Pravin Muthu has experience in robotics and sensor development, stemming from his academic background and professional work in defense and healthcare. At Johns Hopkins University, Pravin teaches AI applications for robotic systems, focusing on training deep learning models for computer vision tasks to enable robot interaction with their environment. His professional work includes designing military-grade software and hardware interfaces for unmanned systems, ensuring compliance with cybersecurity standards and integrating low-latency communication protocols into AI-driven decision-making for critical missions. He led the development of secure, scalable software stacks, integrating AI/ML models with telemetry systems, real-time data pipelines, and databases to support edge AI processing and enable real-time decision-making at the sensor level. He also has expertise in integrating diverse data sources, such as analytical laboratory data, imaging modalities, and natural language processing techniques. Additionally, he served as the principal investigator (PI) for developing multimodal AI for surveillance networks, using natural language and image processing to predict disease transmission and progression by parsing documents. He has also led the development of predictive analytics models by integrating diverse documents within the military supply chain, presenting these models to users through dashboards, reports, and a chatbot interface. Pravin’s work has involved developing multimodal sensors that utilize data fusion from several analytical instruments to identify threats. These experiences across diverse projects solidify Pravin’s deep understanding of robotics and sensor development, encompassing theoretical knowledge and practical implementation for real-world applications.

Education History

  • B.S., Chemical Engineering, Johns Hopkins University
  • M.S., Chemical Engineering, Johns Hopkins University
  • Ph.D., Chemistry, Emory University

Work Experience

Director, Credence

Publications

Muthu, P. and Lutz, S. Quantitative Detection of Nucleoside Analogues by Multi‐enzyme Biosensors using Time‐Resolved Kinetic Measurements. ChemMedChem 11, 660–666 (2016).
Straight, S.; Gardner, M.; Muthu, P.; Williamson, W.; Favela, K.; Menchaca, A.; Harry, E.; Manley, T.; Wilkerson, J.; Krueger, C.; Martinez, R.; Abrha, Y. Chemical Attribution Signatures in VX Produced from DIAEC and EMPTA-Na. Journal of Chemical and Biological Defense (2016.)
Balaji A, Albin, D., Nasko, D., Elworth, R.L., Lu, J., Diaz, C., Shah, N., Selengut, J., Hulme-Lowe, C., Muthu, P. and Godbold, G., SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning. Genome biology. 2022 Jun 20;23(1):133.
Straight, S.; Gardner, M.; Krueger, C.; Muthu, P.; Harry E.; Manley, T; Acevedo, C. Chemical Attribution Signatures of Fentanyl Derivatives Identified by the Application of Data Analytics. Journal of Chemical and Biological Defense, 2016.
Gardner, M.W., Muthu, P.J., McAvey, K.M., Corless, G.P., DeBuck, L.H., Patterson, J.M. Chemical Attribution Signatures of Homemade Explosives, CTTSO US Personnel HME Working Group, Washington, DC, September 7–8, 2016.
Gardner, M.W., Smith, A.R., Krueger, C.J., Manley, T.E., Reaves, M.A., Hulme-Lowe, C., Muthu, P.J. Identification of Chemical Signatures Attributable to Strychnine Sources Using Predictive Modeling of a Fused GC-MS, LC-HRAM-MS, and ICP-MS Dataset, 250th American Chemical Society National Meeting, Boston, MA, August 16–20, 2015.
P Muthu, HX Chen, S Lutz. Redesigning Human 2′-Deoxycytidine Kinase Enantioselectivity for l-Nucleoside Analogues as Reporters in Positron Emission Tomography ACS Chemical Biology 9 (10), 2326-2333.
AB Daugherty, P Muthu, S Lutz Novel protease inhibitors via computational redesign of subtilisin BPN′ propeptide Biochemistry 51 (41), 8247-8255.
S Lutz, E Williams, P Muthu Engineering therapeutic enzymes Directed Enzyme Evolution: Advances and Applications, 17-67.
E Matyugina, M Novikov, D Babkov, A Ozerov, L Chernousova, Chemical biology & Drug Design 86 (6), 1387-1396 5‐Arylaminouracil Derivatives: New Inhibitors of Mycobacterium tuberculosis.
S. Chaudhury, M. Berrondo, BD Weitzner, P. Muthu, H. Bergman, JJ Gray. Benchmarking and analysis of protein docking performance in Rosetta v3. 2. PloS one 6 (8), e22477.
KP Kilambi, MS Pacella, J Xu, JW Labonte, JR Porter, P. Muthu, K. Drew. Extending RosettaDock with water, sugar, and pH for prediction of complex structures and affinities for CAPRI rounds 20–27 Proteins: Structure, Function, and Bioinformatics 81 (12), 2201-2209.
Lensink, Marc F., et al. “Blind prediction of interfacial water positions in CAPRI.” Proteins: Structure, Function, and Bioinformatics 82.4 (2014): 620-632.
Albin, D., Nasko, D., Elworth, R.L., Lu, J., Balaji, A., Diaz, C., Shah, N., Selengut, J., Hulme-Lowe, C., Muthu, P. and Godbold, G., 2019, November. SeqScreen: a biocuration platform for robust taxonomic and biological process characterization of nucleic acid sequences of interest. In 2019 IEEE international conference on bioinformatics and biomedicine (BIBM) (pp. 1729-1736). IEEE.