|
The Biologically Inspired Computing (BIC) research group at K-State is
involved in theoretical and applied research in evolutionary algorithms,
ant colony optimization, particle swarm optimization, artificial immune
systems, memetic algorithms and neural networks for multi-objective and
constrained optimization, prediction, structure discovery, learning and
other tasks. Our funded research is in in the areas of plant gene
regulatory network modeling and power
distribution systems. We are also interested in other applications
(communications & networks, engineering,
computer science, biology & finance).
 |
Sanjoy Das, Ph.D.
Director, BIC
Electrical & Computer Engineering
2063 Rathbone Hall
Tel: (785) 532-4642
Email: (in reverse)edu.ksu@sdas |
|
Prof. Das
completed his Ph.D. in Electrical & Computer Engineering from
Louisiana State University in 1994, and received postdoctoral training at
the University of California, Berkeley. He worked for several years in the
industry before joining Kansas State University in 2001.
Prof. Das is primarily interested in biologically inspired computing, a
field of study within artificial intelligence that borrows algorithms from
various natural paradigms, such as Darwinian evolution, swarm
intelligence, immune systems and neuronal structures to address problems that are
too complex to be solved through conventional means. He is investigating
the applications of these algorithms for specific optimization problems in
plant genomics in collaboration with Prof. Welch and in distribution
systems modeling in collaboration with Prof. Pahwa.
|
|
Praveen Koduru, Ph.D.
Postdoctoral Research Scientist
Electrical & Computer Engineering
2088 Rathbone Hall (BIC lab)
Tel: (785) 532-4664
Email: (in reverse)edu.ksu@praveen
Dr. Koduru received a B. Tech. in Chemical Engineering from Osmania University,
India, an M.S. in Control Systems Engineering from West Virginia University Institute of
Technology under the guidance of Prof. Asad Davari, and an M.S. in Electrical
Engineering from Yale University under the guidance of Prof. Kumpati S.
Narendra. He completed his Ph.D. in Electrical & Computer Engineering
from Kansas State University in 2006 under the guidance of Prof. Sanjoy
Das and Prof. Stephen M. Welch. Dr. Koduru's research interests are in
evolutionary algorithms, genomics, control systems, and artificial
intelligence.
|
 |
Dapeng Li
Ph.D. student
Electrical & Computer Engineering
2088 Rathbone Hall (BIC lab)
Tel: (785) 532-4664
Email: (in reverse)edu.ksu@dapeng |
|
Mr. Li received his
B. Eng. from Xi'an University of Technology, China, and M. Eng. from
Shanghai University, China, in 2001 and 2004 respectively. From 2004 to 2005,
he worked as a full-time research assistant in the Department of Electrical Engineering
at Hong Kong Polytechnic University. From 2005 to 2006, he worked as a power system
engineer in the Hygrand Electronic Equipment Company, China. Mr. Li's current research
interests mainly focus on computational intelligence applications and power system control. At
present he is working with Dr. Das in evolutionary algorithms.
Mr. Li's other interests include Chinese calligraphy, badminton, hiking and music.
|
 |
Xinye Cai
Ph.D. student
Electrical & Computer Engineering
2088 Rathbone Hall (BIC lab)
Tel: (785) 532-4664
Email: (in reverse)edu.ksu@xinye |
|
Xinye Cai received a Bachelor degree in Electronic and Information Engineering
from Huazhong University of Science and Technology in Wuhan, China and an M.S in
Electronic Engineering from Bio-inspired architecture group, University of York.
Currently he is a Ph.D. student under the guidance of Prof. Sanjoy Das and Prof.
Stephen M. Welch. His main interest is in evolutionary computation and other bio-inspired
algorithms, modeling and simulation, engineering design and evolvable hardware.
|
 |
Gui Min
Ph.D. student
Electrical & Computer Engineering
2088 Rathbone Hall (BIC lab)
Tel: (785) 532-4664
Email: (in reverse)edu.ksu@mingui |
|
Gui Min received a B.S. in Electrical
Engineering from Central South University, China, an M.S. in Electrical Engineering from
Central South University, China, under the guidance of Prof.Luo An. She currently is
a graduate student in Electrical & Computer Engineering from Kansas State University
under the guidance of Prof. Anil Pahwa and Prof. Sanjoy Das. Gui Min's research interests
are in power system reliability and artificial intelligence.
|
 |
Eduard Plett
Assistant Professor, K-State Salina,
(pursuing Ph.D.)
Electrical & Computer Engineering
Address:
Tel: (785) xxx-xxxx
Email: (in reverse)edu.ksu@eplett |
|
Eduard Plett received an MS in Electrical Engineering from KSU in 2006.
|
Former advisees/co-advisees (in alphabetic order)
- Kai Ma
- Ashish Ahuja
- Dan Stevens
- Grant Cochenour
- Rajiv Annaluru
- Surasish Nag
- Yujia Zhou
 |
Stephen M. Welch, Ph.D.
Professor
Agronomy
3728 Throckmorton Hall
Tel: (785) 532-7236
Email: (in reverse)edu.ksu@welchsm |
 |
Anil Pahwa, Ph.D.
Professor & Interim Chair
Electrical &
Computer Engineering
261A Rathbone Hall
Tel: (785) 532-5600
Email: (in reverse)edu.ksu@pahwa |
Other major collaborators (in alphabetic
order)
- Bala Natarajan,
Assistant Professor, Electrical & Computer Engineering
- Gurdip Singh,
Professor, Computer & Information Sciences
- Judy Roe, Agronomy (currently in UK)
- Mary Knapp,
State Climatologist
- William H. Hsu,
Associate Professor, Computer & Information Sciences
(The above is only a partial list of collaborators who have authored
multiple papers, obtained funded research, and/or guided students in BIC related fields.
There are a few
other noteworthy collaborators also not listed here.)
- Multi-objective hybrid evolutionary algorithm with Nelder-Mead based
local search
- Parameter estimation of differential equation models of gene
regulatory networks
- Reduced complexity particle swarm hybrid algorithm
with local search for multi-objective/constrained optimization
- Structure discovery of gene regulatory networks using genetic
programming and ant colony algorithms
- Overhead distribution system failure rate prediction using radial
basis function networks and wavelet decomposition
- Overhead distribution system anomaly detection using the negative
selection algorithm
- Distribution system
reconfiguration using multi-objective ant colony
optimization and evolutionary strategies
- Multi-objective DS-CDMA code design using the clonal selection principle
Fuzzy Simplex Genetic Algorithm (FSGA)
FSGA is a general purpose hybrid algorithm for fast
multi-objective optimization that was developed by Praveen Koduru, Sanjoy
Das and Stephen M. Welch that has outperformed all major multi-objective
evolutionary algorithms
- Matlab implementation
- C implementation
- Related documents
BIC projects
External
|