Publications
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
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1 - 15 of 11599 publications
When Android Apps Request Location with a Reason: A Developer Study and User Behavior Experiment
May Smith
Aman Jain
Proceedings on Privacy Enhancing Technologies (2027)
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Recent reports have highlighted how mobile apps share user location data with third parties, risking user privacy and platform trust. Although location data is highly sensitive, when users grant apps location access, they may not know the full extent to which it is used. We study how requiring Android apps to show a reason for location access could impact developers, users, and the platform.
We surveyed 323 Android app developers and found most supported such a requirement. The majority said it would have a positive impact on user privacy, trust for apps, and trust for Android, where impact on user trust for Android correlated most strongly with support. Many developers also said the intervention would increase the number of users granting location access. Yet their open-ended comments also revealed consistent concerns, such as apps providing dishonest reasons and platform verification.
To study the impact on user behavior, we conducted a randomized controlled experiment with 2579 US Android users. We tested how users' decisions to grant location access were impacted by app type, whether reasons were included in the requests, and the content of the reasons, including monetization. We did not find the reasons impacted users' decisions; decisions were instead driven by app type and demographics. Yet we did find the reasons could have a positive impact on user perception for the platform when the reasons did not include using data for ads. Our findings provide insights into developers' willingness to implement privacy-enhancing changes, and expose limits to improving user privacy by simply adding information to user interfaces.
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A 3D Scene Graphs Survey: Open Challenges and Future Directions
Dennis Rotondi
Francesco Argenziano
Sebastian Koch
Nathan Hughes
Martin Büchner
Johanna Wald
Lukas Schmid
Daniele Nardi
Abhinav Valada
Liam Paul
Luca Carlone
Kai Arras
Annual Review of Control, Robotics, and Autonomous Systems (ARCRAS), 10 (2027) (to appear)
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3D Scene Graphs (3DSGs) have emerged as a powerful representation for spatial AI by combining geometric grounding with semantic and relational abstractions of the environment. Their expressiveness has made them relevant to a broad range of problems in robotics and computer vision, including mapping, task and motion planning, scene understanding, and many others. However, the field remains fragmented: different communities adopt distinct formulations, construction pipelines, and evaluation protocols, making it difficult to compare methods, identify common assumptions, and assess remaining challenges for robust real- world deployment. This survey provides a unified and critical review of 3DSGs, with particular emphasis on open challenges and future directions. We first formalize 3DSGs under a common definition and analyze the principal modeling choices that characterize existing formulations, including node and edge attributes, hierarchical structure, dynamic scene representations, and affordance-aware extensions. We then review how 3DSGs are constructed from raw sensory observations, covering both learning-oriented and construction-oriented systems. Finally, we examine downstream applications and evaluation strategies, from intrinsic graph quality to task-level performance. To support the community, we also provide a dedicated website that organizes and extends the surveyed works.
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We study a quantized prefix estimator for inner products that turns a randomly rotated TurboQuant-style representation into a cheap Johnson–Lindenstrauss-like search signal. The idea is simple: rotate the vectors once, keep only a short prefix of coordinates for fast scoring, and quantize the database-side prefix with an unbiased scalar quantizer. We prove that this estimator is unbiased and that its error separates cleanly into two interpretable sources: prefix truncation from using only r coordinates, and quantization error from using b bits per coordinate
This separation is useful in systems because the prefix can be exposed as a lightweight filter without building a separate projection index. In ParlayANN graph search, a 64-coordinate truncated view of existing TQ4 codes can replace a separately stored JL256 filter before full-precision reranking, adding only prefix-scale and query lookup-table bookkeeping. In k-means, the same estimator accelerates the dominant point–centroid assignment kernel while preserving exact centroid norms. Empirically, the truncated-TQ filter tracks the JL recall–throughput frontier across five graph-search datasets while reusing the quantized representation already present in the index.
