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Center for Artificial Intelligence and Modeling: Publications

Raginsky, M. (2023). Biological Autonomy. Biological Theory, 18(4), 303–308. https://doi.org/10.1007/s13752-023-00440-

Song, C. L., Main, E. J., Simmons, F., Liu, S., Phillabaum, B., Dahmen, K. A., Hudson, E. W., Hoffman, J. E., & Carlson, . W. (2023). Critical nematic correlations throughout the superconducting doping range in Bi2−zPb zSr2−yLa yCuO6+x. Nature communications, 14(1), Article 2622. https://doi.org/10.1038/s41467-023-38249-3

Guo, B., Holscher, H. D., Auvil, L. S., Welge, M. E., Bushell, C. B., Novotny, J. A., Baer, D. J., Burd, N. A., Khan, N. A., & Zhu, R. (2023). Estimating Heterogeneous Treatment Effect on Multivariate Responses Using Random Forests. Statistics in Biosciences, 15(3), 545-561. https://doi.org/10.1007/s12561-021-09310-w

Huerta, E. A., Blaiszik, B., Brinson, L. C., Bouchard, K. E., Diaz, D., Doglioni, C., Duarte, J. M., Emani, M., Foster, I., Fox, G., Harris, P., Heinrich, L., Jha, S., Katz, D. S., Kindratenko, V., Kirkpatrick, C. R., Lassila-Perini, K., Madduri, R. K., Neubauer, M. S., ... Zhu, R. (2023). FAIR for AI: An interdisciplinary and international community building perspective. Scientific Data, 10(1), Article 487. https://doi.org/10.1038/s41597-023-02298-6

Salners, T., Avila, K. E., Nicholson, B., Myers, C. R., Beggs, J., & Dahmen, K. A. (2023). Recurrent activity in neuronal avalanches. Scientific reports, 13(1), Article 4871. https://doi.org/10.1038/s41598-023-31851-x

Lozano, A. C., Ding, H., Abe, N., & Lipka, A. E. (2023). Regularized multi-trait multi-locus linear mixed models for genome-wide association studies and genomic selection in crops. BMC bioinformatics, 24(1), Article 399. https://doi.org/10.1186/s12859-023-05519-2

Arya, S., George, A. B., & O’Dwyer, J. P. (2023). Sparsity of higher-order landscape interactions enables learning and prediction for microbiomes. Proceedings of the National Academy of Sciences, 120(48), Article e2307313120. https://doi.org/10.1073/pnas.2307313120

Pesantez, J. E., Li, B., Lee, C., Zhao, Z., Butala, M., & Stillwell, A. S. (2023). A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment. Energy, 283, Article 129142. https://doi.org/10.1016/j.energy.2023.129142

Liu, X., Milenkovic, O., & Moustakides, G. V. (2023). Query-based selection of optimal candidates under the Mallows model. Theoretical Computer Science, 979, Article 114206. https://doi.org/10.1016/j.tcs.2023.114206

Greenberg, G., Ravi, A. N., & Shomorony, I. (2023). LexicHash: Sequence Similarity Estimation via Lexicographic Comparison of Hashes. Bioinformatics, 39(11). https://doi.org/10.1093/bioinformatics/btad652

Nambiar, A., Dubinkina, V., Liu, S., & Maslov, S. (2023). FUN-PROSE: A deep learning approach to predict condition-specific gene expression in fungi. PLoS computational biology, 19(11), 1-21. Article e1011563. https://doi.org/10.1371/journal.pcbi.1011563

Njuguna, J. N., Clark, L. V., Lipka, A. E., Anzoua, K. G., Bagmet, L., Chebukin, P., Dwiyanti, M. S., Dzyubenko, E., Dzyubenko, N., Ghimire, B. K., Jin, X., Johnson, D. A., Nagano, H., Peng, J., Petersen, K. K., Sabitov, A., Seong, E. S., Yamada, T., Yoo, J. H., ... Sacks, E. J. (2023). Genome-wide association and genomic prediction for yield and component traits of Miscanthus sacchariflorus. GCB Bioenergy, 15(11), 1355-1372. https://doi.org/10.1111/gcbb.13097

Della Coletta, R., Fernandes, S. B., Monnahan, P. J., Mikel, M. A., Bohn, M. O., Lipka, A. E., & Hirsch, C. N. (2023). Importance of genetic architecture in marker selection decisions for genomic prediction. Theoretical and Applied Genetics, 136(11), Article 220. https://doi.org/10.1007/s00122-023-04469-w

