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Conferences, Lectures, & Seminars
Events for April
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CS Colloquium: Tian Li (CMU) - Scalable and Trustworthy Learning in Heterogeneous Networks
Mon, Apr 03, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Tian Li, CMU
Talk Title: Scalable and Trustworthy Learning in Heterogeneous Networks
Series: CS Colloquium
Abstract: To build a responsible data economy and protect data ownership, it is crucial to enable learning models from separate, heterogeneous data sources without data centralization. For example, federated learning aims to train models across massive networks of remote devices or isolated organizations, while keeping user data local. However, federated networks introduce a number of unique challenges such as extreme communication costs, privacy constraints, and data and systems-related heterogeneity.
Motivated by the application of federated learning, my work aims to develop principled methods for scalable and trustworthy learning in heterogeneous networks. In the talk, I discuss how heterogeneity affects federated optimization, and lies at the center of accuracy and trustworthiness constraints in federated learning. To address these concerns, I present scalable federated learning objectives and algorithms that rigorously account for and directly model the practical constraints. I will also explore trustworthy objectives and optimization methods for general ML problems beyond federated settings.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Tian Li is a fifth-year Ph.D. student in the Computer Science Department at Carnegie Mellon University working with Virginia Smith. Her research interests are in distributed optimization, federated learning, and trustworthy ML. Prior to CMU, she received her undergraduate degrees in Computer Science and Economics from Peking University. She received the Best Paper Award at the ICLR Workshop on Security and Safety in Machine Learning Systems, was invited to participate in the EECS Rising Stars Workshop, and was recognized as a Rising Star in Machine Learning/Data Science by multiple institutions.
Host: Dani Yogatama
Location: Ronald Tutor Hall of Engineering (RTH) - 115
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Willie Neiswanger (Stanford University) - AI-Driven Experimental Design for Accelerating Science and Engineering
Mon, Apr 03, 2023 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Willie Neiswanger, Stanford University
Talk Title: AI-Driven Experimental Design for Accelerating Science and Engineering
Series: CS Colloquium
Abstract: AI-driven experimental design methods have the potential to accelerate costly discovery and optimization tasks throughout science and engineering-”from materials design and drug discovery to computer systems tuning and instrument control. These methods are promising as they provide the intelligent decision making needed for use in complex real-world problems where experiments are time-consuming or expensive, and efficiency is paramount. In the first part of my talk, I will discuss challenges that I encountered while applying these methods to new types of scientific optimization problems being pursued at national labs. I will then introduce an information-based framework for flexible experimental design, which overcomes these challenges by enabling easy customization to new problem settings. This framework is theoretically principled, and has been used by scientists for efficient materials synthesis and optimization in large scientific instruments. Along the way, I will discuss my vision for reliable systems that expand the scope of AI-driven experimental design and make it easier to use, so that it can be put in the hands of scientists, engineers, and other practitioners everywhere.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Willie Neiswanger is a postdoctoral scholar in the Computer Science Department at Stanford University. Previously, he completed his PhD in machine learning at Carnegie Mellon University. He develops machine learning techniques to perform optimization and experimental design in costly real-world settings, where resources are limited. His work spans topics in active learning, uncertainty quantification, Bayesian decision making, and reinforcement learning, and he applies these methods downstream to solve problems in science and engineering. Willie's work has received honors including a Best Paper Award at OSDI'21, and has been published in top machine learning venues (e.g., NeurIPS, ICML, ICLR, AAAI, AISTATS) and natural science journals (e.g., J Chem Physics, J Immunology, Cell Reports, Nucl Fusion). He has also collaborated with the SLAC National Accelerator Laboratory and the Princeton Plasma Physics Laboratory, where his methods have been run live on particle accelerators and tokamak machines for optimization/control tasks.
