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<h1>Alexandre Evfimievski</h1>
<p align="left"><u><b>Publications and Manuscripts:</b></u></p>
<p align="left">
[1] K. Killamsetty, A. V. Evfimievski, T. Pedapati, K. Kate, L. Popa, R. Iyer, “MILO: Model-agnostic subset selection framework for efficient model training and tuning,” preprint arXiv:2301.13287, Jan 2023.<br><br>
[2] K. Killamsetty, G. S. Abhishek, A. Lnu, G. Ramakrishnan, A. Evfimievski, L. Popa, R. Iyer, “AUTOMATA: Gradient based data subset selection for compute-efficient hyper-parameter tuning,” in NeurIPS’22: Advances in Neural Information Processing Systems, vol. 35, pp. 28721–28733, 2022.<br><br>
[3] A. Chaudhary, A. Issak, K. Kate, Y. Katsis, A. Valente, D. Wang, A. Evfimievski, S. Gurajada, B. Kawas, C. Malossi, L. Popa, T. Pedapati, H. Samulowitz, M. Wistuba, Y. Li, “AutoText: An end-to-end AutoAI framework for text,” Proc. of the AAAI Conference on Artificial Intelligence, vol. 35, no. 18, pp. 16001–16003, 2021.<br><br>
[4] J. Sommer, M. Boehm, A. V. Evfimievski, B. Reinwald, P. J. Haas, “MNC: Structure-exploiting sparsity estimation for matrix expressions,” in SIGMOD’19: Proc. of the 2019 International Conference on Management of Data, Amsterdam, pp. 1607–1623, Jun 2019.<br><br>
[5] M. Boehm, A. Evfimievski, B. Reinwald, “Efficient data-parallel cumulative aggregates for large-scale machine learning,” in BTW’19: Datenbanksysteme für Business, Technologie und Web, pp. 267–286, Gesellschaft für Informatik, Bonn, Mar 2019.<br><br>
[6] M. Boehm, B. Reinwald, D. Hutchison, A. V. Evfimievski, P. Sen, “On optimizing operator fusion plans for large-scale machine learning in SystemML,” preprint arXiv:1801.00829, Jan 2018.<br><br>
[7] X. Chen, L. Chiticariu, M. Danilevsky, A. Evfimievski, P. Sen, “A rectangle mining method for understanding the semantics of financial tables,” in ICDAR’17: Proc. of the 14th IAPR International Conference on Document Analysis and Recognition, Kyoto, Japan, pp. 268–273, IEEE, Nov 2017.<br><br>
[8] T. Elgamal, S. Luo, M. Boehm, A. V. Evfimievski, S. Tatikonda, B. Reinwald, P. Sen, “SPOOF: Sum-product optimization and operator fusion for large-scale machine learning,” in CIDR’17: Proc. of the 8th Biennial Conference on Innovative Data Systems Research, Chaminade, CA, USA, Jan 2017.<br><br>
[9] M. Boehm, M. W. Dusenberry, D. Eriksson, A. V. Evfimievski, F. M. Manshadi, N. Pansare, B. Reinwald, F. R. Reiss, P. Sen, A. C. Surve, S. Tatikonda, “SystemML: Declarative machine learning on Spark,” Proc. of the VLDB Endowment, vol. 9, pp. 1425–1436, Sep 2016.<br><br>
[10] M. Boehm, A. V. Evfimievski, N. Pansare, B. Reinwald, “Declarative machine learning – a classification of basic properties and types,” preprint arXiv:1605.05826, May 2016.<br><br>
[11] S. Schelter, J. Soto, V. Markl, D. Burdick, B. Reinwald, A. Evfimievski, “Efficient sample generation for scalable meta learning,” in Proc. of 2015 IEEE 31st International Conference on Data Engineering, Seoul, Korea, pp. 1191–1202, Apr 2015.<br><br>
[12] M. Boehm, D. R. Burdick, A. V. Evfimievski, B. Reinwald, F. R. Reiss, P. Sen, S. Tatikonda, Y. Tian, “SystemML’s optimizer: Plan generation for large-scale machine learning programs,” IEEE Data Engineering Bulletin, vol. 37, pp. 52–62, Sep 2014.<br><br>