George, A. B., & O'Dwyer, J. P. (2023). Universal abundance fluctuations across microbial communities, tropical forests, and urban populations. Proceedings of the National Academy of Sciences, 120(44), Article e2215832120. https://doi.org/10.1073/pnas.2215832120

Weber, L. L., Zhang, C., Ochoa, I., & El-Kebir, M. (2023). Phertilizer: Growing a clonal tree from ultra-low coverage single-cell DNA sequencing of tumors. PLoS computational biology, 19(10), Article e1011544. https://doi.org/10.1371/journal.pcbi.1011544

Mia, M. S., Abdelmeguid, M., & Elbanna, A. E. (2023). The spectrum of fault slip in elastoplastic fault zones. Earth and Planetary Science Letters, 619, Article 118310. https://doi.org/10.1016/j.epsl.2023.118310

Cannavale, C. N., Keye, S. A., Rosok, L. M., Martell, S. G., Holthaus, T. A., Raine, L. R., Mullen, S. P., Holscher, H. D., Hillman, C. H., Kramer, A. F., Cohen, N. J., Hammond, B. R., Renzi-Hammond, L., & Khan, N. A. (2023). Macular Pigment Optical Density and Skin Carotenoids in a Childhood Sample. Journal of Nutrition, 153(10), 3144-3151. https://doi.org/10.1016/j.tjnut.2023.06.006

Deville, L., Emerson, T., Garibaldi, S., Reed, M. L., Washington, T. M., & Weekes, S. L. (2023). Supporting Faculty in Mentoring Students for Careers Beyond Academia. Notices of the American Mathematical Society, 70(9), 1442-1447. https://doi.org/10.1090/noti2784

Sarker, K., Zhu, R., Holscher, H. D., & Zhai, C. X. (2023). Augmenting nutritional metabolomics with a genome-scale metabolic model for assessment of diet intake. In ACM-BCB 2023 - 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics Article 4 (ACM-BCB 2023 - 14th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics). Association for Computing Machinery. https://doi.org/10.1145/3584371.3612958

Murphy, M. D., & Lipka, A. E. (2023). An application of vGWAS to differences in flowering time in maize across mega-environments. Crop Science, 63(5), 2807-2817. https://doi.org/10.1002/csc2.21051

George, A. B., Wang, T., & Maslov, S. (2023). Functional convergence in slow-growing microbial communities arises from thermodynamic constraints. ISME Journal, 17(9), 1482-1494. https://doi.org/10.1038/s41396-023-01455-4

Saldi, N., Başar, T., & Raginsky, M. (2023). Partially Observed Discrete-Time Risk-Sensitive Mean Field Games. Dynamic Games and Applications, 13(3), 929-960. https://doi.org/10.1007/s13235-022-00453-z

Fan, Y., McMath, A. L., & Donovan, S. M. (2023). Review on the Impact of Milk Oligosaccharides on the Brain and Neurocognitive Development in Early Life. Nutrients, 15(17), Article 3743. https://doi.org/10.3390/nu15173743

Hajarolasvadi, S., Celli, P., Kim, B., Elbanna, A. E., & Daraio, C. (2023). Experimental evidence of amplitude-dependent surface wave dispersion via nonlinear contact resonances. Applied Physics Letters, 123(8), Article 081704. https://doi.org/10.1063/5.0151294

Della Coletta, R., Liese, S. E., Fernandes, S. B., Mikel, M. A., Bohn, M. O., Lipka, A. E., & Hirsch, C. N. (2023). Linking genetic and environmental factors through marker effect networks to understand trait plasticity. Genetics, 224(4), Article iyad103. https://doi.org/10.1093/genetics/iyad103

Pattabiraman, S., Gabrys, R., & Milenkovic, O. (2023). Coding for Polymer-Based Data Storage. IEEE Transactions on Information Theory, 69(8), 4812-4836. https://doi.org/10.1109/TIT.2023.3267620

Cai, T., Sutter, C., Donovan, S. M., & Fiese, B. H. (2023). The Relationship Between Maternal and Infant Sleep Duration Across the First Two Years. Journal of Developmental and Behavioral Pediatrics, 44(6), E421-E428. https://doi.org/10.1097/DBP.0000000000001195