Host: Dani Yogatama
Location: Ronald Tutor Hall of Engineering (RTH) - 105
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Rakshit Trivedi (MIT) - Foundations for Learning in Multi-agent Ecosystems: Modeling, Imitation, and Equilibria
Tue, Apr 04, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Rakshit Trivedi, MIT
Talk Title: Foundations for Learning in Multi-agent Ecosystems: Modeling, Imitation, and Equilibria
Series: CS Colloquium
Abstract: The growing presence of AI in critical domains such as information communication, service, financial markets and agriculture requires designing AI systems capable of seamlessly interacting with other AI, with humans and as part of complex systems in a manner that is beneficial to humans. For an AI to be effective in such settings, a key open challenge is for it to have the ability to effectively collaborate across a broad group of interdependent agents (AI or human) in a variety of one or few-shot interactions. A crucial step towards addressing this is to enable rapid development and safe evaluation of AI agents and frameworks that can incorporate the richness and diversity observed in human behaviors and account for various social and economic factors that drives interactions in the multi-agent ecosystems. In this talk, I will set forth the research agenda of real-world in silico design for such AI systems and discuss methodological advancements in this direction. First, I will focus on automated design of central mechanisms tasked to shape the behavior of self-interested agents and drive them towards improving social welfare. I will introduce a novel multi-agent reinforcement learning technique to solve the resulting bi-level optimization problem and present its effectiveness in a simulated market economy. Next, I will discuss the setting where the self-interested agents interact with each other in a strategic manner to form networks and present our approach on discovering the underlying mechanisms that drives these interactions. This approach considers a game-theoretic formalism, and leverages recent advances in inverse reinforcement learning, thereby serving as a preliminary step towards learning models of optimizing mechanisms directly from observed data. Finally, I will focus on the use of AI agents as surrogate for human actors that can provide simulations of real-world complexity and discuss challenges and opportunities on designing AI that is capable of handling the diversity, richness, and noise that is inherent to human behaviors. I will conclude my talk with an outline of my forward-looking vision on this agenda.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Rakshit Trivedi is a Postdoctoral Associate in the Computational Science and Artificial Intelligence Laboratory (CSAIL) at MIT and a Researcher in EconCS at Harvard School of Engineering and Applied Sciences (SEAS). His research focuses on the development of AI that is capable of learning from human experiences, quickly adapt to evolving human needs and achieve alignment with human values. He is further interested in studying the effectiveness of such an AI in the presence of various socio-economic mechanisms. Towards this goal, he is currently leading a set of efforts on developing and evaluating design strategies for building helpful and prosocial artificial agents in mixed-motive settings, in collaboration with Deepmind and Cooperative AI Foundation. Previously, Rakshit completed his Ph.D. at Georgia Institute of Technology, where he focused on learning in networked and multi-agent systems to improve predictive and generative capabilities of downstream applications, by accounting for the structure and dynamics of interactions in such systems.
Host: Bistra Dilkina
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Evi Micha (University of Toronto) - Fair and Efficient Decision-Making for Social Good
Wed, Apr 05, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Evi Micha, University of Toronto
Talk Title: Fair and Efficient Decision-Making for Social Good
Series: CS Colloquium
Abstract: Algorithms have had a remarkable impact on human lives as they have been used increasingly to automate critical decisions. Consequently, it is more important than ever to design decision-making algorithms that treat people fairly, use limited resources efficiently, and foster social good. To illustrate my research in this direction, I will present two recent examples: in one, we boost the efficiency of COVID testing in a real-world setting, and in the other, we make the selection of citizens' assemblies more representative. Towards the end, I will address the challenging question of algorithmic fairness, making a case that fairness notions emerging from the EconCS literature have far-reaching applications, even to machine learning.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Evi Micha is a Ph.D. candidate in the Computer Science Department at the University of Toronto, advised by Nisarg Shah. She is also an affiliate of the Vector Institute for Artificial Intelligence and a fellow of the Schwartz Reisman Institute for Technology and Society. Her research interests lie at the intersection of computer science and economics, and span areas such as algorithmic fairness and computational social choice.