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This study examines the psychological and ethical implications of generative-AI chatbot use among youth, introducing the CTRL framework (Cognitive Trust, Reliance, and Learning Diminution) to explain how repeated use fosters cognitive offloading and reduced verification behavior. Survey data from 420 participants analyzed through factor analysis and structural equation modeling reveal that higher trust predicts greater reliance and diminished critical evaluation, alongside elevated concerns around privacy and academic integrity. Findings highlight the need for AI literacy and responsible design to mitigate unintended cognitive impacts.
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The Perfection Paradox: From Architect to Curator in AI-Assisted API Design
JJ Geewax
David R Karger
Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems (CHI EA '26), ACM, Barcelona, Spain, TBD
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Enterprise API design is often bottlenecked by the tension between rapid feature delivery and the rigorous maintenance of usability standards. We present an industrial case study evaluating an AI-assisted design workflow trained on API Improvement Proposals(AIPs). Through a controlled study with 16 industry experts, we compared AI-generated API specifications against human-authored ones. While quantitative results indicated AI superiority in 10 of 11 usability dimensions and an 87% reduction in authoring time, qualitative analysis revealed a paradox: experts frequently misidentified AI work as human (19% accuracy) yet described the designs as unsettlingly “perfect.” We characterize this as a “Perfection Paradox”—where hyper-consistency signals a lack of pragmatic human judgment. We discuss the implications of this perfection paradox, proposing a shift in the human designer’s role from the “drafter” of specifications to the “curator” of AI-generated patterns.
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Optical Transport Optimized for AI Infrastructure - Fiber Is The New Wavelength (𝝺)
Ahsaan Yousaf
2026
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With ML/AI as growth drivers for WAN, highlight unique challenges and drive the industry for next gen coherent pluggable and embedded terminal optics solutions with performance and RSP tradeoffs in mind.
Together with other hyperscalers, drive the industry (system and component vendors) to develop highly space and power efficient ILA solutions, compatible with our existing infrastructure. The right technology evolution will have to consider overall TCO and operational/practical aspects such as power consumption and failure domains.
Zero Touch Networking: Highlighting importance of standard open models and APIs across IP/Optical layers across module/host vendors enabling vendor agnostic config, telemetry for autonomous operations
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GroupDPO: Memory-Efficient Group-Wise Direct Preference Optimization
Jixuan Leng
Hsiang-Fu Yu
Vinod Raman
Inderjit Dhillon
The 2026 Conference on Empirical Methods in Natural Language Processing
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Preference optimization is widely used to align Large Language Models (LLMs) with preference feedback. However, most existing methods train on a single positive-negative pair per prompt, discarding additional supervision available in preference datasets that typically contain multiple candidate responses. Motivated by this limitation, recent work explores group-wise preference optimization, which jointly contrasts multiple responses for the same prompt, but its empirical behavior and scalability remain underexplored due to the memory overhead of group-coupled objectives. In this work, we present a unified empirical and systems study of group-wise preference optimization and develop a memory-efficient implementation for group-coupled objectives. By instantiating first-order linearization with objective-specific per-response coefficients, our implementation preserves first-order gradients while decoupling samples during backpropagation, substantially reducing peak memory usage and enabling scalable training with larger groups. Across offline and online settings, we show that leveraging multiple responses consistently outperforms single-pair training. Furthermore, incorporating a negative log-likelihood (NLL) term on positive responses is critical for both performance gains and training stability.
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CoDaS: AI Co-Data-Scientist for Biomarker Discovery via Wearable Sensors
Juro Gottweis
CJ Park
Salman Rahman
Ahmed Metwally
Hong Yu
Ivor Rendulic
Yuzhe Yang
Petar Sirkovic
Daniel McDuff
Shwetak Patel
Nicolas Stroppa
Yubin Kim
Mark Malhotra
Orson Xu
Sam Schmidgall
Tim Althoff
Elahe Vedadi
Cynthia Breazeal
Hae Won Park
(2026)
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The Private Aggregation of Teacher Ensembles (PATE) framework enables privacy-preserving machine learning by aggregating responses from disjoint subsets of sensitive data. Adaptations of PATE to tasks with inherent output diversity such as text generation, where the desired output is a sample from a distribution, face a core tension: as diversity increases, samples from different teachers are less likely to agree, but lower agreement results in reduced utility for the same privacy requirements. Yet suppressing diversity to artificially increase agreement is undesirable, as it distorts the output of the underlying model, and thus reduces output quality.
