CURRICULUM VITAE
Dr. Rage Uday kiran,IEEE Senior Member,
Associate Professor,
Leader of Intelligent Transportation System Cluster,
The University of Aizu,
Aizu-Wakamatsu, Fukushima, Japan.
Address:
Aizu Daigaku Kyoin Kosha C102,
1-17-25, Ikki-machi, Matsunaga,
Aizu-Wakamatsu, Fukushima, Japan 965-0001.
Mobile : (+81)-80-4834-0999
e-mail : udayrage@u-aizu.ac.jp and uday.rage@gmail.com
URL : https://u-aizu.ac.jp/~udayrage/

1. Work history
S. No.

Job title and location

Years

1

Post-doctoral fellow at The
University of Tokyo

2012 to 20142

Researcher at the NICT, Japan

2014 to 20203

Specially Appointed Associate
Professor at the University of Tokyo(part time)2014 to 2020

4

The University of Aizu

2020 to ~College/University

Year of
Passing

Marks

International Institute of Information
Technology, Hyderabad, Telangana,India.

5/200512/2011

7.13/10

2. Education
CoursePh. D.
(Computerscience)Decision
Models andExchange student at IISc, Bangalore,
Methodology of
Karnataka, India.
Management
Research
Dhirubhai Ambani Institute of
MS (IT in
Information and Communications
Agriculture)
Technology, Gandhinagar, Gujarat,
India.
B-TechAgricultural Engineering College,(Agriculture)
Bapatla, Andhra Pradesh, India.
Intermediate
Sri S. Siva Reddy Junior College(12th class)
Nagarjuna English Medium High
10th class
School6/200512/2005

2003-2005

3.24/4

1999-2003

7.25/10

1997-1999

628/1000-1997

413/600

3. Awards
a. Best research paper award in IEA/AIE 2019.
b. Best research poster award in IBM-ICARE 2010.
c. Government merit seat to study B.Tech in India. More than 100,000 students
have written the exam.

4. Research Interest
Database systems; Dataware housing and Data mining; Web mining; Recommendersystems, Urban transportation; Geographical information systems and Remotesensing; Spatio-temporal data mining.

5. Teaching Experiencea. Undergraduate courses:
S. No.
Course title
Years
7
SCCPs
2020, 2021, 2022
6
Ventures Experience Workshops
2021
5
Introduction to Data Mining
2021, 2022
4
Database Management Systems 2020, 2021, 2022
3
Programming in Java-1
2020, 2021, 2022
2 Software Comprehensive Exercise-2 2020, 2021, 2022
1
Web data modeling2020
b. Graduate courses:
S. No.
Course title
Years

4
3
2
1

Practical Data Analysis with Lunar and Planetary Databases 2022
Advanced Topics in Pattern Mining
2022
Advanced Topics in Data Science
2022
2020,
Introduction to Big Data Science
2021,
2022

6. Research projects
6.1. Development of all-weather road congestion prediction model
a.
b.
c.
d.

Place
: National Institute of Information and Communication Technology
Duration: 2017 – 2019Technologies: Java, C++, Postgres
Description: In this project, we have developed a novel association rule based
ensemble classifier for predicting road congestion in Japan. Fast, scalable andparallelizable algorithms were developed for the classifier. Experimental results
demonstrated that the proposed model has outperformed the existing state-of-theart classifier by a very large margin.

6.2. Design and development of eSaguTM system into Smart phonesa. Place: IIIT-H.
b. Funding agency: Nokia, Finland and Media Lab Asia, Ministry of Information
and Communication Technologies, New Delhi, India.
c. Duration: 2006-2010
d. Technologies: Java, J2ME, GPS, MYSQL.
e. Description: The eSagu: An IT based Agro-advisory system (www.esagu.in) is system
aimed at effective delivery of agricultural information to the (Indian) farming
communities. In this project, we are make efforts to develop effective humanmobile interactive systems which will enable illiterate farmers to effective use
mobile phones for acquiring necessary information.