Gu, X., Qi, Y., & El-Kebir, M. (2023). Balancing Minimum Free Energy and Codon Adaptation Index for Pareto Optimal RNA Design. In D. Belazzougui, & A. Ouangraoua (Eds.), 23rd International Workshop on Algorithms in Bioinformatics, WABI 2023 Article 21 (Leibniz International Proceedings in Informatics, LIPIcs; Vol. 273). Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.WABI.2023.21

Roddur, M. S., Snir, S., & El-Kebir, M. (2023). Inferring Temporally Consistent Migration Histories. In D. Belazzougui, & A. Ouangraoua (Eds.), 23rd International Workshop on Algorithms in Bioinformatics, WABI 2023 Article 9 (Leibniz International Proceedings in Informatics, LIPIcs; Vol. 273). Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.WABI.2023.9

Holthaus, T. A., Sethi, S., Cannavale, C. N., Aguiñaga, S., Burd, N. A., Holscher, H. D., & Khan, N. A. (2023). MIND dietary pattern adherence is inversely associated with visceral adiposity and features of metabolic syndrome. Nutrition Research, 116, 69-79. https://doi.org/10.1016/j.nutres.2023.06.001

Stroet, M., Caron, B., Engler, M. S., van der Woning, J., Kauffmann, A., van Dijk, M., El-Kebir, M., Visscher, K. M., Holownia, J., Macfarlane, C., Bennion, B. J., Gelpi-Dominguez, S., Lightstone, F. C., van der Storm, T., Geerke, D. P., Mark, A. E., & Klau, G. W. (2023). OFraMP: a fragment-based tool to facilitate the parametrization of large molecules. Journal of Computer-Aided Molecular Design, 37(8), 357-371. https://doi.org/10.1007/s10822-023-00511-7

Robben, M., Nasr, M. S., Das, A., Veerla, J. P., Huber, M., Jaworski, J., Weidanz, J., & Luber, J. (2023). Comparison of the Strengths and Weaknesses of Machine Learning Algorithms and Feature Selection on KEGG Database Microbial Gene Pathway Annotation and Its Effects on Reconstructed Network Topology. Journal of Computational Biology, 30(7), 766-782. https://doi.org/10.1089/cmb.2022.0370

Baldeon, A. D., McDonald, D., Gonzalez, A., Knight, R., & Holscher, H. D. (2023). Diet Quality and the Fecal Microbiota in Adults in the American Gut Project. Journal of Nutrition, 153(7), 2004-2015. https://doi.org/10.1016/j.tjnut.2023.02.018

Unger, A. L., Astrup, A., Feeney, E. L., Holscher, H. D., Gerstein, D. E., Torres-Gonzalez, M., & Brown, K. (2023). Harnessing the Magic of the Dairy Matrix for Next-Level Health Solutions: A Summary of a Symposium Presented at Nutrition 2022. Current Developments in Nutrition, 7(7), Article 100105. https://doi.org/10.1016/j.cdnut.2023.100105

Baur, B., Shin, J., Schreiber, J., Zhang, S., Zhang, Y., Manjunath, M., Song, J. S., Noble, W. S., & Roy, S. (2023). Leveraging epigenomes and three-dimensional genome organization for interpreting regulatory variation. PLoS computational biology, 19(7), Article e1011286. https://doi.org/10.1371/journal.pcbi.1011286

Ivanovic, S., & El-Kebir, M. (2023). Modeling and predicting cancer clonal evolution with reinforcement learning. Genome Research, 33(7), 1078-1088. https://doi.org/10.1101/gr.277672.123

Morton, J. T., Jin, D. M., Mills, R. H., Shao, Y., Rahman, G., McDonald, D., Zhu, Q., Balaban, M., Jiang, Y., Cantrell, K., Gonzalez, A., Carmel, J., Frankiensztajn, L. M., Martin-Brevet, S., Berding, K., Needham, B. D., Zurita, M. F., David, M., Averina, O. V., ... Taroncher-Oldenburg, G. (2023). Multi-level analysis of the gut–brain axis shows autism spectrum disorder-associated molecular and microbial profiles. Nature Neuroscience, 26(7), 1208-1217. https://doi.org/10.1038/s41593-023-01361-0

Karakoc, D. B., Konar, M., Puma, M. J., & Varshney, L. R. (2023). Structural chokepoints determine the resilience of agri-food supply chains in the United States. Nature Food, 4(7), 607-615. https://doi.org/10.1038/s43016-023-00793-y

Jops, K., & O’Dwyer, J. P. (2023). Life history complementarity and the maintenance of biodiversity. Nature, 618(7967), 986-991. https://doi.org/10.1038/s41586-023-06154-w

Basu, S., Sattigeri, P., Ramamurthy, K. N., Chenthamarakshan, V., Varshney, K. R., Varshney, L. R., & Das, P. (2023). Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models. In B. Williams, Y. Chen, & J. Neville (Eds.), AAAI-23 Technical Tracks 6 (pp. 6788-6796). (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023; Vol. 37). American Association for Artificial Intelligence (AAAI) Press.