[13] D. Burdick, A. Evfimievski, R. Krishnamurthy, N. Lewis, L. Popa, S. Rickards, P. Williams, “Financial analytics from public data,” in DSMM’14: Proc. of the International Workshop on Data Science for Macro-Modeling, Snowbird, Utah, pp. 1–6, Jun 2014.<br><br>
[14] A. Evfimievski, R. Fagin, D. Woodruff, “Epistemic privacy,” Journal of the ACM, vol. 58, pp. 1–45, Dec 2010.<br><br>
[15] A. Evfimievski and T. Grandison, “Privacy-preserving data mining,” in Handbook of Research on Innovations in Database Technologies and Applications: Current and Future Trends, ch. 56, pp. 527–536, Feb 2009.<br><br>
[16] A. Evfimievski, R. Fagin, D. P. Woodruff, “Epistemic privacy,” in PODS’08: Proc. of the 27th ACM Symposium on Principles of Database Systems, Vancouver, Canada, pp. 171–180, Jun 2008.<br><br>
[17] R. Agrawal, A. Evfimievski, J. Kiernan, R. Velu, “Auditing disclosure by relevance ranking,” in SIGMOD’07: Proc. of the 2007 ACM SIGMOD International Conference on Management of Data, Beijing, China, pp. 79–90, Jun 2007.<br><br>
[18] A. V. Evfimievski, Privacy Preserving Information Sharing. PhD thesis, Cornell University, Ithaca, NY, Aug 2004.<br><br>
[19] R. Agrawal, A. Evfimievski, R. Srikant, “Information sharing across private databases,” in SIGMOD’03: Proc. of the 2003 ACM SIGMOD International Conference on Management of Data, San Diego, CA, pp. 86–97, Jun 2003.<br><br>
[20] A. Evfimievski, J. Gehrke, R. Srikant, “Limiting privacy breaches in privacy preserving data mining,” in PODS’03: Proc. of the 22nd ACM Symposium on Principles of Database Systems, San Diego, CA, pp. 211–222, Jun 2003.<br><br>
[21] A. Evfimievski, “Randomization in privacy preserving data mining,” ACM SIGKDD Explorations Newsletter, vol. 4, pp. 43–48, Dec 2002.<br><br>
[22] A. Evfimievski, R. Srikant, R. Agrawal, J. Gehrke, “Privacy preserving mining of association rules,” in KDD’02: Proc. of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Canada, pp. 217–228, Jul 2002.<br><br>
[23] A. Evfimievski, “A probabilistic algorithm for updating files over a communication link,” in SODA’98: Proc. of the 9th Annual ACM-SIAM Symposium on Discrete Algorithms, San Francisco, CA, pp. 300–305, Jan 1998.<br><br>
</p>
<p align="left"><u><b>Patents and Patent Applications:</b></u></p>
<p align="left">
[P1] K. Killamsetty, A. Evfimievski, T. Pedapati, K. A. Kate, L. Popa, R. K. Iyer, “Model-independent data subsets.” U.S. Patent Application # 18/523,958, Nov 30 2023. Filed.<br><br>
[P2] D. Wang, C. Gan, G. Bramble, L. Amini, H. C. Samulowitz, K. A. Kate, B. Chen, M. Wistuba, A. Evfimievski, I. Katsis, Y. Li, A. C. I. Malossi, A. Bartezzaghi, B. Kawas, S. Gurajada, L. Popa, T. Pedapati, A. Gray, “Using meta-learning to optimize automatic selection of machine learning pipelines.” U.S. Patent Application # 16/990,965, Aug 11 2020. Filed.<br><br>
[P3] D. R. Burdick, A. V. Evfimievski, B. Reinwald, S. Schelter, “Single-pass distributed sampling from block-partitioned matrices.” U.S. Patent Application # 16/271,485, Feb 8 2019. Issued on 12/07/2021 as U.S. Patent # 11,194,826.<br><br>