6.3. Design and development of eSaguGIS system.
a. Place: IIIT-H.
b. Funding agency: Media Lab Asia, Ministry of Information and Communication
Technologies, New Delhi, India.
c. Duration: 2005-2007
d. Technologies: Java, Map server, Apache web server and MYSQL (spatial).
e. Description: In eSagu system, a set of agricultural experts delivers advices to avillage. For effective advice delivery and monitoring of farm specific problems,
spatial information is one of the key component. Hence, we are making efforts toprovide necessary spatial information to the agricultural experts which will enable
them to analyze the farm conditions in a particular village effectively.

7. Research publications (SERIAL NUMBER IS BASED ON PUBLICATION ORDER)
7.1. Journal publications
S.
No
.
15

14
13

12

11

10

9
8

7

6

Publication

Citation
count

Im pactfactorNA

4.1

Uday Kiran, R., Veena, P., Ravikumar, P. et al. HDSHUIminer:a novel algorithm for discovering spatial high-utility
itemsets in high-dimensional spatiotemporal databases. ApplIntell 53, 8536–8561 (2023). https://doi.org/10.1007/s10489022-04436-w
José María Luna, Rage Uday Kiran, Philippe Fournier-Viger,
Sebastián Ventura:Efficient mining of top-k high utility itemsets
through genetic algorithms. Inf. Sci. 624:529-553 (2023)
Hong N. Dao, Penugonda Ravikumar, Palla Likhitha, Rage
Uday Kiran, Yutaka Watanobe, Incheon Paik:Finding Stable
Periodic-Frequent Itemsets in Big Columnar Databases. IEEE
Access 11:12504-12524 (2023)
Philippe Fournier-Viger, Zhitian Li, Jerry Chun-Wei Lin, Rage
Uday Kiran, Hamido Fuj
ita:Efficient algorithms to identify
periodic patterns in multiple sequences. Inf. Sci. 489:205-226(2019)
R. Uday Kiran, Alampally Anirudh, Chennupati Saideep, Masashi
Toyoda, P. Krishna Reddy, Masaru Kitsuregawa:Finding PeriodicFrequent Patterns in Temporal Databases using Periodic
Summaries. To be appeared in the Journal of Data Science andPattern Recognition
P Fournier-Viger, Peng Yang, Zhitian Li, JCW Lin, R. Uday Kiran:
Discovering Rare Correlated Periodic Patterns in Multiple
Sequences. To be appeared in the journal of Data and Knowledge
Engineering
P Fournier-Viger, JCW Lin, RU Kiran, YS Koh, R Thomas:A surveyof sequential pattern mining. Data Science and Pattern Recognition 1(1), 54-77 (2018)
J. N. Venkatesh, R. Uday Kiran, P. Krishna Reddy, Masaru
Kitsuregawa:Discovering Periodic-Correlated Patterns in Temporal
Databases. T. Large-Scale Data- and Knowledge-Centered Systems
38:146-172 (2018)[EI]
R. Uday Kiran, J. N. Venkatesh, Masashi Toyoda, Masaru
Kitsuregawa, P. Krishna Reddy:Discovering partial periodic-frequent
patterns in a transactional database. Journal of Systems andSoftware 125:170-182 (2017)[EI]

