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Dr. A. R. Jac Fredo, Ph.D

Assistant Professor
School of Bio-Medical Engineering IIT (BHU)
Phone No(s): 9444555798
Email: jack.bme@iitbhu.ac.in
Area of Interest: Bio-Medical Signal and Image Processing, Bio-Medical Instrumentation, Computational Neuroscience, Developmental Psychology, Neuro-Informatics, Machine Learning, Drug Repurposing/Discovery
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Dr. Jac Fredo did his undergraduate in Electrical and Electronics Engineering from Anna University during 2004- 2008. He did first project study in 2007, as part of his undergraduate degree under Dr. Amalin Prince, BITS Pilani, K.K.Birla Goa Campus, India. This group worked with biomedical signal and image processing methods in application to FPGA devices. During this project work, he developed various filter methods to pre-process the vibro-arthrographic (VAG) signals and he got experience in biomedical signal processing, and coding platform like Labview. Dr. Jac started his research career in 2009 as a Masters student working under Dr. S. Ramakrishnan at Department of Instrumentation Engineering, Madras Institute of Technology, Anna University, Chennai, India. This group worked with different biomedical signal and image processing, pattern recognition, and machine learning algorithms in application to biomedical instrumentation. Dr. Jac participated in developing test systems for human brain evoked auditory signals using Virtual Audiometer and got experience in pattern recognition and machine learning algorithms, and coding platform like Rstudio. The outcome of the study was published in a conference held in Pune, India. Since 2010, Dr. Jac joined as a Junior Research Fellow in Neuro-informatics with the grant from University Grant Commission, Department of Science and Technology, India working under Dr. G. Kavitha, Anna University, Chennai, India and started to work on his Ph.D. thesis. This group worked in close collaboration with Dr. S. Ramakrishnan at the Non-Invasive Imaging and Diagnostics Laboratory, Biomedical Division, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India, on several projects of brain imaging. Dr. Jac’s Ph.D. thesis was devoted to the Neuro-informatics study of identifying structural brain based autistic traits in autism using sMRI, image processing and pattern recognition methods. Dr. Jac was trained in advanced image processing algorithms, brain imaging toolboxes such BSE, BET, Brain suite, Neuron, SPM8, and coding platform like Matlab during this tenure. Dr. Jac and his colleagues found that subcortical regions of brain can serve as an effective biomarker for the diagnosis of autism spectrum disorder and proved the presence of structural brain based autistic traits in autism. Dr. Jac also assisted in Electronics Labs and teaches basic Matlab for the students of Faculty of Electronics Engineering (Anna University) during this tenure. He has published 6 journals and 5 conference publications as part of his Ph.D thesis. Further, Dr. Jac travelled to Germany, The Netherlands, and United States of America through different travel grants and participated in workshops, presented research outcomes in conferences.  
After his Ph.D. in Neuro-informatics in 2015, Dr. Jac joined as a Project Associate in the same Lab under Dr. S. Ramakrishnan and was in charge for the projects in different fields including Brain imaging and EMG processing. He served in organizational committee for the conferences hosted by the lab during this tenure. He obtained skills in planning in vitro and in ​vivo experiments, statistical analysis, writing grant proposals, articles, patents, and working in an interdisciplinary team. Later, Dr. Jac joined as an Assistant Professor (Senior Grade) in 2016 at VIT University, Vellore, India and delivered courses on Bio-Medical Instrumentation, Sensors, Medical Physics and Rstudio for students. Further, Dr. Jac supervised three undergraduate projects, which have improved his skills in mentoring and support in career objectives. He undergone research in VAG signals, developed machine learning model for the diagnosis of articular diseases and published the outcome in a journal. Further, he developed a process pipeline to identify the damages in composite materials using digital images and published in 4 leading journals sooner and later. Dr. Jac was funded grant by Indo-Us Science and Technology Forum/ Science Engineering Research Board, India to pursue Post-Doctoral studies under Prof. Ralph Axel Mueller at Brain Development Imaging Lab, San Diego State University, USA in 2016. This group is pioneer in autism spectrum disorder research using behavioural, clinical measures, multi-model imaging modalities like sMRI, fMRI, DTI and EEG. He worked in two projects including machine learning models for autism using resting-state fMRI and identification of subtypes in autism using multi-model imaging methods. During this tenure, Dr. Jac was trained in resting-state fMRI processing, DTI processing and training in brain imaging tool boxes like FSL, AFNI, Freesurfer, coding in bash script and working on High Performance Computing through remote server. Based on his studies, Dr. Jac has published two papers (1 journal and 1 conference) related to machine learning models for autism. In 2018, Dr. Jac joined as a Research Fellow in Nanyang Technological University, Singapore under the supervision of Prof. Justin Dauwels. The lab is pioneer in developing advanced brain connectivity estimation methods and machine learning algorithms in application to brain development research. He got opportunity to work in brain connectivity analysis methods in collaboration with Prof. Georg Langs, Medical University of Vienna, Austria. The outcomes of this research study are published in a journal and a conference poster. During this tenure, Dr. Jac acquired a strong background in neuro-informatics (brain network connectivity estimation methods), machine learning (deep learning), high complexity algorithms, and coding platforms like python.
In the mid of 2020, Dr. Jac got Advanced Research Opportunities (AROP) Scholarship to wok under Dr. Kerstin Konrad, Clinics & Institute, Clinic for Psychiatry, Psychosomatic, and Psychotherapy of childhood and adolescents in RWTH Aachen, Germany. This group work in autism traits, neural mechanism, child development, gender variation, behavioural dimensions, neuroimaging, functional brain connectivity, and diagnostic classification of neuro developmental disorders. Recently, Dr. Jac joined as an Assistant Professor in the School of Bio-Medical Engineering in IIT (BHU), Varanasi, India.

2020-Present Assistant Professor in the School of Bio-Medical Engineering, IIT (BHU), Varanasi, India. Delivering courses in Bio-Medical Instrumentation and Medical Imaging Modalities, Bio-materials, Artificial Intelligence and its application in Bio-Medical Engineering, Electronic Measurements and Instrumentation for Bio-Medical Applications for undergraduates, Masters and Ph.D students
2020 Research Fellow in Clinics & Institute, Clinic for Psychiatry, Psychosomatic, and Psychotherapy of childhood and adolescents in RWTH Aachen, Germany. Studied a project under the title “Investigation of autistic traits in typical developing fMRI of brain using sparse inverse co-variance estimation methods and machine learning approaches” under Prof. Kerstin Konrad.
2018-2020 Research Fellow in the Department of Electrical Engineering, Nanyang Technological University, Singapore. Participated in a research project “Multimodal brain imaging and network inference to capture changes due to autism” under Prof. Justin Dauwels, Nanyang Technological University, in collaboration with Prof. George Langs, Medical University of Vienna, Austria.
2017-2018 Research Fellow in Brain Development Imaging Lab, Department of Psychology, San Diego State University, USA. Studied gender specific functional brain connectivity in autism which was found in a project on “Performance of machine learning classification models of autism using resting-state fMRI is contingent on sample heterogeneity” under the supervision of Prof. Ralph Axel Mueller.
Participated in a study “Assessment of subtypes of autism spectrum disorder based on structural, functional and clinical scores” under the supervision of Prof. Ralph Axel Mueller.
2016-2017 Assistant Professor, Department of Biomedical Engineering, VIT University, Vellore, India. Delivered courses in Bio-Medical Instrumentation, Sensors, Medical Physics and Rstudio for Beginners courses for groups of about 60 undergraduate students.
2015-2016 Research Associate in Department of Applied Mechanics, Biomedical Division, Indian Institute of Technology Madras, Chennai, India. Project leader in a research project “Corpus callosum: A structural brain biomarker for autism diagnosis” under the supervision of Dr. S. Ramakrishnan.
2011-2015 Teaching Assistant, Department of Electronics Engineering, Anna University. Assisted in Electronics Labs and teaches basic Matlab for Beginners courses for groups of about 30 undergraduate students.
2008-2010 Teaching Assistant, Department of Instrumentation Engineering, Anna University. Assisted in Electrical Machines Lab and teaches Rstudio and Labview for Beginners courses for groups of about 30 undergraduate students.

