Center for Data Foundations in AI

Computational Biology

DISC conducts research by developing and applying appropriate data science methods for bioinformatics and computational biology research, in collaboration with faculty, staff, and stakeholders within and outside Tufts University. Some of current work focuses on single-cell transcriptomics, biological networks, and proteomics. Below are some details about our current projects.

• Single Cell Profiling of Hofbauer Cells and Fetal Brain Microglia Reveals Shared Programs and Functions
• Neutrophils and Macrophages Drive TNF-induced Lethality via TRIF/CD14-mediated Responses
• Multi-resolution Characterization of Molecular Taxonomies in Transcriptomics Data
• System-Level Analysis of Omics Data to Reveal Mechanisms of Head & Neck Cancer
• Proteomics Profiling Approaches to Study Healthy Aging and Longevity

View Bioinformatics and Computational Biology Projects
 

Engineering and Physical Sciences

DISC conducts research by developing and applying appropriate data science methods for engineering and physical sciences research, in collaboration with faculty, staff, and stakeholders within and outside Tufts University. Some of our current work focuses on uncertainty quantification, developing surrogate models for large simulations of hybrid rocket systems, machine learning tools for digital twins of energy infrastructures, and creating data science tools for aviation analyses. Below are some details about our current projects.

• Combined Data and Model Form Uncertainty for Hybrid Rocket Experiments
• Machine Learning Methods for Offshore Wind Energy Infrastructure
• Identification of Phases of Flight for General Aviation
• Large scale Mass Flows and Hazard Risk evaluation
• Cyberinfrastructure Development

View Engineering and Physical Sciences Projects
 

Social Sciences and Humanities

Some of the current work focuses on agent-based modeling for social dynamics, ethics of artificial intelligence tools and their impact, and using data science to uncover policy shortfalls and seek improvements. Below are some details about our current projects in the social sciences and humanities area.

• Hegselmann-Krause Model of Opinion Dynamics
• Precision Nutrition AI Ethics
• Understanding Disparities in Small Town Policing
• Surveying Judges on the Implementation of Artificial Intelligence in Legal Systems

View Social Sciences and Humanities Projects
 

2025

• Gaikwad, Bipin, Abani Patra, Carl R. Crawford, and Eric L. Miller. "Self-supervised anomaly detection and localization for X-ray cargo images: Generalization to novel anomalies." Engineering Applications of Artificial Intelligence 140 (2025): 109675.
   
• Ali SA, Perera G, Laird J, Batorsky R, Maron MS, Rivas VN, Stern JA, Harris S, Chin MT. Single Cell Transcriptomic Profiling of MYBPC3-Associated Hypertrophic Cardiomyopathy Across Species Reveals Conservation of Biological Process But Not Gene Expression. J Am Heart Assoc. 2025 Jan 7;14(1):e035780. doi: 10.1161/JAHA.124.035780. Epub 2024 Dec 24. PubMed PMID: 39719426.
   
• Georgalis, G., Becerra, A., Budzinski, K., McGurn, M., Faghihi, D., DesJardin, P.E. and Patra, A., 2025. Uncertainty Quantification of Slab Burner Simulation: Surrogates, Forward Propagation, and Parameter Calibration, Journal of Propulsion & Power [Under Review].
   
• Haensch, A. (2025). Becoming a Data Scientist: An Impractical Guide. The American Mathematical Monthly, 132(1), 6-13.
   

2024

• Gaikwad, Bipin, Abani Patra, Carl Crawford, and Eric Miller. "Self-Supervised Anomaly Detection and a New Benchmark for X-Ray Cargo Images." In 2024 IEEE International Conference on Image Processing (ICIP), pp. 2175-2181. IEEE, 2024.
   
• Jetton D, Muendlein HI, Connolly WM, Magri Z, Smirnova I, Batorsky R, Mecsas J, Degterev A, Poltorak A. Non-canonical autophosphorylation of RIPK1 drives timely pyroptosis to control Yersinia infection. Cell Rep. 2024 Aug 27;43(8):114641. doi: 10.1016/j.celrep.2024.114641. Epub 2024 Aug 17. PubMed PMID: 39154339; PubMed Central PMCID: PMC11465231.
   
