EE

EE faculty colloquium
2025-02-21 16:00 - 17:30 (Asia/Kolkata)
Title: Power Electronics – A Technology Enabler for a Carbon-free Energy Pathway
Speaker: Dr. Vinod John, Dept. of Electrical Engineering, Indian Institute of Science
Venue: MMCR, EE
Meeting Link (for online audience)
Time: 4 pm, 21 Feb 2024

Abstract:
Power electronics technologies play a pivotal role in efficiently transferring electrical energy from sources and storage elements to loads while minimizing power losses. These technologies are critical for optimizing the utilization of renewable energy sources, enhancing the efficiency of storage systems, and reducing energy wastage—all of which are essential in the global effort to achieve minimal CO₂ emissions. In this talk, I will begin with an introduction to the fundamentals of power electronics, highlighting key research challenges and discussing solutions developed in the Power Electronics Group at the Department of Electrical Engineering, IISc. Specific focus areas include power converters, switching devices, input and output filtering components, and the control of power conversion systems. I will try to summarize the research efforts over time and indicate a few practical applications of power electronics in real-world scenarios.

Speaker’s Bio:
Dr. Vinod John is a Professor in the Department of Electrical Engineering at the Indian Institute of Science (IISc), Bengaluru. He earned his Ph.D. from the University of Wisconsin–Madison and subsequently worked at GE Research, New York, and Northern Power Systems, Vermont, before joining IISc. His research interests encompass
power electronics, switched-mode power conversion, distributed energy resources, and energy storage systems.

EE PhD Defense: Infimal Convolution Based Regularization for Image recovery
2025-02-27 14:00 - 16:00 | Multi-Media Class Room (MMCR), EE Department (Hybrid mode)
Student : Deepak G Skariah
Advisor : Prof. Muthuvel Arigovindan
Title : Infimal Convolution Based Regularization for Image recovery
Date and Time: 27.02.2025 (Thursday), 2 pm.
Venue : MMCR, Department of Electrical Engineering
Meeting link
Thesis examiners: Prof. Kedar Khare, Prof. Naren Nayak
Defense examiner: Prof. Kedar Khare
Abstract
The quality of image captured by acquisition devices has increased drastically over the years largely due to a revolution in imaging sensor capability. But, image acquisition under low illumination continues to be a bottleneck for imaging devices such as optical microscopes leading to blurred and noisy images. A potential solution to this limitation is a computational approach known as image restoration. An image restoration algorithm recovers an estimate of the original image from a noisy blurred observation while assuming a knowledge of the image degradation model. The restoration problem is even more challenging when it comes to a spatio-temporal signal as a good restoration scheme needs to be mindful of presence of motion in the measured signal. This means that in spatio-temporal signal restoration problem, the algorithm should ensure temporal regularity of restored signal in addition to spatial regularity. Regularization based image restoration attempts to pose image restoration problem as a regularized optimization problem from the measured signal. We propose to exploit the concept of infimal convolution from convex analysis to design effective and efficient restoration schemes for images and spatio-temporal images.
In our first work, we address the problem of regularization design. We propose a family of derivative based regularization which we call generalized unitary invariant regularization and it belongs to class of infimal convolution based functionals. We also design an algorithmic scheme to optimize the resultant optimization problem. We demonstrate the quality of proposed algorithm and restoration scheme through multiple experiments on simulated data.
In our second work, we address the restoration of spatio-temporal images measured from TIRF microscopes where a sequence of noisy blurred images are observed over time. We once again exploit the infimal convolution based approach to design a novel spatio-temporal regularizer that is tailor made for above class of signals. The proposed regularization was designed to ensure both spatial and temporal regularity of restored signal. The resultant regularization functional is defined as an optimization problem where the cost is a weighted sum of two constituent functions where the two functions play the role of promoting spatial and temporal regularity respectively. We also design an algorithm to optimize the resultant restoration problem using this regularization. We demonstrate the quality of the proposed algorithm by testing the restoration quality against spatio-temporal measurements collected from TIRF microscopes.
In the third and final work we address the problem of estimating the relative weights in spatio-temporal regularization functional designed based on infimal convolution formulation. We propose a renewed optimization model where the spatio-temporal signal is estimated together with the better quality image estimate by incorporating the weights as part of the optimization problem. We also design an iterative scheme to optimize the resultant joint optimization model. We demonstrate the effectiveness of this scheme against other joint optimization schemes for spatio-temporal signal estimation.

