29 citations · 56 across the 16 of their papers we have counts for
16 papers
From Understanding to Creation: A Prerequisite-Free AI Literacy Course with Technical Depth Across Majors
Amarda Shehu
Most AI literacy courses for non-technical undergraduates emphasize conceptual breadth over technical depth. This paper describes UNIV 182, a prerequisite-free course at George Mas…
Can Consumer Chatbots Reason? A Student-Led Field Experiment Embedded in an "AI-for-All" Undergraduate Course
Amarda Shehu, Adonyas Ababu, Asma Akbary +34
Claims about whether large language model (LLM) chatbots "reason" are typically debated using curated benchmarks and laboratory-style evaluation protocols. This paper offers a comp…
Foundation Models for AI-Enabled Biological Design
Asher Moldwin, Amarda Shehu
This paper surveys foundation models for AI-enabled biological design, focusing on recent developments in applying large-scale, self-supervised models to tasks such as protein engi…
Birdie: Advancing State Space Models with Reward-Driven Objectives and Curricula
Sam Blouir, Jimmy T. H. Smith, Antonios Anastasopoulos +1
Efficient state space models (SSMs), such as linear recurrent neural networks and linear attention variants, offer computational advantages over Transformers but struggle with task…
Accounting for Work Zone Disruptions in Traffic Flow Forecasting
Yuanjie Lu, Amarda Shehu, David Lattanzi
Traffic speed forecasting is an important task in intelligent transportation system management. The objective of much of the current computational research is to minimize the diffe…
Beyond Single-Model Views for Deep Learning: Optimization versus Generalizability of Stochastic Optimization Algorithms
Toki Tahmid Inan, Mingrui Liu, Amarda Shehu
Despite an extensive body of literature on deep learning optimization, our current understanding of what makes an optimization algorithm effective is fragmented. In particular, we…
Multi-objective Deep Data Generation with Correlated Property Control
Shiyu Wang, Xiaojie Guo, Xuanyang Lin +11
Developing deep generative models has been an emerging field due to the ability to model and generate complex data for various purposes, such as image synthesis and molecular desig…
Multiple Instance Learning for Detecting Anomalies over Sequential Real-World Datasets
Parastoo Kamranfar, David Lattanzi, Amarda Shehu +1
Detecting anomalies over real-world datasets remains a challenging task. Data annotation is an intensive human labor problem, particularly in sequential datasets, where the start a…
Transformer Neural Networks Attending to Both Sequence and Structure for Protein Prediction Tasks
Anowarul Kabir, Amarda Shehu
The increasing number of protein sequences decoded from genomes is opening up new avenues of research on linking protein sequence to function with transformer neural networks. Rece…
Interpretable Molecular Graph Generation via Monotonic Constraints
Yuanqi Du, Xiaojie Guo, Amarda Shehu +1
Designing molecules with specific properties is a long-lasting research problem and is central to advancing crucial domains such as drug discovery and material science. Recent adva…
Traffic Flow Forecasting with Maintenance Downtime via Multi-Channel Attention-Based Spatio-Temporal Graph Convolutional Networks
Yuanjie Lu, Parastoo Kamranfar, David Lattanzi +1
Forecasting traffic flows is a central task in intelligent transportation system management. Graph structures have shown promise as a modeling framework, with recent advances in sp…
Space Partitioning and Regression Mode Seeking via a Mean-Shift-Inspired Algorithm
Wanli Qiao, Amarda Shehu
The mean shift (MS) algorithm is a nonparametric method used to cluster sample points and find the local modes of kernel density estimates, using an idea based on iterative gradien…
Decoy Selection for Protein Structure Prediction Via Extreme Gradient Boosting and Ranking
Nasrin Akhter, Gopinath Chennupati, Hristo Djidjev +1
Identifying one or more biologically-active/native decoys from millions of non-native decoys is one of the major challenges in computational structural biology. The extreme lack of…
Interpretable Deep Graph Generation with Node-Edge Co-Disentanglement
Xiaojie Guo, Liang Zhao, Zhao Qin +3
Disentangled representation learning has recently attracted a significant amount of attention, particularly in the field of image representation learning. However, learning the dis…
Generating Tertiary Protein Structures via an Interpretative Variational Autoencoder
Xiaojie Guo, Yuanqi Du, Sivani Tadepalli +2
Much scientific enquiry across disciplines is founded upon a mechanistic treatment of dynamic systems that ties form to function. A highly visible instance of this is in molecular…
ROMEO: A Plug-and-play Software Platform of Robotics-inspired Algorithms for Modeling Biomolecular Structures and Motions
Kevin Molloy, Erion Plaku, Amarda Shehu
Motivation: Due to the central role of protein structure in molecular recognition, great computational efforts are devoted to modeling protein structures and motions that mediate s…