Host: Sven Koenig
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Maithilee Kunda (Vanderbilt University) - Reasoning with visual imagery: Research at the intersection of autism, AI, and visual thinking
Thu, Apr 06, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Maithilee Kunda, Vanderbilt University
Talk Title: Reasoning with visual imagery: Research at the intersection of autism, AI, and visual thinking
Series: CS Colloquium
Abstract: While decades of AI research on high-level reasoning have yielded many techniques for many tasks, we are still quite far from having artificial agents that can just "sit down" and perform tasks like intelligence tests without highly specialized algorithms or training regimes. We also know relatively little about how and why different people approach reasoning tasks in different (often equally successful) ways, including in neurodivergent conditions such as autism. In this talk, I will discuss: 1) my lab's work on AI approaches for reasoning with visual imagery to solve intelligence tests, and what these findings suggest about visual cognition in autism; 2) how imagery-based agents might learn their domain knowledge and problem-solving strategies via search and experience, instead of these components being manually designed, including recent leaderboard results on the very difficult Abstraction & Reasoning Corpus (ARC) ARCathon challenge; and 3) how this research can help us understand cognitive strategy differences in people, with applications related to neurodiversity and employment. I will also discuss 4) our Film Detective game that aims to visually support adolescents on the autism spectrum in improving their theory-of-mind and social reasoning skills.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Maithilee Kunda is an assistant professor of computer science at Vanderbilt University. Her work in AI, in the area of cognitive systems, looks at how visual thinking contributes to learning and intelligent behavior, with a focus on applications related to autism and neurodiversity. She directs Vanderbilt's Laboratory for Artificial Intelligence and Visual Analogical Systems and is a founding investigator in Vanderbilt's Frist Center for Autism & Innovation.
She has led grants from the US National Science Foundation and the US Institute of Education Sciences and has also collaborated on large NSF Convergence Accelerator and AI Institute projects. She has published in Proceedings of the National Academy of Sciences (PNAS) and in the Journal of Autism and Developmental Disorders (JADD), the premier journal for autism research, as well as in AI and cognitive science conferences such as ACS, CogSci, AAAI, ICDL-EPIROB, and DIAGRAMS, including a best paper award at the ACS conference in 2020. Also in 2020, her research on innovative methods for cognitive assessment was featured on the national news program CBS 60 Minutes, as part of a segment on neurodiversity and employment. She holds a B.S. in mathematics with computer science from MIT and Ph.D. in computer science from Georgia Tech.
Host: Jyo Deshmukh
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Daniel Seita (CMU) - Representations in Robot Manipulation: Learning to Manipulate Cables, Fabrics, Bags, Liquids, and Plants
Fri, Apr 07, 2023 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Daniel Seita, Carnegie Mellon University
Talk Title: Representations in Robot Manipulation: Learning to Manipulate Cables, Fabrics, Bags, Liquids, and Plants
Series: CS Colloquium
Abstract: The robotics community has seen significant progress in applying machine learning for robot manipulation. However, much manipulation research focuses on rigid objects instead of highly deformable objects such as cables, fabrics, bags, liquids, and plants, which pose challenges due to their complex configuration spaces, dynamics, and self-occlusions. To achieve greater progress in robot manipulation of such diverse deformable objects, I advocate for an increased focus on learning and developing appropriate representations for robot manipulation. In this talk, I show how novel action-centric representations can lead to better imitation learning for manipulation of diverse deformable objects. I will show how such representations can be learned from color images, depth images, or point cloud observational data. My research demonstrates how novel representations can lead to an exciting new era for robot manipulation of complex objects.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Daniel Seita is a postdoctoral researcher at Carnegie Mellon University's Robotics Institute, advised by David Held. His research interests are in computer vision and machine learning for robot manipulation, with a focus on using and developing novel observation and action representations to improve manipulation of challenging deformable objects. Daniel holds a PhD in computer science from the University of California, Berkeley, advised by John Canny and Ken Goldberg. He received undergraduate degrees in math and computer science from Williams College. Daniel's research has been supported by a six-year Graduate Fellowship for STEM Diversity and by a two-year Berkeley Fellowship. He has the Honorable Mention for Best Paper award at UAI 2017, was an RSS 2022 Pioneer, and has presented his work at premier robotics conferences such as ICRA, IROS, RSS, and CoRL.
