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Events
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Nov20
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Title: The Lifecycle of Data Science: A Framework for Advancing Computational and Data-enabled Research
Abstract
There is no crisis, but also no time for complacency” said the chair of the National Academies of Sciences, Engineering, and Medicine (NASEM) committee on "Reproducibility and Replicability in Science" in May 2019 (https://vimeo.com/335923468). Questions regarding reproducibility have arisen regarding the transparency of computational methods and discovery, in part due to the leveraging of data and compute resources for scientific and engineering advancements, now pervasive in a staggeringly broad range of academic disciplines and activities. In this talk I present the reproducibility definitions that emerged in our NASEM committee deliberations and discuss an abstract framework for conceptualizing and advancing data science as a discipline, called the Lifecycle of Data Science (forthcoming in CACM). This framework integrates the disparate components of data-enabled discovery, from hardware provisioning to applications to dissemination standards for verification and re-use to ethics, and thereby brings into contextual focus salient issues such as computational reproducibility, standards and policy, and curricular development. I then present the "Knowledge Integrator," an effort to conceptualize and enable the dissemination of reproducible research results based on the Lifecycle of Data Science and community engagement.
Bio
Victoria Stodden joined the School of Information Sciences as an associate professor in Fall 2014. She is a leading figure in the area of reproducibility in computational science, exploring how can we better ensure the reliability and usefulness of scientific results in the face of increasingly sophisticated computational approaches to research. Her work addresses a wide range of topics, including standards of openness for data and code sharing, legal and policy barriers to disseminating reproducible research, robustness in replicated findings, cyberinfrastructure to enable reproducibility, and scientific publishing practices. Stodden co-chairs the NSF Advisory Committee for CyberInfrastructure and is a member of the NSF Directorate for Computer and Information Science and Engineering (CISE) Advisory Committee. She also serves on the National Academies Committee on Responsible Science: Ensuring the Integrity of the Research Process.
Previously an assistant professor of statistics at Columbia University, Stodden taught courses in data science, reproducible research, and statistical theory and was affiliated with the Institute for Data Sciences and Engineering. She co-edited two books released in 2014—Privacy, Big Data, and the Public Good: Frameworks for Engagement published by Cambridge University Press and Implementing Reproducible Research published by Taylor & Francis. Stodden earned both her PhD in statistics and her law degree from Stanford University. She also holds a master’s degree in economics from the University of British Columbia and a bachelor’s degree in economics from the University of Ottawa.
TIME Wednesday, November 20, 2019 at 12:00 PM - 1:00 PM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Brianna White brianna.mello@northwestern.edu EMAIL
CALENDAR Department of Computer Science
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Feb19
EVENT DETAILS
lessTitle: We're Stuck With the Internet — We'd Better Quickly Figure Out How It Fails
Abstract
Our society's dependence on the Internet today is unquestioned. Yet the Internet's complexity (e.g., distributed self-governance, constantly-evolving layered technologies, and competing political and commercial interests) makes it hard to understand and manage the risks associated with relying on the proper functioning of its core infrastructure. While efforts to reduce and manage these risks are not limited to the field of computer science and engineering, their progress is hindered by the scarcity of data and a lack of clear understanding of the underlying technical phenomena. For example, we lack a thorough understanding of when, where, how, and why, connectivity fails at large and with which consequences or recovery times. We barely understand the interdependence with other critical infrastructure sectors. We rely on a global routing system that is extremely vulnerable and we do not know to which extent it is exploited, by whom, and for which purposes. In this talk, I will discuss some of my research efforts to develop methods and systems for a better understanding of phenomena affecting the Internet's security and reliability. In particular, I will describe how devising a novel approach to study specific episodes of connectivity disruption due to political and geophysical events, inspired me to build a system (ioda.caida.org) to continuously monitor the Internet for large-scale connectivity outages. Over the past few years, this effort enabled research in various directions, facilitated multi-disciplinary collaborations, and resulted in the engagement of industry, government, and civil society.