Choraria, M., Ferwana, I., Mani, A., & Varshney, L. R. (2023). Learning Optimal Features via Partial Invariance. In B. Williams, Y. Chen, & J. Neville (Eds.), AAAI-23 Technical Tracks 6 (pp. 7175-7183). (Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023; Vol. 37). American Association for Artificial Intelligence (AAAI) Press.

Bleier, N., Wezelis, A., Varshney, L., & Kumar, R. (2023). Programmable Olfactory Computing. In ISCA 2023 - Proceedings of the 2023 50th Annual International Symposium on Computer Architecture (pp. 358-371). (Proceedings - International Symposium on Computer Architecture). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1145/3579371.3589061

Cannavale, C. N., Edwards, C. G., Liu, R., Keye, S. A., Iwinski, S. J., Holscher, H. D., Renzi-Hammond, L., & Khan, N. A. (2023). Macular pigment is inversely related to circulating C-reactive protein concentrations in school-aged children. Nutrition Research, 114, 13-19. https://doi.org/10.1016/j.nutres.2023.03.003

Bailey, M. A., Thompson, S. V., Mysonhimer, A. R., Bennett, J. N., Vanhie, J. J., De Lisio, M., Burd, N. A., Khan, N. A., & Holscher, H. D. (2023). Dietary fiber intake and fecal short chain fatty acid concentrations are associated with lower plasma lipopolysaccharide-binding protein and inflammation. American Journal of Physiology - Gastrointestinal and Liver Physiology, 324(5), G369-G377. https://doi.org/10.1152/ajpgi.00176.2021

Mysonhimer, A. R., & Holscher, H. D. (2023). Nondigestible Carbohydrate Consumption: Balancing Therapeutics With Gastrointestinal Effects and Tolerance. Nutrition Today, 58(3), 100-104. https://doi.org/10.1097/NT.0000000000000605

Pan, C., Chien, E., & Milenkovic, O. (2023). Unlearning Graph Classifiers with Limited Data Resources. In ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023 (pp. 716-726). (ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023). Association for Computing Machinery. https://doi.org/10.1145/3543507.3583547

Hughes, R. L., Pindus, D. M., Khan, N. A., Burd, N. A., & Holscher, H. D. (2023). Associations between Accelerometer-Measured Physical Activity and Fecal Microbiota in Adults with Overweight and Obesity. Medicine and Science in Sports and Exercise, 55(4), 680-689. https://doi.org/10.1249/MSS.0000000000003096

O’Shaughnessy, M. R., Schiff, D. S., Varshney, L. R., Rozell, C. J., & Davenport, M. A. (2023). What governs attitudes toward artificial intelligence adoption and governance? Science and Public Policy, 50(2), 161-176. Article scac056. https://doi.org/10.1093/scipol/scac056

Donovan, S. M., Aghaeepour, N., Andres, A., Azad, M. B., Becker, M., Carlson, S. E., Järvinen, K. M., Lin, W., Lönnerdal, B., Slupsky, C. M., Steiber, A. L., & Raiten, D. J. (2023). Evidence for human milk as a biological system and recommendations for study design—a report from “Breastmilk Ecology: Genesis of Infant Nutrition (BEGIN)” Working Group 4. American Journal of Clinical Nutrition, 117, S61-S86. https://doi.org/10.1016/j.ajcnut.2022.12.021

Kim, J. S., Takahagi, K., Inoue, K., Shimizu, M., Uehara-Yamaguchi, Y., Kanatani, A., Saisho, D., Nishii, R., Lipka, A. E., Hirayama, T., Sato, K., & Mochida, K. (2023). Exome-wide variation in a diverse barley panel reveals genetic associations with ten agronomic traits in Eastern landraces. Journal of Genetics and Genomics, 50(4), 241-252. https://doi.org/10.1016/j.jgg.2022.12.001

