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Events for March 08, 2021
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CS Colloquium: Mariya Toneva (Carnegie Mellon University) - Data-Driven Transfer of Insight between Brains and AI Systems
Mon, Mar 08, 2021 @ 09:00 AM - 10:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Mariya Toneva, Carnegie Mellon University
Talk Title: Data-Driven Transfer of Insight between Brains and AI Systems
Series: CS Colloquium
Abstract: Several major innovations in artificial intelligence (AI) (e.g. convolutional neural networks, experience replay) are based on findings about the brain. However, the underlying brain findings took many years to first consolidate and many more to transfer to AI. Moreover, these findings were made using invasive methods in non-human species. For cognitive functions that are uniquely human, such as natural language processing, there is no suitable model organism and a mechanistic understanding is that much farther away.
In this talk, I will present my research program that circumvents these limitations by establishing a direct connection between the human brain and AI systems with two main goals: 1) to improve the generalization performance of AI systems and 2) to improve our mechanistic understanding of cognitive functions. Lastly, I will discuss future directions that build on these approaches to investigate the role of memory in meaning composition, both in the brain and AI. This investigation will lead to methods that can be applied to a wide range of AI domains, in which it is important to adapt to new data distributions, continually learn to perform new tasks, and learn from few samples.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Mariya Toneva is a Ph.D. candidate in a joint program between Machine Learning and Neural Computation at Carnegie Mellon University, where she is advised by Tom Mitchell and Leila Wehbe. She received a B.S. in Computer Science and Cognitive Science from Yale University. Her research is at the intersection of Artificial Intelligence, Machine Learning, and Neuroscience. Mariya works on bridging language in machines with language in the brain, with a focus on building computational models of language processing in the brain that can also improve natural language processing systems.
Host: Yan Liu
Audiences: By invitation only.
Contact: Assistant to CS chair
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Central Intelligence Agency Office Hours | Pre-Signup Required
Mon, Mar 08, 2021 @ 11:00 AM - 12:00 PM
Viterbi School of Engineering Career Connections
University Calendar
Sign Up For a Time Slot: Link Coming soon!
Schedule a one-on-one phone call with a recruiter to discuss career opportunities at the Central Intelligence Agency!
All degree levels and Viterbi majors welcome.
NOTE: The Central Intelligence Agency cannot sponsor international candidates.
All times are in local Los Angeles time (PST)
Talk one-on-one with a CIA Representative to explore career and paid internship opportunities and life at the CIA. These are informal sessions--not job interviews--but please be prepared with questions about positions found on cia.gov/careers.
If you sign up for a "waitlist" slot, you will be called if the first student signed up for that time does not answer their phone.
We recommend going to the website, taking the short survey through the website's Job Fit Tool and having your results for our conversation. We can discuss resume style, cover letters, preparing for any interview, or anything else regarding working at the CIA. Undergraduates and Graduates of all academic disciplines are encouraged to attend. All CIA positions require US citizenship and relocation to the Washington, DC metropolitan area.Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Connections
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CS Colloquium: Saiph Savage (University of Washington) - The Future of A.I. for Social Good
Mon, Mar 08, 2021 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Saiph Savage, University of Washington
Talk Title: The Future of A.I. for Social Good
Series: CS Colloquium
Abstract: The A.I. industry has powered a futuristic reality of self-driving cars and voice assistants to help us with almost any need. However, the A.I. Industry has also created systematic challenges. For instance, while it has led to platforms where workers label data to improve machine learning algorithms, my research has uncovered that these workers earn less than minimum wage. We are also seeing the surge of A.I. algorithms that privilege certain populations and racially exclude others. If we were able to fix these challenges we could create greater societal justice and enable A.I. that better addresses people's needs, especially groups we have traditionally excluded.
In this talk, I will discuss some of these urgent global problems that my research has uncovered from the A.I. Industry. I will present how we can start to address these problems through my proposed "A.I. For Good" framework. My framework uses value sensitive design to understand people's values and rectify harm. I will present case-studies where I use this framework to design A.I. systems that improve the labor conditions of the workers operating behind the scenes in our A.I. industry; as well as how we can use this framework to safeguard our democracies. I conclude by presenting a research agenda for studying the impact of A.I. in society; and researching effective socio-technical solutions in favor of the future of work and countering techno-authoritarianism.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Saiph Savage conducts research in the intersection of Human Computer Interaction, A.I., and Civic Technology. She is one of the 35 Innovators under 35 by the MIT Technology Review, a Google Anita Borg Scholarship recipient, and a fellow at the Center for Democracy & Technology. Her work has been covered in the BBC, Deutsche Welle, and the New York Times, as well as published in top venues such as ACM CHI, CSCW, and AAAI ICWSM, where she has also won honorable mention awards. Dr. Savage has been awarded grants from the National Science Foundation, the United Nations, industry, and has also formalized new collaborations with Federal and local Governments where she is driving them to adopt Human Centered Design and A.I. to deliver better experiences and government services to citizens. Dr. Savage has opened the research area of Human Computer Interaction at West Virginia University, and Saiph's students have obtained fellowships and internships in industry (Facebook Research, Twitch Research, and Microsoft Research) as well as academia (Oxford Internet Institute). Saiph holds a bachelor's degree in Computer Engineering from the National Autonomous University of Mexico (UNAM), and a master's and Ph.D. in Computer Science from the University of California, Santa Barbara (UCSB). Dr. Savage currently works at the University of Washington; previously she was a Visiting Professor at Carnegie Mellon University (CMU). Additionally, Dr. Savage has been a tech worker at Microsoft Bing, Intel Labs, and a crowd research worker at Stanford.
