Srinivasan Parthasarathy
Professor
Dept. of Computer Science and Engineering
and Department of Biomedical Informatics
The Ohio State University
395 Dreese Lab
2015 Neil Ave
Columbus, OH-43210, USA
Office: 693 Dreese Lab
Phone: (614) 292-2568
Fax: (614) 292-2911
Email: s r i n i
[AT] cse.ohio-state.edu (remove spaces in name and replace [AT] with
appropriate symbol)
WWW: http://www.cse.ohio-state.edu/~
(my-last-name.2)
|
Teaching
I primarily teach courses in the Data and Network Science areas. In the
past I have taught courses in Computer Architecture. Recent courses I have
taught include:
CSE 5243
(Introduction to Data Mining)
CSE 5249
(Special Topics Seminar in Databases and Data Mining)
CSE 5245
(Introduction to Network Science)
Research
I direct the Data Mining Research Laboratory which is a part of the High
End Systems Group and affiliated with the Laboratory for Artificial
Intelligence Research. I am broadly interested in the following areas
- High
Performance Data Analytics
- Graph
Analytics and Network Science
- Machine
Learning and Database Systems
I enjoy applied, systems and theoretical problems in these
areas. The National Science Foundation, the Department of Energy and the
National Institutes of Health have been the primary supporters of my group's
research together with grants and gifts from several industrial sponsors. For a
recent example, read more here.
PhD Advisees
I currently advise the following PhD students (research foci noted):
- Jiongqian
Liang (Research Foci: Human Guided Analytics, Clinical Informatics,
Emergency Response)
- Yu Wang
(Research Focus: Sampling Network Data, Network Science)
- Bortik Bandyopadhyay
(Research Foci: High Performance Graph Mining, GPU computing, Streaming
Algorithms)
- J. Sun (Research
Foci: Database Usability, Network Science)
- Nikhita Vedhula
(Research Foci: Social Media Analytics, Emergency Response)
The following have graduated but continue to be remembered fondly:
o
Matthew Otey (PhD 2006, @Google Research)
o
Hui Yang (PhD 2006, Assoc. Prof
@San Francisco State University)
o
Sameep Mehta (PhD 2006, Manager@IBM
Research )
o
Michael Twa (PhD 2006, Assoc. Dean, @U. Alabama)
o
Keith Marsolo (PhD 2007, Assoc. Prof@Cincinnati
Childrens and Univ. of Cincinnati)
o
Amol Ghoting (PhD 2007,
@LinkedIn)
o
Chao
Wang (PhD 2008,Senior Applied Researcher @Microsoft )
o
Gregory Buehrer (PhD 2008,
Partner Architect@Microsoft)
o
Sitaram
Asur, (PhD 2009,
now @Doximity)
o
Duygu Ucar, (PhD 2009, Asst.
Professor @Jackson Laboratories)
o
Shirish Tatikonda (PhD
2010, @Target Labs)
o
V. Satuluri
(PhD 2011, @Twitter)
o
X. Yang
(PhD 2012, @Google)
o
Y. Wang (PhD 2013, @Airbnb)
o
Y-K
Shih (PhD 2013, @Facebook)
o
T. Clemons, (ABD 2014,
@Manta)
o
S.M. Faisal (PhD 2015,
@Google)
o
Y. Ruan (PhD 2015, @Google)
o
D. Fuhry
(PhD 2015, Senior Lecturer @OSU)
o
Y. Zhang (PhD
2015, @Google)
o
Aniket
Chakrabarti (PhD 2017, @Microsoft Research AI)
o
Anirban Roychowdhury (PhD
2017, Research Scientist@Facebook)
I also advise undergraduate students and masters students
(at some point I will update this list to include them as well).
Selected Awards and Honors
- Best of WWW 2016 selection, 2016
- Google Research Award, 2012
- IBM Faculty Award, 2010
- OSU Lumley Research Award, 2010
- Google Research
Award, 2009
- IBM Faculty Award,
2007
- ACM SIGKDD Best
Applications Paper Award, 2007
- Best of IEEE ICDM Selection, 2006
- OSU Lumley Research
Award, 2005
- VLDB Best Paper
Award, 2005
- Best of SIAM Data Mining
Selection, 2005
- NSF CAREER Award,
2004
- DOE Early Career
Principal Investigator Award, 2004
- SIAM Data Mining Best Paper Award,
2003
- IEEE Data Mining Best Applications
Paper Award, 2002
- Ameritech Faculty Fellow, 2001
- Nominations for Best Paper at KDD
2006, ISMB 2009, ACM BCB 2010
Selected Recent Publications
High Performance Analytics: My earlier
efforts in this space include the development of the ECLAT family of algorithms
(a precursor by at least a decade or two to the current emphasis on column
oriented systems designs) for association analysis in the late nineties, the
development of novel architecture conscious designs for various analytic
algorithms on both shared memory and distributed memory systems (through
careful data placement and the judicious leveraging of architecture and network
capabilities such as RDMA). My current efforts in this space largely target
graph algorithms on GPUs and systems that support scalable energy-conscious
analytics (via elastic fidelity or via green-energy harvesting). Selected
recent papers in this vein include:
o
A. Chakrabarti, S. Parthasarathy and C.
Stewart, A Pareto Framework for Data Analytics on Heterogeneous Systems:
Implications for Green Energy Usage and Performance, IEEE International Conference on Parallel
Processing (ICPP), 2017.