Website: https://www.cs.cmu.edu/~dseita/
Host: Stefanos Nikolaidis
Location: Ronald Tutor Hall of Engineering (RTH) - 115
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Ruishan Liu (Stanford University) - Machine learning for precision medicine
Tue, Apr 11, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ruishan Liu, Stanford University
Talk Title: Machine learning for precision medicine
Series: CS Colloquium
Abstract: Toward a new era of medicine, our mission is to benefit every patient with individualized medical care. This talk explores how machine learning can make precision medicine more effective and diverse. I will first discuss Trial Pathfinder, a computational framework to optimize clinical trial designs (Liu et al. Nature 2021). Trial Pathfinder simulates synthetic patient cohorts from medical records, and enables inclusive criteria and data valuation. In the second part, I will discuss how to leverage large real-world data to identify genetic biomarkers for precision oncology (Liu et al. Nature Medicine 2022), and how to use language models and causal inference to form individualized treatment plans.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Ruishan Liu is a postdoctoral researcher in Biomedical Data Science at Stanford University, working with Prof. James Zou. She received her PhD in Electrical Engineering at Stanford University in 2022. Her research lies in the intersection of machine learning and applications in human diseases, health and genomics. She was the recipient of Stanford Graduate Fellowship, and was selected as the Rising Star in Data Science by University of Chicago, the Next Generation in Biomedicine by Broad Institute, and the Rising Star in Engineering in Health by Johns Hopkins University and Columbia University. She led the project Trial Pathfinder, which was selected as Top Ten Clinical Research Achievement in 2022 and Finalist for Global Pharma Award in 2021.
Host: Yan Liu
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: C. Mohan (Tsinghua University) - Query Optimization and Processing: Trends and Directions
Wed, Apr 12, 2023 @ 02:00 PM - 03:30 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: C. Mohan, Tsinghua University
Talk Title: Query Optimization and Processing: Trends and Directions
Series: CS Colloquium
Abstract: Query optimization and processing (QOP) have been a dominant component of relational database management systems ever since such systems emerged in the research and commercial space more than four decades ago. Technologies related to QOP have received widespread attention and have evolved significantly since the days of IBM Research's System R, the project which gave birth to the concept of cost-based query optimization. Having worked on various database management topics at the birthplace of the relational model and the SQL language, until my retirement 2 years ago as an IBM Fellow at IBM Research in Silicon Valley, I have observed at close quarters a great deal of work in QOP. In this talk, I will give a broad overview of QOP's evolution. I will discuss not only research trends but also trends in the commercial world. Work done in various organizations across the world will be covered.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Dr. C. Mohan is currently a Distinguished Visiting Professor at Tsinghua University in China, a Member of the inaugural Board of Governors of Digital University Kerala, and an Advisor of the Kerala Blockchain Academy (KBA) and the Tamil Nadu e-Governance Agency (TNeGA) in India. He retired in June 2020 from being an IBM Fellow at the IBM Almaden Research Center in Silicon Valley. He was an IBM researcher for 38.5 years in the database, blockchain, AI and related areas, impacting numerous IBM and non-IBM products, the research and academic communities, and standards, especially with his invention of the well-known ARIES family of database locking and recovery algorithms, and the Presumed Abort distributed commit protocol.
This IBM (1997-2020), ACM (2002-) and IEEE (2002-) Fellow has also served as the IBM India Chief Scientist (2006-2009). In addition to receiving the ACM SIGMOD Edgar F. Codd Innovations Award (1996), the VLDB 10 Year Best Paper Award (1999) and numerous IBM awards, Mohan was elected to the United States and Indian National Academies of Engineering (2009), and named an IBM Master Inventor (1997). This Distinguished Alumnus of IIT Madras (1977) received his PhD at the University of Texas at Austin (1981). He is an inventor of 50 patents. During the last many years, he focused on Blockchain, AI, Big Data and Cloud technologies (https://bit.ly/sigBcP, https://bit.ly/CMoTalks). Since 2017, he has been an evangelist of permissioned blockchains and the myth buster of permissionless blockchains. During 1H2021, Mohan was the Shaw Visiting Professor at the National University of Singapore (NUS) where he taught a seminar course on distributed data and computing. In 2019, he became an Honorary Advisor to TNeGA for its blockchain and other projects.