Erickson, B. A., Jiang, J., Lambert, V., Barbot, S. D., Abdelmeguid, M., Almquist, M., Ampuero, J. P., Ando, R., Cattania, C., Chen, A., Dal Zilio, L., Deng, S., Dunham, E. M., Elbanna, A. E., Gabriel, A. A., Harvey, T. W., Huang, Y., Kaneko, Y., Kozdon, J. E., ... Yang, Y. (2023). Incorporating Full Elastodynamic Effects and Dipping Fault Geometries in Community Code Verification Exercises for Simulations of Earthquake Sequences and Aseismic Slip (SEAS). Bulletin of the Seismological Society of America, 113(2), 499-523. https://doi.org/10.1785/0120220066

Mysonhimer, A. R., Cannavale, C. N., Bailey, M. A., Khan, N. A., & Holscher, H. D. (2023). Prebiotic Consumption Alters Microbiota but Not Biological Markers of Stress and Inflammation or Mental Health Symptoms in Healthy Adults: A Randomized, Controlled, Crossover Trial. Journal of Nutrition, 153(4), 1283-1296. https://doi.org/10.1016/j.tjnut.2023.02.015

Pan, C., Chien, E., Tabaghi, P., Peng, J., & Milenkovic, O. (2023). Provably accurate and scalable linear classifiers in hyperbolic spaces. Knowledge and Information Systems, 65(4), 1817-1850. https://doi.org/10.1007/s10115-022-01820-3

Reichhardt, C., Regev, I., Dahmen, K., Okuma, S., & Reichhardt, C. J. O. (2023). Reversible to irreversible transitions in periodic driven many-body systems and future directions for classical and quantum systems. Physical Review Research, 5(2), Article 021001. https://doi.org/10.1103/PhysRevResearch.5.021001

Donovan, S. M., Abrams, S. A., Azad, M. B., Belfort, M. B., Bode, L., Carlson, S. E., Dallas, D. C., Hettinga, K., Järvinen, K., Kim, J. H., Lebrilla, C. B., McGuire, M. K., Sela, D. A., & Neu, J. (2023). Summary of the Joint National Institutes of Health and the Food and Drug Administration Workshop Titled “Exploring the Science Surrounding the Safe Use of Bioactive Ingredients in Infant Formula: Considerations for an Assessment Framework”. Journal of Pediatrics, 255, 30-41.e1. https://doi.org/10.1016/j.jpeds.2022.11.027

Chakraborty, R., Xiong, M., Athreya, N., Tabatabaei, S. K., Milenkovic, O., & Leburton, J. P. (2023). Solid-State MoS2 Nanopore Membranes for Discriminating among the Lengths of RNA Tails on a Double-Stranded DNA: A New Simulation-Based Differentiating Algorithm. ACS Applied Nano Materials, 6(6), 4651-4660. https://doi.org/10.1021/acsanm.3c00129

Alexander, F. J., Reyes, K. R., Varshney, L. R., & Yoon, B. J. (2023). AI for optimal experimental design and decision-making. In Artificial Intelligence For Science: A Deep Learning Revolution (pp. 609-625). World Scientific Publishing Co. Pte Ltd. https://doi.org/10.1142/9789811265679_0032

Burroughs, C. H., Montes, C. M., Moller, C. A., Mitchell, N. G., Michael, A. M., Peng, B., Kimm, H., Pederson, T. L., Lipka, A. E., Bernacchi, C. J., Guan, K., & Ainsworth, E. A. (2023). Reductions in Leaf Area Index, Pod Production, Seed Size and Harvest Index Drive Yield Loss to High Temperatures in Soybean. Journal of experimental botany, 74(5), 1629–1641. Article erac503. https://doi.org/10.1093/jxb/erac503

Wen, B., Ravishankar, S., Zhao, Z., Giryes, R., & Ye, J. C. (2023). Physics-Driven Machine Learning for Computational Imaging: Part 2 [From the Guest Editors]. IEEE Signal Processing Magazine, 40(2), 13-15. https://doi.org/10.1109/MSP.2023.3236492

Rashid, F., Dubinkina, V., Ahmad, S., Maslov, S., & Irudayaraj, J. M. K. (2023). Gut Microbiome-Host Metabolome Homeostasis upon Exposure to PFOS and GenX in Male Mice. Toxics, 11(3), Article 281. https://doi.org/10.3390/toxics11030281

