Ganesh Bagler

Last updated

Ganesh Bagler
Professor Ganesh Bagler.jpg
Born (1977-01-31) 31 January 1977 (age 47)
Solapur, Maharashtra, India
Alma mater
Known for
  • Pioneering work in computational gastronomy
  • Science and education
Scientific career
Fields Computational Biology
Institutions
Website cosylab.iiitd.edu.in

Ganesh Bagler is known for his research [1] [2] [3] [4] [5] [6] [7] [8] [9] in computational gastronomy, an emerging data science of food, flavors and health. [10] [11] By blending food with data and computation he has helped establish the foundations of this niche area. [10] [7] Starting with the investigation of food pairing in the Indian cuisine, [12] [13] [14] [15] [16] his lab has contributed to computational gastronomy with studies on culinary fingerprints of world cuisines, [4] culinary evolution, [17] [18] benevolent health impacts of spices, [19] and taste prediction algorithms. [3]

Contents

Education

Ganesh Bagler was born and raised in Solapur, Maharashtra, where he completed his schooling at Sharada School and Siddheshwar High School,Solapur in the western peninsular Indian state of Maharashtra. He pursued a Bachelor of Science (B.Sc.) degree in Physics from Sangameshwar College, affiliated with Shivaji University, graduating in 1997. Bagler continued his academic journey at the University of Hyderabad, earning a Master of Technology (M.Tech.) in Computational Techniques. He later joined the Centre for Cellular and Molecular Biology (CCMB) for his doctoral studies, where his Ph.D. research focused on the application of graph theoretical models to protein structures. [20] [21] His work contributed to the understanding of assortative mixing in these models, a key observation in the field of Computational Biology. Bagler's diverse academic background laid the foundation for his pioneering work in Computational Gastronomy. [22]

Career

Ganesh Bagler's career blends academic research, teaching, and pioneering efforts in Computational Gastronomy. After earning his Ph.D. from the Centre for Cellular and Molecular Biology, where he focused on graph theoretical models of protein structures, Bagler completed postdoctoral research in computational neuroscience at the National Centre for Biological Sciences. He then worked at the Max Planck Institute for Molecular Genetics in Prof. Michael Lappe's (Otto Warburg Laboratory, Bioinformatics/Structural Proteomics) group in Germany on bioinformatics and structural proteomics. Returning to India, Bagler joined the CSIR-Institute of Himalayan Bioresource Technology as a scientist before moving to the Indian Institute of Technology Jodhpur as an assistant professor and later the Dhirubhai Ambani Institute of Information and Communication Technology. He eventually joined Indraprastha Institute of Information Technology (IIIT-Delhi), [23] where he holds a tenure-track position. There, he is affiliated to the Center for Computational Biology and Department of Computational Biology [24] and has been developing the Computational Gastronomy niche in his lab, the Complex Systems Laboratory. [25]

Research

Ganesh Bagler's research [26] has been rooted in investigation of complex systems, [25] primarily of biological origin: protein structure-function, [27] kinetics, [28] folding, [29] and design; [30] complex network models transportation systems; [31] molecular interactome models of complex diseases; [32] controllability of biological networks; [33] [34] in silico drug discovery; [35] [36] [37] systems biological investigation of brain networks; [38] [39] modeling and prediction of phenotypic side effects of drugs; [40] computational models of biological systems; [41] and computational gastronomy. [1] [2] [3] [4] [5] [6] [7] [8] [9]

Science communication and outreach

Ganesh Bagler has keen interest in science, technology, engineering, and mathematics (STEM) education and public outreach for communicating science. [42] He has been engaged in propagating the cause of leveraging computational gastronomy for data-driven food innovations on various platforms: [42] TEDx; [43] HasGeek's Kilter 2017; [44] Discussion Meeting on Mathematical and Statistical Explorations in Disease Modeling and Public Health at the International Centre for Theoretical Sciences; [45] 7th IFCA International Chefs Congress; IIT Guwahati Research Conclave 2017; GD Goenka University Le Cordon Bleu India's iHOST 2017; Cadence Advanced Technology Talk; [46] 2nd International Meeting on Systems Medicine (Utrecht, Netherlands); [47] Food Safety and Standards Authority of India's (FSSAI) EatRight Mela; Bangalore Science Forum; and SIAL Paris Conference 2019. He has organized five editions of Computational Gastronomy Symposiums at IIIT-Delhi. [11] He conducts the 'Open Computational Gastronomy' course on Google Classroom [48]