EE Talk: Cybersecurity for the Power Grid in the AI Age - 7th March 3PM, MMCR
2025-03-07 15:00 - 17:00 | Multi-Media Class Room (MMCR), EE Department (Hybrid mode)
IEEE PES Student Branch Chapter, IISc and POWERGRID Center of Excellence in Cyber Security (PGCoE) is pleased to invite you for a technical talk by Prof. Manimaran Govindarasu, Iowa State University, USA. The technical talk details are as follows.

Title: Cybersecurity for the Power Grid in the AI Age
Venue: C-241, MMCR, 1st floor, Electrical Engineering Department
Date and Time: 07th March 2025, 03:00 PM IST
Mode: Hybrid (Online and Offline). MS Team link
Talk Abstract: Power grid is a complex cyber-physical system (CPS) that forms the lifeline of our modern society. Reliable, secure, and resilient operation of nation’s energy infrastructure is of paramount importance to national security and economic wellbeing. In recent years, there has been a growing trend of cyber threats/attacks, both in numbers and sophistication, targeting critical infrastructure systems around the globe (e.g., Stuxnet, Ukraine power grid attacks, and Colonial Pipeline attack). This evolving cyber threat landscape, coupled with the leveraging of AI tools by the adversaries, underscores the importance and urgency for CPS security solutions that go beyond the traditional IT cybersecurity by leveraging grid’s cyber-physical properties and harnessing the power of AI models to be able to prevent, detect, and mitigate stealthy cyber attacks. This talk will present an overview of cybersecurity threats to critical energy infrastructures, then it will present a holistic life-cycle model for CPS security with some illustrative example solutions for attack prevention, detection, and mitigation. The talk will also briefly discuss CPS security testbeds for attack-defense evaluations and then conclude with some future research directions.

Speaker Bio: Manimaran Govindarasu is on the faculty of Iowa State University since 1999, and he currently holds the titles of Anson Marston Distinguished Professor in Engineering and Harploe Professor in Electrical and Computer Engineering. Prior to joining Iowa State, he received his Ph.D. in Computer Science and Engineering from Indian Institute of Technology, Madras (Chennai). His research experience includes Cybersecurity for the Smart Grid and Critical Infrastructures, and Real-time Systems and Networks. He has co-authored over 300 peer-reviewed research publications, received multiple conference best paper awards, presented several dozen conference invited talks, tutorials, and industry short-courses, hands-on training sessions, and mentored over 50 graduate students for their dissertation/thesis research. He is currently serving as the Chair of Cybersecurity Working Group for Power Grid in IEEE Power & Energy Society and has served as an Associate Editor and guest co-editor for several flagship IEEE publications. His research is supported over the years by US NSF, DOE, DHS, and DOD. He is a Fellow of the IEEE and an ABET Program Evaluator.