Biography
Alberto Dainotti is an Associate Research Scientist at CAIDA, the Center for Applied Internet Data Analysis at University of California San Diego, USA. His research is at the intersection of Internet measurement and data analysis and Internet security, with a focus on understanding and improving Internet infrastructure security and reliability. He has received the ACM SIGCOMM Internet Measurement Conference Distinguished Paper Award, two IRTF Applied Networking Research Prizes, and the Best of ACM SIGCOMM Computer Communication Review award. Dr. Dainotti received his Ph.D. in Computer Engineering and Systems at University of Napoli "Federico II", Italy.
TIME Wednesday, February 19, 2020 at 12:00 PM - 1:00 PM
LOCATION Room 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Pam Villalovoz pmv@northwestern.edu EMAIL
CALENDAR Department of Computer Science
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Feb21
EVENT DETAILS
lessTitle: Policy Compliance in Online Services
Abstract
In response to incidents of unintended disclosure and misuse of user data by online services, modern data protection regulations require service providers to restrict their collection, processing, sharing and storage of sensitive user data. However, ensuring compliance with such regulation in today's complex and rapidly evolving systems is technically challenging. In my research, I have developed practical systems to prevent unintended disclosures and misuse of data in the face of two broad classes of threats: software bugs and misconfiguration, and side channels.In this talk, I will describe Pacer, a compliance system designed to prevent indirect inference of sensitive data via side channels in shared network links in the Cloud. Pacer shapes the outbound traffic of a Cloud tenant to make it independent of the tenant's secrets. At the same time, Pacer does allow variations in the traffic shape based only on public (non-secret) aspects of the tenants' workloads, thus enabling efficient sharing of network resources and incurring moderate overhead. Implementing Pacer requires modest changes to the Cloud hypervisor and the guest OS, and minimal changes to the guest application.
Biography
Aastha Mehta is a Ph.D. student at the Max Planck Institute for Software Systems (MPI-SWS) in Germany. She is expecting to graduate by mid 2020. Prior to joining MPI-SWS, she worked at NetApp. She received her Bachelors degree from BITS Pilani in India. She is interested in building practical systems that solve security problems. Most recently, she has been focusing on systems for mitigating side channels in Cloud environments. She was invited to attend the 4th Heidelberg Laureate Forum in 2015 and the Rising Stars in EECS Workshop in 2018.
TIME Friday, February 21, 2020 at 12:00 PM - 1:00 PM
LOCATION Room 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Pam Villalovoz pmv@northwestern.edu EMAIL
CALENDAR Department of Computer Science
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Feb24
EVENT DETAILS
lessTitle: Thrifty Data Management
Abstract
The end of Moore’s law will push database system designers to be more judicious with computation as the growth in data outpaces the availability of computational resources. Aggressively consuming resources to immediately and quickly complete the task at hand, is one source of wasted resources in modern data systems, especially when the systems expend unnecessary resources on queries or data that might change later. Building database systems that process more data by consuming the same amount of computing resources requires system re-architecture that recognizes, exploits, and integrates users’ performance goals and workload characteristics.
In this talk, I will present our thrifty database system that aims to achieve performance goals with minimum resource consumption. I will focus on two specific components. First, I will present Predicate-Constraint, a framework that calculates guaranteed query result ranges based on a lightweight summary of data yet to be processed; thus, it saves additional computations when the data scientists accept a slightly approximated answer. Second, I will introduce Intermittent Query Processing, a new query optimization paradigm that allows database systems to reduce memory consumption when inactive without scarifying the performance. I will conclude by briefly discussing other promising directions.
Biography
Zechao Shang is a postdoctoral scholar working with Michael Franklin and Aaron Elmore at the University of Chicago. His research interest centers on the design and implementation of large-scale data management systems. He received his Ph.D. degree at the Chinese University of Hong Kong. He is the head coach of University of Chicago ICPC team.
