Anastasiu, David

I am an Assistant Professor in the Computer Science and Engineering department at Santa Clara University. Between 2016 and 2019, I was an Assistant Professor in the Computer Engineering department at San José State University. In 2016, I received my Ph.D. in computer science from the University of Minnesota. My advisor was George Karypis. In 2011, I received my M.S. in computer science from Texas State University, under the guidance of Byron J. Gao.

Highlights:

• Research in machine learning, data mining, computational genomics, and high-performance computing.
• Received the Next Generation Data Scientist award at IEEE DSAA 2016, for work on efficient exact nearest neighbor search methods.
• Built a supercomputer from scratch, for the research and education benefit of SJSU faculty and students.
• Passionate about teaching and mentoring students.

Student co-authors are denoted with an asterisk (*).

Journals

• Andrea Tagarelli, Ester Zumpano, David C. Anastasiu, Andrea Calì, and Gottfried Vossen. Managing, Mining and Learning in the Legal Data Domain. Inf. Syst., 106(C), Elsevier Science Ltd., 2022. Impact factor: 7.767.
• Bipasa Bose, Taylor Downey*, Anad K. Ramasubramanian, and David C. Anastasiu. Identification of Distinct Characteristics of Antibiolfilm Peptides and Prospection of Diverse Sources for Efficacious Sequences. Frontiers in Microbiology, 12, 2022. Impact factor: 6.064.
• David C. Anastasiu, Jack Gaul*, Maria Vazhaeparambil*, Meha Gaba, and Prajval Sharma. Efficient City-Wide Multi-Class Multi-Movement Vehicle Counting: A Survey. Journal of Big Data Analytics in Transportation, 2(3):235-250, 2020.
• David C. Anastasiu and George Karypis. Parallel cosine nearest neighbor graph construction. Elsevier Journal of Parallel and Distributed Computing, 2017. Impact factor: 1.815.
• David C. Anastasiu and George Karypis. Efficient identification of tanimoto nearest neighbors; all pairs similarity search using the extended jaccard coefficient. Springer International Journal of Data Science and Analytics, 4(3):153–172, Nov 2017.
• David C. Anastasiu and Andrea Tagarelli. Document clustering. Wiley StatsRef: Statistics Reference Online, pages 1–11, 2017.
• David C. Anastasiu, Evangelia Christakopoulou, Shaden Smith, Mohit Sharma, and George Karypis. Big data and recommender systems. Novática: Journal of the Spanish Computer Scientist Association, (237):39–45, October 2016.
• David C. Anastasiu, Byron J. Gao, Xing Jiang, and George Karypis. A novel two-box search paradigm for query disambiguation. World Wide Web, 16(1):1–29, 2013. Impact Factor: 0.653.
• Byron J. Gao, David Buttler, David C. Anastasiu, Shuaiqiang Wang, Peng Zhang, and Joey Jan. User-centric organization of search results. IEEE Internet Computing, 17(3):52–59, May 2013. Impact factor: 0.758.

