8 citations · 11 across the 8 of their papers we have counts for
9 papers
BioAlchemy: Distilling Biological Literature into Reasoning-Ready Reinforcement Learning Training Data
Brian Hsu, Ozan Gökdemir, Carlo Siebenschuh +7
Despite the large corpus of biology training text, the impact of reasoning models on biological research generally lags behind math and coding. In this work, we show that biology q…
Self Distillation Fine-Tuning of Protein Language Models Improves Versatility in Protein Design
Amin Tavakoli, Raswanth Murugan, Ozan Gokdemir +3
Supervised fine-tuning (SFT) is a standard approach for adapting large language models to specialized domains, yet its application to protein sequence modeling and protein language…
Scalable Agentic Reasoning for Designing Biologics Targeting Intrinsically Disordered Proteins
Matthew Sinclair, Moeen Meigooni, Archit Vasan +14
Intrinsically disordered proteins (IDPs) represent crucial therapeutic targets due to their significant role in disease -- approximately 80\% of cancer-related proteins contain lon…
Foundation Models for Discovery and Exploration in Chemical Space
Alexius Wadell, Anoushka Bhutani, Victor Azumah +26
Accurate prediction of atomistic, thermodynamic, and kinetic properties from molecular structures underpins materials innovation. Existing computational and experimental approaches…
Automated MCQA Benchmarking at Scale: Evaluating Reasoning Traces as Retrieval Sources for Domain Adaptation of Small Language Models
Ozan Gokdemir, Neil Getty, Robert Underwood +5
As scientific knowledge grows at an unprecedented pace, evaluation benchmarks must evolve to reflect new discoveries and ensure language models are tested on current, diverse liter…
HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights
Ozan Gokdemir, Carlo Siebenschuh, Alexander Brace +21
The volume of scientific literature is growing exponentially, leading to underutilized discoveries, duplicated efforts, and limited cross-disciplinary collaboration. Retrieval Augm…
AdaParse: An Adaptive Parallel PDF Parsing and Resource Scaling Engine
Carlo Siebenschuh, Kyle Hippe, Ozan Gokdemir +10
Language models for scientific tasks are trained on text from scientific publications, most distributed as PDFs that require parsing. PDF parsing approaches range from inexpensive…
Connecting Large Language Model Agent to High Performance Computing Resource
Heng Ma, Alexander Brace, Carlo Siebenschuh +3
The Large Language Model agent workflow enables the LLM to invoke tool functions to increase the performance on specific scientific domain questions. To tackle large scale of scien…
Speech enhancement deep-learning architecture for efficient edge processing
Monisankha Pal, Arvind Ramanathan, Ted Wada +1
Deep learning has become a de facto method of choice for speech enhancement tasks with significant improvements in speech quality. However, real-time processing with reduced size a…