In 2020, he joined the Advisory Board of KBA. Since 2016, Mohan has been a Distinguished Visiting Professor of China's prestigious Tsinghua University. In 2021, he was inducted as a member of the inaugural Board of Governors of the new Indian university Digital University Kerala (DUK). Mohan has served on the advisory board of IEEE Spectrum, and on numerous conference and journal boards. During most of 2022, he was a non-employee consultant at Google with the title of Visiting Researcher. He has also been a Consultant to the Microsoft Data Team. Mohan is a frequent speaker in North America, Europe and Asia. He has given talks in 43 countries. He is highly active on social media and has a huge network of followers. More information can be found in the Wikipedia page at https://bit.ly/CMwIkP and his homepage at https://bit.ly/CMoDUK
Host: Cyrus Shahabi
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Ibrahim Sabek (MIT) - Building Better Data-Intensive Systems Using Machine Learning
Thu, Apr 13, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ibrahim Sabek, MIT
Talk Title: Building Better Data-Intensive Systems Using Machine Learning
Series: CS Colloquium
Abstract: Database systems have traditionally relied on handcrafted approaches and rules to store large-scale data and process user queries over them. These well-tuned approaches and rules work well for the general-purpose case, but are seldom optimal for any actual application because they are not tailored for the specific application properties (e.g., user workload patterns). One possible solution is to build a specialized system from scratch, tailored for each use case. Although such a specialized system is able to get orders-of-magnitude better performance, building it is time-consuming and requires a huge manual effort. This pushes the need for automated solutions that abstract system-building complexities while getting as close as possible to the performance of specialized systems. In this talk, I will show how we leverage machine learning to instance-optimize the performance of query scheduling and execution operations in database systems. In particular, I will show how deep reinforcement learning can fully replace a traditional query scheduler. I will also show that-”in certain situations-”even simpler learned models, such as piece-wise linear models approximating the cumulative distribution function (CDF) of data, can help improve the performance of fundamental data structures and execution operations, such as hash tables and in-memory join algorithms.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Ibrahim Sabek is a postdoc at MIT and an NSF/CRA Computing Innovation Fellow. He is interested in building the next generation of machine learning-empowered data management, processing, and analysis systems. Before MIT, he received his Ph.D. from University of Minnesota, Twin Cities, where he studied machine learning techniques for spatial data management and analysis. His Ph.D. work received the University-wide Best Doctoral Dissertation Honorable Mention from University of Minnesota in 2021. He was also awarded the first place in the graduate student research competition (SRC) in ACM SIGSPATIAL 2019 and the best paper runner-up in ACM SIGSPATIAL 2018.
Host: Cyrus Shahabi
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Michael Oberst (MIT) - Rigorously Tested & Reliable Machine Learning for Health
Thu, Apr 13, 2023 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Michael Oberst, MIT
Talk Title: Rigorously Tested & Reliable Machine Learning for Health
Series: CS Colloquium
Abstract: How do we make machine learning as rigorously tested and reliable as any medication or diagnostic test?
Machine learning (ML) has the potential to improve decision-making in healthcare, from predicting treatment effectiveness to diagnosing disease. However, standard retrospective evaluations can give a misleading sense for how well models will perform in practice. Evaluation of ML-derived treatment policies can be biased when using observational data, and predictive models that perform well in one hospital may perform poorly in another.
In this talk, I will introduce methods I have developed to proactively assess and improve the reliability of machine learning models. A central theme will be the application of external knowledge, including guided review of patient records, incorporation of limited clinical trial data, and interpretable stress tests. Throughout, I will discuss how evaluation can directly inform model design.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Michael Oberst is a final-year PhD candidate in Computer Science at MIT. His research focuses on making sure that machine learning in healthcare is safe and effective, using tools from causal inference and statistics. His work has been published at a range of machine learning venues (NeurIPS / ICML / AISTATS / KDD), including work with clinical collaborators from Mass General Brigham, NYU Langone, and Beth Israel Deaconess Medical Center. He has also worked on clinical applications of machine learning, including work on learning effective antibiotic treatment policies (published in Science Translational Medicine). He earned his undergraduate degree in Statistics at Harvard.
Host: Yan Liu
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Alvaro Velasquez (DARPA) - Neuro-Symbolic Transfer and Optimization
Thu, Apr 20, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Alvaro Velasquez, DARPA
Talk Title: Neuro-Symbolic Transfer and Optimization
Series: CS Colloquium
Abstract: Neuro-symbolic artificial intelligence (NSAI) has experienced a renaissance and gained much traction in recent years as a potential "third wave" of AI to follow the tremendously successful second wave underpinned by statistical deep learning. NSAI seeks the integration of neural learning systems and formal symbolic reasoning for more efficient, robust, and explainable AI. This integration has been successful in classification and reinforcement learning, among other areas, but its application to transfer learning and combinatorial optimization remains largely unexplored. In this talk, we will cover recent advancements for the integration of symbolic structures in transferring knowledge between agents in the context of reinforcement learning and planning for sequential decision-making. We will also explore the concept of dataless neural networks as a framework for integrating combinatorial optimization problems and learning models. We conclude with a vision for these areas and the technical challenges that follow.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Alvaro Velasquez is a program manager in the Innovation Information Office (I2O) of the Defense Advanced Research Projects Agency (DARPA), where he currently leads programs on neuro-symbolic AI. Before that, Alvaro oversaw the machine intelligence portfolio of investments for the Information Directorate of the Air Force Research Laboratory (AFRL). Alvaro received his PhD in Computer Science from the University of Central Florida in 2018 and is a recipient of the distinguished paper award from AAAI, best paper and patent awards from AFRL, the National Science Foundation Graduate Research Fellowship Program (NSF GRFP) award, the University of Central Florida 30 Under 30 award. He has authored over 60 papers and two patents and serves as Associate Editor of the IEEE Transactions on Artificial Intelligence. His research has been funded by the Air Force Office of Scientific Research.