TIME Monday, February 24, 2020 at 12:00 PM - 1:00 PM
LOCATION Room 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Pam Villalovoz pmv@northwestern.edu EMAIL
CALENDAR Department of Computer Science
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Feb28
EVENT DETAILS
lessTitle: Knowledge Is Power: Sensing The Physical World Around Devices
Abstract
Function-as-a-Service "serverless" platforms like Amazon Lambda have quickly emerged as a popular environment for developers since their arrival in 2014. FaaS offers developers automatic scaling of their applications with user demand and the promise to only pay for the resources their application uses, down to the millisecond range. Despite its desirability and positive features, FaaS has its dirty secrets. In this talk, I will reveal how the paradigm disregards decades of architectural wisdom and rewards misbehaviour from the FaaS provider. These issues require a rethink of the hardware and software stack to make today's markets more efficient and to enable the applications of tomorrow that will make FaaS shine in the post-Moore era.
To prototype these changes, we need to build full-stack prototype systems ready to run complex virtualised or containerised applications like FaaS, as high-level simulation alone abstracts away the complex system interactions. I will introduce OpenPiton, our open-source manycore research platform used to evaluate architecture research ideas for the cloud and beyond. OpenPiton enables the exact kind of full-stack system-on-chip prototyping we need in a push-button way, making it ideal for evaluating serverless applications and future research ideas. By embracing open-source hardware, the platform reduces the barrier to entry for new users across Computer Science and Electrical Engineering and creates a nexus where research ideas can come together to build a single coherent system.
One example of such a system I will introduce is our "Bring Your Own Core" (BYOC) platform for enabling heterogeneous-ISA systems research. BYOC enabled the development of both the first open-source, general-purpose, heterogeneous-ISA processor and the first open-source, SMP Linux-booting RISC-V manycore. By providing our novel interface for the connection of processor cores, one can bring a core of their choice and turn it into a heterogeneous manycore system with minimal effort. With this we have built multiple heterogeneous-ISA prototypes and connected ten cores of ISAs including RISC-V, X86, and SPARC. BYOC brings the potential to break down the ISA barrier, treating it as "just another interface" in the broader system stack, and to enable a new class of application-optimised heterogeneous systems to live alongside emerging application-specific accelerators.
Biography
Jonathan Balkind is a PhD Candidate in Computer Science at Princeton University. His research interests lie at the intersection of Computer Architecture, Programming Languages, and Operating Systems. He is the Lead Architect of OpenPiton and its heterogeneous-ISA descendent, BYOC, which are productive research platforms with thousands of downloads from over 70 countries worldwide. In developing Piton (a 25-core OpenPiton ASIC), Jonathan's verification effort helped ensure a functional, Linux-booting chip, a notable accomplishment for a small academic team's first chip. Jonathan was also a developer of FaaSProfiler, a tool for profiling Function-as-a-Service "serverless" applications. His research on FaaS unveiled a series of architectural problems with today's FaaS workloads which will only be effectively addressed through hardware-software co-design. Jonathan was a Class of 2018 Siebel Scholar and recipient of the Gordon Y.S. Wu Fellowship in Engineering. He received his Master's in Science in Computing Science with Honours of the First Class from the University of Glasgow in June 2013. He has completed internships at Samsung Electronics (Suwon, South Korea), Arm Ltd (Cambridge, UK), and Microsoft Research (Cambridge, UK).
TIME Friday, February 28, 2020 at 12:00 PM - 1:00 PM
LOCATION Room 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Pam Villalovoz pmv@northwestern.edu EMAIL
CALENDAR Department of Computer Science
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Mar2
EVENT DETAILS
lessTitle: Distributive Justice for Machine Learning: An Interdisciplinary Perspective on Defining, Measuring, and Mitigating Algorithmic Unfairness
Abstract
Automated decision-making tools are increasingly in charge of making high-stakes decisions for people—in areas such as education, credit lending, criminal justice, and beyond. These tools can exhibit and exacerbate existing undesirable biases and adversely impact already disadvantaged and marginalized social groups and individuals. In this talk, I will illustrate how we can bring together tools and methods from computer science, economics, and political philosophy to define, measure, and mitigate algorithmic unfairness. In particular, I will address two key questions:
▪ Given the decision-making context, how should we define fairness as the equality of some notion ofbenefit or harm across socially salient groups? First, I will offer a framework to think about this question normatively. I map the recently proposed notions of group-fairness to models of equality of opportunity. This mapping provides a unifying framework for understanding these notions, and importantly, allows us to spell out the moral assumptions underlying each one of them. Second, I give a descriptive answer to the question of “fairness as equality of what?”. I mention a series of adaptive human-subject experiments we conducted to understand which existing notion best captures laypeople’s perception of fairness.