Controversy

In April 2015, soon after his research reporting the food pairing investigation of Indian cuisine was touted as an emerging technology by the MIT Technology Review, [12] Bagler was unceremoniously removed from the position of assistant professor at the Indian Institute of Technology Jodhpur in a controversial decision. [49] The students protested against the decision of termination. [49] [50] [51] [52] Ministry of Human Resource Development constituted a three-member committee for the investigation of the matter. Subsequently, Bagler moved to Indraprastha Institute of Information Technology on a tenure track position and has graduated his PhD students [53] from Indian Institute of Technology Jodhpur. He has been tenured and promoted to the position of Professor [23] at Indraprastha Institute of Information Technology.

Selected bibliography

Related Research Articles

<span class="mw-page-title-main">Computational biology</span> Branch of biology

Computational biology refers to the use of techniques in computer science, data analysis, mathematical modeling and computational simulations to understand biological systems and relationships. An intersection of computer science, biology, and data science, the field also has foundations in applied mathematics, molecular biology, cell biology, chemistry, and genetics.

<span class="mw-page-title-main">Self-organized criticality</span> Concept in physics

Self-organized criticality (SOC) is a property of dynamical systems that have a critical point as an attractor. Their macroscopic behavior thus displays the spatial or temporal scale-invariance characteristic of the critical point of a phase transition, but without the need to tune control parameters to a precise value, because the system, effectively, tunes itself as it evolves towards criticality.

<span class="mw-page-title-main">Interactome</span> Complete set of molecular interactions in a biological cell

In molecular biology, an interactome is the whole set of molecular interactions in a particular cell. The term specifically refers to physical interactions among molecules but can also describe sets of indirect interactions among genes.

<span class="mw-page-title-main">Effective fitness</span> Reproductive success given genetic mutation

In natural evolution and artificial evolution the fitness of a schema is rescaled to give its effective fitness which takes into account crossover and mutation.

In academia, computational immunology is a field of science that encompasses high-throughput genomic and bioinformatics approaches to immunology. The field's main aim is to convert immunological data into computational problems, solve these problems using mathematical and computational approaches and then convert these results into immunologically meaningful interpretations.

In the study of complex networks, assortative mixing, or assortativity, is a bias in favor of connections between network nodes with similar characteristics. In the specific case of social networks, assortative mixing is also known as homophily. The rarer disassortative mixing is a bias in favor of connections between dissimilar nodes.

Anders Krogh is a bioinformatician at the University of Copenhagen, where he leads the university's bioinformatics center. He is known for his pioneering work on the use of hidden Markov models in bioinformatics, and is co-author of a widely used textbook in bioinformatics. In addition, he also co-authored one of the early textbooks on neural networks. His current research interests include promoter analysis, non-coding RNA, gene prediction and protein structure prediction.

<span class="mw-page-title-main">Google matrix</span> Stochastic matrix representing links between entities

A Google matrix is a particular stochastic matrix that is used by Google's PageRank algorithm. The matrix represents a graph with edges representing links between pages. The PageRank of each page can then be generated iteratively from the Google matrix using the power method. However, in order for the power method to converge, the matrix must be stochastic, irreducible and aperiodic.

The clique percolation method is a popular approach for analyzing the overlapping community structure of networks. The term network community has no widely accepted unique definition and it is usually defined as a group of nodes that are more densely connected to each other than to other nodes in the network. There are numerous alternative methods for detecting communities in networks, for example, the Girvan–Newman algorithm, hierarchical clustering and modularity maximization.

Dante R. Chialvo is a professor at Universidad Nacional de San Martin. Together with Per Bak, they put forward concrete models considering the brain as a critical system. Initial contributions focussed on mathematical ideas of how learning could benefit from criticality. Further work provided experimental evidence for this conjecture both at large and small scale. He was named Fulbright Scholar in 2005 and elected as a Fellow of the American Physical Society in 2007 and as Member of the Academia de Ciencias de America Latina in 2022.

<span class="mw-page-title-main">Friendship paradox</span> Phenomenon that most people have fewer friends than their friends have, on average

The friendship paradox is the phenomenon first observed by the sociologist Scott L. Feld in 1991 that on average, an individual's friends have more friends than that individual. It can be explained as a form of sampling bias in which people with more friends are more likely to be in one's own friend group. In other words, one is less likely to be friends with someone who has very few friends. In contradiction to this, most people believe that they have more friends than their friends have.

