend on whether some unrelated person happened to be in the training set). Secondly, I will discuss how considering process can help us find bias mitigation techniques which avoid a tradeoff between predictive utility and fairness, with case studies from my collaborations with Stanford RegLab and the Internal Revenue Service (IRS) investigating tax auditing practices, and with Cornell, Microsoft Research, Upturn, and others to examine the role of criminal risk assessment models in racial disparities in pre-trial detention.

Emily Black is a PhD candidate in the Accountable Systems Lab at Carnegie Mellon University, advised by Matt Fredrikson. Her research centers around understanding the impacts of machine learning and deep learning models in society. In particular, she focuses on showing ways in which commonly used machine learning models may act unfairly; finding ways to pinpoint when models are behaving in a harmful manner in practice; developing ways to mitigate harmful behavior when possible; and translating technical insights into technology policy recommendations. She is currently supported by an Amazon Graduate Research Fellowship. (For more information, please see https://www.cs.cmu.edu/~emilybla/).

Limits and Applications of Natural Language Processing

Natural Language Processing (NLP) is a branch of Artificial Intelligence that aims to build machines that humans can seamlessly interact with through spoken and written language. As NLP becomes more ubiquitous in our daily lives, through technologies like Google Translate and Apple’s Siri, understanding the limits of these systems is critical. In the first half of the talk, we will cover my work developing diagnostics that test such systems. With the remaining time, we will discuss how my research group has applied Natural Language Processing to leverage data from social media (e.g. Twitter and Reddit) in fields like psychology, public health, and nursing. 

Adam Poliak completed his Ph.D. in Computer Science at Johns Hopkins University where he was a member of the Center for Language and Speech Processing. He is currently a Roman Family Faculty Fellow at Barnard College, Columbia University in the Computer Science Program where he teaches courses in Data Science and runs a research group focused on Natural Language Processing. He has done research stints at the MIT Lincoln Laboratory,  Bloomberg L.P.,  and the Human Language Technology Center of Excellence. Adam's research has been funded by the FDA, Google, Columbia School of Nursing, and DARPA, and others. Prior to graduate school, Adam spent time at numerous start-ups.

Polynomial Counting in Anonymous Dynamic Networks with Applications to Anonymous Dynamic Algebraic Computations

In recent years, the problem of Counting the number of nodes in Anonymous Dynamic Networks (ADNs) has attracted a lot of attention. ADNs are algorithmically challenging because nodes are indistinguishable (they lack identifiers) and the topology is adversarial (i.e. network links may change arbitrarily from a communication round to the next one) limited only to maintain the network connected. Counting is central in distributed computing because the number of participants is frequently needed for algorithmic decisions, such as termination, agreement, and synchronization.

A variety of distributed algorithms built on top of mass-distribution techniques have been presented, analyzed, and also experimentally evaluated; some of them assumed additional knowledge of network characteristics, such as bounded degree or given upper bound on the network size. However, the question of whether Counting can be solved deterministically in sub-exponential time remained open until recently.

In this talk, I will present our recent Methodical Counting Algorithm for ADNs, which runs in polynomial time and requires no knowledge of network characteristics. I will also show how to extend Methodical Counting to compute the sum of input values and more complex functions without extra cost. The Methodical Counting Algorithm and its extensions to other algebraic and Boolean function computations were the first that could be implemented in practice on large ADNs with worst-case guarantees. I will also overview some of the successful follow-up work on this and other lines of research involving undergraduate students.

Miguel A. Mosteiro is an Associate Professor of Computer Science Pace University. Before, he was Assistant Professor at Kean University, Research Professor at Rutgers University and Research Fellow at  the Universidad Rey Juan Carlos (Spain). He obtained his PhD in Computer Science from Rutgers University, and his BE in Electronics from Universidad Tecnologica Nacional (Argentina). His research interests span the broad areas of algorithms and distributed computing. He focuses on algorithms for restricted wireless networks, crowd computing, cloud computing, and more recently spammability and fairness of ranking algorithms such as PageRank. His research work is conducted in collaboration with scholars from various countries, involving graduate as well as undergraduate students. Prof. Mosteiro has received the 2016 Kean University Undergraduate Research Mentor of the Year Award, concurrently with the Undergraduate Researcher of the Year Award for one of his students.