Thesis Defense (Online)
2025-03-19 16:00 - 17:30 | Online, India
Title: Parallel Algorithms for Efficient Utilization of Multiprocessor Architectures for Transient Stability
Student: Francis C Joseph
Faculty Advisor: Dr. Gurunath Gurrala.
Examiner: Prof Saikat Chakrabarti, IIT Kanpur
Date : 19th March 2025
Time: 4 PM – 5.30 PM
Mode: ONLINE
TEAMS LINK
ABSTRACT:
Computer hardware capabilities have been enormously increasing over the years. Multi-core processors, graphic processing units (GPUs), and field programmable gate array (FPGA) accelerators have grown significantly recently. They have opened new computational paradigms such as edge computing, fog computing, grid computing, distributed computing, cloud computing, and exascale supercomputing. However, efficiently utilising most of these computational paradigms in traditional engineering disciplines, such as power engineering, is challenging. In this thesis, efficient algorithms for multiprocessor-based high-performance computing and edge computing platforms for two power system applications are developed, power system stability assessment and power quality measurements respectively. Faster than real-time transient stability assessment of large power grids using time domain simulations with detailed models is computationally challenging. Today, the commercial tools used for this application in Energy Management Systems (EMS) worldwide rely on parallel batch processing methods, which don’t efficiently utilise the architecture of the computational paradigms. For transient stability simulations, this thesis explores a time parallel algorithm, Parareal in Time, which belongs to a class of temporal decomposition methods for time parallel solutions of differential equations. Two effective implementation approaches, Master Worker and Distributed, are analysed for large systems, and scaling tests are performed using a state space model with a Message Passing Interface (MPI) in a multiprocessor environment. One of the findings was that the performance of the Parareal depends on the accuracy and the computational cost of the coarse solver used for initialisation and subsequent correction steps. A potential coarse solver, Modified Euler (ME), a well-known solver for transient stability simulations even in commercial packages, has been explored to adapt its step size by controlling the Local Truncation Error (LTE) to achieve the desired accuracy. An LTE estimator using a Multistage Homotopy Analysis Method (MHAM), which gives an approximate solution to a set of non-linear equations in the form of a power series, is proposed to control the LTE at each integration step to enable adaptation of the ME step size. The proposed MHAM-assisted adaptive ME solver is faster and has comparable accuracy to the conventional fixed and adaptive Modified Euler solver for large systems’ transient stability simulations. Since MHAM is lighter than the ME solver and the LTE estimate is sufficient for step size adaptation, an adaptive MHAM coarse solver is proposed for the Parareal. However, MHAM provides a non-zero auxiliary parameter `c’ to select a family of solutions. Hence, an optimisation framework is also proposed to automatically select this parameter based on the system’s dynamics. Based on many case studies on test systems of different sizes, it is found that maintaining the LTE lower than the Parareal convergence tolerance improves the speedup of the Master-Worker paradigm; however, for the distributed implementation, maintaining LTE higher than the convergence tolerance gives improved speedup. An approach to include unscheduled events which arise in power system operation due to the operation of protective relays is also proposed for Parareal. The impact of frequency estimation on Parareal is evaluated using three estimation methods. It was found that the network admittance-based method has the lowest execution time. Many different types of disturbance types are performed on systems of different sizes and see that Parareal can maintain its performance. In Parareal implementation, each coarse time segment is assigned to one processor in the MPI environment. Multiple processors in a node can be assigned to a coarse time segment to improve speedup. Therefore, a shared memory-based space parallel transient stability solver is also considered for further performance enhancement. Space parallelisation of transient stability simulation involves breaking the network into subnetworks and solving each part independently while ensuring the original network’s convergence. Therefore, a Multi Area Thevenin Equivalent (MATE) based parallel solver implementation on a shared memory platform is proposed, and both space parallelisation and task parallelisation are explored. It is shown that the space parallelism can closely match the ideal speedup and can be exceeded by space + task parallelism while the network is well-partitioned. It can be further improved when combined with time parallelism. A hybrid time-space solver using OpenMP MATE, space + task parallelism, and MPI Parareal is proposed using two scheduling schemes: homogeneous and heterogeneous for both communication paradigms. The homogeneous scheduling enabled a faster-than-real time solution even for the PEGASE 13659 bus system and provided multiple combinations to achieve it. The heterogeneous can increase the performance of the hybrid solver when homogeneous scheduling is unavailable. A particular case for Hybrid Master with a single core worker was used to showcase the initialisation phase’s time reduction by reducing the coarse solver’s computational time. The current state-of-the-art chips also provide multicore architectures for edge computing applications. One such low-cost, open-source, heterogeneous, resource-constrained hardware platform is called Parallella. The unique hardware architecture of the Parallella provides many edge computing resources in the form of a Zynq SoC (dual-core ARM + FPGA) and a 16-core co-processor called Epiphany. This Parallella device was used as a measurement device for edge computing applications research in smart grids, and it could sample 3 voltages and four currents at a 32 kHz sampling rate. The thesis explores one application of such a device to measure the harmonics and compute various Power Quality (PQ) indices. A parallel implementation of multichannel FFT on Epiphany for the streaming data is developed in this regard. Epiphany 16-core architecture has very limited memory resources, and the order in which the cores are to be accessed significantly impacts the execution. Proper decomposition of the FFT algorithm tasks and scheduling the tasks for efficient core and memory usage are crucial, requiring a good understanding of the Epiphany architecture. The obtained PQ measurements from the proposed implementation are comparable to those of a commercial power analyser.

