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

Jennifer Lam will present her FPO "Rethinking Distributed Systems for High Contention Workloads" on Monday, 5/4/26 at 10am in CS 301
by Gradinfo 28 Apr '26

28 Apr '26

Jennifer Lam will present her FPO "Rethinking Distributed Systems for High Contention Workloads" on Monday, 5/4/26 at 10am in CS 301 and Zoom Zoom link: [ https://princeton.zoom.us/j/91095935061?pwd=heXLEXo7YrQLfGSdK8ZSVN6r5zv3AF.… | https://princeton.zoom.us/j/91095935061?pwd=heXLEXo7YrQLfGSdK8ZSVN6r5zv3AF.… ] Committee: Examiners: Wyatt Lloyd (adviser), Mae Milano, and Jialin Ding, Zachary Kincaid Readers: Wyatt Llyod and Haonan Lu (co-adviser, external at SUNY Buffalo) Abstract: Strongly consistent distributed systems support large-scale applications by sharding data across many machines to provide capacity greater than what can fit on a single machine. They are designed to run on asynchronous datacenter networks, where transaction requests may be dropped, delayed, or interleaved across shards. Interleaving requests is a root cause of performance bottlenecks. When transactions overlap across shards, their requests arrive in opposite orders. If at least one request is a write, it becomes challenging for serializable systems to guarantee that transactions appear in a matching total order across all shards; writes make the execution order non-commutative, so a read ordered before a write cannot observe it, while a read ordered after must. Consequently, interleaving may result in a request order on one shard contradicting another. Such conflicts degrade performance by consuming extra CPU, memory, and network round-trips. The frequency of conflicts increases under workload skew, where access patterns favor a subset of keys. Skew causes overlap between transactions accessing the same shards and, combined with writes, leads to high contention. Consequently, concurrency control protocols that performed well under low contention often perform poorly under high contention. Many use conflict-based ordering, which orders overlapping transactions using conflict resolution mechanisms such as wait-wait or wait-die. These mechanisms typically allow at most one transaction to proceed at a time, forcing others to wait or be aborted. Under high contention, this worsens overall latency and throughput. This dissertation recognizes that high-contention workloads are challenging for existing distributed systems and calls for new architectures robust against them. We introduce two such systems. The first is TurboDB, a process-ordered serializable distributed database that mitigates conflict on the hottest keys by co-locating them on a hotshard and efficiently serializing requests to that shard. It improves performance by up to an order of magnitude. The second is Whiptail, a strictly serializable distributed database that eliminates conflicts. It exploits improved datacenter networks and tightly synchronized clocks to enable clients to predetermine the total order of transactions, eliminating conflicts. Whiptail achieves up to an order-of-magnitude improvement in throughput on high-contention workloads.

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Arpon Basu will present his General Exam "Sparsifying Sums of Positive Semidefinite Matrices"on April 30, 2026 at 12pm in CS 402.
by Gradinfo 27 Apr '26

27 Apr '26

Arpon Basu will present his General Exam "Sparsifying Sums of Positive Semidefinite Matrices"on April 30, 2026 at 12pm in CS 402. The members of his committee are as follows: Pravesh Kothari (adviser), Zeev Dvir, and Ran Raz Link to Abstract and Reading List: [ https://docs.google.com/document/d/1Ue2YLJakw_u57pi0Fqa5uW57-DNx_GTV0bgifJD… | https://docs.google.com/document/d/1Ue2YLJakw_u57pi0Fqa5uW57-DNx_GTV0bgifJD… ] Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.

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Xuyang Cao will present his General Exam "Exploring a 5G Architecture for Efficient, QoE-Aware Video Conferencing" on May 4th, 2026 at 12:30pm in Friend Center 005 and Zoom.
by Gradinfo 27 Apr '26

27 Apr '26

Xuyang Cao will present his General exam "Exploring a 5G Architecture for Efficient, QoE-Aware Video Conferencing" on May 4th, 2026 at 12:30pm in Friend Center 005 and Zoom. Zoom Link: [ https://princeton.zoom.us/j/7013752837 | https://princeton.zoom.us/j/7013752837 ] Committee: Kyle Jamieson (adviser), Wyatt Lloyd, Jialin Ding Link to Abstract and Reading List: [ https://docs.google.com/document/d/1HH4njRWhRznymhE3xvzYRwWzwokmKZCWefbC2yP… | https://docs.google.com/document/d/1HH4njRWhRznymhE3xvzYRwWzwokmKZCWefbC2yP… ]