Conference Proceedings

• Alex Whelan*, Soham Phadke*, and David C. Anastasiu. On-Device Prediction for Chronic Kidney Disease. In 2022 IEEE Global Humanitarian Technology Conference (GHTC) (GHTC 2022), 2022.
• Arpita Vats*, Gheorghi Guzun, and David C. Anastasiu. CLP: A Platform for Competitive Learning. In Educating for a New Future: Making Sense of Technology-Enhanced Learning Adoption (EC-TEL 2022), pages 615-622, Springer International Publishing, 2022.
• Zheng Tang, Milind Naphade, Ming-Yu Liu, Xiaodong Yang, Stan Birchfield, Shuo Wang, Ratnesh Kumar, David C. Anastasiu, and Jenq-Neng Hwang. Cityflow: A city-scale benchmark for multi-target multi-camera vehicle tracking and re-identification. In CVPR 2019: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, 2019. Acceptance rate: 25%.
• Manika Kapoor* and David C. Anastasiu. A data-driven approach for detecting autism spectrum disorders. In Peter Haber, Thomas Lampoltshammer, and Manfred Mayr, editors, Data Science – Analytics and Applications, IDSC 2019, Wiesbaden, 2019. Springer Fachmedien Wiesbaden.
• Saloni Mohan*, Sahitya Mullapudi*, Sudheer Sammeta*, Parag Vijayvergia*, and David C. Anastasiu. Stock price prediction using news sentiment analysis. In 2019 IEEE Fourth International Conference on Big Data Computing Service and Applications (BigDataService), BDS 2019. IEEE, April 2018.
• Shuai Hua* and David C. Anastasiu. Effective vehicle tracking algorithm for smart traffic networks. In Thirteenth IEEE International Conference on Service-Oriented System Engineering (SOSE), SOSE 2019. IEEE, April 2019. Acceptance rate: 28%.
• Anupama Upadhyayula*, Avinash Ravilla*, Ishwarya Varadarajan*, Sowmya Viswanathan*, and David C. Anastasiu. Study area recommendation via network log analytics. In The Seventh IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MCCSE 2019. IEEE, April 2019.
• Swapnil Gaikwad*, Melody Moh, and David C. Anastasiu. Data structure for efficient line of sight queries. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM’18, New York, NY, USA, 2018. ACM. Acceptance rate: 18%.
• Manika Kapoor*, Shuai Hua*, and David C. Anastasiu. Improving student motivation through competitive active learning. In 2018 IEEE Frontiers in Education Conference, FIE 2018. IEEE, October 2018.
• Swapnil Gaikwad* and David C. Anastasiu. Optimal constrained wireless emergency network antenna placement. In Proceedings of the IEEE Smart City Innovations 2017 Conference, IEEE SCI 2017, 2017.
• David C. Anastasiu. Cosine approximate nearest neighbors. In Peter Haber, Thomas Lampoltshammer, and Manfred Mayr, editors, Data Science – Analytics and Applications, iDSC 2017, pages 45–50, Wiesbaden, 2017. Springer Fachmedien Wiesbaden.
• David C. Anastasiu and George Karypis. Efficient identification of tanimoto nearest neighbors. In 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), DSAA’16, pages 156–165, 2016. Best Research Paper Award. Acceptance rate: 20.2%.
• David C. Anastasiu and George Karypis. L2knng: Fast exact k-nearest neighbor graph construction with l2- norm pruning. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management, CIKM’15, pages 791–800, New York, NY, USA, 2015. ACM. Acceptance rate: 26%.
• David C. Anastasiu, Al M. Rashid, Andrea Tagarelli, and George Karypis. Understanding computer usage evolution. In 31st IEEE International Conference on Data Engineering, ICDE 2015, pages 1549–1560, 2015. Acceptance rate: 25%.
• David C. Anastasiu and George Karypis. L2ap: Fast cosine similarity search with prefix l-2 norm bounds. In The 30th IEEE International Conference on Data Engineering, ICDE 2014, pages 784–795, 2014. Acceptance rate: 20%.
• David C. Anastasiu, Byron J. Gao, and David Buttler. A framework for personalized and collaborative clustering of search results. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM’11, pages 573–582, New York, NY, USA, 2011. ACM. Acceptance rate: 25%.
• David C. Anastasiu, Byron J. Gao, and David Buttler. Clusteringwiki: personalized and collaborative clustering of search results. In The 34th ACM SIGIR International Conference on Research and Development in Information Retrieval, SIGIR 2011, pages 1263–1264, 2011. Acceptance rate: 20%.
• Byron J. Gao, David C. Anastasiu, and Xing Jiang. Utilizing user-input contextual terms for query disambiguation. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters, COLING’10, pages 329–337, Stroudsburg, PA, USA, 2010. Association for Computational Linguistics. Acceptance rate: 41%.
• Byron J. Gao, Mingji Xia, Walter Cai, and David C. Anastasiu. The gardener’s problem for web information monitoring. In Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM’09, pages 1525–1528, New York, NY, USA, 2009. ACM. Acceptance rate: 15%.
• Walter Cai, David C. Anastasiu, Mingji Xia, and Byron J. Gao. Olap for multicriteria maintenance scheduling. In The 5th International Conference on Data Mining, DMIN’09, pages 35–41. CSREA Press, 2009.

Book Chapters

• Evangelia Christakopoulou, Shaden Smith, Mohit Sharma, Alex Richards*, David C. Anastasiu, and George Karypis. Scalability and distribution of collaborative recommenders. In Collaborative Recommendations: Algorithms, Practical Challenges, and Applications. World Scientific Publishing, Singapore, April 2019.
• David C. Anastasiu, Jeremy Iverson, Shaden Smith, and George Karypis. Big data frequent pattern mining. In Frequent Pattern Mining, pages 225–260. Springer International Publishing, Switzerland, 2014.
• David C. Anastasiu, Andrea Tagarelli, and George Karypis. Document clustering: The next frontier. In Data Clustering: Algorithms and Applications, pages 305–338. CRC Press, Boca Raton, FL, USA, 2013.