NA

7.69

NA

1.467

102

NA

1

NA

9

2.278

22

2.278

5
4
3

2

1

R. Uday Kiran, Masaru Kitsuregawa, P. Krishna Reddy:Efficientdiscovery of periodic-frequent patterns in very large databases.
Journal of Systems and Software 112:110-121 (2016)[EI]
R. Uday Kiran, Masaru Kitsuregawa:Efficient discovery of correlated
patterns using multiple minimum all-confidence thresholds. J. Intell.
Inf. Syst. 45(3):357-377 (2015)[EI]
P. Gowtham Srinivas, P. Krishna Reddy, A. V. Trinath, Bhargav
Sripada, R. Uday Kiran(:Mining coverage patterns from transactional
databases. J. Intell. Inf. Syst. 45(3):423-439 (2015)[EI]
Wei Guo, R. Uday Kiran, and Sesishi Ninomiya:Illumination invariant
segmentation of vegetation for time series wheat images based on
decision tree model, Journal of Computers and Electronics inAgriculture, Vol. 96, pp. 58-66, 2013. [EI]
M.Kumara Swamy, P. Krishna Reddy, R. Uday Kiran, and M.
Venugopal Reddy:Temporality-based User Interface Design
Approaches for Desktop and Small Screens. Appeared inInternational Journal of Computational Science and Engineering(IJCSE), Vol.7, No.1, 2012. [EI]
R. Uday Kiran, P. Krishna Reddy, Mittapally Kumara Swamy, G.
Syamasundar Reddy:Analysing dynamics of crop problems byapplying text analysis methods on farm advisory data of eSaguTM.
IJCSE 5(2):154-164 (2010)[EI]

5

1.107

13

1.107

96

2.427

5

NA

3

NA

7.2. Conference publications
S.No
.
43

42

41

40

39

38

Publication

Citation count
Philippe Fournier-Viger, Peng Yang, Jerry Chun-Wei Lin, Rage Uday NA
Kiran: Discovering Stable Periodic-Frequent Patterns in Transactional
Data. IEA/AIE 2019: 230-244
T. Yashwanth, R. Uday Kiran, Masashi Toyoda, P. Krishna Reddy and NA
Masaru Kitsuregawa: Discovering Partial Periodic High UtilityItemsets in
Temporal Databases. To be appeared in DEXA 2019.
R. Uday Kiran, Koji Zettsu, Yuji Uchiyama, Masashi Toyoda, Masaru NA
Kitsuregawa, Philippe Fournier-Viger: Discovering Spatial High Utility
Itemsets in Spatiotemporal Databases. To be appeared in SSDBM 2019.
R. Uday Kiran, T. Yashwanth, Philippe Fournier-Viger, Masashi Toyoda, NA
P. Krishna Reddy, Masaru Kitsuregawa: Efficient Discovery of High
Utility-Frequent Itemsets using Cutoff Utility and Suffix Utility. PAKDD(2) 2019: 191-203 [EI]
R. Uday Kiran, Amulya Kotni, Masashi Toyoda, P. Krishna Reddy, Subash NA
Bhalla, Masaru Kitsuregawa: Efficient Discovery of Weighted Frequent
Itemsets in Very Large Transactional Databases: A Re-visit. BigData
2018: 723-732.
3