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Sowmya Thanvantri will present her General Exam "Generalized Combinations of Experts in Multi-Head Attention" on Monday, April 27, 2026 at 1:30 PM in CS 401.
by CS Grad Department 24 Apr '26

24 Apr '26

Sowmya Thanvantri will present her General Exam "Generalized Combinations of Experts in Multi-Head Attention" on Monday, April 27, 2026 at 1:30 PM in CS 401. Committee Members: Ryan Adams (advisor), Tom Griffiths, Karthik Narasimhan Abstract: The success of large language models today is attributed largely to the attention mechanism used in transformers, which assigns a measure of uncertainty over other tokens. This idea was extended to multi-head attention, allowing models to learn several different distributions of uncertainty over tokens. However, multi-head attention does not take full advantage of the different distributions because the outputs of the heads are concatenated and passed through a linear map, which prevents learning more expressive mappings. To enhance expressivity, a mixture model could aggregate information between these distributions. Alternatively, taking a Bayesian perspective, a product-based model across distributions could lead to a more accurate representation. To achieve both of these, we draw on ideas from mixture of experts (MoE) and product of experts (PoE) and propose a generalized combination of expert heads in multi-head attention to allow models to learn a richer representation of attention. Reading List: https://docs.google.com/document/d/1PaOPsZcP-rYhBvUYQBqSR-c_BAB3HIYI4rGCSE7… Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.

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Jiatong Yu will present her MSE thesis "On the Impossibility of Retrain Equivalence in Machine Unlearning" on Friday, April 24th, 2026 in CS 301 at 10:00am.
by gradinfo＠cs.princeton.edu 24 Apr '26

24 Apr '26

Jiatong Yu will present her MSE thesis "On the Impossibility of Retrain Equivalence in Machine Unlearning" on Friday, April 24th, 2026 in CS 301 at 10:00am. Advisor: Sanjeev Arora Reader: Kai Li All are welcome to attend. Abstract: {\em Machine unlearning} seeks to selectively remove the ``influence'' of specific training data on a model's outputs. The ideal goal is {\em Retrain Equivalence}---behavior identical to a model trained from scratch on only the retained data. This goal was originally formulated for models trained on {\em i.i.d.}\ data batches, but modern pipelines often involve multi-stage training, with each stage having a distinct data distribution and objective. Examples include LLM finetuning for alignment, reasoning ability, etc. Building on prior work that has shown retrain-equivalent behavior can be ill-defined due to forgeable datasets in mini-batch training, we study a complementary source of fragility that arises in multi-stage training. In an overparameterized linear regression model, we prove an exponential lower bound on {\em path-dependent divergence} under a family of unlearning objectives, unifying purely local gradient ascent and weakly local algorithms with retain-set regularization. Models trained on the same stages in different orders can be driven exponentially far apart during unlearning, and we show that this pointwise divergence implies violation of the distributional $(\varepsilon,\delta)$-certified unlearning definition. We then empirically measure the same kind of path dependence in LLM post-training across Llama and Qwen models (1B--14B) with gradient ascent, NPO, and SimNPO algorithms.Models finetuned via different orderings of identical training stages diverge in behavior during unlearning, with the degradation in GSM8K accuracy after unlearning varying by over $20\%$ across paths. We also observe that some learning paths consistently produce models that unlearn slowly while preserving higher retained utility---a phenomenon we term the \emph{recency effect}---and that the fate of probability mass during unlearning (e.g., paraphrasing vs.\ alternative concepts) is likewise path-dependent. Taken together, these results quantitatively demonstrate that local, path-oblivious unlearning in multi-stage pipelines can be highly sensitive to training history, adding a multi-stage perspective to the impossibility results and motivating the search for new unlearning desiderata and evaluations.

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Nanqinqin Li will present his FPO "System Idle Time Need Not Be Wasted" on Thursday, 5/7/2026 at 11:30 AM at 194 Nassau Street conference room.
by Gradinfo 23 Apr '26

