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Main profile
Sumedha
School:
Physical Sciences
Designation:
Reader-F
Simons Associate, ICTP, Trieste, Italy (2020-2025)
Education:
BSc.(Phys(H)) Delhi University
MSc(Physics) IIT Delhi
PhD Tata Institute of Fundamental Research(Mumbai)
PostDoc:
Laboratoire de Physique Theorique et Modeles Statistiques, Universite Paris-Sud,Paris, France
Institute for Scientific Interchange, Torino,Italy
Brandeis University, Boston, USA
HRI Allahabad,India
Specialisation:
Statistical mechanics and interdisciplinary applications
Awards:
Simons Associateship, International Centre for Theoretical Physics( ICTP), Trieste, Italy(2020-2025)
Publications:
List of Publications:
21. Emergence of a bicritical end point in the random crystal field Blume-Capel model, Sumedha and Soheli Mukherjee, Phys. Rev. E, 101,042125( https://arxiv.org/abs/1907.06454)
20. Conformal Bootstrap Signatures of the Tricritical Ising Universality Class, Chethan N Gowdigere, Jagannath Santara and Sumedha, Phys. Rev. D 101, 116020 (https://arxiv.org/abs/1811.11442)
19. Effect of ring topology in a stochastic model for Z-ring dynamics in bacteria,Arabind Swain, A.V. Anil Kumar and Sumedha, The European Physical Journal E volume 43, Article number: 43 (2020), arXiv:1810.03371
18. Absence of first order transition in random crystal field Blume-Capel model on a fully connected graph, Sumedha and Nabin K Jana, J. Phys. A: Math. Theor. 50 015003 (2017).
17. Effect of random field disorder on the first order transition in p-spin interaction model, Sumedha and Sushant K singh, Physica A, 276(2016).
16. Exact satisfiability threshold for k-satisfiability problems on a Bethe lattice, Supriya Krishnamurthy and Sumedha, Phys. Rev. E ,92, 042144(2015)
15. Some results for k-sat on trees, Sumedha and Supriya Krishnamurthy, J. Phys. Conf. Series(2015).
14. Balanced k-satisfiability and biased random k-satisfiability on trees, Sumedha, Supriya Krishnamurthy and Sharmistha Sahoo, Phys. Rev. E, 87, 042130(2013).
13. On the behaviour of random k-SAT on trees, Supriya Krishnamurthy and Sumedha, J. Stat. Mech. P05009 (2012)
12. Prolonging assembly through dissocaition : A self assembly paradigm in microtubules,Sumedha, Michael F Hagan and Bulbul Chakraborty, Phys. Rev. E , 83, 051904 (2011)
11. A thermodynamic model for agglomeration of DNA-looping proteins, Sumedha and Martin Weigt, J. Stat. Mech. P11005(2008).
10. Unsupervised and semi-supervised clustering by message passing:Soft-constraint affinity propagation, Michele Leone, Sumedha, and Martin Weigt, European Physics Journal B,vol. 66, 125(2008).
9. Clustering by soft-constraint affinity propagation:Applications to gene-expression data, Michele Leone, Sumedha and Martin Weigt, Bioinformatics vol. 23, 2708 (2007).
8. New structural variation in evolutionary searches of RNA neutral networks, Sumedha, Olivier C Martin and Andreas Wagner,Biosystems, vol. 90, 475-485 (2007).
7. Population size effects in evolutionary dynamics on neutral networks and toy landscapes, Sumedha, Olivier C Martin and Luca Peliti, J. Stat. Mech. P05011(2007).
6. Quenched averages for self-avoiding walks and polygons on a deterministic fractal, Sumedha and Deepak Dhar, J. Stat. Phys., Vol. 125, 55-76(2006).
5. Rooted Spiral Lattice Trees on Hyper-cubic lattices, Sumedha, J. Stat. Phys., Vol. 120, 101-123(2005).
4. Efficiency of the Incomplete Enumeration algorithm for Monte-Carlo simulation of linear and branched polymers, Sumedha and Deepak Dhar, J. Stat. Phys., Vol. 120, 71-100(2005).
3. Directed Branched Polymer near an Attractive Line,Sumedha, J. Phys. A:Math. Gen., Vol. 37, 3673(2004).
2. Distribution of Transverse Distances in Directed Animals,Sumedha and Deepak Dhar, J. Phys. A:Math. Gen., Vol. 36,3701(2003).
1. Transition curves for the variance of the nearest neighbor spacing distribution for Poisson to Gaussian orthogonal and unitary ensemble transitions, V. K. B. Kota and S. Sumedha, Phys.Rev. E, Vol. 60, 3405-3408(1999).