37

36

35

34

33

32

31

30
29
28

27
26

25

Philippe Fournier-Viger, Zhitian Li, Jerry Chun-Wei Lin, Rage Uday Kiran,
Hamido Fujita:Discovering Periodic Patterns Common to Multiple
Sequences. DaWaK 2018: 231-246 [EI]
Amulya Kotni, R. Uday Kiran, Masashi Toyoda, P. Krishna Reddy, Masaru
Kitsuregawa:Novel Data Segmentation Techniques for Efficient
Discovery of Correlated Patterns Using Parallel Algorithms. DaWaK2018: 355-370 [EI]
Qian Li, Ziwei Li, Jin-Mao Wei, Zhenglu Yang, Yanhui Gu, R. Uday Kiran:A
Story Coherence based Neural Network Model for Predicting Story
Ending. WWW (Companion Volume) 2018: 119-12
Alampally Anirudh, R. Uday Kiran, P. Krishna Reddy, Masashi Toyoda,
Masaru Kitsuregawa:An Efficient Map-Reduce Framework to Mine
Periodic Frequent Patterns. DaWaK 2017: 120-129 [EI]
R. Uday Kiran, J. N. Venkatesh, Philippe Fournier-Viger, Masashi
Toyoda, P. Krishna Reddy, Masaru Kitsuregawa: Discovering Periodic
Patterns in Non-uniform Temporal Databases. PAKDD (2) 2017: 604617 [EI]
R. Uday Kiran, Haichuan Shang, Masashi Toyoda, Masaru
Kitsuregawa:Discovering Partial Periodic Itemsets in Temporal
Databases. SSDBM 2017: 30:1-30:6
J. N. Venkatesh, R. Uday Kiran, P. Krishna Reddy, Masaru Kitsuregawa:Discovering Periodic-Frequent Patterns in Transactional Databases
Using All-Confidence and Periodic-All-Confidence. DEXA (1) 2016: 55-70[EI]
Alampally Anirudh, R. Uday Kiran, P. Krishna Reddy, Masaru
Kitsuregawa:Memory efficient mining of periodic-frequent patterns intransactional databases. SSCI 2016: 1-8
R. Uday Kiran, Haichuan Shang, Masashi Toyoda, Masaru Kitsuregawa:Discovering Recurring Patterns in Time Series. EDBT 2015: 97-108
R. Uday Kiran, Masaru Kitsuregawa: Finding Periodic Patterns in Big
Data. BDA 2015: 121-133 [EI]Haichuan Shang, Xiang Zhao, R. Uday Kiran, Masaru
Kitsuregawa:Towards Scale-out Capability on Social Graphs. CIKM 2015:
253-262
R. Uday Kiran and Masaru Kitsuregawa: Discovering Chronic-Frequent
Patterns in Transactional Databases. DNIS 2015: 12-26
R. Uday Kiran and Masaru Kitsuregawa: Novel Techniques to Reduce
Search Space in Periodic-Frequent Pattern Mining. DASFAA (2) 2014:377-391 [EI]

NA

NA

2

3

4

5

8

18
2
3

6
14

7

24

23
22

21

20
19

18

17

16

15
14
13

12

11

R. Uday Kiran, Masaru Kitsuregawa: Mining Correlated Patterns with
Multiple Minimum All-Confidence Thresholds. PAKDD Workshops
2013: 295-306
R. Uday Kiran and Masaru Kitsuregawa: An Improved NeighborhoodRestricted Association Rule-based Recommender System. ADC 2013: 43-50
R. Uday Kiran and Masaru Kitsuregawa: Discovering Quasi-PeriodicFrequent Patterns in Transactional Databases. BDA 2013: 97-115 [EI]
R. Uday Kiran, Masaru Kitsuregawa:Towards Addressing the
Coverage19 Problem in Association Rule-Based Recommender
Systems. DEXA (2) 2013: 418-425[EI]
R. Uday Kiran, Masashi Toyoda, Masaru Kitsuregawa:Towards efficientdiscovery of coverage patterns in transactional databases. SSDBM
2013: 38:1-38:4
R. Uday Kiran, Masaru Kitsuregawa:Towards Efficient Discovery ofFrequent Patterns with Relative Support. COMAD 2012: 92-99
M. Venu, R. Uday Kiran, R. Kiranmai: A robust neural network classifier
to model the compressive strength of high performance concrete using
feature subset selection. COMPUTE 2012: 1
R. Uday Kiran, Masaru Kitsuregawa:Efficient Discovery of Correlated
Patterns in Transactional Databases Using Items' Support Intervals.
DEXA (1) 2012: 234-248 [EI]
P. Gowtham Srinivas, P. Krishna Reddy, Bhargav Sripada, R. Uday Kiran,D. Satheesh Kumar:Discovering Coverage Patterns for Banner
Advertisement Placement. PAKDD (2) 2012: 133-144
Akshat Surana, R. Uday Kiran, P. Krishna Reddy:An Efficient Approach toMine Periodic-Frequent Patterns in Transactional Databases. PAKDD
Workshops 2011: 254-266
Somya Srivastava, R. Uday Kiran, P. Krishna Reddy:Discovering DiverseFrequent Patterns in Transactional Databases. COMAD 2011: 69-78
R. Uday Kiran, P. Krishna Reddy:An Alternative Interestingness Measure
for Mining Periodic-Frequent Patterns. DASFAA (1) 2011: 183-192 [EI]
Mohak Sharma, P. Krishna Reddy, R. Uday Kiran, Thirumalaisamy
Ragunathan:Improving the Performance of Recommender System byExploiting the Categories of Products. DNIS 2011: 137-146
R. Uday Kiran, P. Krishna Reddy:Novel techniques to reduce search
space in multiple minimum supports-based frequent pattern mining
algorithms. EDBT 2011: 11-20