23 Apr '26

Nanqinqin Li will present his FPO "System Idle Time Need Not Be Wasted" on Thursday, 5/7/2026 at 11:30 AM at 194 Nassau Street conference room. Committee: Examiners: Mike Freedman (adviser), Wyatt Lloyd, Asaf Cidon (Columbia University) Readers: Mike Freedman and Jialin Ding Abstract: Modern computer systems routinely lose performance and availability to time spent waiting on blocking events: CPUs stall on memory, services block on storage and networks, and stateful applications wait through conservative failover protocols. This dissertation studies a common question: when a system must wait, how can it safely run other useful work in parallel with that wait? The dissertation develops this theme through two systems projects. The first, Speculative Recovery, targets failover for stateful applications using recovery from disaggregated storage (REDS). REDS is resource efficient because only one instance runs during normal operation, but failover is slow because timeout and recovery run sequentially. Speculative Recovery starts backup recovery as soon as the primary appears unavailable, while letting the primary continue in case it recovers first. The work introduces disk superposition and the super/collapse abstractions, allowing temporary divergence of disk state while ensuring only one version becomes externally observable. The design includes collocated-clone for near-normal clone performance and dirty-bit-based rules for correctness. Implemented in Ceph and evaluated with MySQL, PostgreSQL, and MariaDB, the approach improves failover while preserving the resource efficiency of REDS. The second project, LiteSwitch, targets sub-microsecond CPU stall cycles caused by CXL-attached memory. CXL expands memory capacity but increases access latency and amplifies memory-induced stalls. Existing harvesting techniques are mismatched: profiling-based methods struggle with CXL latency variation, and interruptbased delivery is too expensive for hundreds-of-nanoseconds windows. LiteSwitch uses a lightweight hardware-software co-design. On the hardware side, location-dependent memory branching (LDMB) detects long-latency accesses online and delivers control via direct branching. On the software side, Bundled Handoff provides fast scavenger selection, and xstate-aware context switching avoids unnecessary SIMD/FP iiisave/restore overhead. Evaluation shows substantial slowdown reductions across representative workloads and CXL latency settings. Taken together, these projects show that idle time can be an opportunity rather than unavoidable loss. The central lesson is that useful parallelization with waiting is effective only when systems codesign performance mechanisms with correctness constraints. By combining overlap with careful control over observability, ordering, and runtime overhead, this dissertation demonstrates practical ways to improve both availability and performance in modern systems.

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Carlos Jimenez will present his FPO "Agent in the Shell: Establishing Agentic Software Engineering" on Tuesday, April 28, 2026 at 3:30 in Friend 110.
by CS Grad Department 23 Apr '26

23 Apr '26

Carlos Jimenez will present his FPO "Agent in the Shell: Establishing Agentic Software Engineering" on Tuesday, April 28, 2026 at 3:30 in Friend 110. The members of Carlos’ committee are as follows: Examiners: Karthik Narasimhan (Adviser), Danqi Chen, Sanjeev Arora Readers: Karthik Narasimhan, H. Sebastian Seung A copy of his thesis is available upon request. Please email gradinfo(a)cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend the talk. Abstract follows below: Neural language models were put to work on code almost as soon as they were useful for anything. Their capabilities have grown from auto-complete to chat interactions, and we establish a third paradigm, in which language model work as agents that autonomously complete full software engineering tasks. The first benchmarks for code language models focused on isolated function synthesis: a specification goes in, a function comes out. Real software engineering is a different activity; it requires directory navigation, running code, and editing files that already exist inside a larger system. We propose an evaluation task that measures progress using precise execution verification and demonstrate the benefits of the agentic approach, in which the model works on the repository interactively. We first introduce SWE-bench, a benchmark drawn from real GitHub issues across twelve open-source Python repositories, each scored by whether a system's patch passes the repository's own tests. The strongest retrieval-augmented baselines resolve under 2% of the test set, establishing that single-shot generation is not enough. We then present SWE-agent, an agent that gives a language model interactive access to the repository through the shell and an interface specially designed for software engineering agents. With model weights fixed, SWE-agent resolves 12.47% of SWE-bench, and ablations isolate the interface as an axis of progress separable from model scale. We finally introduce SWE-bench Multimodal, which reapplies the collection pipeline to visual JavaScript repositories and shows that the task definition transfers across language and modality while systems tuned to Python largely do not. In the years since its release, SWE-bench has been widely adopted as a core evaluation target for frontier models, and reported resolve rates have risen above 90% on a human-validated subset as of April 2026. We analyze the work that has followed and find that model improvement has far outpaced scaffold design: a minimal bash-only agent tracks the frontier within a few points, reflecting both raw capability gains and the tailoring of frontier models toward agentic software engineering as an explicit training target.