Wang, T., Wang, X. W., Lee-Sarwar, K. A., Litonjua, A. A., Weiss, S. T., Sun, Y., Maslov, S., & Liu, Y. Y. (2023). Predicting metabolomic profiles from microbial composition through neural ordinary differential equations. Nature Machine Intelligence, 5(3), 284-293. https://doi.org/10.1038/s42256-023-00627-3

Fei, F., Mia, M. S., Elbanna, A. E., & Choo, J. (2023). A phase-field model for quasi-dynamic nucleation, growth, and propagation of rate-and-state faults. International Journal for Numerical and Analytical Methods in Geomechanics, 47(2), 187-211. https://doi.org/10.1002/nag.3465

Holscher, H. D. (2023). Let's do the math: embracing mathematical modeling to advance nutrition research. American Journal of Clinical Nutrition, 117(2), 220-221. https://doi.org/10.1016/j.ajcnut.2022.12.011

Splichal, I., Donovan, S. M., Kindlova, Z., Stranak, Z., Neuzil Bunesova, V., Sinkora, M., Polakova, K., Valaskova, B., & Splichalova, A. (2023). Release of HMGB1 and Toll-like Receptors 2, 4, and 9 Signaling Are Modulated by Bifidobacterium animalis subsp. lactis BB-12 and Salmonella Typhimurium in a Gnotobiotic Piglet Model of Preterm Infants. International journal of molecular sciences, 24(3), Article 2329. https://doi.org/10.3390/ijms24032329

Rana, P., & Varshney, L. R. (2023). Exploring limits to tree planting as a natural climate solution. Journal of Cleaner Production, 384, Article 135566. https://doi.org/10.1016/j.jclepro.2022.135566

Shinn, L. M., Mansharamani, A., Baer, D. J., Novotny, J. A., Charron, C. S., Khan, N. A., Zhu, R., & Holscher, H. D. (2023). Fecal Metabolites as Biomarkers for Predicting Food Intake by Healthy Adults. The Journal of nutrition, 152(12), 2956-2965. Article nxac195. https://doi.org/10.1093/jn/nxac195

Holthaus, T. A., Kashi, M., Cannavale, C. N., Edwards, C. G., Aguiñaga, S., Walk, A. D. M., Burd, N. A., Holscher, H. D., & Khan, N. A. (2023). MIND Dietary Pattern Adherence Is Selectively Associated with Cognitive Processing Speed in Middle-Aged Adults. The Journal of nutrition, 152(12), 2941-2949. Article nxac203. https://doi.org/10.1093/jn/nxac203

Kim, Y., Shin, J., Cassuto, Y., & Varshney, L. R. (2023). Distributed Boosting Classification Over Noisy Communication Channels. IEEE Journal on Selected Areas in Communications, 41(1), 141-154. https://doi.org/10.1109/JSAC.2022.3221972

Zhao, Z., Ye, J. C., & Bresler, Y. (2023). Generative Models for Inverse Imaging Problems: From mathematical foundations to physics-driven applications. IEEE Signal Processing Magazine, 40(1), 148-163. https://doi.org/10.1109/MSP.2022.3215282

Wen, B., Ravishankar, S., Zhao, Z., Giryes, R., & Ye, J. C. (2023). Physics-Driven Machine Learning for Computational Imaging [From the Guest Editor]. IEEE Signal Processing Magazine, 40(1), 28-30. https://doi.org/10.1109/MSP.2022.3222888

Gabrys, R., Pattabiraman, S., & Milenkovic, O. (2023). Reconstruction of Sets of Strings From Prefix/Suffix Compositions. IEEE Transactions on Communications, 71(1), 3-12. https://doi.org/10.1109/TCOMM.2022.3222341

Rana, V., Chien, E., Peng, J., & Milenkovic, O. (2023). Small-Sample Estimation of the Mutational Support and Distribution of SARS-CoV-2. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20(1), 668-682. https://doi.org/10.1109/TCBB.2022.3165395

Nambiar, A., Liu, S., Heflin, M., Forsyth, J. M., Maslov, S., Hopkins, M., & Ritz, A. (2023). Transformer Neural Networks for Protein Family and Interaction Prediction Tasks. Journal of Computational Biology, 30(1), 95-111. https://doi.org/10.1089/cmb.2022.0132

McMath, A. L., Iwinski, S., Shen, S., Bost, K. F., Donovan, S. M., & Khan, N. A. (2023). Adherence to screen time and physical activity guidelines is associated with executive function in US toddlers participating in the STRONG Kids 2 birth cohort study. The Journal of Pediatrics, 252, 22-30.e6. https://doi.org/10.1016/j.jpeds.2022.08.026

