קולוקוויום וסמינרים

Scaffold decoration is a central task in structure-based drug design: starting from a fixed molecular core, the goal is to add peripheral fragments that improve target binding while preserving drug-like properties. Existing scaffold-completion methods often ignore the three-dimensional structure of the target protein, whereas structure-aware generative models are typically designed for unconstrained de novo generation rather than scaffold-preserving lead elaboration. We introduce MODOLO (Molecular Docking-based Lead Elaboration), an attachment-point-centric generative model for target-aware scaffold decoration. Given an incomplete scaffold, its docking pose, and the surrounding protein pocket, MODOLO represents the protein-ligand complex as a sparse heterogeneous 3D graph and uses grouped vector attention to encode local geometric interactions around each attachment point. A VAE-based Transformer then generates SMILES fragments conditioned on this pocket-aware representation, which are reattached to the scaffold to form complete molecules. On CrossDock2020, MODOLO achieves the best overall performance across affinity, pose score, scaffold similarity, synthetic accessibility, and success rate, improving success from 0.17 for the strongest non-random baseline to 0.41. The model also generalizes to an out-of-distribution PoseBusters subset, supporting its utility for structure-based lead elaboration on unseen targets.

DNA-based storage offers exceptional density and durability, but its scalability depends on efficiently synthesizing many strands in parallel under physical constraints. We study two complementary models capturing limitations arising from nucleotide availability and spatial interactions in large strand arrays.

First, we introduce complex synthesis sequences, a hybrid framework that bridges enzymatic synthesis, which permits strand-specific nucleotide additions, and photolithographic synthesis, which applies common additions across many strands. We characterize the achievable information rate through an analogue of the deletion ball, derive tight expressions for the maximal rate and its asymptotic behavior, and present a dynamic programming algorithm for computing an optimal synthesis sequence for known strands.

We then consider a two-dimensional array model motivated by optical and chemical coupling, in which strands follow a fixed global synthesis sequence and at most one strand per row may advance in each cycle. For two strands in a single row, we show that the laggard-first policy is asymptotically optimal among online policies without look-ahead, while one-symbol look-ahead yields a strict improvement in the binary case.

We further show that even globally optimal scheduling incurs an unavoidable expected overhead that grows linearly with strand length. These results are complemented by an offline dynamic programming algorithm and a constant-redundancy binary coding scheme with a deterministic worst-case synthesis-time guarantee. Together, the two works establish a unified framework for understanding the information-theoretic and scheduling limits of constrained DNA synthesis.

My research focuses on mechanistic interpretability-understanding and improving multi-modal models by analyzing their internal computations. I will present recent studies, including Diffusion Lens (ACL 2024), Padding Tone (NAACL 2025), and Follow the Flow (ACL 2026), and DeLeaker (ICL 2026). These works propose methods to reveal what text-to-image models learn and how computations are performed internally. These insights are then applied to solve concrete problems, such as semantic leakage, paving the way for more reliable and transparent generative AI.

Machine learning models are increasingly deployed in high-stakes applications where reliable uncertainty quantification is as important as predictive accuracy. In such settings, learning algorithms must not only generate accurate predictions but also communicate their uncertainty in a statistically principled manner. 

The first part of the research investigates predictive inference under imperfect training data. It first analyzes the robustness of conformal prediction and risk-controlling procedures to noisy labels, identifying conditions under which these methods construct reliable estimates with theoretical guarantees despite corrupted observations. Building upon these theoretical insights, the research studies predictive inference where additional features, referred to as privileged information, are available during training but unavailable at deployment. We develop calibration methods that account for the distribution shifts induced by missing or corrupted data: one based on distribution re-weighting and one relying on uncertainty-preserving imputation. We analyze the robustness of weighting-based calibration approaches to inaccuracies in the estimated weights and propose a triply robust calibration strategy that provides statistically valid prediction sets under complementary assumptions.

The second part considers uncertainty quantification for modern interactive AI systems, with a particular focus on large language models (LLMs). We formulate the evaluation of model safety and utility of agentic systems or LLMs as a time-to-event prediction problem and develop conformal survival methods that construct statistically valid predictive bounds on the number of interactions required before events of interest occur, such as an unsafe response of an LLM or a successful task completion of an agent. To improve the statistical efficiency of these procedures under limited computational resources, we introduce a theoretically valid dynamic budget allocation framework for sequential evaluation, enabling adaptive allocation of computational resources while preserving finite-sample distribution-free guarantees of the predictive bounds.

Collectively, the contributions presented in this research extend the scope of reliable predictive inference from classical supervised learning to increasingly practical machine learning settings.