List of Book Publications

  1. Abirami S, John T, Yuvaraj R and Jac Fredo AR (2021), “A comparative study on EEG features for neonatal seizure detection”, edited book chapter in Springer, titled as, Biomedical signal-based computer aided diagnosis for neurological disorders.

List of Journal Publications

  1. Sriram Kumar P and Jac Fredo AR, (2024) ‘Emotion classification through optimal segments of EDA and texture analysis of time-encoded images with artificial intelligence', IEEE Transactions on Instrumentation & Measurement (IEEE publishers, IF: 5.6)

  2. Ahsan A,  Jac Fredo AR, Ramakrishnan S (2024) 'Characterization of Fornix Structural Changes in Controls, MCI and AD Using sMR Images and Rotational Invariant Moments', IEEE Sensors Letters (IEEE publishers, IF: 2.2)

  3. Vinay Kumar B, Swarubini PJ, Sriram Kumar P, Nagarajan G, Karthik PA, Deepesh Kumar, Jac Fredo AR, (2024) ‘Evaluating the effectiveness of machine learning in identifying the optimal facial electromyography location for emotion detection’, Biomedical Signal Processing and Control (Elsevier publishers, IF: 4.9)

  4. Sanju Kumari, Bhavana P, Essha C, Nancy T, Sanheeta C, Nivedita B, Santosh Kumar G, Jac Fredo AR, and Shreyans KJ (2024) ‘Prioritization of the Secondary Metabolites for the Rapid Annotation Based on Liquid Chromatography-High Resolution Mass Spectrometry Assessment: Varanasine and Schroffanone from Murraya paniculata and Cytotoxic Evaluation’, Journal of Proteome Research (ACS publishers, IF: 3.8)

  5. Abirami S, Tikaram, Kathiravan M, Yuvraj R, Ramshekhar NM, John T, Karthick PA, Amalin PA and Jac Fredo AR (2024) 'Automated Multi-class Seizure-type Classification System using EEG Signals and Machine Learning Algorithms', IEEE Access (IEEE publishers, IF: 3.9).

  6. Sanju K, Sanheeta C, Sanjay K, Sanjeev K, Jac Fredo AR and Shreyans KJ (2024) 'Prioritization before dereplication, an effective strategy to target new metabolites in whole extracts: ghosalin from Murraya paniculata root', Analytical Methods (The Royal Society of Chemistry publishers, IF: 2.7)

  7. Ahsan A,  Jac Fredo AR, Ramakrishnan S (2024) 'Assessment of Structural Variations in Fornix of MCI and AD using MR Images and Geometrical Features', Journal of Medical and Biological Engineering (Springer publishers, IF: 2).

  8. Abhavya R, Pragya, Sabitha R, Brijesh K, Jac Fredo AR (2024) 'Biomarker identification and gene-drug interaction prediction for breast cancer using machine learning algorithms', Current Directions in Biomedical Engineering (De Gruyter publishers)

  9. Ahsan A, Jac Fredo AR, Ramakrishnan S (2024) 'Assessment of Invariant Moments of Fornix in Brain Structural MR Images to Differentiate Mild Cognitive Impairment from Alzheimer’s Disease', Current Directions in Biomedical Engineering, (De Gruyter publishers).

  10. Sriram Kumar P, Praveen Kumar G, Abdul Aleem SG, Nagarajan G, Jac Fredo AR (2024) 'Deep Learning-Based Automated Emotion Recognition Using Multimodal Physiological Signals and Time-Frequency Methods', IEEE Transactions on Instrumentation & Measurement (IEEE publishers, IF: 5.6)

  11. Praveen Kumar G, Sriram Kumar P, Nagarajan G, Jac Fredo AR (2024), 'Emotion Classification using Electrocardiogram and Machine Learning: A Study on the Effect of Windowing Techniques', Expert Systems With Applications (Elsevier, IF: 8.5)

  12. Chetan R, Suja K, Soumya S, Murugappan M, Jac Fredo AR (2024), ‘Autism Spectrum Disorder Diagnosis using Fractals and Non-Fractals based Functional Connectivity Analysis and Machine Learning Methods’, Neural Computing and Applications (Springer publishers, IF: 6).

  13. Pragya, Praveen Kumar Govarthan, Malay Nayak, Sudip Mukherjee,  Jac Fredo AR (2024), ‘Establishment of three gene prognostic markers in pancreatic ductal adenocarcinoma using machine learning approach’, Journal of Medical and Biological Engineering (Springer publishers, IF: 2).
  14. Gokul M, Vaibhavi G, Aditi B, Shaik GAA, Dhanvi V, Amalin PA, Jac Fredo AR (2024), “Diagnostic classification of autism spectrum disorder using sMRI improves with the morphological distance-related features compared to morphological features”, Multimedia Tools and Applications (Springer publishers, IF: 3.6)

  15. Sriram Kumar P and Jac Fredo AR (2024) 'Optimal Electrodermal Activity Segment for Enhanced Emotion Recognition Using Spectrogram-based Feature Extraction with Machine Learning Approach', International Journal of Neural Systems  (World Scientific publishers, IF: 8)

  16. Ankit Kumar M, Aseem S, Abhishesh Kumar M, Vishnu Priya, Nikitha Lakshmi Suseela M, Patharaj G, Jac Fredo AR, Shreyans Kumar J, Joseph S, Muthu MS (2024), 'Green analytical chemistry: Experimental and chemometric methods for the detection of therapeutics using liquid chromatography in wastewater samples', Analytical Chemistry Letters, vol. 14 (Taylor & Francis publishers, IF: 2.267)

  17. Vaibhav J, Chetan TR, Sandeep SS, Murugappan M and Jac Fredo AR (2023) 'Age and Severity Specific Deep Learning Models for Autism Spectrum Disorder Classification Using Functional Connectivity Measures', Arabian Journal for Science and Engineering (Springer publishers, IF: 2.9)