We propose Hot PATE, a variant of PATE designed for diverse generative settings. We formalize the notion of a diversity-preserving ensemble sampler and introduce an efficient sampler that provably transfers diversity without incurring additional privacy cost. Hot PATE requires only API access to proprietary models and can be used as a drop-in replacement for existing Cold PATE samplers. Our empirical evaluations corroborate and quantify the benefits, showing significant improvements in the privacy–utility trade-off on evaluated in-context learning tasks, both in preserving diversity and in returning relevant responses.
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Mobility-Embedded POIs: Learning What a Place Is and How It’s Used from Human Movement
Shushman Choudhury
Shang-Ling Hsu
Cyrus Shahabi
Forty-third International Conference on Machine Learning (2026)
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Recent progress in geospatial foundation models (GeoFMs) has highlighted the
importance of learning general-purpose representations for real-world locations,
particularly Points of Interest (POIs) where human activity concentrates. Yet, ex-
isting POI representations remain largely static, drawing from textual metadata
(e.g., category labels, descriptions) and spatial attributes (e.g., coordinates, neigh-
borhood context), all of which describe what a place is, but not how it is actu-
ally used. We argue that human mobility provides a complementary and dynamic
signal, capturing real-world visitation patterns that reveal how places function in
practice. To this end, we introduce Mobility Embedded POIs (ME-POIs), a
pretraining framework that learns POI representations directly from sequences of
human visits. Each visit is encoded as a contextualized embedding that captures
the POI’s static attributes as well as its temporal and sequential context, includ-
ing when the visit occurs and which visits surround it. These visit embeddings
are aligned with learnable POI embeddings via a contrastive objective, grounding
POI representations in their real-world usage patterns. To address the long tail of
sparsely visited POIs, we transfer visitation distributions from data-rich anchors
to sparse locations, leveraging multi-scale spatial proximity to capture local and
regional patterns, and functional similarity to enable transfer across semantically
related POIs. We demonstrate the utility of ME-POIs for a set of automated map
enrichment tasks, critical in geospatial intelligence. We show empirically that
by embedding visitation dynamics, ME-POIs outperform text- and location-only
baselines, proving that mobility-informed embeddings provide a stronger founda-
tion for modeling place function and change.
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Latent Diffusion Models (LDM) face three compounding efficiency challenges in practical deployment: i) the temporal latency of iterative sampling; ii) the architectural overhead of heavy backbone parameter counts; and iii) the spatial cost of high-dimensional latent grids. While recent acceleration methods have made substantial progress on temporal distillation and architectural compression, the spatial axis is often inherited from the teacher tokenizer and treated as fixed. In this work, we recast latent resolution as an optimizable efficiency axis and introduce a unified framework that optimizes all three dimensions simultaneously. We introduce a novel strategy of Score-Compatible Tokenizer Distillation (SCTD), which leverages score-matching principles to align a spatially compact latent space with the induced distribution of a frozen, powerful teacher model, distilling the teacher's generative prior into a compressed, lower-dimensional compatible manifold. With flexibility provided by SCTD, we can surrogate a computationally heavy teacher backbone with a lightweight student architecture operating strictly within this new compressed space. Finally, we apply temporal distillation to collapse the sampling trajectory, producing a one-step generator that operates at peak efficiency. Our method yields a student generator outperforming existing single-axis acceleration methods in efficiency and throughput, while maintaining competitive generation quality. With reduced peak memory usage and latency, our method enables resource-constrained deployment and high-volume serving of high-fidelity LDM.