• Joshi VR, Claiborne DT, Pack ML, Power KA, Newman RM, Batorsky R, Bean DJ, Goroff MS, Lingwood D, Seaman MS, Rosenberg E, Allen TM. A VRC13-like bNAb response is associated with complex escape pathways in HIV-1 envelope. J Virol. 2024 Mar 19;98(3):e0172023. doi: 10.1128/jvi.01720-23. Epub 2024 Feb 27. PubMed PMID: 38412036; PubMed Central PMCID: PMC10949433.select
   
• Retfalvi, K., Georgalis, G., Patra, A. and DesJardin, P.E., 2024. TCP-UQ: two-color pyrometry uncertainty quantification using high speed color cameras. Measurement Science and Technology, 36(1), p.015211.
   
• Bourgeois, J. W., Haensch, A., Kher, S., Knox, D., Lanzalotto, G., & Wong, T. A. (2024). How to Use Causal Inference to Study Use of Force. CHANCE, 37(4), 6–10.
   
• Shook, L.L., Batorsky, R.E., De Guzman, R.M. et al. "Maternal SARS-CoV-2 impacts fetal placental macrophage programs and placenta-derived microglial models of neurodevelopment". J Neuroinflammation 21, 163 (2024).
   
• Batorsky R, Ceasrine AM, Shook LL, Kislal S, Bordt EA, Devlin BA, Perlis RH, Slonim DK, Bilbo SD, Edlow AG. "Hofbauer cells and fetal brain microglia share transcriptional profiles and responses to maternal diet-induced obesity". Cell Reports, Volume 43, Issue 6, 25 June 2024, 114326
   
• Haensch, A., Gordon, D., Knudson, K., & Cheng, J. (2024). "A Multi-Method Data Science Pipeline for Analyzing Police Service." The American Statistician, 1–18.
   
• Bourgeois, J. W., Haensch, A., Kher, S., Knox, D., Lanzalotto, G., & Wong, T. A. (2024). How to Use Causal Inference to Study Use of Force. CHANCE, 37(4), 6-10.
   
• Jiri Bonaventura, Ethan J. Rowin, Raymond H. Chan, Michael T. Chin, Veronika Puchnerova, Eva Polakova, Milan MacekJr, Pavel Votypka, Rebecca Batorsky, Gayani Perera, Benjamin Koethe, Josef Veselka, Barry J. Maron and Martin S. Maron. "Relationship Between Genotype Status and Clinical Outcome in Hypertrophic Cardiomyopathy". Journal of the American Heart Association. 2024
   
• Hend Alqedari, Khaled Altabtbaei, Josh L Espinoza, Saadoun Bin-Hasan, Mohammad Alghounaim, Abdullah Alawady, Abdullah Altabtabae, Sarah AlJamaan, Sriraman Devarajan, Tahreer AlShammari, Mohammed Ben Eid, Michele Matsuoka, Hyesun Jang, Christopher L Dupont, Marcelo Freire, "Host–microbiome associations in saliva predict COVID-19 severity", PNAS Nexus, Volume 3, Issue 4, April 2024, Page 126.
   
• Martinho, A., Kroesen, M., & Chorus, C. (2024). "Moral foundations in gender violence cases decided in Portuguese courts." European Journal of Criminology, 0(0).
   
• Gritzer, L., Zavras, A., Macek, M. and Alqaderi, H., 2024. "Bridging gaps: Transforming dental public health training for modern job market demands." Journal of Dental Education.
   
• Dinh, Y., Alawady, A., Alhazmi, H., Altabtbaei, K., Freire, M., Alghounaim, M., Devarajan, S., Bin-Hassan, S. and Alqaderi, H., 2024. "Association between risk of obstructive sleep apnea severity and risk of severe COVID-19 symptoms: insights from salivary and serum cytokines". Frontiers in Public Health, No. 12, p.1348441.
   
• Martinho, A. "Surveying Judges about artificial intelligence: profession, judicial adjudication, and legal principles". AI & Soc (2024).
   
• Haensch, A., Tronci, E. M., Moynihan, B., and Moaveni, B., 2024. "Regularized hidden markov modeling with applications to wind speed predictions in Offshore Wind," Mechanical Systems and Signal Processing, 211, 111229.
   