1

9
1

3

1
1

7

13

36

4
21
NA

55

10

10

9
8

7

6

5

4

3
2
1

Bhargav Sripada, Polepalli Krishna Reddy, Rage Uday Kiran: Coverage
patterns for efficient banner advertisement placement. WWW(Companion Volume) 2011: 131-132
R. Uday Kiran, Polepalli Krishna Reddy:An Efficient Approach to Mine
Rare Association Rules Using Maximum Items' Support Constraints.
BNCOD 2010: 84-95
Akshat Surana, R. Uday Kiran, P. Krishna Reddy: Selecting a Right
Interestingness Measure for Rare Association Rules. COMAD 2010: 115
R. Uday Kiran, P. Krishna Reddy:Mining periodic-frequent patterns withmaximum items' support constraints. Bangalore Compute Conf. 2010:
1:1-1:8
R. Uday Kiran, P. Krishna Reddy:Mining Rare Association Rules in theDatasets with Widely Varying Items' Frequencies. DASFAA (1) 2010: 4962 [EI]
R. Uday Kiran, P. Krishna Reddy:Towards Efficient Mining of PeriodicFrequent Patterns in Transactional Databases. DEXA (2) 2010: 194-208[EI]
Mittapally Kumara Swamy, P. Krishna Reddy, R. Uday Kiran, M.
Venugopal Reddy:Interface Tailoring by Exploiting Temporality ofAttributes for Small Screens. DNIS 2010: 284-295
R. Uday Kiran, P. Krishna Reddy:An improved multiple minimum
support based approach to mine rare association rules. IEEE CIDM
2009: 340-347
R. Uday Kiran, P. Krishna Reddy:Mining Rare Periodic-Frequent Patterns
Using Multiple Minimum Supports. COMAD 2009
R. Uday Kiran, P. Krishna Reddy:An Improved Frequent Pattern-growth
Approach to Discover Rare Association Rules. KDIR 2009: 43-52
R. Uday Kiran, P. Krishna Reddy:Understanding the Dynamics of Crop
Problems by Analyzing Farm Advisory Data in eSagu TM . DNIS 2007:272-284

11

31
8

33

33

3

99

9
24
1

8. Academic service
1. Program committee member for DEXA, DAWAK, PAKDD, AUSDM, TENCON, NCAAand BDA.
2. Reviewer for many reputed Journals IEEE TKDE, IEEE TKDD, JSS and DMKD

References
Prof. Masaru Kitsuregawa

Director General at National Institute of Informatics/Professor at the University of Tokyo,
Tokyo, Japan.
kitsure@tkl.iis.u-tokyo.ac.jp

Prof. Masashi Toyoda
Professor at the University of Tokyo,
Tokyo, Japan.
toyoda@tkl.iis.u-tokyo.ac.jp

Prof. P. Krishna Reddy
Professor at International Institute of Information Technology-Hyderabad,
Telangana, India.
pkreddy@iiit.ac.in

Prof. Sanjay Madria
Curators' Distinguished Professor
ACM Distinguished Scientist
Director, Web and Wireless Computing Lab
Department of Computer Science,
Missouri University of Science and Technology, Rolla, MO 65409, USA
madrias@mst.edu
Anirban Mandal, Ph.D.,
Associate Professor,
Ashoka University,
Rajiv Gandhi Education City, Sonipat, Haryana 131029, India
anirban.mondal@ashoka.edu.in