Molecular communications systems use the presence or absence of a selected type of molecule to digitally encode messages. The molecules are delivered into communications media such as air and water for transmission. The technique also is not subject to the requirement of using antennas that are sized to a specific ratio of the wavelength of the signal. Molecular communication signals can be made biocompatible and require very little energy.

<span class="mw-page-title-main">Integrated information theory</span> Theory within consciousness research

Integrated information theory (IIT) proposes a mathematical model for the consciousness of a system. It comprises a framework ultimately intended to explain why some physical systems are conscious, and to be capable of providing a concrete inference about whether any physical system is conscious, to what degree, and what particular experience it has; why they feel the particular way they do in particular states, and what it would take for other physical systems to be conscious.

<span class="mw-page-title-main">SATB2</span> Protein-coding gene in the species Homo sapiens

Special AT-rich sequence-binding protein 2 (SATB2) also known as DNA-binding protein SATB2 is a protein that in humans is encoded by the SATB2 gene. SATB2 is a DNA-binding protein that specifically binds nuclear matrix attachment regions and is involved in transcriptional regulation and chromatin remodeling. SATB2 shows a restricted mode of expression and is expressed in certain cell nuclei. The SATB2 protein is mainly expressed in the epithelial cells of the colon and rectum, followed by the nuclei of neurons in the brain.

<span class="mw-page-title-main">Robustness (evolution)</span> Persistence of a biological trait under uncertain conditions

In evolutionary biology, robustness of a biological system is the persistence of a certain characteristic or trait in a system under perturbations or conditions of uncertainty. Robustness in development is known as canalization. According to the kind of perturbation involved, robustness can be classified as mutational, environmental, recombinational, or behavioral robustness etc. Robustness is achieved through the combination of many genetic and molecular mechanisms and can evolve by either direct or indirect selection. Several model systems have been developed to experimentally study robustness and its evolutionary consequences.

Transfer entropy is a non-parametric statistic measuring the amount of directed (time-asymmetric) transfer of information between two random processes. Transfer entropy from a process X to another process Y is the amount of uncertainty reduced in future values of Y by knowing the past values of X given past values of Y. More specifically, if and for denote two random processes and the amount of information is measured using Shannon's entropy, the transfer entropy can be written as:

Direct coupling analysis or DCA is an umbrella term comprising several methods for analyzing sequence data in computational biology. The common idea of these methods is to use statistical modeling to quantify the strength of the direct relationship between two positions of a biological sequence, excluding effects from other positions. This contrasts usual measures of correlation, which can be large even if there is no direct relationship between the positions. Such a direct relationship can for example be the evolutionary pressure for two positions to maintain mutual compatibility in the biomolecular structure of the sequence, leading to molecular coevolution between the two positions.

In chemical kinetics, the Aquilanti–Mundim deformed Arrhenius model is a generalization of the standard Arrhenius law.

<span class="mw-page-title-main">Taha Yasseri</span> Physicist and sociologist

Taha Yasseri is a physicist and sociologist known for his research on crowdsourcing, collective intelligence and computational social science. He is a full professor at the School of Social Sciences and Philosophy at Trinity College Dublin, Ireland. He is the inaugural Workday Chair of Technology and Society. He was formerly a professor of sociology at University College Dublin, a senior research fellow in computational social science at the Oxford Internet Institute (OII), University of Oxford, a Turing Fellow at the Alan Turing Institute for data science and artificial intelligence, and a research fellow in humanities and social sciences at Wolfson College, Oxford. Yasseri is one of the leading scholars in computational social science and his research has been widely covered in mainstream media. Yasseri obtained his PhD in theoretical physics of complex systems at the age of 25 from the University of Göttingen, Germany.