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Tim Kosfeld will present his MSE thesis "Supervised linear modeling and integration of ATAC-seq" on Monday, April 27th, 2026 in Bendheim House 103 at 10:30am.
by CS Grad Department 23 Apr '26

23 Apr '26

Tim Kosfeld will present his MSE thesis "Supervised linear modeling and integration of ATAC-seq" on Monday, April 27th, 2026 in Bendheim House 103 at 10:30am. Advisor: Yuri Pritykin Reader: Ben Raphael All are welcome to attend. Abstract: Single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq) has been widely used to profile chromatin accessibility and explore the epigenomic properties in heterogeneous cell populations. Moreover, transcription factors (TFs) bind to accessible sites, and scATAC-seq analysis could associate TF binding specificities, typically available as sequence motifs, with variance in chromatin accessibility. Here we present a new supervised TF motif regression method, MotifReg, for computational analysis and interpretation of scATAC-seq data. We comprehensively compare MotifReg with other popular methods for motif and sequence analysis in scATAC-seq data and demonstrate superior performance and benefits of MotifReg. MotifReg can efficiently analyze large-scale scATAC-seq datasets and learn interpretable associations between TF motifs and accessible peaks. The resultant low-dimensional cell representations in the TF activity space facilitate downstream cell type-specific analyses of scATAC-seq data. The supervised formulation in MotifReg enables ranking TF motifs by significance of their association with chromatin accessibility, implicating cell type-specific TFs. Furthermore, by comparing MotifReg performance across data analysis settings, we develop guidelines and best practices for scATAC-seq peak calling, data preprocessing and normalization. In sum, MotifReg is a convenient and statistically sound new open source computational method for scATAC-seq data analysis and characterization of genome-wide TF activities.

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Ryan Oet will present his MSE thesis "Organizing the Organizer: Developing Digital Tools to Support Gig Worker Labor Advocacy" on Friday, April 24th, 2026 in Friend 110 at 11:30am and Zoom.
by CS Grad Department 23 Apr '26

23 Apr '26

Ryan Oet will present his MSE thesis "Organizing the Organizer: Developing Digital Tools to Support Gig Worker Labor Advocacy" on Friday, April 24th, 2026 in Friend 110 at 11:30am and Zoom. Zoom Link: https://princeton.zoom.us/j/94830827553 Advisor: Andrés Monroy-Hernández Reader: Parastoo Abtahi All are welcome to attend. Abstract: Algorithmic management practices in the ridehail industry cause financial, physical, and emotional harm to drivers. New regional policies in the United States mandate partial protections for platform workers, including minimum pay guarantees, paid sick leave, and appeals processes for wrongful deactivations. Organizers require easily accessible data and efficient workflows to effectively advocate for workers, enforce legal compliance from platforms, and support drivers navigating bureaucratic systems. Existing workflows are tedious and error-prone. To address this challenge, we collaborated with the labor association Drivers Union to design and implement automated data access tools for policy advocacy and sick leave auditing. We conducted semi-structured evaluative interviews with organizers and, through thematic analysis, found that these tools accurately target their current operational needs. Organizers also anticipate that the tools will substantially expand the efficiency and scale of worker-support processes. Looking ahead, they expressed a clear need for descriptive analytical solutions to answer increasingly complex questions and new methods of data collection to capture key metrics missing from existing aggregation approaches. We contribute reflections on researcher-organizer collaborations in the ridehail industry, two computational artifacts for supporting labor advocacy, and directions for further work in this space.

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Lana Glisic will present her MSE thesis "LiDAR for Children's Coding and Creativity in Augmented Reality" on Friday, April 24th, 2026 in Green Hall 3-S-14 at 11:00am.
by CS Grad Department 23 Apr '26

23 Apr '26

Lana Glisic will present her MSE thesis "LiDAR for Children's Coding and Creativity in Augmented Reality" on Friday, April 24th, 2026 in Green Hall 3-S-14 at 11:00am. Advisor: Andrés Monroy-Hernández Reader: Parastoo Abtahi All are welcome to attend. Abstract: Abstract: Children often encounter Augmented Reality (AR) through social media and video games, where engagement is largely limited to consumption rather than creation. An AR authoring environment is presented that enables the construction of interactive experiences by programming a virtual character to navigate and climb obstacles within the physical environment. The system leverages LiDAR-based depth sensing to reconstruct surrounding geometry and provides a block-based programming interface to define character behaviors and interactions. By combining spatial sensing with visual programming, expanded opportunities for coding, creativity, and the development of spatial reasoning are supported. A user study involving 6 children aged 5-11 in the United States was conducted to evaluate the usability and engagement of the LiDAR-driven features. Participants successfully created interactive AR narratives featuring virtual characters that traversed and climbed real-world structures in three-dimensional space. The implementation and potential applications of the system in educational contexts are examined. Findings indicate that the approach promotes learning through creative exploration while also revealing challenges related to the accuracy and robustness of real-time scene reconstruction and geometric traversal.

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