  18. Sudharshan R, Kshitij S, Pragya P, Gowri MB, Jac Fredo AR  (2023) ‘Comparing Pertubagens from Differential Gene Expression Data Analysis of ASD using Random Forest and Statistical Test’, Current Directions in Biomedical Engineering,  vol. 9, no. 1, pp. 682-685 (De Gruyter publishers)

  19. Devesh J, Praveen Kumar G, Abirami S, Christy T, John T, Jac Fredo AR (2023) ‘A feasibility study on using EEG for Biometric Trait Authentication System’, Current Directions in Biomedical Engineering,  vol. 9, no. 1, pp. 690-693 (De Gruyter publishers)

  20. Swarubini PJ, Sriram Kumar P, Praveen Kumar G, Meghraj P, Parbani D, Shivabhijit S, Mythili A, Jac Fredo AR (2023) ‘ECG based Categorical emotion classification using time-domain features and Machine Learning’, Current Directions in Biomedical Engineering,  vol. 9, no. 1, pp. 694-697 (De Gruyter publishers)

  21. Sriram Kumar P, Praveen Kumar G, Nagarajan G, Jac Fredo AR (2023) ‘A comparative analysis of EDA decomposition methods for improved emotion recognition’, Journal of Mechanics in Medicine and Biology (World Scientific publishers, IF:0.8)

  22. Sriram Kumar P, Praveen Kumar G, Nagarajan G, Jac Fredo AR (2023) ‘Electrodermal activity based analysis of emotion recognition using temporal-morphological features and machine learning algorithms’, Journal of Mechanics in Medicine and Biology  (World Scientific publishers, IF: 0.8)

  23. Manoj K, Chandrakanta H, Sunil Kumar K, Eshita B, Kommu John VS, Hansashree P, Akhila SG, Shweta N, Thomas Kishore M, Yamini BK, Jac Fredo AR, Jitender S, Rose Dawn B (2023) ‘Altered cerebellar lobular volumes correlate with clinical deficits in siblings and children with ASD: evidence from toddlers’ Journal of Translational Medicine, 21 (1), 246 (BMC publishers, IF: 8.448)

  24. Aditi B, Gokul M, Vaibhavi G, Abdul ASG, Dhanvi V, Amalin PA. Priya R, Anandh KR, Jac Fredo AR (2022) ‘Comparative evaluation of geometrical, Zernike moments, and volumetric features of the corpus callosum for discrimination of ASD using machine learning algorithms’, International Journal of Biomedical Engineering and Technology (Inderscience publishers)

  25. Rohan Kotha, Priya Rani, Femi Robert, Christy Bobby Thomas, Suresh Kumar C, Jac Fredo AR (2022) ‘Damage monitoring in fibre-reinforced polymer composites using adaptive threshold methods and geometric features’, Journal of the Brazilian Society of Mechanical Sciences and Engineering (Springer publishers, IF: 2.361)

  26. S Shaji, Jac Fredo AR, AK Ramaniharan, R Swaminathan (2022) ‘Study on the Effect of Extreme Learning Machine and its Variants in Differentiating Alzheimer Conditions from Selective Regions of Brain MR Images, Expert Systems with Applications, 118250 (Elsevier publishers, IF: 8.665)

  27. Rakshit Mittal, Amalin Prince A, Jac Fredo AR (2022) ‘Time-sliced architecture for efficient accelerator to detrend high-definition electroencephalograms’, IEEE Transactions on Instrumentation and Measurement, vol. 71 (IEEE publishers, IF: 5.6).

  28. Femi R, Amalin Prince A,  Jac Fredo  AR (2022) ‘Investigation of using CNT and Cu/CNT Wires for Replacing Cu for Power Electronics and Electrical Applications', ECS Journal of Solid State Science and Technology, Vol. 11, No. 2 (IOP Science publishers, IF:2.2)

  29. Femi Robert, Amalin Prince A, JacFredo AR (2021), ‘Influence of wire electrical discharge machine cutting parameters on the magnetization characteristics of electrical steel laminations’ Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2021.10.141 (Elsevier publishers, IF: 1.24)

  30. Amalin Prince, Rakshit Mittal, Saif Nalband, Femi Robert, Jac Fredo AR (2021), ‘Modified-MaMeMi Filter Bank for Efficient Extraction of Brainwaves from Electroencephalograms’, Biomedical Signal Processing & Control, https://doi.org/10.1016/j.bspc.2021.102927 (Elsevier publishers, IF: 5.1)

  31. Rakshit M, Amalin PA, Saif N, Femi R, & Jac Fredo AR (2020), ‘Low-power hardware accelerator for detrending measured biopotential data’, IEEE Transactions on Instrumentation and Measurement. (IEEE publishers, IF: 5.6)

  32. Jac Fredo AR, John T, Prasanth T, Vineetha K, Georg L & Justin D (2020), ‘Diagnostic classification of autism using resting-state fMRI data improves with full correlation functional brain connectivity compared to partial correlation’, Journal of Neuroscience Methods, https://doi.org/10.1016/j.jneumeth.2020.108884 (Elsevier publishers, IF: 2.785)

  33. Maya AR, Afrooz J, Jac Fredo AR, Inna F, Barbara B & Ralph-Axel M (2020), ‘Performance of machine learning classification models of autism using resting-state fMRI is contingent on sample heterogeneity’, Neural Computing and Applications, https://doi.org/10.1007/s00521-020-05193-y (Springer publishers, IF: 4.774)

  34. Jac Fredo AR, Abilash RS, Femi R, Sri Madhava Raja N, & Suresh Kumar C (2020) ‘Automated damage detection and characterization of polymer composite images using Tsallis-particle swarm optimisation based multi-level threshold and multifractals’ Polymer Composites, https://doi.org/10.1002/pc.25611 (Wiley publishers, IF: 2.268)

  35. Suresh Kumar C, Arumugam V, Kenned JJ, Karthikeyan R, & Jac Fredo AR (2019) ‘Experimental investigation on the effect of glass fiber orientation on impact damage resistance under cyclic indentation loading using AE monitoring’ Nondestructive Testing and Evaluation, https://doi.org/10.1080/10589759.2019.1684491 (Taylor and Francis publishers, IF: 1.735)

  36. Femi R, Sharma A, Katare H, & Jac Fredo AR (2019) ‘Investigation of graphene as a material for electrical contacts in the application of microrelays using finite element modeling’ Materials Research Express, vol. 6, no. 9, pp. 1-14 (IOP Science publishers, IF: 1.449)

  37. Jac Fredo AR, Abilash RS, Femi R, Sri Madhava Raja N, & Suresh Kumar C (2019), ‘Characterization of global and local damages in composite images using geometrical and Fourier-Hu moment based shape descriptors’, Journal of Testing and Evaluation, https://doi.org/10.1520/JTE20180701 (ASTM International publishers, IF: 0.711)

  38. Jac Fredo AR, Abilash RS, Femi R, Mythili A, & Suresh Kumar C (2018), ‘Classification of damages in composite images using Zernike moments and support vector machines’, Composites B: Engineering, vol. 168, pp. 77-86. (Elsevier publishers, IF: 13.1)

  39. Jac Fredo AR, Abilash RS, & Suresh Kumar C (2017), ‘Segmentation and analysis of damages in composite images using multi-level threshold methods and geometrical features’, Measurements, vol. 100, pp. 270-278. (Elsevier publishers, IF: 5.6)

  40. Jac Fredo AR, Josena TR, Rajkumar E, & Mythili A (2017), ‘Classification of normal and knee joint disorder vibroarthrographic signals using multi fractals and support vector machines’, Biomedical Engineering: Applications, Basis and Communications, vol. 29, no. 3, pp. 1-9. (World Scientific publishers)

  41. Karthick PA, Navaneethakrishna M, Punitha N, Jac Fredo AR, & Ramakrishnan S (2016), ‘Analysis of muscle fatigue conditions using time-frequency images and GLCM features’, Current Directions in Biomedical Engineering, vol. 2, pp. 1-4. (De Gruyter publishers)

  42. Jac Fredo AR,  Kavitha G & Ramakrishnan S (2015), ‘Automated segmentation and analysis of corpus callosum in autistic MR images using fuzzy c-means based level set’, Journal of Medical and Biological Engineering, vol. 35, no. 3, pp. 331-337. (Springer publishers, IF: 1.211) 

  43. Jac Fredo AR,  Kavitha G & Ramakrishnan S (2015), ‘Segmentation and analysis of corpus callosum in autistic MR brain images using reaction diffusion level sets’, Journal of Medical Imaging and Health Informatics, vol. 5, pp. 1-5. (American Scientific publishers, IF: 0.549)

  44. Jac Fredo AR,  Kavitha G & Ramakrishnan S (2015), ‘Subcortical region segmentation using fuzzy based augmented Lagrangian multiphase level set method in autistic MR brain images’, Biomedical Sciences Instrumentation, vol. 51, pp. 323-331.  (Instrumentation Society of America)

  45. Jac Fredo AR,  Kavitha G & Ramakrishnan S (2014), ‘Segmentation and analysis of brain sub-cortical regions using regularized multi-phase level set in autistic MR Images’, International Journal of Imaging Systems and Technology, vol. 24, no. 3, pp. 256-262. (Wiley publishers, IF: 1.423)

  46. Jac Fredo AR, Kavitha G & Ramakrishnan S (2014), ‘Segmentation and morphometric analysis of sub-cortical regions in autistic MR brain images using fuzzy Gaussian model based distance regularized multi-phase level set’, International Journal of Biomedical Engineering and Technology, vol.15, no.3, pp. 211-223. (Inderscience publishers)

  47. Jac Fredo AR,  Kavitha G & Ramakrishnan S (2014), ‘Analysis of cortical and subcortical regions in autistic MR images using level set method and structure tensors’, Biomedical Sciences Instrumentation, vol. 50, pp. 140-149. (Instrumentation Society of America)

List of Conference Publications

  1. Nour Alshawa, Ravi Ratnaik, Ramesh M, Anandh KR, and Jac Fredo AR (2024) ‘ASD Diagnosis using selective slices of sMRI and deep learning’, International Conference on Experimental Mechanics (ICEM), IIT Madras, Chennai, India.

  2. Chetan Rakshe, Christy Bobby T, Mohanavelu K, Sreeraj VS, Venkat S, and Jac Fredo AR (2024) ‘Leveraging Brain Channels for EEG Authentication Across Emotional States’, International Conference on Experimental Mechanics (ICEM), IIT Madras, Chennai, India.

  3. Ravi Ratnaik and Jac Fredo AR (2024) ‘Structural Connectivity and Support Vector Machine Demonstrate Improved Diagnostic Classification of ASD: A DTI Study’, International Conference on Experimental Mechanics (ICEM), IIT Madras, Chennai, India.

  4. Pragya, Sabitha R, Jac Fredo AR (2024) ‘Gene expression analysis for identifying prognostic markers in pancreatic ductal adenocarcinoma through machine learning’, International Conference on Experimental Mechanics (ICEM), IIT Madras, Chennai, India.

  5. Ahsan A, Jac Fredo AR, Subha DP and Ramakrishnan S (2024) ‘Investigation of fornix circularity in normal cognitive and Alzheimer's disease conditions using structural MR images’, International Conference on Experimental Mechanics (ICEM), IIT Madras, Chennai, India.

  6. Sushma S, Venkat S, Mohanavelu K, Jac Fredo AR, Christy Bobby, T (2024) ‘EEG-Based User Authentication using Machine Learning and Deep Learning Techniques’, 58th Annual Conference of the German Society for Biomedical Engineering, Stuttgart, Germany.

  7. Ravi Ratnaik and Jac Fredo AR (2024) ‘DTI study on structural connectivity and random forest reveals improved diagnostic classification of ASD’, 23rd International Conference on Mechanics in Medicine and Biology, Bruxelles, Belgium.

  8. Nour Alshawa, Ravi Ratnaik, Ramesh M, Anandh KR, and Jac Fredo AR (2024) ‘Selective sMRI Slice analysis for ASD diagnosis with Deep Learning’, 23rd International Conference on Mechanics in Medicine and Biology, Bruxelles, Belgium.
  9. Abdul Aleem Shaik Gadda, Abirami S, John Thomas, Yuvaraj R and Jac Fredo AR (2024) ‘Epileptic Seizure Prediction using Wavelet Features and Machine Learning’, 23rd International Conference on Mechanics in Medicine and Biology, Bruxelles, Belgium.
  10. Lokesh Naidu Lebaka, Sriram Kumar P, Jac Fredo AR (2024) ‘Automated Emotion Detection via Blood Volume Pulse: Machine learning approaches’, 23rd International Conference on Mechanics in Medicine and Biology, Bruxelles, Belgium.
  11. Chetan Rakshe, Christy Bobby Thomas, Mohanavelu Kalathe, Sreeraj VS, Venkat Subramaniam, Deepesh Kumar, and Jac Fredo AR (2024) ‘Optimizing EEG Channels for Emotion-aware Biometric Authentication using Machine Learning’, 23rd International Conference on Mechanics in Medicine and Biology, Bruxelles, Belgium.
  12. Pragya, Sunil Kumar V, and Jac Fredo AR (2024) ‘Genomics Data to Therapeutics: Enabled Drug Repurposing Opportunities Targeting DEGs in PDAC using Machine Learning and Molecular Docking’, 23rd International Conference on Mechanics in Medicine and Biology, Bruxelles, Belgium.
  13. Ahsan Ali, Jac Fredo AR and Ramakrishnan S (2024) ‘Analysis of Fornix in Normal Cognitive, MCI, and AD conditions in MR images using Fractional Order Jacobi Fourier Moments’, 23rd International Conference on Mechanics in Medicine and Biology, Bruxelles, Belgium.

  14. Sriram Kumar P and Jac Fredo AR (2024) ‘Identification of Optimal EDA Segment for Improved Emotion Detection Using Time-Series Image Encoding and Machine Learning’, 61st International Biomedical Sciences Instrumentation Symposium (IBSIS) and the 61st Rocky Mountain Bioengineering Symposium (RMBS), Kenner, USA.

  15. Sriram Kumar P and Jac Fredo AR (2024) ‘A Comparative Study of Time-Frequency Methods for the Effective Classification of Categorical Emotions Using EDA and Deep Learning’, 61st International Biomedical Sciences Instrumentation Symposium (IBSIS) and the 61st Rocky Mountain Bioengineering Symposium (RMBS), Kenner, USA.

  16. Smarth Sood, Chetan Tanaji Rakshe, Jac Fredo AR (2024) ‘Diagnostic Classification of ASD improves with Dynamic FC of fMRI compared to static FC’, 61st International Biomedical Sciences Instrumentation Symposium (IBSIS) and the 61st Rocky Mountain Bioengineering Symposium (RMBS), Kenner, USA.

  17. Pragya, Sunil Kumar V, Sabitha R, Jac Fredo AR (2024) ‘Predicting biological activity on targeted therapeutics with the integration of machine learning and virtual screening for PDAC drug repurposing’, 61st International Biomedical Sciences Instrumentation Symposium (IBSIS) and the 61st Rocky Mountain Bioengineering Symposium (RMBS), Kenner, USA.

  18. Vaibhav Jain, Sandeep Singh Sengar, Jac Fredo AR (2023) ‘Age-specific diagnostic classification of autism spectrum disorder using deep learning approaches’, Studies in Health Technology and Informatics, European Federation for Medical Informatics (EFMI) Special Topic Conference (STC), Turin, Italy, 309, 267-271.
  19. Vaibhavi Gupta, Gokul Manoj, Aditi Bhattacharya, Sandeep Singh Sengar, Rakesh Mishra, Bhoomika R. Kar, Chhitij Srivastava, Jac Fredo AR (2023) ‘A framework to diagnose autism spectrum disorder using morphological connectivity of sMRI and XGBoost’ Studies in Health Technology and Informatics, European Federation for Medical Informatics (EFMI) Special Topic Conference (STC), Turin, Italy, 309, 33-37.
  20. Praveen Kumar G, Sriram Kumar P, Nagarajan G, Jac Fredo AR (2023) ‘Investigating Windowing Techniques in Emotion Classification with ECG and Machine Learning’, 5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, Elmshorn, Germany.
  21. Tikaram, Manasi BI, Chetan R, Jac Fredo AR (2023) ‘Advancing ASD Diagnostic Classification with features of Continuous Wavelet Transform of fMRI and machine learning algorithms’, 5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, Elmshorn, Germany.
  22. Nivedhitha S, Akshaya MV, Praveen Kumar G, Ramesh M, Anandh KR, Jac Fredo AR (2023) ‘Deep learning based Diagnosis of ASD using Pretrained Convolutional Neural Network’, 5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, Elmshorn, Germany.
  23. Sriram Kumar P, Jac Fredo AR (2023) ‘Investigating the Effects of Two-Class Categorical Emotion Classification through Electrodermal activity and Machine Learning’, 5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, Elmshorn, Germany.
  24. Pragya, Praveen Kumar G, Malay Nayak, Sudip Mukherjee, Jac Fredo AR (2023) 'Implantable immunoprotective hydrogels containing engineered cells secreting ATP2A3 for the treatment of PDAC', International Conference on Advanced Materials for Better Tomorrow, Varanasi, India
  25. Sanju Kumari, Jac Fredo AR, Shreyans Kumar Jain (2023) 'Extraction and Purification of New Compounds from Murraya paniculata to investigate their cytotoxic potential', International Conference on seperation and purification technologies, IIT Patna, India.
  26. Sriram Kumar P, Jac Fredo AR (2023) ‘Enhancing Emotion Recognition: Machine Learning with Phasic Spectrogram Texture Features’, 5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, Elmshorn, Germany.

  27. Praveen Kumar G, Sriram Kumar P, Nagarajan G, Jac Fredo AR (2023) ‘Deep Learning Framework for Categorical Emotional States Assessment using Electrodermal Activity Signals’, Studies in Health Technology and Informatics, 21st International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH) Athens, Greece, 305, 40-43.

  28. Lokesh NL, Sriram Kumar P, Praveen Kumar G, Priya R, Nagarajan G, Jac Fredo AR  (2023) ‘Automated Emotion Recognition System using Blood Volume Pulse and XGBoost Learning’, Studies in Health Technology and Informatics, 21st International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH) Athens, Greece, 305, 52-55.

  29. Chetan R, Suja K, Soumya S, Jac Fredo AR  (2023) ‘Diagnostic Classification of ASD using Fractal Functional Connectivity of fMRI and Logistic Regression’, Studies in Health Technology and Informatics, 21st International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH) Athens, Greece, 305, 60-63.

  30. Ravi R, Chetan R, Manoj K, Jac Fredo AR  (2023) ‘Diagnostic Classification of ASD Improves with Structural Connectivity of DTI and Logistic Regression’, Studies in Health Technology and Informatics, 21st International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH) Athens, Greece, 305, 64-67

  31. Abirami S, Swarubini PJ, John T, Yuvraj R, Ramshekhar NM, Jac Fredo AR (2023) ‘Multi-class Seizure type Classification using Features Extracted from the EEG’, Studies in Health Technology and Informatics, 21st International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH) Athens, Greece, 305, 68-71.

  32. Vinay Kumar B, Sriram Kumar P, Praveen Kumar G, Swarubini PJ, Mythili A, Nagarajan G, Karthick PA, Deepesh K, Jac Fredo AR (2023) ‘ Identifying the Optimal Location of Facial EMG for Emotion Detection using Logistic Regression’, Studies in Health Technology and Informatics, 21st International Conference on Informatics, Management, and Technology in Healthcare (ICIMTH) Athens, Greece, 305, 81-84.

  33. Jahanvi J, Praveen Kumar G, Abirami S, Amalin P, John T, Mohanavelu K, Venkat S, Jac Fredo AR ‘Low Valence Low Arousal Stimuli: An Effective Candidate for EEG-Based Biometrics Authentication System’, Studies in Health Technology and Informatics, 33rd Medical Informatics Europe Conference (MIE), European Federation for Medical Informatics (EFMI) and Swedish Medical Informatics Association (SFMI), Gothenburg, Sweden, 302, 257-261.

  34. Shaik Gadda AA, Dhanvi V, John T, Yuvaraj R, Ramshekhar NM, Jac Fredo AR (2023) ‘Optimization of Pre-Ictal Interval Time Period for Epileptic Seizure Prediction Using Temporal and Frequency Features.’ Studies in Health Technology and Informatics, 33rd Medical Informatics Europe Conference (MIE), European Federation for Medical Informatics (EFMI) and Swedish Medical Informatics Association (SFMI), Gothenburg, Sweden, 302, 232-236.

  35. Sriram Kumar P, Praveen Kumar G, Nagarajan G, Jac Fredo AR (2023) ‘Comparative Analysis of Electrodermal Activity Decomposition Methods in Emotion Detection Using Machine Learning’, Studies in Health Technology and Informatics, 33rd Medical Informatics Europe Conference (MIE), European Federation for Medical Informatics (EFMI) and Swedish Medical Informatics Association (SFMI), Gothenburg, Sweden, 302, 73-77.

  36. Pragya, Praveen Kumar G, Kshitij S, Sudip M, Jac Fredo AR (2023) ‘Differential Gene Expression Data Analysis of ASD Using Random Forest’, Studies in Health Technology and Informatics, 33rd Medical Informatics Europe Conference (MIE), European Federation for Medical Informatics (EFMI) and Swedish Medical Informatics Association (SFMI), Gothenburg, Sweden, 302:1047-1051.

  37. Chetan Tanaji Rakshe and Jac Fredo AR (2022) ‘Investigation of brain networks in autism using fractal, non-fractal and Pearson correlation methods’, XXII International Conference on Mechanics in Medicine and Biology, Bologna, Italy, September 19-21.

  38. Sriram Kumar P and Jac Fredo AR (2022) ‘Classification of emotional states using electrodermal activity and random forest’, XXII International Conference on Mechanics in Medicine and Biology, Bologna, Italy, September 19-21.

  39. Abirami S, John Thomas, Rajamanickam Yuvaraj, Jac Fredo AR (2022) ‘Characterization of seizure subtypes using time-frequency features from scalp EEG signals’, XXII International Conference on Mechanics in Medicine and Biology, Bologna, Italy, September 19-21.

  40. Aditi Bhattacharya, Gokul Manoj, Vaibhavi Gupta, Shaik Gadda Abdul Aleem, Amalin Prince A, Priya Rani, Jac Fredo AR (2022) ‘Evaluation of Zernike moments of corpus callosum for discrimination of autism using Random Forest’, XXII International Conference on Mechanics in Medicine and Biology, Bologna, Italy, September 19-21.

  41. Praveen kumar G, Nagarajan Ganapathy and Jac Fredo AR (2022) ‘Deep-learning framework for ECG based categorical emotional states assessment’, XXII International Conference on Mechanics in Medicine and Biology, Bologna, Italy, September 19-21.

  42. Aalan Natarajan, Deepesh Kumar and Jac Fredo AR (2022) ‘Vision-guided autonomous robotic system for pick and place tasks in healthcare settings’, XXII International Conference on Mechanics in Medicine and Biology, Bologna, Italy, September 19-21.

  43. Vaibhav Jain, Abirami Selvaraj, Rakshit Mittal, Priya Rani, Anandh Kilpattu Ramaniharan, Jac Fredo AR (2022) ‘Automated diagnosis of autism spectrum disorder condition using shape-based features extracted from brainstem’, Challenges of Trustable AI and Added-Value on Health,European Federation for Medical Informatics (EFMI), 32nd Medical Informatics Europe Conference (MIE2022), Nice, France, May 27-30, 53-57.

  44. Arijit Gupta, Amalin Prince A, Jac Fredo AR, Femi Robert, (2022) 'Transformer-based models for supervised monocular depth estimation', International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP), Hyderabad, India, pp. 1-5.

  45. Edwin M, Saif N, Jac Fredo AR, & Amalin Prince A (2020) ‘Analysis and classification of vibroarthrographic signals using tuneable ‘Q’wavelet transform’, 7th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, pp.65-70.

  46. Jac Fredo AR, Georg L. & Justin D, (2019) ‘Classification of typical developing and autism spectrum disorder using connectivity matrix and support vector machines’, IEEE International conference on Engineering in Medicine and Biology, Berlin, Germany.

  47. Jac Fredo AR, Maya R, Afrooz J & Ralph AM, (2018) ‘Classification of autism and control on resting state fMRI using conditional random forest’, IEEE International conference on Engineering in Medicine and Biology, Honolulu, Hawaii, USA.

  48. Jac Fredo AR,  Kavitha G & Ramakrishnan S (2015) ‘Analysis of corpus callosum and its sub-anatomical regions in autistic MR brain images using structure tensors’, Second International Conference on Biomedical Systems, Signals and Images, IIT Madras, Chennai, India. 

  49. Jac Fredo AR, Kavitha G & Ramakrishnan S (2014) ‘Segmentation of sub-cortical regions of autistic MR brain images using combination of fuzzy c-means and Gaussian distribution model based augmented Lagrangian multi-phase level sets’, Nineteenth International Conference on Mechanics in Medicine and Biology, Bologna, Italy, pp. 431-434, ISBN: 978-88-901675-1-5.

  50. Jac Fredo AR,  Kavitha G & Ramakrishnan S (2014) ‘Analysis of sub-cortical regions in cognitive processing using fuzzy c-means clustering and geometrical measure in autistic MR images’, 40th North East Bio-Engineering Conference, Boston, United States of America, pp. 1-2, doi: 10.1109/NEBEC.2014.6972791. 

  51. Jac Fredo AR, Kavitha G & Ramakrishnan S (2014) ‘Segmentation and analysis of subcortical regions of autistic MR brain images using Gaussian distribution model based reaction diffusion multi-phase level sets and geometric feature’, Frontiers in Neuroinformatics, doi: 10.3389/conf.fninf.2014.18.00090. Leiden, Netherlands.

  52. Jac Fredo AR, Kavitha G & Ramakrishnan S (2014) ‘Characterization of autistic MR brain images using fuzzy c-means based reaction diffusion multi-phase level sets and structure tensor’, National Conference on Present Scenario and Future Trends in Biomedical Engineering and Healthcare Technologies, IIT (BHU), Varanasi, India. 

  53. Jac Fredo AR, Ramakrishnan S, Jaginth C, & Srinivasan S (2010) ‘Virtual Audiometer‘, International Conference on Instrumentation, Instrumentation Society of India, Pune, India.

  54. Jac Fredo AR (2007) ‘SCADA-Automation in thermal power plant‘, Conference on Efactory Technologies and Challenges, IRTT Erode, India.

Conferences
  • IEEE International conference on Engineering in Medicine and Biology, Berlin, Germany, 2019 (poster presentation), Hawaii, USA, 2018 (oral presentation).
  • IEEE North East Bio-Engineering Conference, Boston, USA, 2014 (oral presentation).
  • International Conference on Mechanics in Medicine and Biology, Bologna, Italy, 2014 (oral presentation).
  • INCF congress of Neuro-informatics, University of Leiden, Leiden, The Netherlands, 2014 (oral presentation).
  • National Conference on Present Scenario and Future Trends in Biomedical Engineering and Healthcare Technologies, IIT Varanasi, India, 2014 (oral presentation).
  • International Conference on Instrumentation, Instrumentation Society of India, Pune, India, 2010 (oral presentation).
Courses
August, 22-23, 2014 INCF short course on “Introduction to neuro-informatics”, University of Leiden, Leiden, The Netherlands
September, 20-28, 2012 BBCI summer school on “Advances in neuro-technology”, Technical University of Berlin, Berlin, Germany
October, 15-16, 2012 Workshop on “Image processing framework using FPGA”, Anna university, Chennai, India

Grants and Funding:
 

Sl.No Type of funding Scheme Agency Location Amount
1.  Research Grant  Small Extramural Grant ICMR India 169k Euros approximately
2. Research Grant  Hackathon  MSME India 17k Euros approximately
3.  Research Grant  Rare Diseases ICMR India 45k Euros approximately
4.  Research Grant  LSRB DRDO India 78k Euros approximately
5. Research Grant Startup Research Grant SERB, DST India 20k Euros approximately
6. Research Grant Seed Grant IIT (BHU) India 11.5k Euros
7. Exchange Fellowship Grant AROP RWTH Aachen Germany 11k Euros
8. Post-doctoral Fellowship Grant Overseas Grant IUSSTF, SERB/DST India-USA 42k Euros approximately
9. Doctoral Fellowship Grant MANF UGC India 20k Euros approximately
10. Travel Grant BBCI Summer workshop Technical University of Berlin Germany 500 Euros
11. Travel Grant Neuro-informatics workshop INCF The Netherlands 500 Euros
12. Travel Grant Conference TSCST India 500 Euros approximately

Current Team

Name Position  Area of Research Modality Status
Sriram Kumar P Ph.D Student (QIP scheme) Emotion Detection EDA Awarded
Pragya  Ph.D Student (CSIR Fellowship) Drug Repurposing fro PDAC Genetic, Cell Sequence and Drug Data Pursuing
Chetan Rakshe Ph.D Student (Institute Teaching Assistance Fellowship) Biometrics EEG Pursuing
Sanju Kumari Ph.D Student (CSIR Fellowship) Drug Discovery Cell Analysis Pursuing
Ravi Ratnaik Ph.D Student and Junior Research Fellow (SRG/LSRB/ICMR Project) Autism Diagnosis DTI Pursuing
Pratima Ph.D Student (Institute Teaching Assistance Fellowship) Emotion Detection EEG Pursuing
Renu Ph.D Student (CSIR Fellowship) Drug Repurposing for autism Genetic, Cell Sequence and Drug Data Pursuing
Saumya Ph.D Student (CSIR Fellowship) Seizure Prediction EEG Pursuing
Jaya Devi Ph.D Student (CSIR Fellowship) Drug Repurposing for autism Genetic, Cell Sequence and Drug Data Pursuing
Pravin Mewda Ph.D Student (CSIR Fellowship) Blood Glucose Monitor PPG Pursuing
Hrithika Junior Research Fellow Autism Diagnosis sMRI Pursuing
Dibyasree Junior Research Fellow Autism Diagnosis fMRI Pursuing
Nour Alshawa Masters Student Seizure Classification EEG Pursuing
Aleem IDD Student Seizure Prediction EEG Pursuing
Lokesh IDD Student Emotion detection PPG Pursuing
Abhijeet IDD Student Seizure Prediction EEG Pursuing
Utkarsh IDD Student Emotion detection PPG Pursuing

Collaborators
 

Name Institute
Dr. John Thomas McGill University, Canada
Dr. Priya Rani RMIT University, Australia
Dr. Yuvraj Rajamanickom National Institute of Education, Nanyang Technological University, Singapore
Dr. Murugappan Kuwait College of Science and Technology 
Dr. Sandeep Singh Sengar  Cardiff Metropolitan University, United Kingdom
Dr. Nagarajan Indian Institute of Technology Hyderabad
Dr. Anandh Kilpattu Ramaniharan Cincinnati Children's Hospital Medical Center,  Ohio, United States America
Dr. Karthick PA National Institute of Technology Trichy
Dr. Ramshekhar Menon Sree Chitra Tirunal Institute for Medical Sciences and Technology 
Dr. Soumya Sree Chitra Tirunal Institute for Medical Sciences and Technology 
Dr. Manoj Kumar National Institute of Mental Health and Neurosciences
Dr. Sabitha Ramanathan Adyar cancer institute
Dr. Bhoomika R. Kar Centre of Behavioural and Cognitive Sciences (CBCS), University of Allahabad

Aluminees 

Name Position in Lab Thesis Current Position
Abirami S Masters Student Seizure type classification using EEG Project Fellow in IIT Kanpur
Praveen Kumar Masters Student Improving communication for dysarthric individuals using speech signals Ph.D in IIT Madras
Gokul Manoj IDD Student Diagnosis of ASD using sMRI Research Assistant 
Vinay Kumar IDD Student Emotion detection using facial EMG Research Assistant at University of Rochester, New York, USA
Vaibhav IDD Student Diagnosis of ASD using fMRI Software Engineer, JP Morgan Chase & Co, Mumbai, India
Ranga Sudharshan IDD Student Biomarkers for ASD using gene expresion data Research Lab Associate at the University of Michigan Ann Arbor, USA
Tikaram Masters Student Diagnosis of ASD using fMRI Ph.D in IIT Bombay
Siddhartha IDD Student Seizure type classification using EEG Data Analyst in Axtria

S. No. Title of Lecture/Lecture Series Date, Place and Programme where lectures delivered
1. Emotion detection IEEE Signal Processing Society “IEEE SPS Day” student branch chapter, IIT (BHU), Varanasi
2. Applications of AI in Neuroimaging Workshop SMARTBEING 2023 - Sensors and Wellbeing in Department of Biomedical Engineering, IIT Hyderabad,, 22/11/2023
3. AI for Biomedical Applications  Seven days high-end workshop on "AI for Biomedical Applications", School of Biomedical Engineering, IIT (BHU), Varanasi, 22/05/2023-28/05/2023
4. Wearable devices for biomedical applications  Eight days high-end workshop on "Wearable Intelligent Devices: Next Generation Technology”, IIITDM Kancheepuram, 15/02/2023 to  22/02/2023
5.  Teaching seminar of electrodes for ECG, EEG, and EMG Department of Electrical and Electronics Engineering, SRM University, Chennai,17/06/2022
6. Neuroimaging methods for neurodevelopmental disorders Keynote talk in International Conference on Innovative Engineering and Technology, 02/06/2022, DMI College of Engineering, Chennai, Tamilnadu. 
7.  fMRI time series analysis methods  Five days FDP on "Internet of Everything - IoE", 21/05/2022, VIT Univerisity, Vellore, Tamilnadu.
8. Sparse inverse covariance methods for fMRI time series  Six days online AICTE-AU-STTP on "Data Science Applied to Measurement and Control", 03/02/2022, Department of Instrumentation Engineering, Madras Institute of Technology, Anna University, Chennai, Tamilnadu. 
9. Artificial intelligence methods for time series fMRI data analysis Guest Lecture on 21/12/2022 in Department of Medical Informatics, TU Brunsweig, Germany
10. Artificial intelligence methods for volumetric sMRI data analysis Guest Lecture on 16/12/2022 in Clinical Child Neuropsychology, RWTH Aachen Univeristy, Aachen, Germany
11. Neuroimage processing for ASD diagnosis Instrumentation, signals and images for the evolution of physiological systems, 18/08/2021, at Department of Instrumentation and Control Engineering, NIT-Trichy, Tamilnadu.
 12. Application of Machine Learning in Medical Imaging Advances in Medical Imaging, on 18/03/2021, at IIT (BHU), Varanasi, Uttar Pradesh.
 13. Neuro-informatics methods for neuroimaging Hands on project based approach for biomedical signal analysis using MATLAB, on 06/02/2021 at Kakatiya Institute of Technology and Science Warangal, Telangana.
 14. Machine Learning in Neuroimaging Biomedical Signal Processing in Precision Health on 18/12/2020, organized by ECE Department, Thiagarajar College of Engineering, Madurai, Tamilnadu.
 15. Multi-model Neuroimage analysis Research Scholars Day, on 05/09/2020, Department of Instrumentation, MIT Campus, Anna University, Chennai, Tamilnadu.

IIT (BHU)

Level Course No. & Title
P.G BM511: Artificial Intelligence and its Application in Biomedical Engineering
P.G. BM509: Bioinstrumentation
U.G. BM401: Bioinstrumentation and Medical Imaging Modalities
U.G BM301: Electronic Measurement and Instruments for Biomedical Applications
U.G. BM204: Biomaterials

VIT University:

Level Course No. & Title
UG ECE1011: Medical Physics and Biomedical Instrumentation
UG ECE1005: Sensors and Instrumentation
UG ECE1001: Fundamentals of Electrical Circuits Laboratory

Post-doctoral Positions

Postdoc candidates (having fellowship from CSIR, DST, DBT etc.)  or wish to apply may contact me by sending detailed CV with research interests.(Candidates with Engineering backgrounds are preferred)

Ph.D. Positions

Ph.D. positions are available in my lab for students who have qualified CSIR/UGC/DBT JRF/ DST inspire fellowship. Interested candidate have to follow guidelines of IIT-BHU PhD admission (Candidates with Engineering backgrounds or proficiency in coding platforms like Matlab, Python or Rstudio are preferred)

Summer or Winter internships

Summer or Winter internships are available in my lab for Masters and Undergraduate students. Candidates can sent their CV and project proposal.

  1. We have EDA, ECG, EMG and EEG sensors which can record the physiological signals from the subjects during different experiments. 
  2. We have five high computing workstations which helps us to process the computationally complex dimensional data.
  3. Abdul Aleem, a Fourth-year student awarded MITACS Globalink Research Internship to pursue research under Dr. Albert Vette, Neuromuscular Control & Biomechanics Laboratory, University of Alberta, Canada, from May 2023 to July 2023.
  4. Lokesh Lebaka, a Fourth-year student awarded ANU-FRT (Future Research Talent) program Internship to pursue research under Dr. Gaetan Burgio in Gene Editing Lab, Australian National University, Australia, from May 2023 to July 2023.
  5. Successfully conducted seven-dayshop program on "AI for Biomedical Applications" on 22/05/2023-28/05/2023 in the ABLT IIT (BHU), Vananasi (Served as the co-convener).
  6. Successfully conducted one day training program on "Introduction to Computational Neuroscience-2022" on 09/09/2022 in the seminar hall of school of Biomedical Engineering with Guest Speakers Dr. Manoj Kumar (NIMHANS, Bengaluru), Dr. Yuvraj Rajamanickom (NTU, Singapore) and Dr. Nagarajan (IIT Hyderabad).

  8. Mr. Gokul Manoj, Fourth year IDD student awarded DAAD-Combined Study and Practice Stays for Engineers from Developing Countries (KOSPIE) to pursue research under Prof. Kerstin Konrad, RWTH Aachen University, Aachen, Germany from September 2022 to March 2023.
  9. NAS server available in our lab, which permits to access the medical data available in our lab from anywhere around the campus. 
  10. UPS available in our lab, which can support the NAS and workstation to get continues power supply. 
  1. DPGC Convener, September, 2024-August, 2025
  2. DPGC Convener, September, 2023-August, 2024
  3. School Web Convener, September, 2023-August, 2024
  4. DUGC Convener, September, 2022-August, 2023
  5. Time table Convener, June, 2022-August, 2023
  6. Examination Convener, June, 2022-August, 2023

 

 

1. Awarded Distinguished Alumina Award 14/05/2022 by Department of Instrumentation Engineering, Madras Institute of Technology, Anna University, Chennai, India.

 

​

Name and Designation of speakers Title of talk Year

Dr. Camille Lambert

Laboratory of Biophysics and Evolution (LBE),

UMR 8231, ESPCI-PSL,

Paris, France,

 Exploration of RNA fitness landscapes by statistical and structure-based generative models coupled with high-throughput sequencing 18/03/2024

Dr. Shubham Sahu

Laboratoire Physico-Chimie Curie Institut Curie,

Institut de Chimie du CNRS,

Sorbonne Université,

Centre National de la Recherche Scientifique,

Paris, France

 Spatiotemporal dynamics of developmental timing genes during C. elegans larval development 18/03/2024

Dr. Nagarajan Ganapathy

Assistant Professor,

Department of Biomedical Engineering,

Indian Institute of Technology Hyderabad,

Kandi, Sangareddy, Telangana, India.

 Technology advances in wearables sensing - challenges with AI 22/03/2024

Dr. Kirti Prakash, FRMS,

Senior Staff Scientist,

Integrated Pathology Unit,

Center for Molecular Pathology,

The Royal Marsden Trust,

The Institute of Cancer Research,

United Kingdom

 Laser-free super-resolution microscopy: a cost-efficient tool for biological discoveries 11/10/2023

Prof. Sujatha N,

Professor,

Biophotonics Lab,

Biomedical Division,

Department of Applied Mechanics,

Indian Institute of Technology Madras, Chennai, Tamilnadu, India.

Photonics in Medical Diagnostics 13/03/2023

Dr. Praful P,

Senior Engineer,

Education Team,

MathWorks India.

Medical Device Design and Medical Signal/Image Processing with MATLAB & Simulink

21/12/2022
Dr. Priya Rani,
Lecturer,
Discipline of Electrical and Biomedical Engineering,
School of Engineering,
RMIT University,
Melbourne, Australia		 Deep Learning to Detect COVID Infections 16/12/2022

Dr. Rajkumar Elagiri Ramalingam,

Program Manager,

Apex semiconductor,

Bangalore, India.

Intelligent non invasive sensing system proof of concept to product development

07/11/2022
Vijay Pratap Singh
Application Engineer
Integrated Microsystem
Gurgaon-122018 (HR), India		 Simpleware for Clinical Applications & Life Sciences (3D Medical Image Segmentation, 3D Printing, Analysis, Processing and Meshing software- Synopsys Simpleware demo) 05/08/2022