• Borgers, C., Dragovic, N., Haensch, A., Kirshtein, A., and Orr, L., 2024. "ODEs and Mandatory Voting," CODEE Journal: Vol. 17, Article 11
   
• Georgalis, G., Nathawani, D., Knepley, M., and Patra, A., 2024. “Uncertainty Quantification of Shear-induced Paraffin Droplet Pinch-off in Hybrid Rocket Motors,” AIAA Scitech ’24.

2023

• Perera G, Power L, Larson A, Codden CJ, Awata J, Batorsky R, Strathdee D, Chin MT. Single Cell Transcriptomic Analysis in a Mouse Model of Barth Syndrome Reveals Cell-Specific Alterations in Gene Expression and Intercellular Communication. Int J Mol Sci. 2023 Jul 18;24(14). doi: 10.3390/ijms241411594. PubMed PMID: 37511352; PubMed Central PMCID: PMC10380964.
   
• Laird J, Perera G, Batorsky R, Wang H, Arkun K, Chin M. Spatial Transcriptomic Analysis of Focal and Normal Areas of Myocyte Disarray in Human Hypertrophic Cardiomyopathy. International Journal of Molecular Sciences. 2023; 24(16). doi: 10.3390/ijms241612625.
   
• Prashant, S., Babu, M., and Patra, A., 2023. “Hierarchical Regularization Networks for Sparsification Based Learning on Noisy Datasets.” Foundations of Data Science 5, No. 4, pp. 520–57.
   
• Reed E., Jankowski S.A., Spinella A.J., Noonan V., Haddad R., Nomoto K., Matsui J., Bais M.V., Varelas X., Kukuruzinska M.A., and Monti S., 2023. "β-catenin/CBP activation of mTORC1 signaling promotes partial epithelialmesenchymal states in head and neck cancer", Translational Research.
   
• Vora N., Polleys C. M., Sakellariou F., Georgalis G.,  Thieu HT., Genega E., Jahanseir N., Patra A., Miller E., and Georgakoudi I., 2023. "Restoration of metabolic functional metrics from label-free, two-photon cervical tissue images using multiscale deep-learning-based denoising algorithms". bioRxiv 2023.06.07.544033; doi: https://doi.org/10.1101/2023.06.07.544033. 
   
• Börgers, C., Boghosian, B., Dragovic, N., and Haensch, A., 2023. "A blue sky bifurcation in the dynamics of political candidates," To appear in The American Mathematical Monthly.
   
• Georgalis, G., Retfalvi, K., DeJardin, P.E., and Patra, A., 2023. “Combined Input Data and Deep Learning Model Uncertainty: An Application to the Measurement of Solid Fuel Regression Rate,” International Journal of Uncertainty Quantification, Vol. 13, No. 5, pp. 23-40.
   
• Fala, N., Georgalis, G., and Arzamani, N., 2023. “Study on Machine Learning Methods for General Aviation Flight Phase Identification,” Journal of Aerospace Information Systems, Vol. 20, No. 10, pp. 636-647.
   
• Salunkhe, A., Georgalis, G., Patra, A., and Chandola, V., 2023. “An Ensemble-Based Deep Framework for Estimating Thermo-Chemical State Variables from Flamelet Generated Manifolds,” AIAA Scitech ’23.
   
• Georgalis, G., and Fala, N., 2023. “Automated Identification of Phase of Flight via Probabilistic Clustering for General Aviation Operations,” AIAA Aviation ’23.
   
• H. I. Muendlein, W. M. Connolly, J. Cameron, D. Jetton, Z. Magri, I. Smirnova, E. Vannier, X. Li, R.E. Batorsky, and A. Poltorak., 2023. “Neutrophils and macrophages drive TNF-induced lethality via TRIF/CD14-mediated responses,” Science Immunology, Vol. 7, No. 78.
   
• Haensch, A., Dragovic, N., Börgers, C. and Boghosian, B., 2022. “A geospatial bounded confidence model including mega-influencers with an application to Covid-19 vaccine hesitancy.” Journal of Artificial Societies and Social Simulation,  Vol. 26, No. 1.
   