[P4] D. R. Burdick, W. Cheng, A. Evfimievski, M. D. Hailpern, R. Krishnamurthy, S. I. Mohamed, P. Sen, S. Vaithyanathan, “Table recognition in portable document format documents.” U.S. Patent Application # 16/050,803, Jul 31 2018. Issued as U.S. Patent # 11,200,413 on 12/14/2021.<br><br>
[P5] X. Chen, L. Chiticariu, A. Evfimievski, M. D. Hailpern, P. Sen, “Extracting structure and semantics from tabular data.” U.S. Patent Application # 15/916,535, Mar 9 2018. Issued on 05/16/2023 as U.S. Patent # 11,650,970.<br><br>
[P6] A. Ashari, M. Boehm, K. W. Campbell, A. Evfimievski, J. D. Keenleyside, B. Reinwald, S. Tatikonda, “Pipelined approach to fused kernels for optimization of machine learning workloads on graphical processing units.” U.S. Patent Application # 15/924,029, Mar 16 2018. Issued on 03/05/2019 as U.S. Patent # 10,223,762.<br><br>
[P7] R. Agrawal, A. V. Evfimievski, G. Kiernan, R. Velu, “System and method for tracking database disclosures.” U.S. Patent Application # 12/131,079, May 31 2008. Filed.<br><br>
[P8] W. Chen, A. V. Evfimievski, Z. Liu, R. Rantzau, A. V. Riabov, P. Rohatgi, A. M. Schuett, R. Srikant, G. Wagner, “Access control method and a system for privacy protection.” U.S. Patent Application # 12/130,308, May 30 2008. Issued on 10/04/2011 as U.S. Patent # 8,032,924.<br><br>
[P9] R. Agrawal, A. V. Evfimievski, R. Srikant, “Information integration across autonomous enterprises.” U.S. Patent Application # 11/924,519, Oct 25 2007. Issued on 10/18/2011 as U.S. Patent # 8,041,706.<br><br>
[P10] K. S. Anderson, A. V. Evfimievski, M. D. Feblowitz, G. Grabarnik, N. Halim, Z. Liu, R. Rantzau, A. V. Riabov, A. Schuett, R. Srikant, G. Wagner, “Mitigating and managing privacy risks using planning.” U.S. Patent Application # 11/493,321, Jul 26 2006. Issued on 03/20/2012 as U.S. Patent # 8,141,160.<br><br>
[P11] R. Agrawal, A. Evfimievski, R. Srikant, “Mining association rules over privacy preserving data.” U.S. Patent Application # 10/624,069, Jul 21 2003. Filed.<br><br>
</p>
<p align="left"><u><b>Tutorials and Links:</b></u></p>
<p align="left">
[L1] D. Burdick, M. Danilevsky, A. V. Evfimievski, Y. Katsis, N. Wang, “Table extraction and understanding for scientific and enterprise applications (tutorial),” Proc. of the VLDB Endowment, vol. 13, pp. 3433–3436, Aug 2020.<br><br>
[L2] D. Burdick, M. Danilevsky, A. V. Evfimievski, Y. Katsis, N. Wang, “Table extraction and understanding for scientific and enterprise applications (tutorial),” Nov 2019. ICDM’19: The 19th IEEE International Conference on Data Mining.<br><br>
[L3] M. Boehm, A. Evfimievski, N. Pansare, B. Reinwald, P. Sen, “Declarative, large-scale machine learning with Apache SystemML (tutorial),” Aug 13–17 2017. KDD’17: The 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining.<br><br>
[L4] <a href="https://developer.ibm.com/learningpaths/get-started-autoai-for-text-api/what-is-autotai-text/?mhsrc=ibmsearch_a&mhq=AutoAI%20for%20Text">What is AutoAI for Text?</a><br><br>
[L5] <a href="https://systemml.apache.org">Apache SystemML.</a><br><br>
[L6] <a href="https://systemds.apache.org/docs/1.2.0/algorithms-reference">SystemML algorithms reference.</a><br><br>
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