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STORM Research Group

Our research group is led by Prof. Can Firtina. We are part of the Department of Computer Science at the University of Maryland.
Our research focuses on problems in bioinformatics to enable fast, accurate, energy-efficient and scalable analysis of biological data such as genomics. To this end, we develop algorithmic, machine learning, and hardware solutions.

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Research in Bioinformatics

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Real-Time and Portable Nanopore Sequencing

Nanopore sequencing is a commonly used technology to sequence biological molecules such as DNA, RNA, and proteins. Translating their initial raw data, electrical signals, into human-readable sequences of characters (e.g., DNA characters of A, C, T, G), a process called basecalling, is costly and ineffective. We design solutions that can directly analyze nanopore electrical signals without basecalling. These solutions help us build solutions that better utilize resource constrained devices (e.g., mobile devices or drones) to enable in-the-field and real-time biological data analysis. Additionally, we explore integrating our signal analysis solutions into standard genomics pipelines further to improve their accuracy and speed.

Algorithms and Machine Learning for Genome Analysis

Analyzing genomic data is challenging as solutions must analyze very large volumes of data quickly and accurately. Such an analysis usually requires designing effective algorithmic and machine learning solutions in the genome analysis pipeline (e.g., read mapping, de novo genome assembly, error correction, metagenomics, and basecalling). We explore improving the accuracy and speed of analyzing genomic data to better generate insights from them.

Hardware Acceleration for Bioinformatics

To substantially improve speed and energy-efficiency of the computational approaches in genomics, we explore hardware acceleration of the underlying analysis. To this end, we explore designing solutions for GPUs, FPGAs, as well as emerging technologies such as processing in-/near-memory (i.e., data-centric computing), analog computing, and neuromorphic computing.

News

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  • Ege Sirvan, Quang Nhat Ngo, and Ming Gao join STORM as the group’s first PhD students. Welcome!
  • Our undergraduate researcher Simon Ambrozak joins Johns Hopkins University as a PhD student. Best wishes to Simon!
  • Ulysse McConnell presents his summer internship project in a CBCB Seminar
  • Our paper “De Bruijn Graphs for Pangenomics: In-depth Performance Benchmarking of de Bruijn Graph-Based Tools for Read Mapping” is accepted for publication in Briefings in Bioinformatics
  • Can Firtina gives an invited talk, “Lost in Translation: Harnessing the Power of Nanopore Electrical Signals in Genomics,” at the Computational Genomics Summer Institute 2026 in Rancho Palos Verdes, California

Selected publications

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CRANE: Correcting Errors in Raw Nanopore Signals Using Hidden Markov Models

Simon Ambrozak, Ulysse McConnell, Bhargav Srinivasan, Burak Ozkan, Ernest Zhang, Can Firtina

arXiv, March 2026.

[PDF][Code][Link]

ISMB 2026 (HiTSeq) Talk[Slides (pptx)][Slides (pdf)]

BLEND: a fast, memory-efficient and accurate mechanism to find fuzzy seed matches in genome analysis

Can Firtina, Jisung Park, Mohammed Alser, Jeremie S. Kim, Damla Senol Cali, Taha Shahroodi, Nika Mansouri Ghiasi, Gagandeep Singh, Konstantinos Kanellopoulos, Can Alkan, Onur Mutlu

NAR Genomics and Bioinformatics (NARGAB), March 2023.

[PDF][Code][DOI]

Preprint (bioRxiv)[Link][PDF]

RECOMB 2023 Talk[Video][Slides (pptx)][Slides (pdf)]

Social media thread[Twitter (X), publication][Twitter (X), preprint]

Join Us!

We are actively looking for motivated students to join us.

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