o
G. Tziantzioulis, A. Gok, S. Faisal,
N. Hardavellas, S. Memik
and S. Parthasarathy, Lazy Pipelines:
Enhancing quality in approximate computing. IEEE DATE 2016.
o
G. Tziantzioulis, A. Gok,
S. Faisal, N. Hardevallas, S. Memik
and S. Parthasarathy: b-HiVE: a bit-level history-based error model with value
correlation for voltage-scaled integer and floating point units. IEEE DAC 2015: 105:1-105:6
o
S. M. Faisal, G. Tziantzioulis, A. M. Gok, N. Hardavellas, S. Memik, S. Parthasarathy: Edge Importance
Identification for Energy Efficient Graph Processing, IEEE Big Data Conference, 2015.
o
Gregory Buehrer, Roberto L. de Oliveira Jr.,
David Fuhry, Srinivasan Parthasarathy: Towards a
parameter-free and parallel itemset mining algorithm
in linearithmic time. ICDE 2015: 1071-1082
o
Naser Sedaghati, Te Mu, Louis-Noel Pouchet,
Srinivasan Parthasarathy, P. Sadayappan:
Automatic Selection of Sparse Matrix Representation on GPUs. ACM International Conference on
Supercomputing (ICS) 2015: 99-108
o
Arash Ashari, Naser Sedaghati, John Eisenlohr, Srinivasan Parthasarathy,
P. Sadayappan: Fast Sparse
Matrix-Vector Multiplication on GPUs for Graph Applications. SC 2014: 781-792
o
S. M. Faisal, Srinivasan Parthasarathy, P. Sadayappan:
Global graphs: A middleware for large scale graph
processing. IEEE Big Data Conference 2014: 33-40
o
Qingpeng Niu, Pai-Wei
Lai, S. M. Faisal, Srinivasan Parthasarathy, P. Sadayappan:
A fast implementation of MLR-MCL algorithm on multi-core
processors. HiPC 2014: 1-10
Graph Analytics and Network Science: My work in this
space was initiated roughly a decade ago. Our initial efforts in this space
focused on event-based algorithms for dynamic network analysis, Markov
clustering and ensemble clustering of networks with applications in
bioinformatics and clinical informatics. Current foci are in the space of computational social science (with applications to emergency
response and behavioral health) and the theoretical understanding of various
network sampling and localized sparsification
algorithms. Selected recent papers in this vein include:
- J.
Liang P. Jacobs, J. Sun and S. Parthasarathy. “SEANO: Semi-supervised Embedding in
Attributed Networks with Outliers”. To appear in Proceedings of SIAM International Conference
on Data Mining (SDM’18), 2018.
o
J.
Liang, P. Jacobs, S. Parthasarathy.
“Human-Guided Flood Mapping: From
Experts to the Crowd”. To appear in Proceedings
of the Web Conference (WWW’18), 2018
- Yu
Wang, Aniket Chakrabarti,
David Sivakoff, Srinivasan Parthasarathy:
Fast Change Point Detection on
Dynamic Social Networks. IJCAI 2017: 2992-2998
- Yu
Wang, Aniket Chakrabarti,
David Sivakoff, Srinivasan Parthasarathy:
Hierarchical Change Point Detection
on Dynamic Networks. ACM WebSci 2017:
171-179
o
N.Vedula and S. Parthasarathy:
Emotional and Linguistic Cues of Depression from Social Media, In ACM International Conference
on Digital Health (ACM-DH17), 2017
- S.
Parthasarathy, D. Sivakoff,
M. Tian, Y. Wang: A Quest to
Unravel the Metric Structure Behind Perturbed
Networks. To appear in Symposium on Computational Geometry (SoCG), 2017 (full version available on arXiv)
- Nikhita Vedula,
Srinivasan Parthasarathy, Valerie L. Shalin:
Predicting Trust Relations Within a Social
Network: A Case Study on Emergency Response. ACM WebSci
2017: 53-62
- Jiankai Sun, Deepak Ajwani,
Patrick K. Nicholson, Alessandra Sala, Srinivasan Parthasarathy:
Breaking Cycles In
Noisy Hierarchies. ACM WebSci 2017: 151-160
o
P. Gupte,
B. Ravindran and S. Parthasarathy, Role Discovery in Graphs using Global Features:
Algorithms, Applications and a Novel Evaluation Strategy, IEEE International Conference on Data Engineering, ICDE 2017.
- B.
Bandyopadhyay, D. Fuhry,
A. Chakrabarti, S. Parthasarathy: Topological Graph Sketching for
Incremental and Scalable Analytics. ACM Conference on Information and
Knowledge Management (CIKM 2016).