2022

• Karagiannis, T., Dowrey, T., Villacorta-Martin, C., Montano, M., Reed, E., Andersen, S., Perls, T., Monti, S., Murphy, G. and Sebastiani, P., 2022. "Multi-modal profiling of peripheral blood cells across the human lifespan reveals distinct immune cell signatures of aging and longevity." Submitted to Immunology.
   
• Haensch, A. and Knudson, K., 2022. “Python for Global Applications: teaching scientific Python in context to law and diplomacy students,” Proc. Of the 21st Python in Science Conference (Scipy 2022).
   
• Hanscom, T., Woodward, N., Batorsky, R., Brown, A.J., Roberts, S.A. and McVey, M., 2022. “Characterization of sequence contexts that favor alternative end joining at Cas9-induced double-strand breaks,” Nucleic acids research, 50(13), pp.7465-7478.
   
• Surina III, G., Georgalis, G., Aphale, S.S., Patra, A. and DesJardin, P.E., 2022. “Measurement of hybrid rocket solid fuel regression rate for a slab burner using deep learning.” Acta Astronautica, 190, pp.160-175.
   
• Haensch, A., Gordon, D., Knudson, K., & Cheng, J., 2022. “A Multi-method Data Science Pipeline for Analyzing Police Service in the Presence of Misconduct.” SocArXiv. November 5.
   
• Fiore, N.J., Ganat, Y.M., Devkota, K., Batorsky, R., Lei, M., Lee, K., Cowen, L.J., Croft, G., Noggle, S.A., Nieland, T.J. and Kaplan, D.L., 2022. “Bioengineered models of Parkinson’s disease using patient-derived dopaminergic neurons exhibit distinct biological profiles in a 3D microenvironment,” Cellular and Molecular Life Sciences, 79(2), pp.1-20.
   
• Wojnowicz, M. T., Aeron, S., Miller, E. L., & Hughes, M., 2022.  “Easy Variational Inference for Categorical Models via an Independent Binary Approximation,” In International Conference on Machine Learning (pp. 23857-23896).

2021

• Ceasrine, A.M., Batorsky, R., Shook, L.L., Kislal, S., Bordt, E.A., Devlin, B.A., Perlis, R.H., Slonim, D.K., Bilbo, S.D. and Edlow, A.G., 2021. Single cell profiling of Hofbauer cells and fetal brain microglia reveals shared programs and functions. bioRxiv 2021.12.03.471177
   
• Reed, E.R. and Monti, S., 2021. Multi-resolution characterization of molecular taxonomies in bulk and single-cell transcriptomics data. Nucleic acids research, 49(17), pp.e98-e98.
   
• Kim, S., Reed, E., Monti, S. and Schlezinger, J.J., 2021. “A data-driven transcriptional taxonomy of adipogenic chemicals to identify white and brite adipogens,” Environmental health perspectives, 129(7), p.077006.
   
• Georgalis, G. and Marais, K., 2021. “Predicting failure events from crowd-derived inputs: schedule slips and missed requirements”, In INCOSE International Symposium, Vol. 31, No. 1.
   
• Reed, E., Moses, E., Xiao, X., Liu, G., Campbell, J., Perdomo, C. and Monti, S., 2019. “Assessment of a highly multiplexed RNA sequencing platform and comparison to existing high-throughput gene expression profiling techniques,” Frontiers in genetics, 10, p.150.
   

DISC seeks to recruit several student interns for Fall 2025. During the semester the interns will be expected to work 10 hours per week, have weekly assignment deadlines, and weekly meetings with their supervisor.

Students will assist faculty and staff on ongoing projects at the center. Students will also have the opportunity to participate in other center related learning activities. Students will be assigned a faculty/staff mentor that they will assist. Exceptional juniors, seniors, and graduate students are encouraged to apply with:

1. A one-page resume highlighting knowledge and skills in data science, mathematics, computer science, and statistics. Please specifically highlight prior programming/coding experience.
2. A brief (no more than one-page) cover letter that addresses your interest in machine learning and how an internship relates to your broader educational goals.
3. A copy of latest transcript.

Applications instructions and deadlines will be provided at a later date.