Knowledge-enhanced Text Generation: The Curious Case of Figurative Language and Argumentation 

Large-scale language models based on transformer architectures, such as GPT-3 or BERT, have advanced the state of the art in Natural Language Understanding and Generation. However, even though these models have shown impressive performance for a variety of tasks, they often struggle to model implicit and/or non-compositional meaning, such as figurative language and argumentative text. In this talk, I will present some of our recent work on text generation models for figurative language and argumentation. There are two main challenges we have to address to make progress in this space: 1) the need to model common sense and/or connotative knowledge required for these tasks; and 2) the lack of large training datasets. I will discuss our proposed theoretically-grounded knowledge-enhanced text generation models for figurative language such as metaphor and simile, as well as for enthymeme reconstruction and if time permits argument reframing. I will conclude by discussing opportunities and remaining challenges for incorporating knowledge in neural text generation systems.

Smaranda Muresan is a Research Scientist at the Data Science Institute at Columbia University and an Amazon Scholar. Before joining Columbia, she was a faculty member in the School of Communication and Information at Rutgers University where she co-founded the Laboratory for the Study of Applied Language Technologies and Society. At Rutgers, she was the recipient of the Distinguished Achievements in Research Award. Her research interests are in computational semantics and discourse, particularly figurative language understanding and generation, argument mining and generation, and fact-checking. Most recently, she has been interested in applying NLP to education and public health, as well as in building NLP technologies for low resource languages. She received best papers awards at SIGDIAL 2017 and ACL 2018 (short paper). She is currently serving as a board member of the North American Chapter of the Association for Computational Linguistics (NAACL) and as a Program Co-Chair for ACL 2022.

Building the Audio Programmer: Power through Inexperience, Transparency, and Sharing

In 2017, Joshua Hodge started a YouTube channel called The Audio programmer to teach a skill that he knew hardly anything about - audio software development.  Since then, The Audio Programmer has become a central hub for audio developers of all levels. How is this possible?

In this talk, Joshua Hodge will discuss lessons learned while creating the Audio Programmer - how his inexperience became the best experience, how being excluded allowed him to be inclusive, and the ultimate power of sharing your work.  

Joshua Hodge is the founder of The Audio Programmer (TAP).  As a student in 2017, he started TAP as a YouTube channel to absorb the basics of audio software development and share knowledge.  In the 4 years since, TAP has grown into one of the largest audio development communities on the web, along with a thriving software development agency and recruitment service. Since joining the industry in 2018, Josh's primary goal has been to lower the threshold of entry into this industry.  As a person who is enthusiastic about teaching and sharing, he's expanded the YouTube channel for others to offer tutorials on audio development with Max MSP, JavaScript, SOUL, and JUCE.  He has a weekly tutorial series where he teaches his son audio coding, and hosts a monthly meetup with guests to share their perspectives and breakthroughs on audio development.  His list of past and current collaborators include Ableton, Focusrite, Native Instruments, InMusic, ROLI, JUCE, Cycling'74, and more.

 Algebraic Problems: The Frontier of Efficient Randomized Computation

Randomness is a valuable resource in many computational tasks. Indeed, the security and/or the accuracy of many randomized algorithms and protocols rely on the random bits being truly random and independent. However, in practice such random bits are elusive, which may compromise the performance of the underlying systems. This motivates the following fundamental question:-

Can every computational task that requires randomness be carried out deterministically, paying, perhaps only a small overhead?

Meanwhile, the nature of many algebraic problems makes them amenable to randomized algorithms. For example: a random set of vectors is independent, a random assignment to a low-degree polynomial is non-zero etc. Thus, you can easily find a set of independent vectors and a non-zero assignment by picking them uniformly at random. Indeed, it is not surprising that the frontier of efficient randomized computation consists of algebraic problems. Among the frontier problems are Polynomial Identity Testing, Polynomial Factorization and others.

In this talk, I will discuss my research on the relationship between randomness, computation and algebra. Time permitting, I will also discuss the problems I have been working on and some recent connections to cryptography and machine learning.

Dr. Ilya Volkovich is a Senior Lecturer in the Department of Computer Science and Engineering at the University of Michigan, where he has taught courses in the theory of computation for several years. Previously, he was a Postdoctoral Research Associate in the Computer Science Department at Princeton University and held a visiting position at the Institute of Advanced Study. In 2012, he obtained his Ph.D. in Computer Science from Technion, Israel Institute of Technology, advised by Prof. Amir Shpilka. His research interests are in the broad area of theoretical computer science and discrete mathematics. More specifically, he is interested in aspects of algebraic complexity, randomness in computation, computational learning theory, and their applications to cryptography and machine learning. 