Workshops

• Dorian Clay* and David C. Anastasiu. Expanding Neuro-Symbolic Artificial Intelligence for Strategic Learning. In The 2022 KDD undergraduate Consortium (KDD-UC), 2022.
• Raghav Kapoor*, Casey Nguyen*, and David Anastasiu. InterpNet: Interpretability for Autonomous Driving Neural networks. In The 2022 KDD undergraduate Consortium (KDD-UC), 2022.
• Arpita Vats* and David C. Anastasiu. Key Point-Based Driver Activity Recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pages 3274-3281, 2022.
• M. Naphade, S. Wang, D.C. Anastasiu, Z. Tang, M. Chang, Y. Yao, L. Zheng, M. Shaiqur Rahman, A. Venkatachalapathy, A. Sharma, Q. Feng, V. Ablavsky, S. Sclaroff, P. Chakraborty, A. Li, S. Li, and R. Chellappa. The 6th AI City Challenge. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (CVPRW'22), pages 3346-3355, IEEE Computer Society, 2022.
• Milind Naphade, Shuo Wang, David C. Anastasiu, Zheng Tang, Ming Ching Chang, Xiaodong Yang, Yue Yao, Liang Zheng, Pranamesh Chakraborty, Christian E. Lopez, Anuj Sharma, Qi Feng, Vitaly Ablavsky, and Stan Sclaroff. The 5th AI City Challenge. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (CVPRW'21), pages 4263-4273, 2021.
• Milind Naphade, Shuo Wang, David C. Anastasiu, Zheng Tang, Ming Ching Chang, Xiaodong Yang, Liang Zheng, Anuj Sharma, Rama Chellappa, and Pranamesh Chakraborty. The 4th AI City Challenge. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (CVPRW'20), 1:2665-2674, 2020.
• Milind Naphade, Zheng Tang, Ming-Ching Chang, David C. Anastasiu, Anuj Sharma, Rama Chellappa, Shuo Wang, Pranamesh Chakraborty, Tingting Huang, Jenq-Neng Hwang, and Siwei Lyu. The 2019 ai city challenge. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. IEEE, June 2019.
• Shuai Hua*, Manika Kapoor*, and David C. Anastasiu. Vehicle tracking and speed estimation from traffic videos. In 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW’18. IEEE, July 2018.
• Milind Naphade, Ming-Ching Chang, Anuj Sharma, David C. Anastasiu, Vamsi Jagarlamudi, Pranamesh Chakraborty, Tingting Huang, Shuo Wang, Ming-Yu Liu, Rama Chellappa, Jenq-Neng Hwang, and Siwei Lyu. The 2018 nvidia ai city challenge. In 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW’18. IEEE, July 2018.
• Niveditha Bhandary*, Charles MacKay*, Alex Richards*, Ji Tong*, and David C. Anastasiu. Robust classification of city roadway objects for traffic related applications. In 2017 IEEE Smart World NVIDIA AI City Challenge, SmartWorld’17, Piscataway, NJ, USA, 2017. IEEE.
• Milind Naphade, David C. Anastasiu, Anuj Sharma, Vamsi Jagrlamudi, Hyeran Jeon, Kaikai Liu, Ming-Ching Chang, Siwei Lyu, and Zeyu Gao. The nvidia ai city challenge. In 2017 IEEE SmartWorld Conference, SmartWorld’17, Piscataway, NJ, USA, 2017. IEEE.
• David C. Anastasiu and George Karypis. Fast parallel cosine k-nearest neighbor graph construction. In 2016 6th Workshop on Irregular Applications: Architecture and Algorithms (IA3), IA3 2016, pages 50–53, Nov 2016. Acceptance rate: 48.3%.
• David C. Anastasiu and George Karypis. Pl2ap: Fast parallel cosine similarity search. In Proceedings of the 5th Workshop on Irregular Applications: Architectures and Algorithms, in conjunction with SC’15, IA3 2015, pages 1–8, New York, NY, USA, 2015. ACM. Acceptance rate: 58.3%.

Technical Reports

• Bipasa Bose, Taylor Downey*, Anand K. Ramasubramanian, and David C. Anastasiu. Identification of distinct characteristics of antibiofilm peptides and prospection of diverse sources for efficacious sequences. bioRxiv, Cold Spring Harbor Laboratory, 2021.
• Manika Kapoor* and David C. Anastasiu. A data-driven approach for detecting autism spectrum disorders. Technical Report 2019-1, San José State University, San José, CA, USA, 2019.
• David. J. Buttler, David Andrzejewski, Keith D. Stevens, David C. Anastasiu, and Byron J. Gao. Rapid exploitation and analysis of documents. Technical Report LLNL-TR-517731, Lawrence Livermore National Laboratory, Livermore, CA, USA, 2011.

