Bahrad Sokhansanj - Personal Page

I recently started a position as a Senior Research Scholar at the Institute for Law & AI. My work focuses on the legal frameworks shaping AI regulation and safety, with a particular emphasis on AI-biosecurity and intellectual property (IP). I’m especially interested in how AI systems that interact with biological data or biological design capabilities should be governed, and how legal mechanisms -- privacy, liability, IP, and regulatory oversight -- can be adapted to address novel risks. My perspective on AI law and policy builds on my previous experiences as an IP litigator and in computational genomics and machine learning research.

Education

Experience

Institute for Law and Artificial Intelligence (LawAI)

Senior Research Scholar | 2025 – Present

Conducting legal and policy research on AI governance as part of the U.S. Policy team, including analysis of federal and state legal developments, policy writing for legislators, policymakers, and the public, and consultation on draft legislation. Focus areas include AI-biosecurity, AI-for-science risk and governance, and intellectual property in AI regulation.

Russ August & Kabat / McKool Smith

Associate | 2014 – 2019

Represented clients in multi-hundred-million-dollar patent and trade secret disputes across U.S. federal courts, the ITC, and the PTAB, including semiconductors, cloud computing, and consumer electronics matters. Represented tenants facing eviction and prisoners suing to defend their civil rights in state and federal courts pro bono.

U.S. Court of Appeals for the Federal Circuit

Judicial Law Clerk to Chief Judge Sharon Prost | 2013 – 2014

Drafted opinions on patent appeals involving complex computational and biological technologies from district courts, US Patent and Trial Appeals Board (PTAB), and International Trade Commission (ITC), as well as trade and veteran's rights matters.

Selected Publications

AI Policy

Publications for LawAI are available at my LawAI profile. Link to Articles

Sokhansanj B.A., Rosen G.L., Regulating genome language models: navigating policy challenges at the intersection of AI and genetics, Human Genetics, 2025. Link to Article

Sokhansanj B.A., Uncensored AI in the Wild: Tracking Publicly Available and Locally Deployable LLMs, Future Internet, 2025. Link to Article

Sokhansanj B.A., Local AI Governance: Addressing Model Safety and Policy Challenges Posed by Decentralized AI, AI, 2025. Link to Article

Sokhansanj B.A., Beyond protecting genetic privacy: understanding genetic discrimination through its disparate impact on racial minorities, Columbia Journal of Race & Law, 2012. Link to Article

Legal AI

Sokhansanj B.A., Rosen G.L., Predicting Institution Outcomes for Inter Partes Review Proceedings at the United States Patent Trial & Appeal Board by Deep Learning of Patent Owner Preliminary Response Briefs, Applied Sciences, 2022. Link to Article

Genomic AI

Refahi M., Sokhansanj B.A., Mell J.C., Brown J.R., Yoo H., Hearne G., Rosen G.L., Enhancing nucleotide sequence representations in genomic analysis with contrastive optimization, Communications Biology, 2025. Link to Article

Yoo H., Refahi M., Polikar R., Sokhansanj B.A., Brown J.R., Rosen G.L., iSeqSearch: incremental protein search for iBlast/iMMSeqs2/iDiamond, PeerJ, 2025. Link to Article

Yoo H., Sokhansanj B.A., Brown J.R., Enhancing Antimicrobial Drug Resistance Classification by Integrating Sequence-Based and Text-Based Representations, Proceedings of the 24th Workshop on Biomedical Language Processing, 2025. Link to Article

Refahi M.S., Sokhansanj B.A., Rosen G.L., Leveraging Large Language Models for Metagenomic Analysis, IEEE Signal Processing in Medicine and Biology Symposium, 2023. Link to Article

Nguyen R., Sokhansanj B.A., Polikar R., Rosen G.L., Complet+: a computationally scalable method to improve completeness of large-scale protein sequence clustering, PeerJ, 2023. Link to Article

Sokhansanj B.A., Rosen G.L., Mapping Data to Deep Understanding: Making the Most of the Deluge of SARS-CoV-2 Genome Sequences, mSystems, 2022. Link to Article

Zhao Z., Woloszynek S., Agbavor F., Mell J.C., Sokhansanj B.A., Rosen G.L., Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network, PLoS Computational Biology, 2021. Link to Article

ValizadehAslani T., Zhao Z., Sokhansanj B.A., Rosen G.L., Amino Acid k-mer Feature Extraction for Quantitative Antimicrobial Resistance Prediction by Machine Learning and Model Interpretation for Biological Insights, Biology, 2020. Link to Article

Zhao Z., Sokhansanj B.A., Malhotra C., Zheng K., Rosen G.L., Genetic grouping of SARS-CoV-2 coronavirus sequences using informative subtype markers for pandemic spread visualization, PLoS Computational Biology, 2020. Link to Article

Rosen G., Garbarine E., Caseiro D., Polikar R., Sokhansanj B., Metagenome fragment classification using N-mer frequency profiles, Advances in Bioinformatics, 2008. Link to Article

Computational Drug Design

Refahi M., Sokhansanj B.A., Brown J.R., Rosen G., Learning to Align Molecules and Proteins: A Geometry-Aware Approach to Binding Affinity, arXiv preprint, 2025. Link to Article

Chandraghatgi R., Ji H.F., Rosen G.L., Sokhansanj B.A., Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening, Journal of Chemical Information and Modeling, 2024. Link to Article

Wilson J., Sokhansanj B.A., Chong W.C., Chandraghatgi R., Rosen G.L., Ji H.F., Fragment databases from screened ligands for drug discovery (FDSL-DD), Journal of Molecular Graphics and Modelling, 2024. Link to Article

Machine Learning in Biology/Health

Sokhansanj B.A., Zhao Z., Rosen G.L., Interpretable and Predictive Deep Neural Network Modeling of the SARS-CoV-2 Spike Protein Sequence to Predict COVID-19 Disease Severity, Biology, 2022. Link to Article

Sokhansanj B.A., Rosen G.L., Predicting COVID-19 disease severity from SARS-CoV-2 spike protein sequence by mixed effects machine learning, Computers in Biology and Medicine, 2022. Link to Article

Woloszynek S., Pastor S., Mell J.C., Nandi N., Sokhansanj B., Rosen G.L., Engineering Human Microbiota: Influencing Cellular and Community Dynamics for Therapeutic Applications, International Review of Cell and Molecular Biology, 2016. Link to Article

Hu X., Ng M., Wu F.X., Sokhansanj B.A., Mining, modeling, and evaluation of subnetworks from large biomolecular networks and its comparison study, IEEE Transactions on Information Technology in Biomedicine, 2009. Link to Article

Sokhansanj B.A., Wilson D.M., Estimating the effect of human base excision repair protein variants on the repair of oxidative DNA base damage, Cancer Epidemiology Biomarkers & Prevention, 2006. Link to Article

Kriete A., Sokhansanj B.A., Coppock D.L., West G.B., Systems approaches to the networks of aging, Ageing Research Reviews, 2006. Link to Article

Sokhansanj B.A., Wilson D.M., Oxidative DNA damage background estimated by a system model of base excision repair, Free Radical Biology and Medicine, 2004. Link to Article

Sokhansanj B.A., Rodrigue G.R., Fitch J.P., Wilson D.M., A quantitative model of human DNA base excision repair. I. Mechanistic insights, Nucleic Acids Research, 2002. Link to Article

Bahrad A. Sokhansanj, J.D., Ph.D.

Education

Columbia University Law School

University of California, Davis

University of Saskatchewan