Complete list: https://arxiv.org/find/grp_q-bio,cond-mat/1/au:+sumedha/0/1/0/all/0/1
Recent Activities:
1. Organised conference on"Recent Topics in Statistical Mechanics" from 11-13 th December 2019 at NISER, Bhubaneswar: https://www.niser.ac.in/RTSM/
2. Organised a session on "Physics of soft-matter &biological systems" from 19-20th February 2015 in CTCMP2015: http://www.niser.ac.in/~ctcmp2015/
Research:
We are interested in understanding and developing mathematical and numerical approaches to study disordered systems. Our recent work involves:
1. Effect of quenched disorder on first order transitions
Typically correlation length is finite near the first order transitions and they are more stable than the continuous transitions. It is found though that in two dimensions, even an infinitesimal amount of quenched disorder either destroys transition, or converts it into a continuous transition. What happens in higher dimensions is still not clear. We have looked at three models with random field disorder: Random field Ising Model, p-spin interaction model and random crystal field Blume Capel model. We find that typically there is a threshold of disorder, beyond which the transition is always continuous.
Typically correlation length is finite near the first order transitions and they are more stable than the continuous transitions. It is found though that in two dimensions, even an infinitesimal amount of quenched disorder either destroys transition, or converts it into a continuous transition. What happens in higher dimensions is still not clear. We have looked at three models with random field disorder: Random field Ising Model, p-spin interaction model and random crystal field Blume Capel model. We find that typically there is a threshold of disorder, beyond which the transition is always continuous.
2. Phase transitions in random k-Satisfiability problems.
In computer science, it is now believed that computational complexity is connected to phase
transitions. k-satisfiability is one of the most fundamental complex optimization problems. The problem is known to undergo phase transitions as a function of the ratio of constraints and variables. While polynomial time algorithms are known to solve the problem for k = 2, for k ≥ 3 the problem is known to be NP-complete. We define the model on a tree and find that the solvability threshold for k = 2 matches the exact value of the threshold on regular random graphs. For higher k, the values are very close to those predicted using other techniques. Our method can be extended to many other optimisation problems.
In computer science, it is now believed that computational complexity is connected to phase
transitions. k-satisfiability is one of the most fundamental complex optimization problems. The problem is known to undergo phase transitions as a function of the ratio of constraints and variables. While polynomial time algorithms are known to solve the problem for k = 2, for k ≥ 3 the problem is known to be NP-complete. We define the model on a tree and find that the solvability threshold for k = 2 matches the exact value of the threshold on regular random graphs. For higher k, the values are very close to those predicted using other techniques. Our method can be extended to many other optimisation problems.
3. Stochastic modelling of cellular processes.
We are interested in understanding the role of stochasticity in biological processes. We are working on understanding the role of stochasticity on the dynamics of biopolymers like microtubules and actin, which play a crucial role during cell division in bacteria.
Teaching:
Courses Taught:
P-141 Physics Laboratory I
P 205 Mathematical Methods II
P 206 Quantum Mechanics l
P-302 Statistical Mechanics
P 455 Phase Transitions and Critical Phenomena
P 452 Computational Physics
P 614 Statistical Mechanics for PhD students
P-602 Mathematical Methods for PhD Students
Phone No.:
+91 674-2494241
Room No.:
403 FC
Mail ID:
sumedha@niser.ac.in
Group Members:
Present
PhD Students
1. Jagannath Santara(Jointly with Dr. Chethan N Gowdigere) 2016-
2. Soheli Mukherjee 2017-
Alumni
PostDocs
1. Abdul Khaleque Nov 2016-Apr 2017; Now Assistant Professor, Department of Physics, Bidhan Chandra College, University of Calcutta
Master's Thesis Students:
Arabind Swain(2017-2018), on "Role of GTP hydrolysis in FtSz dynamics", Next position: PhD student at Emory University.
Jyotiranjan Parida(2017-2018), on "Large Deviation Theory and its Application to the Blume-Capel model"
Abu James(2017-2018), on "Community detection algorithms for networks"
Varghese Babu(2015-2016), on " Study of randomness in spin models ", Next position: PhD student at JNCSR(Bangalore)
Kunal Garg (Jan-Apr 2015), on "Study of Microscopic Model of Dynamic Instability in Microtubules "
Jetin E Thomas(2013-2014) on "Study of hard core lattice gases using entropic sampling ", Next position: PhD student at Brandeis Univeristy
Sushant K Singh(2012-2013) on "Large Deviation Theory and Its Application to Disordered Systems "; Next position: Scientific Officer at VECC Kolkata
Sharmistha Sahoo(2011-2012) on "Boolean Satisfiability Problem on Regular Random Graphs "; Next position: PhD student at University of Virginia Charlotsville.