Colloquium
2025-03-20 11:30 - 13:00 | MMCR, Hall C 241, 1st floor, EE department
Date & Time: 20th March 2025, at 11:30 am
Speaker: Mr. Varun Krishna P S
Venue: EE, MMCR [1st Floor, C241]

Title: Self-Supervised Learning Approaches for Content Factor Extraction from Raw Speech

Abstract:
The rapid expansion of the digital data has led to a growing interest in self-supervised learning (SSL) techniques, particularly for speech processing tasks where labeled data is often scarce. SSL enables models to learn meaningful representations directly from raw data by capturing inherent structures and patterns without requiring explicit supervision. To be effective, speech representations must not only capture content-related information—such as phonetic, lexical, and semantic features—but also remain robust against speaker variations, co-articulation effects, channel distortions, and background noise. The focus of this talk is to describe our efforts in developing self-supervised learning techniques in extracting semantic content from raw speech while remaining invariant to non-semantic speech factors.

In the first part of the talk, we propose the Hidden Unit Clustering (HUC) framework, which integrates contrastive learning with deep clustering techniques to enhance representation quality. A speaker normalization strategy is incorporated to mitigate speaker variability, ensuring that the extracted representations focus primarily on the content-related information. Additionally, a heuristic data sampling method is introduced to generate pseudo-targets for deep clustering, further refining the learned representations. The framework is evaluated across multiple SSL models, demonstrating significant improvements in phonetic and semantic benchmarks, as well as superior performance in the low-resource ASR settings.

The second part of the talk focuses on the efforts to achieve context-invariant representations to address the challenges posed by co-articulation effects and variations in speaker and channel characteristics. To achieve this, a pseudo-con loss framework is proposed, leveraging pseudo-targets to guide the contrastive learning and enhance robustness. This approach serves as an effective auxiliary module that can be seamlessly integrated into SSL models based on deep clustering. Extensive evaluations demonstrate state-of-the-art performance across multiple ZeroSpeech 2021 sub-tasks, as well as significant improvements in phoneme recognition and ASR performance.

In the final part of the talk, we explore the integration of adversarial learning to obtain semantic representations that are invariant to non-semantic factors. A gradient-reversal mechanism is employed to suppress non-semantic variations explicitly within the SSL models, thereby refining the learned representations. The proposed adversarial learning approach effectively disentangles content from non-semantic factors, leading to robust semantic representations. Experimental results confirm that the proposed approach enhances the ability of speech processing models to generalize across different acoustic conditions while preserving the linguistic information.

Bio:
Varun Krishna is a PhD scholar at the Department of Electrical Engineering, Indian Institute of Science Bengaluru. He obtained his B.Tech from the Department of Electronics and Communications from NITK, Surathkal, in 2017. Later completed his M.Tech in Signal Processing from the Department of Electrical Engineering, Indian Institute of Science, Bengaluru, in 2019. His research interests include self-supervised representation learning, large language models, and generative AI.

PhD Oral Examination
2025-03-25 15:00 - 17:00 | MMCR, Hall C 241, 1st floor, EE department
The oral examination of Lalgy Gopi will be held at the Multi-Media Class Room (MMCR), EE Department, between 3 pm and 5 pm on Tuesday, March 25.