Host: Jyo Deshmukh
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Wanrong Zhang (Harvard) - Enabling Interactivity to Move Differential Privacy Closer to Practice
Tue, Apr 25, 2023 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Wanrong Zhang, Harvard University
Talk Title: Enabling Interactivity to Move Differential Privacy Closer to Practice
Series: CS Colloquium
Abstract: With growing concerns about large-scale data collection and surveillance, the development of privacy-preserving tools can help alleviate public fears about the misuse of personal data. The field of differential privacy (DP) offers powerful data analysis tools that provide worst-case privacy guarantees. However, most of the existing tools in the differential privacy literature only apply to static databases with non-interactive analysis, which release query answers in a single shot. In practice, data analysts often need to perform a sequence of adaptive analyses on data arriving online, which raises the need for interactive data analysis. This development poses two major questions: 1. How can we design interactive mechanisms that strike a better trade-off between privacy and accuracy? 2. Can we combine multiple interactive mechanisms as building blocks to create a more complex DP algorithm?
In this talk, I will discuss some of my work that answers these questions. To answer the first question, I have created a wide set of tools for private online decision-making problems. I will present one example problem for handling online databases---differentially private change-point detection. Second, I will show the optimal composition theorems for composing multiple interactive mechanisms. My work is among the first to address this long-standing gap in the understanding of composition for differential privacy. I will conclude the talk with my future directions.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Wanrong Zhang is an NSF Computing Innovation Fellow in the Theory of Computing group at Harvard John A. Paulson School of Engineering and Applied Sciences. She is also a member of the Harvard Privacy Tools/OpenDP project. Her primary focus is to address new challenges introduced by real-world deployments of differential privacy. Before joining Harvard, she received her Ph.D. from Georgia Institute of Technology. She was selected as a rising star in EECS in 2022 and a rising star in Data Science in 2021. She is a recipient of the Computing Innovation Fellowship from CCC/CRA/NSF.
Host: Jiapeng Zhang
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Silvia Sellan (University of Toronto) - "Geometry +": A Tour of Geometry Processing Research
Tue, Apr 25, 2023 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Silvia Sellan, University of Toronto
Talk Title: "Geometry +": A Tour of Geometry Processing Research
Series: CS Colloquium
Abstract: From virtual reality to 3D printing, all the way through self-driving cars and the metaverse, today's technological advances rely more and more on capturing, creating and processing three-dimensional geometry. In this talk, we will show how geometry processing can empower other areas of Computer Science to find new research questions and solutions. Specifically, we will focus on our latest progress on realtime fracture simulation for video games, an algorithmic fairness analysis of gender in the Computer Graphics literature and a quantification of the uncertainty associated with several steps of the Geometry Processing pipeline
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Silvia is a fourth year Computer Science PhD student at the University of Toronto. She is advised by Alec Jacobson and working in Computer Graphics and Geometry Processing. She is a Vanier Doctoral Scholar, an Adobe Research Fellow and the winner of the 2021 University of Toronto Arts & Science Dean's Doctoral Excellence Scholarship. She has interned twice at Adobe Research and twice at the Fields Institute of Mathematics. She is also a founder and organizer of the Toronto Geometry Colloquium and a member of WiGRAPH. She is currently looking to survey potential future postdoc and faculty positions, starting Fall 2024
Host: Oded Stein
Location: Seeley G. Mudd Building (SGM) - 124
Audiences: Everyone Is Invited
Contact: Melissa Ochoa