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The planning horizon in a Markov Decision Process (MDP) determines how far into the future an agent reasons. In practice, shorter horizons are commonly associated with policies that exhibit simpler or more interpretable decision-making behavior. In this paper, we establish a formal connection between environmental stochasticity and planning horizon in MDPs. We show that for broad classes of transition noise, solving a noisy MDP can be formally related to solving a noise-free MDP with a shorter effective discount factor, leading to identical optimal policies in some cases and near-optimal ones in others. We further characterize settings in which this correspondence breaks down, clarifying when horizon-based interpretations of noise are not valid. These results, which are supported by both theory and experiments, also give some insight into the common practice of using smaller discount factors for reinforcement learning than those that can be justified by standard modeling interpretations.
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Can AI systems be aligned to human values? The popularization of large language models (LLMs) and multi-modal foundation models
has seen a commensurate rise in ways these models cause harm, spanning areas from toxic speech and hallucinations to AI agents
executing unauthorized actions. Given that these models are probabilistic and general-purpose by nature, it is impossible to enumerate
all possible uses and outputs of the model to reach a fully aligned end state. Within the field of AI safety, these harmful instances are
often framed as “the alignment problem,” of models being “misaligned” with human values. Researchers have responded by pursuing
applied and theoretical AI “value alignment” efforts, often without specifying what they mean by human values. How does the field of
AI value alignment conceive of human values? How are these conceptions of values technically operationalized and evaluated? What
does the emergent theory of value from this field signify for the future of AI?
The study of human values has long been part of many academic disciplines outside of computer science, yet these disciplines
are seldom consulted in AI alignment. Building on the theoretical insights of Zhi-Xuan’s (2024) "preferentist paradigm" critique, we
conduct a review of influential AI alignment literature. We also draw from conceptions of human values from philosophy, anthropology,
and sociology, to create an analytical schema. We annotated 94 AI value alignment research papers to discern their implicit theory of
values in AI. The majority do not define values, relying heavily on “preferences” as a stand-in that runs the risk of reducing complex,
culturally situated concepts down to binary choices. As researchers dispense with using human annotators for model training and
evaluation, turning instead to synthetic data and LLM-as-a-judge approaches to aligning and evaluating models, we identify the
potential to close off alternative methods for contesting and enacting values in foundation models. Overall, value alignment is often
reduced to an exercise in utility maximization, which we argue abstracts human values away from their lived context. In making AI
value alignment’s philosophical commitments explicit, we seek to bring greater specificity and under-explored perspectives into the
debate on whether and how AI can address human values
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**Agentic Engineering** is the rigorous discipline of treating Large Language Models as semi-autonomous systems that execute complex, multi-step workflows (trajectories) based on verifiable specifications, rather than using them as simple autocomplete engines.
Here is a brief summary of its core principles:
* **Main Goals:** It aims to maximize the agent's autonomous run-time, multiply a single engineer's impact by running parallel tasks, and offload tedious boilerplate coding.
* **The "Harness":** A raw model is virtually useless without heavy investment in a harness—comprising tools, system prompts, and strict guardrails—to reliably guide the model and enforce coding policies.
* **Loss of Micro-Control:** Engineers must surrender idiosyncratic stylistic preferences; if the agent's code passes automated linters and tests, it is accepted.
* **Meta-Debugging:** When failures occur, engineers no longer fix code syntax. Instead, they debug the workflow itself—adjusting the agent's tools, search queries, or prompt constraints to ensure repeatable success.
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While the Latin script is used informally by speakers of many languages with more complex native scripts, high quality Latin script corpora for such languages that reflect actual natural romanizations are scarce and often difficult to collect. In this work, we propose a method for mining romanized language corpora in languages for which we do not have any pre-existing samples of naturally romanized text, focusing on Tigrinya as a test case. First we examine the efficacy of learning romanizations for a language based on observed romanizations in other languages that use the same native script. We then extrinsically assess such methods by using a romanization model trained on Amharic data to bootstrap coverage of romanized Tigrinya in a language identification system. Manual evaluation by two L1 and one L2 Tigrinya speakers suggests our method extracts romanized Tigrinya text with acceptably high precision. We release code to run our mining pipeline on public web corpora, such as MADLAD-400.
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