References

  1. 1 2 Jain, Anupam; Rakhi N K; Bagler, Ganesh (2015). "Spices form the basis of food pairing in Indian cuisine". arXiv: 1502.03815 [physics.soc-ph].
  2. 1 2 Jain, Anupam; n k, Rakhi; Bagler, Ganesh (2015). "Analysis of Food Pairing in Regional Cuisines of India". PLOS ONE. 10 (10): e0139539. arXiv: 1505.00890 . Bibcode:2015PLoSO..1039539J. doi: 10.1371/journal.pone.0139539 . PMC   4592201 . PMID   26430895.
  3. 1 2 3 Tuwani, Rudraksh; Wadhwa, Somin; Bagler, Ganesh (9 May 2019). "BitterSweet: Building machine learning models for predicting the bitter and sweet taste of small molecules". Scientific Reports. 9 (1): 7155. Bibcode:2019NatSR...9.7155T. doi:10.1038/s41598-019-43664-y. PMC   6509165 . PMID   31073241.
  4. 1 2 3 Bagler, Ganesh; Singh, Navjot (2018). "Data-Driven Investigations of Culinary Patterns in Traditional Recipes Across the World". 2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW). pp. 157–162. arXiv: 1803.04343 . doi:10.1109/ICDEW.2018.00033. ISBN   978-1-5386-6306-6. S2CID   4941349.
  5. 1 2 Garg, Neelansh; Sethupathy, Apuroop; Tuwani, Rudraksh; Nk, Rakhi; Dokania, Shubham; Iyer, Arvind; Gupta, Ayushi; Agrawal, Shubhra; Singh, Navjot; Shukla, Shubham; Kathuria, Kriti; Badhwar, Rahul; Kanji, Rakesh; Jain, Anupam; Kaur, Avneet; Nagpal, Rashmi; Bagler, Ganesh (4 January 2018). "FlavorDB: a database of flavor molecules". Nucleic Acids Research. 46 (D1): D1210 –D1216. doi:10.1093/nar/gkx957. PMC   5753196 . PMID   29059383.
  6. 1 2 Rakhi, N. K.; Tuwani, Rudraksh; Mukherjee, Jagriti; Bagler, Ganesh (2018). "Data-driven analysis of biomedical literature suggests broad-spectrum benefits of culinary herbs and spices". PLOS ONE. 13 (5): e0198030. Bibcode:2018PLoSO..1398030R. doi: 10.1371/journal.pone.0198030 . PMC   5973616 . PMID   29813110.
  7. 1 2 3 "Can A Computer Cook Up The Perfect Recipe?". HuffPost India. 16 December 2018.
  8. 1 2 "Food Scientists Say AI Can Give Ayurveda Scientific Rigor". HuffPost India. 5 January 2019.
  9. 1 2 "Computational Gastronomy: Leveraging food for better health through... by Ganesh Bagler". YouTube. 8 August 2019. Retrieved 23 February 2020.
  10. 1 2 "Data" (PDF). www.currentscience.ac.in. Retrieved 24 February 2020.
  11. 1 2 "Symposium on Computational Gastronomy". IIIT-Delhi. 10 December 2023.
  12. 1 2 arXiv, Emerging Technology from the. "Data Mining Indian Recipes Reveals New Food Pairing Phenomenon". MIT Technology Review.
  13. ↑ "This is why Indian food is so delicious... - Times of India". Timesofindia.indiatimes.com. 6 December 2015. Retrieved 23 February 2020.
  14. ↑ "Why Indian Food Is So Tasty". Time.
  15. ↑ "Researchers Explain Why Indian Cuisine Is Exquisite". NPR.org.
  16. ↑ "The tastes of India: Spices give Indian food the edge". The Jakarta Post.
  17. ↑ Jain, Anupam; Bagler, Ganesh (1 August 2018). "Culinary evolution models for Indian cuisines". Physica A: Statistical Mechanics and Its Applications. 503: 170–176. arXiv: 1505.00155 . Bibcode:2018PhyA..503..170J. doi:10.1016/j.physa.2018.02.176. S2CID   16910527.
  18. ↑ Tuwani, Rudraksh; Sahoo, Nutan; Singh, Navjot; Bagler, Ganesh (2019). "Computational models for the evolution of world cuisines". arXiv: 1904.10138 [physics.soc-ph].
  19. ↑ Rakhi, N. K.; Tuwani, Rudraksh; Mukherjee, Jagriti; Bagler, Ganesh (2018). "Data-driven analysis of biomedical literature suggests broad-spectrum benefits of culinary herbs and spices". PLOS ONE. 13 (5): e0198030. Bibcode:2018PLoSO..1398030R. doi: 10.1371/journal.pone.0198030 . PMC   5973616 . PMID   29813110.