Host: Bistra Dilkina
Audiences: By invitation only.
Contact: Assistant to CS chair
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CS Colloquium: Sanghamitra Dutta (Carnegie Mellon University) - Reliable Machine Learning for High-Stakes Applications: Approaches Using Information Theory
Mon, Mar 08, 2021 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Sanghamitra Dutta , Carnegie Mellon University
Talk Title: Reliable Machine Learning for High-Stakes Applications: Approaches Using Information Theory
Series: CS Colloquium
Abstract: How do we make machine learning (ML) algorithms not only ethical, but also intelligible, explainable, and reliable? This is particularly important today as ML enters high-stakes applications such as hiring and education, often adversely affecting people's lives with respect to gender, race, etc. Identifying bias/disparity in a model's decision is often insufficient. We really need to dig deeper and bring in an understanding of anti-discrimination laws. For instance, Title VII of the US Civil Rights Act includes a subtle and important aspect that has implications for the ML models being used today: Disparities in hiring that can be explained by a business necessity are exempt. E.g., disparity arising due to code-writing skills may be deemed exempt for a software engineering job, but the disparity due to an aptitude test may not be (e.g. Griggs v. Duke Power '71). This leads us to a question that bridges the fields of fairness, explainability, and law: How can we identify and explain the sources of disparity in ML models, e.g., did the disparity arise due to the critical business necessities or not? In this talk, I propose the first systematic measure of "non-exempt disparity," i.e., the illegal bias which cannot be explained by business necessities. To arrive at a measure for the non-exempt disparity, I adopt a rigorous axiomatic approach that brings together concepts in information theory, in particular, an emerging body of work called Partial Information Decomposition, with causal inference tools. This quantification allows one to audit a firm's hiring practices, to check if they are compliant with the law. This may also allow one to correct the disparity by better explaining the source of the bias, also providing insights into accuracy-bias tradeoffs.
My research bridges reliability in learning with reliability in computing, which has led to an emerging interdisciplinary area called "coded computing". Towards the end of this talk, I will also provide an overview of some of my results on coded reliable computing that addresses long-standing computational challenges in large-scale distributed machine learning (namely, stragglers, faults, failures) using tools from coding theory, optimization, and queueing.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Sanghamitra Dutta (B. Tech. IIT Kharagpur) is a Ph.D. candidate at Carnegie Mellon University, USA. Her research interests revolve around machine learning, information theory, and statistics. She is currently focused on addressing the emerging reliability issues in machine learning concerning fairness, explainability, and law with recent publications at AAAI'20, ICML'20 (also featured in New Scientist and CMU Engineering News). In her prior work, she has also examined problems in reliable computing, proposing novel algorithmic solutions for large-scale distributed machine learning in the presence of faults and failures, using tools from coding theory (an emerging area called "coded computing"). Her results on coded computing address problems that have been open for several decades and have received substantial attention from across communities (published at IEEE Transactions on Information Theory'19,'20, NeurIPS'16, AISTATS'18, IEEE BigData'18, ICML Workshop Spotlight'19, ISIT'17,'18, Proceedings of IEEE'20 along with two pending patents). She is a recipient of the 2020 Cylab Presidential Fellowship, 2019 K&L Gates Presidential Fellowship, 2019 Axel Berny Presidential Graduate Fellowship, 2017 Tan Endowed Graduate Fellowship, 2016 Prabhu and Poonam Goel Graduate Fellowship, the 2015 Best Undergraduate Project Award at IIT Kharagpur, and the 2014 HONDA Young Engineer and Scientist Award. She has also pursued research internships at IBM Research and Dataminr.
Host: Bistra Dilkina
Audiences: By invitation only.
Contact: Assistant to CS chair
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ACM Front End Web Dev Workshop
Mon, Mar 08, 2021 @ 07:00 PM - 08:00 PM
Thomas Lord Department of Computer Science
Student Activity
Want to learn more about front-end Web Development? Join ACM on Monday, March 8, from 7-8 PM for a web dev workshop.
During this workshop, ACM will teach you about HTML, CSS, and JavaScript. If you're not familiar with those terms or if you need to brush up on your front-end web developing skills, this workshop is your calling!
Learn more at https://www.facebook.com/events/758195448435348/
Location: Online - Zoom
Audiences: Undergraduate and Graduate Students
Contact: ACM