Tutorials, Keynotes, and Invited Talks

• David C. Anastasiu, Huzefa Rangwala, and Andrea Tagarelli. Tutorial: Are You My Neighbor? Bringing Order to Neighbor Computing Problems. In The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'19), ACM. Anchorage, AK, USA, 2019-08-04.
• David C. Anastasiu. Keynote: The AI Data Revolution: Doing More With Less Data Labeling. 2nd International Data Science Conference 2019, Salzburg, Austria, 2019-05-22.
• David C. Anastasiu. Keynote: The Biomedical AI Revolution. 10th Annual Bay Area Biomedical Device Conference, San Jose, CA, 2019-04-03.
• David C. Anastasiu. Fireside Chat on Current Trends in Machine Learning. IBM Machine Learning Hub, San Jose, CA, 2019-09-25.
• Alessandro Bellofiore, Ragwa Elsayed*, David C. Anastasiu, and Rathna Ramesh*. A novel image-based creatinine monitor for chronic kidney disease. Dublin, Ireland, 2019-07-11.
• David C. Anastasiu. Efficient Neighborhood Graph Construction for Sparse High Dimensional Data. Lawrence Livermore National Laboratory, Livermore, CA, 2017-02-08.

In Preparation

• Darshit Thesiya*, Tanmay Bhatt*, Shruti Padmanabhan*, Vikas Miyani*, and David C. Anastasiu. Human activity recognition and pattern discovery from time series, 2018. Manuscript submitted for publication.
• David C. Anastasiu, Farshid Marbouti, Manika Kapoor*, and Shuai Hua*. Improving student academic performance through a competitive learning platform, 2018. Manuscript submitted for publication.
• Rathna Ramesh*, Ragwa Elsayed*, Alessandro Bellofiore, and David C. Anastasiu. Kidney disease screening as easy as saying “cheese". Manuscript submitted for publication.
• Rathna Ramesh*, Shaden Smith, and David C. Anastasiu. Bipartite network projection analysis.

Tutorials, Keynotes, and Invited Talks

• David C. Anastasiu, Huzefa Rangwala, and Andrea Tagarelli. Are you my neighbor? bringing order to neighbor computing problems. In The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD’19, New York, NY, USA, 2019. ACM. Tutorial presented at KDD’19, Aug 04, 2019.
• David C. Anastasiu. The ai data revolution: Doing more with less data labeling. Keynote at the 2nd International Data Science Conference 2019, Salzburg, Austria. May 22, 2019.
• David C. Anastasiu. The biomedical ai revolution. Keynote at the 10th Annual Bay Area Biomedical Device Conference, San José, CA. Apr 03, 2019.
• David C. Anastasiu. Fireside chat on current trends in machine learning. Talk at IBM Machine Learning Hub, San José, CA. Sep 25, 2018.
• Alessandro Bellofiore, Ragwa Elsayed, David C. Anastasiu, and Rathna Ramesh. A novel image-based creatinine monitor for chronic kidney disease. Dublin, Ireland, 2018-07-11.
• David C. Anastasiu. Efficient neighborhood graph construction for sparse high dimensional data. Talk at Lawrence Livermore National Laboratory, Livermore, CA, Feb 08, 2017.

Posters

• Yijia Li, David C. Anastasiu, and Edgar Arriaga. CosTal: A network-based algorithm for clustering high-dimensional single-cell datasets. 2022. Abstract and poster presented by Yijia Li at the 30th annual conference on Intelligent Systems for Molecular Biology (ISMB), Madison, WI. Acceptance rate: 19.8%
• David C. Anastasiu and Gheorghi Guzun. Using Competitive Learning to Increase Student Engagement. 2021. Poster presented by David C. Anastasiu at the 8th Academic Technology Expo (ATXpo 2021), Berkeley, CA.
• Manika Kapoor* and David C. Anastasiu. A data-driven approach for detecting autism spectrum disorders, 2018. Poster presented by Manika Kapoor at the 2018 Grace Hopper Celebration conference, Houston, TX.
• David C. Anastasiu. Teaching with jupyter in-class activities: Lessons learned and next steps, 2018. Poster presented at the 20th CSU Symposium on University Teaching, held at Cal-Poly Pomona, Pomona, CA.
• Ragwa M. El Sayed*, Rathna Ramesh*, Alessandro Bellofiore, David C. Anastasiu, and Melinda Simon. Patient-friendly kidney function screening, 2018. Poster presented by Ragwa M. El Sayed at the National Kidney Foundation 2018 Spring Clinical Meeting, Austin, TX.

Software

• David C. Anastasiu and George Karypis. Tapnn: Efficient identification of tanimoto nearest neighbors. http://davidanastasiu.net/software/tapnn/, 2016.
• David C. Anastasiu and George Karypis. L2knng: Fast exact k-nearest neighbor graph construction with l2- norm pruning. http://davidanastasiu.net/software/l2knng/, 2015.
• David C. Anastasiu and George Karypis. Orion: Multivariate resource utilization time series evolution analysis. http://davidanastasiu.net/software/orion/, 2014.
• David C. Anastasiu and George Karypis. L2ap: Fast cosine similarity search with prefix l-2 norm bounds. http://davidanastasiu.net/software/l2ap/, 2014.