PhD Thesis Defense
2025-03-27 14:00 - 17:00 | Multi-Media Class Room (MMCR), EE Department (Hybrid mode)
Name of the Candidate: Lalit Manam
Research Supervisor: Venu Madhav Govindu
Date and Time: March 27, 2025, Thursday, 2:00 PM
Venue: C-241, First Floor, Multimedia Classroom (MMCR), EE
Title: Global Methods for Camera Motion Estimation
Abstract:
In computer vision, Structure-from-Motion (SfM) aims to recover a 3D reconstruction of a scene from a collection of images of the scene. SfM has been of interest to the 3D computer vision community for the last few decades, with a wide range of scientific, industrial and domestic applications. A key component of global approaches to SfM is estimating the motion of individual cameras, i.e. their rotation and translation with respect to a frame of reference. The camera motions are generally not available a priori, making their estimation a crucial component. This thesis focuses on accurate, reliable and efficient estimation of camera motions in SfM. We examine a number of issues concerning the estimation of camera motions, namely input quality, choice of cost function and the underlying graph representation, and develop methods to address these issues.
Input Quality: We examine the problem of translation averaging, where all camera translations are simultaneously estimated, given relative directions between them as the input. The accuracy of relative camera directions is limited due to multiple factors relating to how they are obtained. We take recourse to keypoint correspondences between image pairs from which relative motions between the cameras are estimated. We propose a modular framework to iteratively reweight keypoint correspondences based on their global consistency via translation averaging methods. Our proposed framework improves relative translation directions in comparison to the translation averaging scheme used.
Choice of Cost Function: In translation averaging, recovering camera translations from relative directions involves two types of optimization costs, either comparing directions or displacements. These cost functions are often relaxed to obtain simpler optimization approaches. We observe that neither cost performs the best under varying distributions of the underlying camera translations and the noise present in the input directions. We propose a principled approach to recursively fuse the estimates obtained from both the relaxed costs using a principled uncertainty model. Our method leads to improvement in camera translation estimates compared to the individual costs.
Graph Representation: We leverage the underlying graph representation that arises due to relationships between cameras in two applications to improve camera motion estimates. In the first application, we introduce the idea of sensitivity in translation averaging, which analyzes the change in camera translations with small perturbations to input directions. We develop two different formulations to theoretically analyze the sensitivity/conditioning of the problem based solely on the inputs. We propose efficient algorithms to remove the ill-conditioned configuration of inputs, which is abundant in real data. Removal of ill-conditioned inputs significantly improves the translation estimates, revealing the benefits of our analysis.
The second application that leverages graph representation solves the tasks of graph sparsification and disambiguation of repeated structures in a unified manner. We present a scoring mechanism to identify redundant and false edges and remove them with a threshold that is optimal under an edge selection cost. We design efficient algorithms which can be applied as a preprocessing step to any SfM pipeline. Our approach handles both tasks in a unified manner, making it practical for use. Applying our methods reduces reconstruction time due to the sparsification of graphs and avoids superimposed reconstructions because of repeated structures being disambiguated.

EECS RESEARCH STUDENTS SYMPOSIUM - 2025
2025-04-03 08:00 - 2025-04-04 17:00 | IISc
EECS RESEARCH STUDENTS SYMPOSIUM – 2025

The following is the link:
(https://eecs.iisc.ac.in/EECS2025/

Talk on Noise in imaging: focus on correlation and nonlinearity
2025-04-16 02:00 - 17:00 | MMCR, Hall C 241, 1st floor, EE department
A two-part talk on

“Noise in imaging: focus on correlation and nonlinearity”
by

Professor Alessandro Foi*, Tampere University (TAU), Finland
(* of the BM3D fame, among other things)

on April 16, 2025 (Wednesday)

from 2 PM to 5.30 PM (coffee break at 3.30 PM)

Venue: Multimedia Classroom (MMCR), EE Department

Abstract:
Understanding and characterizing noise is a foundational part of the design and analysis of an imaging system, and it is also essential for the development of the corresponding image processing modules. In this talk we consider broad classes of heteroskedastic image observations and specifically focus on the noise correlation, the noise anisotropy, and on the nonlinear effects that can arise when dealing with capture at low signal-to-noise ratio or when maximizing the coverage of a narrow dynamic range. We demonstrate possibly unexpected and perhaps counter-intuitive phenomena which, unless suitably modeled and accounted for, can significantly disrupt the noise analysis and other operations in an image processing pipeline.
The talk is divided into two parts.
In the first part, I will introduce concrete examples and the relevant mathematical models of noise found in various imaging and image processing systems used in biomedical, defense, security, as well as consumer applications, including x-ray tomography, infrared thermography, confocal fluorescence microscopy, and on-demand video streaming.

In the second part, I will discuss the spectral distortion that takes place when nonlinear transformations are applied to correlated noise. In particular, I will consider the case of clipping (e.g., saturation, over-exposure, under-exposure) and the application of variance-stabilizing transformations.

Biography of the speaker:
Alessandro Foi is a Professor of Signal Processing at Tampere University (TAU), Finland. He leads the Signal and Image Restoration group and he is the director of TAU Imaging Research Platform. He is also the CTO of Noiseless Imaging, a company specialized in noise-removal, restoration, and enhancement technology for the imaging industry.

