קולוקוויום וסמינרים

Distributed asynchronous systems often require explicit synchronization to ensure the correct implementation of shared objects. In this talk, I introduce the Delaying the Future approach for reasoning about the ordering of events in distributed executions. Its key idea is that, under certain conditions, events can be postponed without any process noticing the change.

I will show how this technique leads to characterizations of communication requirements in asynchronous message-passing systems and in shared-memory systems under the TSO memory model. The Delaying the Future approach provides a unified way to understand the synchronization required by linearizable implementations of common objects such as registers, stacks, and snapshots.

Powered prosthetic hands are frequently abandoned due to limited dexterity and unintuitive control. Most commercial devices rely on surface electromyography (sEMG) and support only grasping gestures, falling short of the fine, continuous finger motions required for everyday tasks such as typing on a keyboard or playing a musical instrument.

In this talk, we present a series of studies addressing this gap from three complementary angles. First, we introduce an end-to-end system that infers fine finger motions in real time by modeling the hand as a robotic manipulator and encoding muscle dynamics from ultrasound video. Second, we present a low-cost, 3D-printed prosthetic hand engineered for enhanced dexterity, featuring adjustable finger spacing, a two-degree-of-freedom wrist, and independent finger pressing. Third, we propose SonoRank, a step towards calibration-free finger flexion detection from forearm ultrasound.

SonoRank learns to rank ultrasound sequence pairs by relative motion magnitude, then fine-tunes using a rest reference to classify active flexion across all five fingers without user training data. Together, these papers advance prosthetic control toward practical, calibration-free deployment with fine-finger activation, bringing us closer to restoring native hand function for individuals with upper-limb amputation.

Machine learning has become a central tool in molecular design, enabling the generation and optimization of candidate therapeutics across vast chemical spaces; however, real-world drug development is governed by multiple practical constraints that are rarely addressed jointly in computational models. Beyond optimizing chemical and pharmacological properties, viable molecules must avoid intellectual property conflicts, exhibit tissue-specific biological activity, and remain relevant when translating from preclinical animal models to humans—constraints that, if ignored, often yield candidates that are computationally promising yet infeasible in practice.

In this work, we develop machine learning frameworks that explicitly incorporate these legal, biological, and translational constraints into molecular design and evaluation, demonstrating that constraint-aware approaches produce candidates that are more realistic and better aligned with the requirements of real-world drug discovery.

Consider a model in which we can access a parity function through random uniformly distributed labeled examples in the presence of random classification noise. In this thesis, we study learning in this model and show that approximating the number of relevant variables of a parity function is as hard as properly learning it.

More specifically, let $\gamma : \mathbb{R}^+ \to \mathbb{R}^+$ be any strictly increasing function satisfying $\gamma(x) \ge x$. In our first result, we show that from any polynomial-time algorithm that returns a $\gamma$-approximation $D$ (i.e., $\gamma^{-1}(d(f)) \leq D \leq \gamma(d(f))$), of the number of relevant variables~$d(f)$ of any parity function $f$, we can, in polynomial time, construct a solution to the long-standing open problem of polynomial-time learning $k(n)$-sparse parities (parities with $k(n)\le n$ relevant variables), where $k(n) = \omega_n(1)$.

In our second result, we show that from any $T(n)$-time algorithm that, for any parity $f$, returns a $\gamma$-approximation of the number of relevant variables $d(f)$ of $f$, we can, in polynomial time, construct a $poly(\Gamma(n))T(\Gamma(n)^2)$-time algorithm that properly learns parities, where $\Gamma(x)=\gamma(\gamma(x))$.

If $T(\Gamma(n)^2)=\exp({o(n/\log n)})$, this would resolve another long-standing open problem of properly learning parities in the presence of random classification noise in time~$\exp({o(n/\log n)})$.

Recent advances in computer vision, foundation models, and transformer architectures have transformed computational pathology, enabling deep learning systems to extract clinically actionable information directly from digitized tissue slides. This seminar explores how these technologies come together in modern digital pathology frameworks, and presents two studies demonstrating their clinical impact.

The first study addresses a critical diagnostic gap in low-resource settings, showing that convolutional neural networks applied to Giemsa-stained bone marrow aspirates can predict B/T-cell lineage and ETV6–RUNX1 translocation status in pediatric acute lymphoblastic leukemia — tasks that traditionally require expensive molecular assays unavailable in many parts of the world.

The second study tackles overtreatment in breast cancer. The TAILORx trial established that adjuvant chemotherapy can be spared for postmenopausal HR+/HER2− node-negative breast cancer patients with a 21-gene Recurrence Score (RS) of 11–25. However, among premenopausal women with RS 16–25, a small benefit from chemotherapy could not be ruled out. Consequently, guidelines suggest considering chemotherapy for this population, creating a therapeutic dilemma and leading to widespread overtreatment of patients who may not benefit from chemotherapy. Using deep survival analysis on H&E whole-slide images, we identify which women in this group truly benefit from adjuvant chemotherapy. Our model stratifies 76% of this population as low-risk, for whom chemotherapy can be safely omitted, while correctly identifying the high-risk subset that benefits from treatment.

Together, these works illustrate how digital pathology can democratize access to precision diagnostics and enable more personalized, less toxic cancer care.

Self-Organizing Maps are unsupervised machine learning algorithms used primarily for clustering and dimensionality reduction. They map high-dimensional data for improved interpretability using a competitive learning approach. Each data point is mapped with some distance to a point on the map. These distances, called activations, show underlying trajectories in the data that can be explored. This is done in two studies.

The first study seeks vulnerabilities in public data by using self-organizing maps to bring people's sensitive attributes to the surface. This can reveal sensitive attributes with low correlation to the data are recoverable, thus leaving people's personal data at risk.

The second study looks into finding underlying trajectories in cell types. Cell type trajectory inference, also called pseudotime analysis, maps developmental and state changes in cells. Using trajectory inference to order single-cell omics data is used in stem cell differentiation, disease progression, and cell response to stimuli among other things.

These studies open the door to new research into applications of self-organizing maps from a 3rd dimension.

A "named page" is a memory page whose content originates from and is backed by a file. Because named pages are regularly read from and written to persistent storage, filesystems strive to preserve file content contiguity, thereby enabling sequential I/O, which can be much faster than random I/O. No analogous effort to preserve contiguity exists for "anonymous pages," which hold unnamed data such as stack or heap bytes. Consequently, swapping a region of anonymous pages in or out can be much slower than reading or writing a region of named pages.

We observe (1) that the main advantage of the existing swap mechanism is high swap area utilization, since any anonymous page can be placed at any offset within the swap file, so there is no fragmentation; but (2) that secondary storage is commonly underutilized, so the cost of random I/O may be unwarranted. We therefore propose "named swapping," which associates each anonymous region with its own (swap) file and thus benefits from the underlying filesystem's efforts to maintain contiguity, improving swap performance by up to an order of magnitude. A key challenge we address is anonymous pages shared across multiple regions due to fork-based copy-on-write.