  20. ↑ Bagler, Ganesh (2007). "Modeling Protein Contact Networks". arXiv: 0711.2616 [q-bio.MN].
  21. ↑ Bagler, Ganesh; Sinha, Somdatta (2007). "Assortative mixing in Protein Contact Networks and protein folding kinetics". Bioinformatics. 23 (14): 1760–1767. arXiv: 0711.2723 . Bibcode:2007Bioin..23.1760B. doi:10.1093/bioinformatics/btm257. PMID   17519248.
  22. ↑ "Computational Gastronomy: Data Science Approach to Food | World IA Day". worldiaday.org. Retrieved 15 October 2024.
  23. 1 2 "Ganesh Bagler | IIIT-Delhi". Iiitd.ac.in. Retrieved 23 February 2020.
  24. ↑ "Department of Computational Biology - IIIT Delhi". Cb.iiitd.ac.in. Retrieved 23 February 2020.
  25. 1 2 "Complex Systems Lab, IIIT-Delhi". Cosylab.iiitd.edu.in. Retrieved 23 February 2020.
  26. ↑ "Ganesh Bagler - Google Scholar Citations" . Retrieved 23 February 2020.
  27. ↑ Bagler, Ganesh; Sinha, Somdatta (1 February 2005). "Network properties of protein structures". Physica A: Statistical Mechanics and Its Applications. 346 (1): 27–33. arXiv: q-bio/0408009 . Bibcode:2005PhyA..346...27B. doi:10.1016/j.physa.2004.08.046. S2CID   9539809.
  28. ↑ Bagler, Ganesh; Sinha, Somdatta (15 July 2007). "Assortative mixing in Protein Contact Networks and protein folding kinetics". Bioinformatics. 23 (14): 1760–1767. arXiv: 0711.2723 . Bibcode:2007Bioin..23.1760B. doi:10.1093/bioinformatics/btm257. PMID   17519248.
  29. ↑ Lappe, Michael; Bagler, Ganesh; Filippis, Ioannis; Stehr, Henning; Duarte, Jose M; Sathyapriya, Rajagopal (1 August 2009). "Designing evolvable libraries using multi-body potentials". Current Opinion in Biotechnology. 20 (4): 437–446. doi:10.1016/j.copbio.2009.07.008. PMID   19713097.
  30. ↑ Kumar, Arun; Dutt, Som; Bagler, Ganesh; Ahuja, Paramvir Singh; Kumar, Sanjay (30 April 2012). "Engineering a thermo-stable superoxide dismutase functional at sub-zero to >50°C, which also tolerates autoclaving". Scientific Reports. 2 (1): 387. Bibcode:2012NatSR...2..387K. doi:10.1038/srep00387. PMC   3339387 . PMID   22548128.
  31. ↑ Bagler, Ganesh (1 May 2008). "Analysis of the airport network of India as a complex weighted network". Physica A: Statistical Mechanics and Its Applications. 387 (12): 2972–2980. arXiv: cond-mat/0409773 . Bibcode:2008PhyA..387.2972B. doi:10.1016/j.physa.2008.01.077. S2CID   119387720.
  32. ↑ Vashisht, Shikha; Bagler, Ganesh (2012). "An Approach for the Identification of Targets Specific to Bone Metastasis Using Cancer Genes Interactome and Gene Ontology Analysis". PLOS ONE. 7 (11): e49401. arXiv: 1112.1510 . Bibcode:2012PLoSO...749401V. doi: 10.1371/journal.pone.0049401 . PMC   3498148 . PMID   23166660.
  33. ↑ Ravindran, Vandana; Nacher, Jose C.; Akutsu, Tatsuya; Ishitsuka, Masayuki; Osadcenco, Adrian; Sunitha, V.; Bagler, Ganesh; Schwartz, Jean-Marc; Robertson, David L. (14 February 2019). "Network controllability analysis of intracellular signalling reveals viruses are actively controlling molecular systems". Scientific Reports. 9 (1): 2066. Bibcode:2019NatSR...9.2066R. doi:10.1038/s41598-018-38224-9. PMC   6375943 . PMID   30765882.
  34. ↑ Ravindran, Vandana; V., Sunitha; Bagler, Ganesh (15 May 2017). "Identification of critical regulatory genes in cancer signaling network using controllability analysis". Physica A: Statistical Mechanics and Its Applications. 474: 134–143. Bibcode:2017PhyA..474..134R. doi:10.1016/j.physa.2017.01.059.