▪ How can we measure unfairness (both at the individual and group level) and bound it in a computationally efficient manner? Existing notions of fairness focus on defining conditions of fairness, but they do not offer a proper measure of unfairness. In practice, however, designers often need to select the least unfair model among a feasible set of unfair alternatives. I present (income) inequality indices from economics as a unifying framework for measuring unfairness, both at the individual- and group-level. I propose the use of cardinal social welfare functions as an alternative measure of fairness beyond equality and an effective method for bounding inequality.
Biography
Hoda Heidari is currently a Postdoctoral Associate at the Department of Computer Science at Cornell University, where she collaborates with Professors Jon Kleinberg, Karen Levy, and Solon Barocas through the AIPP (Artificial Intelligence, Policy, and Practice) initiative. Hoda’s research is broadly concerned with the societal and economic aspects of Artificial Intelligence, and in particular, the issues of fairness and explainability for Machine Learning. She utilizes tools and methods from Computer Science (Algorithms, AI, and ML) and Social Sciences (Economics and Political Philosophy) to quantify and mitigate the inequalities that arise when socially consequential decisions are automated. Her work has appeared in top-tier Computer Science venues, such as ICML, NeurIPS, KDD, AAAI, IJCAI, and EC. Before coming to Cornell, Hoda was a Postdoctoral Fellow at the Machine Learning Institute of ETH Zürich, working under the supervision of Professor Andreas Krause. Hoda completed her doctoral studies in Computer and Information Science at the University of Pennsylvania, where she was advised by Professors Michael Kearns and Ali Jadbabaie. Hoda has organized multiple academic events on the topic of her research, including a tutorial at the Web Conference (WWW) and a workshop at the Neural and Information Processing Systems (NeurIPS) conference. Beyond computer science venues, she has been invited to and participated in numerous interdisciplinary panels and discussions addressing the implications of AI for society.
TIME Monday, March 2, 2020 at 12:00 PM - 1:00 PM
LOCATION Room 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Pam Villalovoz pmv@northwestern.edu EMAIL
CALENDAR Department of Computer Science
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Mar6
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lessProspective PhD Students for Fall 2020 will be visiting the Department of Computer Science
TIME Friday, March 6, 2020 at 8:00 AM - 6:00 PM
LOCATION 3514, Mudd Hall ( formerly Seeley G. Mudd Library) map it
CONTACT Judith Hernandez judith.hernandez@northwestern.edu EMAIL
CALENDAR Department of Computer Science
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Mar14
EVENT DETAILS
lessWinter Classes End
TIME Saturday, March 14, 2020
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Mar21
EVENT DETAILS
lessSpring Break Begins
TIME Saturday, March 21, 2020
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Mar30
EVENT DETAILS
lessSpring Break Ends
TIME Monday, March 30, 2020
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Mar31
EVENT DETAILS
lessSpring Classes begin 8 a.m.
Northwestern Monday: In the fall of 2019, President Schapiro and Provost Holloway announced changes to commencement, spring break and the start of the spring quarter: spring quarters will now routinely start on Tuesday, rather than Monday, to afford two full weekends of break.
Though spring quarters will begin on a Tuesday, the Monday class schedule will be in effect, a practice referred to as “Northwestern Monday.” Classes that usually meet only on Tuesdays will not meet during the first week of the term. This calendar applies to all quarter classes except those offered in the evening programs in the Kellogg School of Management and the School of Professional Studies (SPS).
TIME Tuesday, March 31, 2020
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar