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Sourajeet Roy

Associate Professor

Email : sourajeet.roy[at]ece.iitr.ac.in
Phone: 01332-285762
Room No. : S 209
My Website

  • Numerical modeling and simulation of high-speed devices/circuits,
  • Machine learning based tools for EDA of circuits
  • Statistical machine learning for uncertainty quantification and stochastic modeling of circuits
  • Machine learning-based generative models of emerging passive and active devices
From To Designation Organisation
November 2013 January 2019 Assistant Professor Colorado State University
January 2019 October 2023 Assistant Professor Indian Institute of Technology Roorkee
October 2023 Present Associate Professor Indian Institute of Technology Roorkee
DegreeSubjectUniversityYear Studied
Ph. D.Electrical and Computer EngineeringUniversity of Western Ontario2013
MEScElectrical and Cmputer EngineeringUniversity of Western Ontario2009
B. TechElectrical and Electronics EngineeringSikkim Manipal University2006
AwardInstituteYear Awarded
Graduate Thesis Research AwardUniversity of Western Ontario2012
Queen Elizabeth II Graduate Scholarship in Science and TechnologyUniversity of Western Ontario2012
Ontario Graduate ScholarshipUniversity of Western Ontario2012
Vice-Chancellors Gold Medal in Electrical EngineeringSikkim Manipal Institute of Technology2006
Award for Academic Excellence in Electrical EngineeringSikkim Manipal Institute of Technology2004
Global Opportunities AwardUniversity of Western Ontario2012
IEEE Senior MemberIEEE2021
  • IEEE , Senior Member
TopicScholar NameStatus of PhDRegistration Year
Uncertainty Quantification and Statistical Modeling of High Speed CircuitsAditi K. PrasadA2014
Fast Uncertainty Quantification of On-Chip Graphene-Based Nanointerconnects and Nanoscale Field-Effect Transistors (FETs)Surila GuglaniA2019
Polymorphic Polynomial Chaos for Mixed Epistemic-Aleatory Uncertainty Quantification of High-speed Circuits and DevicesMohd. YusufO2020
Variability Analysis and Design Optimization using Machine Learning for Photonic ICsAsha JakharO2020
Statistical Signal Integrity Analysis using Polynomial Chaos and Machine Learning for Hybrid Cu-Graphene NanointerconnectsDimple SinghO2020
Stochastic Modeling for Signal Integrity Analysis of Hybrid Copper-Graphene InterconnectsSuyash KushwahaO2020
Delay analysis for Interconnects in Ultra-scaled ICsAdeeba SharifO2021
Machine Learning Based Small Signal Models of HEMT DevicesAasim AshaiO2021
TitlePlaceDate Delivered
Signal Spectra in EMCIEEE Symposium on Electromagnetic Compatibility and Signal Integrity, Washington D. C.August 8th, 2017
Non-Intrusive polynomial chaos approaches with waveform relaxation for rapid exploration of large dimensional random spaces in interconnect and packaging simulationDepartment of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana-ChampaignOctober 7th, 2014
Parallel simulation of massively coupled distributed networks – challenges and viable methodologiesMcGill University, MontrealMarch 10th, 2014
Recent Advances in Statistical Machine Learning for Uncertainty Quantification of High-Speed CircuitsWorkshop on Next Generation Electronic Systems: Heterogeneous Integration, Thermal and Power Management, Related Machine Learning organized by SUNY BinghamtonOctober 8th, 2020
Graphene-Based Emerging Interconnects – From Physics-Based Deterministic SPICE Models to Uncertainty QuantificationIEEE 27th Conference on Electrical Performance of Electronic Packaging and SystemsOctober 7th, 2020
Implications of Thermal Aspects on Interconnect and Packaging Technology – An Electro-Thermal Co-Design PerspectiveIEEE Electrical Design of Advanced Packaging and Systems ConferenceDecember 14th, 2020
Understanding the Impact of Surface Roughness on the Performance of Next Generation Cu InterconnectsIEEE Electrical Design of Advanced Packaging and Systems ConferenceDecember 14th, 2019
TopicOrganisationLevel
Computational algorithms for variation-aware design optimization of silicon photonics and photonic integrated circuitsColorado State University, USAPHD
Uncertainty Quantification for Emerging Interconnects for End-of-Roadmap Technology NodesIndian Institute of Technology RoparPhD
Stochastic Macromodeling in High-Speed InterconnectsCarleton University, CanadaPhD
Uncertainty Quantification for Spintronic DevicesIndian Institute of Technology RoorkeePhD
Title of ProjectName of Student
Novel Methods to Quantify Aleatory and Epistemic Uncertainty in High Speed NetworksIshan D. Kapse
Efficient Multidimensional Uncertainty Quantification of High Speed Circuits using Advanced PolynomiMajid A. Dolastara

Books Authored/Edited

  1. Roy (editor), Uncertainty Quantification of Electromagnetic Devices, Circuits, and Systems. UK: IET Press, 2022

Book Chapters Authored

  1. Roy, “A dimension reduction approach to address the curse of dimensionality in polynomial chaos,” Chapter 4, Uncertainty Quantification of Electromagnetic Devices, Circuits, and Systems. UK: IET Press, 2022
  2. Guglani and S. Roy, “A predictor-corrector algorithm for fast polynomial chaos based statistical modeling of carbon nanotube interconnects,” Chapter 5, Uncertainty Quantification of Electromagnetic Devices, Circuits, and Systems. UK: IET Press, 2022

Peer Reviewed Journal Papers (Total: 39)

  1. Mishra, A. Kumar, A. Dasgupta, and S. Roy, “Study of enhanced ferromagnetic behavior of Mn-doped Janus Cr2S2I2 monolayer,” accepted for publication of IEEE Journal of Electron Devices Society, Oct. 2024
  2. Yusuf, S. Singh, A. Dasgupta, B. Sarkar, and S. Roy, “A space mapping augmented compact model for uncertainty quantification for GaN HEMT devices and circuits in the presence of trap effects,” IEEE Transactions on Electron Devices, vol. 71, no. 11, pp. 6581-6587, Nov. 2024
  3. Guglani, A. K. Jakhar, A. Dasgupta, and S. Roy, “Combining Prior knowledge with transfer learning (PKID-TL) for fast neural network enabled uncertainty quantification of graphene on-chip interconnects,” accepted for publication in IEEE Transactions on Components, Packaging, and Manufac. Tech., June 2024 (DOI: 10.1109/TCPMT.2024.3436672) (Invited Special Issue on EPEPS 2023)
  4. Sehgal, A. K. Shukla, S. Roy, and B. K. Kaushik, “On-chip learning of neural network using spin-based activation function nodes,” IEEE Transactions on Electron Devices, vol. 71, no. 8, pp. 5118-5124, Aug. 2024
  5. Sehgal, S. Dhull, S. Roy, and B. K. Kaushik, “Advances in memory technologies for artificial synapses,” J. Materials Chemistry C, Feb. 2024
  6. Guglani et. al., “Artificial neural networks with fast transfer learning for statistical signal integrity analysis of MWCNT and MLGNR interconnect networks,” IEEE Transactions on Electromagnetic Compatibility, vol. 66, no. 3, pp. 939-948, March 2024
  7. Ehtashmuddin, K. Sheelvardhan, S. Guglani, S. Roy, and A. Dasgupta, “Machine learning-assisted multiobjective optimization of advanced node gate-all-around transistor for logic and RF applications,” IEEE Transactions on Electron Devices, vol. 71, no. 2, pp. 976-982, Feb. 2024
  8. Mishra, P. Ranjan, A. Dasgupta, B. P. Pandey, S. Kumar, and S. Roy, “Theoretical insights into the photocatalytic and hydrogen storage ability of two-dimensional (2D) MoSe2 (MX2) and MoSSe (MXY) (X = Se, Y = S) ML using DFT study,” IEEE Sensors Journal, vol. 24, no. 1, pp. 223-230, Jan. 2024
  9. Sheelvardhan, S. Guglani, M. Ehteshamuddin, S, Roy, and A. Dasgupta, “Machine learning augmented compact modeling for simultaneous improvement in computational speed and accuracy,” IEEE Transactions on Electron Devices, vol. 71, no. 1, pp. 239-245, Jan. 2024 (Special Issue on Device Modeling and Simulation)
  10. Kushwaha, A. Dasgupta, S. Roy, and R. Sharma, “Fast multi-ANN composite models for repeater optimization in presence of parametric uncertainty for on-chip hybrid copper-graphene interconnects,” IEEE Access, vol. 11, pp. 131191-131204, Nov. 2023
  11. Dimple, S. Guglani, A. Dasgupta, R. Sharma, S. Roy, and B. K. Kaushik, “Modified knowledge based neural networks using control variates for the fast uncertainty quantification of on-chip MWCNT interconnects,” IEEE Transactions on Electromagnetic Compatibility, vol. 65, no. 4, pp. 1232-1246, Aug. 2023
  12. Ashai, A. Jadhav, A. K. Behera, S. Roy, A. Dasgupta, and B. Sarkar, “Deep learning based fast BSIM-CMG parameter extraction for general input dataset,” IEEE Transactions on Electron Devices, vol. 70, no. 7, pp. 3437-3441, July 2023
  13. Guglani, Km. Dimple, S. Roy, R. Sharma, and B. K. Kaushik, “A bilevel multi-fidelity polynomial chaos approach for the uncertainty quantification of MWCNT interconnect networks with variable imperfect contact resistances,” IEEE Access, vol. 10, pp. 109925-109936, Oct. 2022
  14. Kushwaha, N. Soleimani, F. Trevisio, R. Kumar, R. Trinchero, F. G. Canavero, S. Roy, and R. Sharma, “Comparative analysis of prior knowledge-based machine learning metamodels for modeling hybrid copper-graphene on-chip interconnects,” IEEE Transactions on Electromagnetic Compatibility, vol. 64, no. 6, pp. 2249-2260, Dec. 2022
  15. Jadhav et.al., “An accurate approach to develop small signal circuit models for AlGaN/GaN HEMTs using rational functions and dependent current sources,” IEEE Journal of Electron Devices Society, vol. 10, pp. 797-807, Sept. 2022
  16. Kumar, S. S. Likith Narayan, S. Kumar, S. Roy, B. K. Kaushik, R. Achar, and R. Sharma, “Knowledge based neural networks for fast design space exploration of hybrid copper-graphene on-chip interconnect networks,” IEEE Transactions on Electromagnetic Compatibility, vol. 64, no. 1, pp. 182-195, Feb. 2022
  17. Yusuf and S. Roy, “A polymorphic polynomial chaos formulation for mixed epistemic-aleatory uncertainty quantification of RF/Microwave circuits,” IEEE Transactions on Microwave Theory and Techniques, vol. 70, no. 1, pp 926-937, Jan. 2022 (Mini Special Issue)
  18. Guglani and S. Roy, “Two-level multi-fidelity algorithm with dimension reduction for efficient uncertainty quantification of MWCNT interconnects,” IEEE Transactions on Electromagnetic Compatibility, vol. 63, no. 6, pp. 1941-1950, Dec. 2021
  19. Jadhav et.al., “Modified small signal circuit of AlGaN/GaN MOS-HEMTs using rational functions,” IEEE Transactions on Electron Devices, vol. 68, no. 12, pp. 6059-6064, Dec. 2021
  20. Jadhav et.al., “Generalized frequency dependent small signal model for high frequency analysis of AlGaN/GaN MOS-HEMTs,” IEEE Journal of Electron Devices Society, vol. 9, pp. 570-581, May 2021
  21. Kumar, A. Kumar, S. Guglani, S. Kumar, S. Roy, B. K. Kaushik, R. Sharma, and R. Achar, “A temperature and dielectric roughness-aware matrix rational approximation (MRA) model for the reliability assessment of copper-graphene hybrid on-chip interconnects,” IEEE Transactions on Components, Packaging, and Manufacturing Technology, vol. 10, no. 9, pp. 1454-1465, Sept. 2020 (Special Issue on Reliability of Microelectronic Packaging)
  22. Li, S. Bhatnagar, A. Merkley, D. Weber, and S. Roy, “A predictor-corrector algorithm for fast polynomial chaos-based uncertainty quantification of multi-walled carbon nanotube interconnects,” IEEE Transactions on Components, Packaging, and Manufacturing Technology, vol. 9, no. 10, pp. 1963-1975, Oct. 2019 (Special Issue on Packaging and Interconnects: Cutting-edge Solutions in Modeling, Design, and Characterization)
  23. K. Prasad and S. Roy, “Reduced dimensional Chebyshev-polynomial chaos approach for fast mixed epistemic-aleatory uncertainty quantification of transmission line networks,” IEEE Transactions on Components, Packaging, and Manufacturing Technology, vol. 9, no. 6, pp. 1119-1132, June 2019
  24. Weinmeister, X. Gao, and S. Roy, “Analysis of a Polynomial Chaos-Kriging Metamodel for Uncertainty Quantification in Aerodynamics,” American Institute of Aeronautics and Astronautics (AIAA) Journal, vol. 57, no. 6, pp. 2280-2296, June 2019
  25. K. Prasad and S. Roy, “Multi-fidelity approach for polynomial chaos based statistical analysis of microwave networks,” Applied Computational Electromagnetics (ACES) Journal, vol. 34, no. 2, pp. 358-359, Feb. 2019 (Special Issue on Cutting-Edge Modeling and Applications of Electromagnetic Devices and Fields)
  26. K. Prasad and S. Roy, “Accurate reduced dimensional polynomial chaos for efficient uncertainty quantification of microwave/RF networks,” IEEE Transactions on Microwave Theory and Techniques, vol. 65, no. 10, pp. 3697-3708, Oct. 2017
  27. A. Maciejewski et. al., “A holistic approach to transforming undergraduate electrical engineering education,” IEEE Access, Special Section on Innovations in Electrical and Computer Engineering Education, vol. 5, pp. 8148-8161, May 2017
  28. Ahadi and S. Roy, “Sparse linear regression (SPLINER) approach for efficient multidimensional uncertainty quantification of high-speed circuits,” IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems, vol. 35, no.10, pp. 1640-1652, Oct. 2016
  29. K. Prasad, M. Ahadi, and S. Roy, “Multidimensional uncertainty quantification of microwave/RF networks using linear regression and optimal design of experiments,” IEEE Transactions on Microwave Theory and Techniques, vol. 64, no.8, pp. 2433-2446, Aug. 2016 (Mini Special Issue)
  30. K. Prasad and S. Roy, “Multidimensional variability analysis of complex power distribution networks via scalable stochastic collocation approach,” IEEE Transactions on Components, Packaging, and Manufacturing Technology, vol. 5, no.11, pp.1656-1668, Nov. 2015
  31. Roy and A. Dounavis, “Parallel transient simulation of package/board power distribution networks based on a two-dimensional overlapping partitioning methodology,” IEEE Transactions on Components, Packaging, and Manufacturing Technology, vol. 3, no. 12, pp. 2101-2112, Dec. 2013
  32. Roy and A. Dounavis, “Macromodeling of multilayered power distribution network based on a multiconductor transmission line approach,” IEEE Transactions on Components, Packaging, and Manufacturing Technology, vol. 3, no. 6, pp. 1047-1056, June 2013
  33. Roy, A. Beygi, and A. Dounavis, “Electromagnetic interference analysis of multiconductor transmission line networks using longitudinal partitioning-based waveform relaxation algorithm,” IEEE Transactions on Electromagnetic Compatibility, vol. 55, no. 2, pp. 395-406, April 2013
  34. Roy and A. Dounavis, “Delay extraction based waveform relaxation algorithm for fast transient analysis of power distribution networks,” IEEE Transactions on Components, Packaging, and Manufacturing Technology, vol. 2, no. 12, pp. 2044-2056, December 2012
  35. Roy, A. Dounavis, and A. Beygi, “Longitudinal-partitioning-based waveform relaxation algorithm for efficient analysis of distributed transmission-line networks,” IEEE Transactions on Microwave Theory and Techniques, vol. 60, no. 3, pp. 451-463, March 2012
  36. Roy and A. Dounavis, “Efficient modeling of power/ground planes using delay extraction based transmission lines,” IEEE Transactions on Components, Packaging, and Manufacturing Technology, vol. 1, no. 5, pp. 761-771, May 2011
  37. Roy and A. Dounavis, “Transient simulation of distributed networks using delay extraction based numerical convolution,” IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems, vol. 30, no. 3, pp. 364-373, March 2011
  38. Roy and A. Dounavis, “Efficient delay and crosstalk modeling of RLC interconnects using delay algebraic equations,” IEEE Transactions on Very Large Scale Integrated Systems, vol. 19, no. 2, pp. 342-346, February 2011
  39. Roy and A. Dounavis, “Closed form delay and crosstalk models for RLC on-chip interconnects using a matrix rational approximation,” IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems, vol. 28, no. 10, pp. 1481-1492, October 2009

Peer Reviewed International Conference Papers (Total: 69)

  1. K. Jakhar, R. Sharma, A. Dasgupta, and S. Roy, “Spacer optimization using a neuro-PSO approach for improving FinFET repeater performance in on-chip global MLGNR interconnects,” accepted for oral presentation in IEEE 31st Conference on Electrical Performance of Electronic Packaging and Systs., July 2024
  2. K. Jakhar, D. Basu, Km. Dimple, S. Guglani, A. Dasgupta, and S. Roy, “A fast metalearning algorithm for neural network enabled uncertainty quantification of graphene based interconnects with passive shielding,” accepted for oral presentation in IEEE Symp. Electromagn. Compatibility and Signal/Power Integrity, March 2024 (Invited paper in Special Session: Machine Learning Aided Signal Integrity, Power Integrity, EMC, and EMI)
  3. Dimple, M. Ehtashmuddin, S. Guglani, A. Dasgupta, and S. Roy, “Optimization of eye diagram characteristics of MLGNR interconnects using fast ML assisted evolutionary algorithm,” in Proc. IEEE Conference on Electrical Design of Advanced Packaging, Dec. 2023, pp. 1-3
  4. Jakhar, S. Guglani, A. Dasgupta, and S. Roy, “Noise-aware uncertainty quantification of MLGNR interconnects using fast trained artificial neural networks,” in Proc. IEEE Conference on Electrical Design of Advanced Packaging, Dec. 2023, pp. 1-3
  5. Kushwaha et. al., “Space mapped neuromodeling for fast and accurate signal integrity analysis of rough on-chip copper interconnects,” in Proc. IEEE Conference on Electrical Design of Advanced Packaging, Dec. 2023, pp. 1-3
  6. Sehgal, G. Verma, S. Dhull, S. Roy, and B. K. Kaushik, “Machine learning assisted analysis of advanced STDP for neuromorphic computing using MRAM,” in Proc. 2023 IEEE Nanotechnology Materials and Devices Conference, Sept. 2023, pp. 793-797
  7. K. Jakhar, S. Guglani, A. Dasgupta, and S. Roy, “Prior knowledge accelerated transfer learning (PKI-TL) for machine learning assisted uncertainty quantification of MLGNR interconnect networks,” in Proc. IEEE 30th Conference on Electrical Performance of Electronic Packaging and Systs., Sept. 2023, pp. 1-3 (Invited paper in Special Session: Machine Learning in Packaging Electrical Analysis)
  8. Sehgal, K. K. Das, S. Dhull, S. Roy, and B. K. Kaushik, “Variability analysis of multilevel spin-orbit torque MRAMs using machine learning,” in Proc. IEEE 23rd Conference on Nanotechnology, July 2023, pp. 793-797
  9. Sehgal, S. Dhull, S. Roy, and B. K. Kaushik, “Energy-efficient on-chip learning for a fully connected neural network using domain wall device,” in Proc. SPIE AI and Optical Data Sciences IV, Jan.-Feb. 2023, pp. 35-43
  10. Yusuf, S. Singh, A. Dasgupta, B. Sarkar, and S. Roy, “A deep learning space mapping based enhancement of compact models for accurate prediction of trapping in GaN HEMTs from DC to mm-wave frequency,” in Proc. IEEE MTT-S 71st International Microwave Symposium, June 2023, pp. 89-92
  11. Sheelvardhan, S. Guglani, M. Ehteshamuddin, S. Roy and A. Dasgupta, “Variability aware FET model with physics knowledge based machine learning,” in Proc. 7th IEEE Electron Devices Technology and Manufacturing Conference (EDTM), March 2023, pp. 1-3
  12. Subramaniyan, N. Chauhan, N. Bagga, A. Kumar, S. K. Banchhor, S. Roy, A. Dasgupta, A. Bulusu and S. Dasgupta, “Analysis and modeling of leakage currents in stacked gate-all-around nanosheet transistors,” accepted for oral presentation in 6th IEEE International Conference on Emerging Electronics, Dec 2022
  13. Pandiyal, A. Singh, K. Sheelvardhan, S. Guglani, M. Ehteshamuddin, S. Roy, and A. Dasgupta, “An efficient variability-aware control variate-assisted neural network model for advanced nanoscale transistors,” accepted for oral presentation in 6th IEEE International Conference on Emerging Electronics, Dec 2022
  14. Guglani, Km Dimple, A. Dasgupta, R. Sharma, B. K, Kaushik, and S. Roy, “A transfer learning approach to expedite training of artificial neural networks for variability-aware signal integrity analysis of MWCNT interconnects,” in Proc. IEEE 29th Conference on Electrical Performance of Electronic Packaging and Systs., Oct. 2022, pp. 1-3
  15. Patel, S. Banchhor, S. Guglani, A. Dasgupta, S. Roy, A. Bulusu, and S. Dasgupta, “Design optimization using symmetric/asymmetric spacer for 14nm multi-fin tri-gate Fin-FET for midband 5G applications,” in Proc. 35th International Conference on VLSI Design, Feb.-March 2022, pp. 292-296
  16. Guglani, J. Patel, A. Dasgupta, S. Dasgupta, M.-Y. Kao, and C. Hu, “Artifical neural network surrogate models for efficient design space exploration of 14nm FinFETs,” accepted for oral presentation in 2022 Design Research Conference, June 2022
  17. Sharif, S. Pathania, S. Kushwaha, S. Roy, R. Sharma, and B. K, Kaushik, “An artificial neural network surrogate model for repeater optimization in the presence of parametric uncertainty for hybrid copper-graphene interconnect networks,” in Proc. 2022 IEEE International Conf. on Numerical Electromagnetics, Multiphysics Modeling, and Optimization, June 2022, pp. 1-4 (Invited paper in Special Session: Machine Learning, AI, and Uncertainties)
  18. Km Dimple, S. Guglani, R. Kumar, S. Kushwaha, S. Roy, R. Sharma, and B. K, Kaushik, “Exploring the impact of parametric variability on eye diagram of on-chip multi-walled carbon nanotube interconnects using fast machine learning techniques,” in  72nd IEEE Electronic Components and Technology Conference, May 2022, pp. 981-986
  19. Banchhor, N. Bagga, N. Chauhan, S. Manikandan, A. Dasgupta, S. Roy, A. Bulusu, and S. Dasgupta, “Analysis of self-heating in 5nm stacked nanosheet transistor applications,” accepted for poster presentation in XXth International Workshop on Physics of Semiconductor Devices, Dec. 2021
  20. Kumar, S. Sarkar, A. Dasgupta, S. Dasgupta, and S. Roy, “Fast extraction of quantum confinement effect on threshold voltage of gate-all-around FETs using machine learning methods,” accepted for poster presentation in XXth International Workshop on Physics of Semiconductor Devices, Dec. 2021
  21. Yusuf and S. Roy, “Statistical sensitivity analysis in distributed circuits using compact polymorphic polynomial chaos surrogates,” in Proc. IEEE Conference on Electrical Design of Advanced Packaging, Dec. 2021, pp. 1-4
  22. Kushwaha, A. Attar, R. Trinchero, F. Canavero, R. Sharma, and S. Roy, “Fast extraction of per-unit-length parameters of hybrid copper-graphene interconnects via generalized knowledge-based machine learning,” in Proc. IEEE 28th Conference on Electrical Performance of Electronic Packaging and Systs., Oct. 2021, pp. 1-3
  23. Yusuf and S. Roy, “A polymorphic polynomial chaos for fast uncertainty quantification of RF/microwave circuits in presence of design variables,” in Proc. IEEE MTT-S 69th International Microwave Symposium, June 2021, pp. 1-4 (Invited paper in Focus Session: Emerging Machine Learning techniques for CAD of RF/Microwave Circuits)
  24. Guglani, K. M. Dimple, S. Roy, B. K. Kaushik, and R. Sharma, “A multi-fidelity polynomial chaos approach for uncertainty quantification of MWCNT interconnect networks in the presence of imperfect contacts,” in Proc. 25th IEEE Workshop on Signal and Power Integrity, May 2021, pp. 1-4
  25. Guglani and S. Roy, “Predictor-Corrector algorithm with embedded dimension reduction for uncertainty quantification of MWCNT on-chip interconnect networks,” in Proc. IEEE 27th Conference on Electrical Performance of Electronic Packaging and Systs., Oct. 2020, pp. 1-3
  26. Rahul Kumar, S. S. Likith Narayan, Somesh Kumar, S. Roy, R. Sharma, B. K. Kaushik, and R. Achar, “Estimating per-unit-length resistance parameter in emerging copper-graphene hybrid interconnects via prior knowledge based accelerated neural networks,” in  IEEE 27th Conference on Electrical Performance of Electronic Packaging and Systs., Oct. 2020, pp. 1-3
  27. K. Nishad, A. K. Nishad, S. Roy, B. K. Kaushik, and R. Sharma, “First principle analysis of Os-passivated armchair graphene,” in Proc. 20th IEEE International Conference on Nanotechnology, July 2020, pp. 155-158 (Best Student Paper Award).
  28. Kumar, S. Pathania, S. Kumar, S. Guglani, A. Kumar, S. Roy, and R. Sharma, “Role of grain size on the effective resistivity of copper-graphene hybrid interconnects,” in Proc. 70th IEEE Electronic Components and Technology Conference, May 2020, pp. 1620-1625
  29. Kumar, S. Kumar, S. Guglani, S. Roy, B. K. Kaushik, R. Sharma, and R. Achar, “Temperature-aware compact modelling for resistivity in ultra-scaled Cu-graphene hybrid interconnects,” in Proc. 24th IEEE Workshop on Signal and Power Integrity, May 2020, pp. 1-4
  30. Guglani and S. Roy, “Development of improved predictor for expedited training of polynomial chaos metamodels of multi-walled carbon nanotube interconnects,” in Proc. 24th IEEE Workshop on Signal and Power Integrity, May 2020, pp. 1-4
  31. Shreya, S. Guglani, B. K. Kaushik, and S. Roy, “Statistical analysis of temperature variability on the write efficiency of spin-orbit torque MRAM using polynomial chaos metamodels,” in Proc. Of 21st International Symp. On Quality Electronic Design, March 2020, pp. 87-92
  32. Guglani, A. Kumar, R. Kumar, B. K. Kaushik, R. Sharma, R. Achar, and S. Roy, “Temperature-aware closed-form matrix rational approximation model for crosstalk analysis of MWCNTs,” in Proc. IEEE Conference on Electrical Design of Advanced Packaging, Dec. 2019, pp. 1-3
  33. Kumar, B. K. Kaushik, S. Roy, and R. Achar, “Crosstalk analysis in MWCNTs using a closed-form matrix rational approximation technique,” in Proc. IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systs., Oct. 2019, pp. 1-3
  34. Bhatnagar, Y. Li, A. Merkley, D. Weber, and S. Roy, “Predictor-corrector algorithms and their scalability analysis for fast stochastic modeling of multi-walled carbon nanotube interconnects using a predictor-corrector polynomial chaos scheme,” in Proc. IEEE Asia-Pacific International Symposium on Electromagnetic Compatibility, June 2019, pp. 560-563 (Invited paper in Special Session: Advanced Macromodeling Techniques for EMC & SI/PI))
  35. Cao, S. Bhatnagar, M. Nikdast, and S. Roy, “Hierarchical polynomial chaos for variation analysis of silicon photonics microresonators,” in Proc. 2019 International Applied Computational Electromagnetics Society (ACES) Symposium, April 2019 (Invited paper in Special Session: Uncertainty Quantification Analysis in Network, Devices, and Fields)
  36. Bhatnagar, A. Merkley, R. Berdine, Y. Li, and S. Roy, “Variability-aware performance assessment of multi-walled carbon nanotube interconnects using a predictor-corrector polynomial chaos scheme,” in Proc. IEEE Conference on Electrical Design of Advanced Packaging, Dec. 2018
  37. Siller, A. Hicks, A. Pezeshki, S. Roy, B. M. Notaros, T. W. Chen, and A. A. Maciejewski, “Using student video presentations to develop communications skills,” in Proc. 125th American Association of Engineering Education (ASEE) Annual Conference and Exposition, June 2018
  38. A. Maciejewski, A. Pezeshki, S. Roy, B. M. Notaros, and T. W. Chen, “Throwing away the course centric teaching model to enable change,” in Proc. 125th American Association of Engineering Education (ASEE) Annual Conference and Exposition, June 2018
  39. Roy, A. Pezeshki, B. M. Notaros, T. W. Chen, T. Siller, and A. A. Maciejewski, “Active learning model as a way to prepare students for knowledge integration,” in Proc. 125th American Association of Engineering Education (ASEE) Annual Conference and Exposition, June 2018
  40. K. Prasad and S. Roy, “Multi-fidelity approach for polynomial chaos based statistical analysis of microwave networks,” in Proc. 2018 International Applied Computational Electromagnetics Society (ACES) Symposium, March 2018, pp. 1-2 (Invited paper in Special Session: Uncertainty Quantification and Modeling for Complex Applications)
  41. Weinmeister, N. Xie, X. Gao, A. K. Prasad, and S. Roy, “Analysis of a polynomial chaos-kriging metamodel in aerospace applications,” in Proc. AIAA SciTech Forum, Jan. 2018, AIAA 2018-0911
  42. Weinmeister, N. Xie, X. Gao, A. K. Prasad, and S. Roy, “Combining a reduced polynomial chaos expansion approach with universal kriging,” in Proc. AIAA Aviation and Aeronautics Forum and Exposition, June 2017, AIAA 2017-3481
  43. Weinmeister, X. Gao, A. K. Prasad, and S. Roy, “Uncertainty quantification for combined polynomial chaos-kriging surrogate models,” in Proc. 70th Annual Meeting of APS Division of Fluid Dynamics, Nov. 2017
  44. K. Prasad and S. Roy, “Mixed aleatory-epistemic uncertainty quantification using reduced dimensional polynomial chaos and parametric ANOVA,” in Proc. IEEE 26th Conference on Electrical Performance of Electronic Packaging and Systs., October 2017, pp. 1-3 (Invited paper in Special Session: Stochastic Modeling and Uncertainty Quantification)
  45. K. Prasad and S. Roy, “A novel dimension fusion based polynomial chaos approach for mixed aleatory-epistemic uncertainty quantification of carbon nanotube interconnects,” in Proc. IEEE Symposium on Electromagnetic Compatibility and Signal Integrity, August 2017, pp. 108-111 (Invited paper in Special Session: Advances in Uncertainty Quantification and Modeling in Computational EM and SIPI Applications)
  46. Kapse, A. K. Prasad, and S. Roy, “Analyzing impact of epistemic uncertainty in high-speed circuit simulation using fuzzy variables and global polynomial chaos surrogates,” in Proc. IEEE International Conf. on Numerical Electromagnetics, Multiphysics Modeling, and Optimization, June 2017, pp. 320-322 (Invited paper in Special Session: Stochastic Electromagnetic Modeling)
  47. W. Chen, B. M. Notaros, A. Pezeshki, S. Roy, A. A. Maciejewski, and M. D. Reese, “Knowledge integration to understand why,” in Proc. 124th American Association of Engineering Education (ASEE) Annual Conference and Exposition, June 2017, paper ID 18611
  48. Liu, A. Pezeshki, S. Roy, B. M. Notaros, T. W. Chen, and A. A. Maciejewski, “Why math matters: demonstrating the relevance of mathematics in ECE education,” in Proc. 124th American Association of Engineering Education (ASEE) Annual Conference and Exposition, June 2017
  49. Kapse and S. Roy, “Anisotropic formulation of hyperbolic polynomial chaos expansion for high-dimensional variability analysis of nonlinear circuits,” in Proc. IEEE 25th Conference on Electrical Performance of Electronic Packaging and Systs., October 2016, pp. 123-126
  50. Gao, Y. Wang, N. Spotts, S. Roy, and A. K. Prasad, “Fast uncertainty quantification in engine nacelle inlet design using a reduced dimensional polynomial chaos approach,” in Proc. AIAA Propulsion and Energy Forum and Exposition 2016, July 2016, AIAA 2016-5057
  51. Ahadi, A. K. Prasad, and S. Roy, “Hyperbolic polynomial chaos expansions (HPCE) and its application to statistical analysis for nonlinear circuits,” in Proc. IEEE 20th Workshop on Signal and Power Integrity, May 2016, pp. 1-4
  52. Kapse, A. K. Prasad, and S. Roy, “Generalized anisotropic polynomial chaos approach for expedited statistical analysis of nonlinear radio-frequency (RF) circuits,” in Proc. IEEE 20th Workshop on Signal and Power Integrity, May 2016, pp. 1-4
  53. K. Prasad, D. Zhou, and S. Roy, “Reduced dimension polynomial chaos approach for efficient uncertainty analysis of multi-walled carbon nanotube interconnects,” in Proc. IEEE MTT-S 64th International Microwave Symposium, May 2016, pp. 1-3
  54. K. Prasad and S. Roy, “Global sensitivity-based dimension reduction for fast variability analysis of nonlinear circuits,” in Proc. IEEE 24th Conference on Electrical Performance of Electronic Packaging and Systs., October 2015, pp. 97-99
  55. Ahadi, M. Kabir, S. Roy, and R. Khazaka, “Fast multidimensional statistical analysis of microwave networks via Stroud cubature approach,” in Proc. IEEE International Conf. on Numerical Electromagnetics, Multiphysics Modeling, and Optimization, August 2015, pp. 1-3
  56. K. Prasad, M. Ahadi, B. S. Thakur, and S. Roy, “Accurate polynomial chaos expansion for variability analysis using optimal design of experiments,” in Proc. IEEE International Conf. on Numerical Electromagnetics, Multiphysics Modeling, and Optimization, August 2015, pp. 1-4
  57. Ahadi, M. Vempa, and S. Roy, “Efficient multidimensional statistical modeling of high-speed interconnects in spice via stochastic collocation using Stroud cubature,” in Proc. IEEE Symposium on Electromagnetic Compatibility and Signal Integrity, March 2015, pp. 300-305 (Invited paper in Special Session: Stochastic Analysis for SI/PI/EMC)
  58. K. Prasad, M. Ahadi, and S. Roy, “Polynomial chaos-based variability analysis of power distribution networks using a 3D topology of multiconductor transmission lines,” in Proc. IEEE 23rd Conference on Electrical Performance of Electronic Packaging and Systs., October 2014, pp. 21-24
  59. Ahadi, M. Kabir, A. Smull, E. Chobanyan, Md. A. H. Talukder, S. Roy, R. Khazaka and B. M. Notaros, “Non-Intrusive pseudo spectral approach for stochastic macromodeling of EM systems using deterministic full-wave solvers,” in Proc. IEEE 23rd Conference on Electrical Performance of Electronic Packaging and Systs., October 2014, pp. 235-238 (Best Poster Paper Award)
  60. A. H. Talukder, M. Kabir, S. Roy and R. Khazaka, “Efficient stochastic transient analysis of high-speed passive distributed networks using Loewner matrix macromodels,” in Proc. IEEE Symposium on Electromagnetic Compatibility and Signal Integrity, August 2014, pp. 209-212 (Invited paper in Special Session: Numerical Methods for Signal and Power Integrity Analysis)
  61. A. H. Talukder, M. Kabir, S. Roy and R. Khazaka, “Efficient generation of macromodels via the Loewner matrix approach for the stochastic analysis of high-speed passive distributed networks,” in Proc. IEEE 18th Workshop on Signal and Power Integrity, May 2014, pp. 1-4
  62. Roy and A. Dounavis, “Hybrid approach for accelerated convergence of waveform relaxation-based simulation of package/board power distribution networks,” in Proc. IEEE MTT-S 61st International Microwave Symposium, June 2013, pp. 1-4
  63. Roy and A. Dounavis, “Waveform relaxation with overlapping based partitioning for fast transient simulation of package/board power distribution networks,” in Proc. 21st IEEE Conference on Electrical Performance of Electronic Packaging and Systs., October 2012, pp. 252-255
  64. Roy, A. Beygi and A. Dounavis, “Fast electromagnetic interference analysis of distributed networks using longitudinal partitioning-based waveform relaxation,” in Proc. IEEE MTT-S 60th International Microwave Symposium, June 2012, pp. 1-3
  65. Roy and A. Dounavis, “Waveform relaxation-based analysis of noise propagation in power distribution networks,” in Proc. 20th IEEE Conference on Electrical Performance of Electronic Packaging and Systs., October 2011, pp. 255-258
  66. Roy and A. Dounavis, “Longitudinal partitioning-based waveform relaxation algorithm for transient analysis of long delay transmission lines,” in Proc. IEEE MTT-S 59th International Microwave Symposium, June 2011, pp. 1-4
  67. Roy and A. Dounavis, “Efficient macromodeling of power distribution planes using delay extraction-based transmission line representation,” in Proc. 14th International Symposium on Antenna Technology and Applied Electromagnetics, July 2010, pp. 1-4
  68. Ahmadloo, S. Roy and A. Dounavis, “Parameterized model order reduction of power distribution planes,” in Proc. 14th International Symposium on Antenna Technology and Applied Electromagnetics, July 2010, pp. 1-4
  69. Roy and A. Dounavis, “RLC interconnect modeling using delay algebraic equations,” in Proc. 8th IEEE Dallas Circuits and Systems Workshop, October 2009, pp. 1-4
Faculty List