- J.Liang, D. Ajwani,
P. Nicholson, A. Sala and S. Parthasarathy: What Links Alice and Bob?: Matching and
Ranking Semantic Patterns in Heterogeneous Networks. WWW 2016. Best of Selection. Expanded
version in ACM TKDD 2017.
o
Sarvenaz Choobdar, Pedro Manuel
Pinto Ribeiro, Srinivasan Parthasarathy, Fernando M. A. Silva: Dynamic inference of
social roles in information cascades. Data Min. Knowl. Discov. 29(5): 1152-1177 (2015)
o
Yu-Keng Shih, Sungmin
Kim, Yiye Ruan, Jinxing
Cheng, Abhishek Gattani, Tao Shi, Srinivasan
Parthasarathy: Component Detection in Directed
Networks. CIKM 2014.
o
Yiye Ruan, Srinivasan Parthasarathy: Simultaneous
detection of communities and roles from large networks. ACM COSN 2014: 203-214
o
Hemant Purohit, Yiye Ruan, David Fuhry, Srinivasan Parthasarathy, Amit P. Sheth: On Understanding the Divergence of
Online Social Group Discussion. ICWSM 2014
Machine Learning and Database Systems: My past work in
this space focused on scalable anomaly detection, similarity search (for
non-metrics) and the application of machine learning algorithms arising in a
clinical context. My current interests include usable database systems (e.g.
query by output, visual query interfaces), algorithms for similarity search
(with an emphasis on locality sensitive hashing), contextual anomaly detection,
and optimization algorithms that arise in machine learning. Selected recent
papers in this vein include:
- A.
Roychowdhury and S. Parthasarathy,
“Adaptive Bayesian Sampling with
Monte Carlo EM”, In Proc. 30th
Advances in Neural Information Processing Systems (NIPS), 2017.
- Yang
Zhang, Yusu Wang, Srinivasan
Parthasarathy: Visualizing Attributed Graphs via Terrain Metaphor. In ACM
SIGKDD 2017: 1325-1334
- Nikhita
Vedula, Wei Sun, Hyunhwan
Lee, Harsh Gupta, Mitsunori Ogihara,
Joseph Johnson, Gang Ren and Srinivasan Parthasarathy.
"Multimodal Content Analysis
for Effective Advertisements on YouTube." In Proceedings of the IEEE International Conference on Data Mining
(ICDM), 2017.
o
J. Liang, S.
Parthasarathy: Robust Contextual Outlier
Detection: Where Context Meets Sparsity. ACM Conference on Information and
Knowledge Management (CIKM 2016) (full version available on arXiv)
o
A. Chakrabarti, V. Satuluri,
A. Srivathsan and S. Parthasarathy, A
Bayesian Perspective on Locality Sensitive Hashing with Extensions for Kernel
Methods, in ACM Transactions on KDD, 2016.
o
A. Roychowdhury, B. Kulis
and S. Parthasarathy, Robust Monte Carlo Sampling using Riemannian
Nose Poincare Hamiltonian Dynamics,ICML 2016.
o
R. Cai, Z. Zhang, S. Parthasarathy, A. Tung, Z.
Hao and W. Zhang., Multi-Domain Manifold
Learning for Drug Target Interaction Prediction, SIAM SDM 2016.
o
J. Liang, D. Fuhry, D. Maung, A. Borstad, R. Crawfis, L. Gauthier, A.
Nandi and S. Parthasarathy, A Data Analytics Framework for A
Game-based Rehabilitation System. ACM Conference on Digital Health, 2016.
o
Aniket Chakrabarti, Srinivasan Parthasarathy: Sequential Hypothesis
Tests for Adaptive Locality Sensitive Hashing. WWW 2015: 162-172
o
Quoc Trung Tran, Chee Yong Chan, Srinivasan Parthasarathy: Query reverse
engineering. VLDB J. 23(5): 721-746 (2014)
o
Roberto Lourenco de Oliveira Jr., Adriano Veloso,
Adriano M. Pereira, Wagner Meira Jr., Renato
Ferreira, Srinivasan Parthasarathy: Economically-efficient
sentiment stream analysis. SIGIR 2014: 637-646
For a complete
listing of publications please refer to one of the links below: Google
Scholar, DBLP or PubMed.
If you are at this page searching for a frequently requested code base please
check here.
Service
I serve or have served on the program
committees or organizing committees of leading conferences in the fields of
Machine Learning, Data Mining, Parallel and Distributed Computing, and Database
Systems. I serve or have served as action or associate editors of several
leading journals in these fields. Since 2012 I am the Steering Committee Chair
of the SIAM Data Mining Conference Series.
At OSU, I helped create, and am a
founding co-director of its undergraduate major in data analytics. This
major among the first of its kind nationwide spans the core areas of
Computer Science, Statistics and Mathematics while offering specializations in
a diverse range of domains ranging from business analytics to cybersecurity and
from text analytics to health informatics. For more details on this effort
please go to the DA major website.
Last Update: April 2017