April 2026 - talks - lists.cs.princeton.edu

Emerging Frontiers in Computer Architecture and Systems Seminar: Wed. April 8, 4:30 PM, B205 EQuad
by Emily C. Lawrence 07 Apr '26

07 Apr '26

Design in Motion: Specialization as an Ongoing Architectural Journey Emerging Frontiers in Computer Architecture and Systems Seminar Date: Apr 8, 2026, 4:30 pm – 5:30 pm Location: B205 Engineering Quadrangle Speaker: Martha Kim, Columbia University Abstract For decades, computing advanced through Moore’s Law and Larus’ virtuous cycle, where hardware improvements automatically accelerated existing software, enabling higher abstraction and continuously fueling demand for faster chips. However, as emerging workloads like deep learning outstrip general-purpose CPU capabilities, the industry increasingly turns to specialized accelerators that sacrifice generality for performance, introducing fragmented programming models, scarce expertise requirements, and complex system integration challenges. As a result, while accelerators deliver significant speedups, they also create a less-virtuous cycle in which software brittleness, high development costs, and rapid workload evolution risk rendering even highly optimized hardware obsolete. There is a strong and understandable predisposition to design accelerators in an ad hoc, reactive manner, but this limits the architecture community’s ability to systematically shape long-term computing trends. Without a structured understanding of acceleration dynamics, it is difficult to anticipate end states, evaluate tradeoffs, or avoid wasted design effort. This talk will present a simple model of the acceleration process, grounded in practical experience, to enable more proactive and principled decisions that steer innovation toward sustainable and virtuous outcomes. Bio Martha Kim is an Associate Professor of Computer Science at Columbia University. Kim leads the ARCADE Lab where she conducts research in computer architecture, parallel programming, compilers, and low-power computing. Her work has explored low-cost chip manufacturing systems, reconfigurable communication networks, and fine-grained parallel application profiling techniques. Kim holds a PhD in Computer Science and Engineering from the University of Washington and a bachelors in Computer Science from Harvard University.

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CS Colloquium Speaker: Luay Nakhleh, Mon, April 6 at 12:10pm
by Emily C. Lawrence 06 Apr '26

06 Apr '26

CS Colloquium Speaker: Luay Nakhleh , Rice University Date & Time: Monday, April 6 - 12:10pm Location: CS 105 Host: Ben Raphael Event page: https://www.cs.princeton.edu/events/towards-building-phylogenetic-network-l… Webinar registration: https://princeton.zoom.us/webinar/register/WN_37fnuL32R1OvNSUqK2wsOw Title: Towards Building the Phylogenetic Network of Life: Models, Methods, and Challenges Abstract: In Charles Darwin’s Origin of Species, the evolutionary history of species takes the shape of a tree. Ever since, trees have been a mainstay of evolutionary biology and, indeed, almost all of biology. However, genomic data is revealing a picture of evolution that is too complex to be captured by a single, simple tree. Horizontal gene transfer (HGT) is rampant in the bacterial world. Hybridization is estimated to be involved in the evolutionary history of over 25% of all plants. Gene flow has occurred between humans and Neanderthals and its extent in many groups of eukaryotic species, including our own order—the primates. In the presence of horizontal gene transfer, hybridization, or gene flow, the evolutionary history takes the shape of a rooted, directed, acyclic graph (rDAG), commonly referred to as ‘phylogenetic network.’ The main computational task in this field is inferring these phylogenetic networks (the rDAG structure itself as well as parameters associated with its nodes and edges) from genomic sequences that are available only for the leaves of the rDAG. Accurate reconstruction of these networks facilitates multiple biological tasks including, but not limited to, understanding the mode of speciation, adaptation to environmental changes (e.g., antibiotic resistance in bacteria), gene functions, and how traits evolve. The talk is divided into three parts. I will first describe models and optimization criteria of increasing complexity for phylogenetic networks. I will then describe methods for inferring phylogenetic networks under these models and criteria. Finally, I will discuss computational challenges that currently limit the scalability of phylogenetic network inference to large data sets. Bio: Luay Nakhleh is the William and Stephanie Sick Dean of Engineering and Professor of Computer Science at Rice University in Houston, Texas. He received a B.Sc. degree in Computer Science (1996) from the Technion (Israel), a master’s degree in Computer Science (1998) from Texas A&M University, and a Ph.D. degree in Computer Science (2004) from The University of Texas in Austin. He conducts research in the areas of bioinformatics and computational biology, focusing mainly on questions in evolutionary biology and genomics. Luay is a recipient of multiple awards including the Department of Energy CAREER award, the National Science Foundation CAREER award, the Sloan Fellowship, and the Guggenheim Fellowship. He is Fellow of the International Society for Computational Biology, the American Institute for Medical and Biological Engineering, and the American Association for the Advancement of Science.

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Romina Mahinpei will present her MSE talk "AI Mediation for Discretionary Work: Reducing the Activation Energy of Feedback Provision" on Monday, April 6 , 2026 at 11am in CS 302.
by Gradinfo 02 Apr '26

02 Apr '26

Romina Mahinpei will present her MSE talk "AI Mediation for Discretionary Work: Reducing the Activation Energy of Feedback Provision" on Monday, April 6 , 2026 at 11am in CS 302. Thesis Committee: Manoel Horta Ribeiro (adviser), Andrés Monroy-Hernández (reader) Thesis Title: AI Mediation for Discretionary Work: Reducing the Activation Energy of Feedback Provision Thesis Abstract: AI systems are increasingly integrated into human workflows, often as mediators that shape human behavior and decision-making by generating intermediate artifacts. While prior work shows that AI mediation can improve task efficiency and accuracy, it remains unclear whether it can increase participation in discretionary but beneficial tasks that people often intend to perform but frequently skip. To explore this idea, we study one such task: feedback provision in a university course. We conduct a randomized field experiment in a 300-level machine learning course in which students' question-level submissions are assigned to either (1) a treatment condition where teaching assistants (TAs) receive AI-mediated feedback templates that they can choose to use or (2) a control condition where TAs do not receive any templates. Using behavioral logs, surveys, and interviews, we examine how AI mediation influences participation, interaction patterns, and downstream student experiences. We find that AI-mediated feedback templates significantly increase feedback provision and that they lower the small but persistent barriers required to initiate feedback, which we refer to as the task's activation energy, rather than reducing the total effort required to produce it. Importantly, this increased provision does not degrade downstream experience of students, suggesting that AI mediation can scale access to beneficial practices while maintaining satisfactory quality. Together, our results position AI mediation as a mechanism for increasing participation in discretionary work and surface important tradeoffs for the design of systems that position AI as mediators. All are welcome to attend.

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