  35. ↑ Pathania, Shivalika; Randhawa, Vinay; Bagler, Ganesh (2013). "Prospecting for Novel Plant-Derived Molecules of Rauvolfia serpentina as Inhibitors of Aldose Reductase, a Potent Drug Target for Diabetes and Its Complications". PLOS ONE. 8 (4): e61327. Bibcode:2013PLoSO...861327P. doi: 10.1371/journal.pone.0061327 . PMC   3629236 . PMID   23613832.
  36. ↑ Pathania, Shivalika; Ramakrishnan, Sai Mukund; Randhawa, Vinay; Bagler, Ganesh (2015). "SerpentinaDB: A database of plant-derived molecules of Rauvolfia serpentina". BMC Complementary and Alternative Medicine. 15: 262. doi: 10.1186/s12906-015-0683-7 . PMC   4523024 . PMID   26238452.
  37. ↑ Pathania, Shivalika; Ramakrishnan, Sai Mukund; Bagler, Ganesh (2015). "Phytochemica: A platform to explore phytochemicals of medicinal plants". Database. 2015: bav075. doi:10.1093/database/bav075. PMC   4529746 . PMID   26255307.
  38. ↑ Badhwar, Rahul; Bagler, Ganesh (2015). "Control of Neuronal Network in Caenorhabditis elegans". PLOS ONE. 10 (9): e0139204. Bibcode:2015PLoSO..1039204B. doi: 10.1371/journal.pone.0139204 . PMC   4586142 . PMID   26413834.
  39. ↑ Badhwar, Rahul; Bagler, Ganesh (1 March 2017). "A distance constrained synaptic plasticity model of C. elegans neuronal network". Physica A: Statistical Mechanics and Its Applications. 469: 313–322. arXiv: 1603.03867 . Bibcode:2017PhyA..469..313B. doi:10.1016/j.physa.2016.11.055. S2CID   2795267.
  40. ↑ Wadhwa, Somin; Gupta, Aishwarya; Dokania, Shubham; Kanji, Rakesh; Bagler, Ganesh (2018). "A hierarchical anatomical classification schema for prediction of phenotypic side effects". PLOS ONE. 13 (3): e0193959. Bibcode:2018PLoSO..1393959W. doi: 10.1371/journal.pone.0193959 . PMC   5832387 . PMID   29494708.
  41. ↑ Yadav, Reena; Ghatge, Mayur; Hiremath, Kirankumar; Bagler, Ganesh (2015). "Numerical study of variable lung ventilation strategies". arXiv: 1509.05163 [q-bio.QM].
  42. 1 2 "Outreach: Talks, Presentations, Past members of the lab. Ganesh Bagler (IIIT-Delhi) - Google My Maps" . Retrieved 23 February 2020.
  43. ↑ "Making Food Computable | Dr. Ganesh Bagler | TEDxIISERPune". YouTube. 10 July 2023. Retrieved 29 September 2024.
  44. ↑ "Data-driven Food Pairings - Ganesh Bagler". YouTube. 6 April 2017. Retrieved 23 February 2020.
  45. ↑ "Computational Gastronomy: Leveraging food for better health through... by Ganesh Bagler". YouTube. 8 August 2019. Retrieved 23 February 2020.
  46. ↑ "Cadence Advanced Technology Talk :: Can a biologist fix a radio? (Ganesh Bagler)". YouTube. 11 September 2019. Retrieved 23 February 2020.
  47. ↑ "2018 Utrecht Conference Program – easym". Easym.eu. Retrieved 23 February 2020.
  48. ↑ "OPEN Computational Gastronomy [Week01/Class01] Ganesh Bagler". YouTube. 8 September 2019. Retrieved 23 February 2020.
  49. 1 2 "Students want 'terminator' IIT-Jodhpur chief to go - Times of India". The Times of India. 3 April 2015.
  50. ↑ Press Trust of India (3 April 2015). "IIT Jodhpur students urge Prez, HRD Minitsry to sack Director | Business Standard News". Business Standard India. Business-standard.com. Retrieved 23 February 2020.
  51. ↑ "These IIT Students Are Waging A Social Media War On Their Director #DictatorFreeIITJ". Indiatimes.com. 3 April 2015. Retrieved 23 February 2020.
  52. ↑ Press Trust of India (April 2015). "IIT-J students protest against faculty termination | Business Standard News". Business Standard India. Business-standard.com. Retrieved 23 February 2020.
  53. ↑ "Khamma Ghani" (PDF). iitj.ac.in. 2018. Retrieved 24 February 2020.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.