Exploring Reasoning Capabilities in Natural Language Processing Models

Natural Language Processing (NLP) is the field of building machines that humans can seamlessly interact with through spoken and written language. As these machines become more ubiquitous in our daily lives through technologies like Google Translate and Apple’s Siri, it is important to develop methods that provide insight into understanding the reasoning capabilities of these systems. Adam will discuss his work on developing diagnostic test-suites composed of fine-grained semantic phenomena. He will demonstrate how to use these tests to explore the reasoning capabilities of contemporary NLP systems. Additionally, he will also discuss biases in prior datasets that the research community has accepted as gold standards. He will discuss how these biases limit the previous datasets’ usefulness in testing how well NLP systems successfully understand natural language. With the remaining time, he will discuss how lessons from these studies can be applied to identifying emergency needs during disaster scenarios.

Adam Poliak is a final year Ph.D. Candidate in Computer Science at Johns Hopkins University advised by Dr. Benjamin Van Durme. Adam is an affiliate of the Center for Language & Speech Processing as his research focuses on Natural Language Processing and Computational Semantics. In particular, his research analyses NLP systems and datasets to provide insight into the failures of NLP systems as well as biases in textual data. His work has been published in top-tier NLP conferences and he won Best Paper Awards in 2018 and 2019 at The Joint Conference on Lexical and Computational Semantics. Adam was a 2017 GEM Fellow and he has performed research at Bloomberg L.P. and the MIT Lincoln Laboratory.

True or False? Automatic Detection of Deception and Trust in Spoken Dialogue

Spoken language processing (SLP) aims to teach computers to understand human speech.  Automatic deception detection from speech is one of the few problems in AI where machines can potentially perform significantly better than humans, who can only detect lies about 50% of the time.  In this talk, I will discuss my work on training computers to distinguish between deceptive and truthful speech using language features. My work combines machine learning with insights from psychology and linguistics to develop robust techniques to detect deceptive speech.  I will also present ongoing research aimed at understanding the characteristics of trustworthy language. This work improves our scientific understanding of deception and trust, and has implications for security applications and for increasing trust in human-computer interaction.

Sarah Ita Levitan is a postdoctoral Research Scientist in the Department of Computer Science at Columbia University.  Her research interests are in spoken language processing, and she is currently working on identifying acoustic-prosodic and linguistic indicators of trustworthy speech, as well as identifying linguistic characteristics of trustworthy news.  She received her PhD in Computer Science at Columbia University, advised by Dr. Julia Hirschberg, and her dissertation addressed the problem of automatic deception detection from speech.  Sarah Ita was a 2018 Knight News Innovation Fellow and a recipient of the NSF Graduate Research Fellowship and the NSF IGERT From Data to Solutions fellowship.  She has interned at Google Research and at Interactions LLC.

Program Synthesis for Software Systems

Program synthesis is the process of automatically generating code from specifications. This specification, describing the intended code behavior, can be either explicitly expressed as formulas, can be given in the form of illustrative examples, or it can be inferred from the context. There are decades of research into program synthesis, but only recently we have seen synthesis scale to industrial benchmarks. However, these applications have been limited to simple data transformations and automation tasks.

In this talk, I outline new directions in software synthesis targeted at increasing scalability and expressivity so that synthesis tools can assist in the development of real-world large software systems. With these advances, we have successfully synthesized systems such as mobile apps, self-driving car controllers, and embedded systems. We have also applied synthesis to novel domains, including configuration file analysis and digital signal processing. I conclude by describing future work on exploring usability of program synthesis and challenges we face when integrating synthesis into developer workflow.

Mark Santolucito is completing his PhD from Yale University under the supervision of Ruzica Piskac. Mark’s work has been focused on program synthesis and computer music. His research has been published at top conferences including, CAV, OOPSLA, CHI, and SIGCSE. His work has also been recognized by industry, including Amazon Web Services, where he interned and applied his work on configuration file analysis. He was invited to the Heidelberg Laureate Forum and has received the Advanced Graduate Leadership award from Yale. He helped found the computer science department at Geumgang University in South Korea, and has taught a Creative Embedded Systems course at Yale.