Luo, D., Chen, Z., Hu, K., Zhao, Z., Hur, V. M., & Clark, B. K. (2023). Gauge-invariant and anyonic-symmetric autoregressive neural network for quantum lattice models. Physical Review Research, 5(1), Article 013216. https://doi.org/10.1103/PhysRevResearch.5.013216

Varshney, K. R., & Varshney, L. R. (2023). A Banal Account of a Safety-Creativity Tradeoff in Generative AI. CEUR Workshop Proceedings, 3359, 163-165.

Liu, X., Milenkovic, O., & Moustakides, G. V. (2023). A Combinatorial Proof for the Dowry Problem. In 2023 IEEE Information Theory Workshop, ITW 2023 (pp. 538-543). (2023 IEEE Information Theory Workshop, ITW 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITW55543.2023.10161638

Shin, S., Zhao, H., & Shomorony, I. (2023). Adaptive Power Method: Eigenvector Estimation from Sampled Data. Proceedings of Machine Learning Research, 201, 1387-1410.

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Abolfathi, M., Shomorony, I., Vahid, A., & Jafarian, J. H. (2022). A Game-Theoretically Optimal Defense Paradigm against Traffic Analysis Attacks using Multipath Routing and Deception. In SACMAT 2022 - Proceedings of the 27th ACM Symposium on Access Control Models and Technologies (pp. 67-78). (Proceedings of ACM Symposium on Access Control Models and Technologies, SACMAT). Association for Computing Machinery. https://doi.org/10.1145/3532105.3535015

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Goyal, M., Serrano, G., Argemi, J., Shomorony, I., Hernaez, M., & Ochoa-Alvarez, I. (2022). JIND: Joint integration and discrimination for automated single-cell annotation. Bioinformatics, 38(9), 2488-2495. https://doi.org/10.1093/bioinformatics/btac140

Bhimaraju, A., Chatterjee, A., & Varshney, L. R. (2022). Expected Extinction Times of Epidemics With State-Dependent Infectiousness. IEEE Transactions on Network Science and Engineering, 9(3), 1104-1116. https://doi.org/10.1109/TNSE.2021.3131954

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Levick, K., Heckel, R., & Shomorony, I. (2022). Achieving the Capacity of a DNA Storage Channel with Linear Coding Schemes. In 2022 56th Annual Conference on Information Sciences and Systems, CISS 2022 (pp. 218-223). (2022 56th Annual Conference on Information Sciences and Systems, CISS 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISS53076.2022.9751151

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Ravi, A. N., Vahid, A., & Shomorony, I. (2022). Capacity of the Shotgun Sequencing Channel. In 2022 IEEE International Symposium on Information Theory, ISIT 2022 (pp. 210-215). (IEEE International Symposium on Information Theory - Proceedings; Vol. 2022-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIT50566.2022.9834409

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Raman, R. K., & Varshney, L. R. (2022). Information-Theoretic Approaches to Blockchain Scalability. In Springer Optimization and Its Applications (pp. 257-296). (Springer Optimization and Its Applications; Vol. 194). Springer. https://doi.org/10.1007/978-3-031-07535-3_8

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Saldi, N., Başar, T., & Raginsky, M. (Accepted/In press). Partially Observed Discrete-Time Risk-Sensitive Mean Field Games. Dynamic Games and Applications. https://doi.org/10.1007/s13235-022-00453-z

Bhimaraju, A., & Varshney, L. R. (2022). Scheduling Group Tests over Time. In 2022 IEEE International Symposium on Information Theory, ISIT 2022 (pp. 886-891). (IEEE International Symposium on Information Theory - Proceedings; Vol. 2022-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIT50566.2022.9834434

Marla, L., Varshney, L. R., Shah, D., Prakash, N. A., & Gale, M. E. (2022). Short and Wide Network Paths. IEEE Transactions on Network Science and Engineering, 9(2), 524-537. https://doi.org/10.1109/TNSE.2021.3123311

Wu, X., Hanganu, A., Hoshino, A., & Varshney, L. R. (2022). Source Identification for Exosomal Communication via Protein Language Models. In 2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing, MLSP 2022 (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol. 2022-August). IEEE Computer Society. https://doi.org/10.1109/MLSP55214.2022.9943418

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