He received the M.Sc. degree in Mathematics from the Università degli Studi di Milano, Italy, in 2001, the Ph.D. degree in Mathematics from the Politecnico di Milano in 2005, and the D.Sc.Tech. degree in Signal Processing from Tampere University of Technology, Finland, in 2007. His research interests include mathematical and statistical methods for signal processing, functional and harmonic analysis, and computational modeling of the human visual system. His work focuses on spatially adaptive algorithms for the restoration and enhancement of digital images, on noise modeling for imaging devices, and on the optimal design of statistical transformations for the stabilization, normalization, and analysis of random data. He is a Fellow of the IEEE for his contributions to image restoration and noise modeling.

He was the Editor-in-Chief of the IEEE Transactions on Image Processing from 2021 to 2023. He previously served as a Senior Area Editor for the IEEE Transactions on Computational Imaging and as an Associate Editor for the IEEE Transactions on Image Processing, the SIAM Journal on Imaging Sciences, and the IEEE Transactions on Computational Imaging.

Host faculty: Prof. Soma Biswas (EE) and Prof. Chandra Sekhar Seelamantula (EE)

Talk on Things you should know before submitting your next paper
2025-04-17 14:00 - 17:00 | MMCR, Hall C 241, 1st floor, EE department
A two-part talk on
“Things you should know before submitting your next paper”

by

Professor Alessandro Foi, Tampere University (TAU), Finland
Former Editor-in-Chief, IEEE Transactions on Image Processing

on April 17, 2025 (Thursday)

from 2 PM to 5 PM (coffee break at 3.15 PM)

Venue: Multimedia Classroom (MMCR), EE Department

Hosts: Prof. Chandra Sekhar Seelamantula (EE) and Prof. Soma Biswas (EE)

Abstract:
1) Publishing process; Ethics and Etiquette : “Things you should know before submitting your next paper”
The lecture is particularly addressed to new authors with little experience about the publication workflow and principles, and it is focused on the dos and don’ts for successfully publishing a technical paper. It offers an overview of the peer-review process and discusses the ethics and etiquette standards that authors are expected to uphold and that reviewers and editors are looking for. The lecture is based on material used at IEEE training events for authors and volunteers, supplemented by additional material by the Committee on Publication Ethics (COPE); it discusses general principles followed by various publishers and communities in science and engineering.

Topics include: choice of the publication venue, framing of the contribution with respect to prior art and literature, authorship and acknowledgment, fair reporting of own and others’ results, appropriate disclosure and referencing to own previous work, appropriate description and citing of prior work, plagiarism, duplicate submissions, inappropriate replication, and bibliometric manipulation, author’s responsibilities.

2) Responding to reviews and managing the revision process
It is extremely rare (though not impossible) that a manuscript submitted to a top-tier journal gets accepted “as is” after the review of the original submission. Most often the manuscript goes through an iterative process, where editors interact with authors and reviewers with the goal of revising the manuscript to make it suitable for publication in the journal. The lecture discusses the manuscript revision process, highlighting the editorial perspective and the typical mistakes that authors make. Participants will learn how to manage and execute a productive and efficient revision, ultimately maximizing the chances that the revised manuscript will get accepted, and in fewer iterations.

Speaker’s biography:
Alessandro Foi is a Professor of Signal Processing at Tampere University (TAU), Finland. He leads the Signal and Image Restoration group and he is the director of TAU Imaging Research Platform. He is also the CTO of Noiseless Imaging, a company specialized in noise-removal, restoration, and enhancement technology for the imaging industry.
He received the M.Sc. degree in Mathematics from the Università degli Studi di Milano, Italy, in 2001, the Ph.D. degree in Mathematics from the Politecnico di Milano in 2005, and the D.Sc.Tech. degree in Signal Processing from Tampere University of Technology, Finland, in 2007. His research interests include mathematical and statistical methods for signal processing, functional and harmonic analysis, and computational modeling of the human visual system. His work focuses on spatially adaptive algorithms for the restoration and enhancement of digital images, on noise modeling for imaging devices, and on the optimal design of statistical transformations for the stabilization, normalization, and analysis of random data. He is a Fellow of the IEEE for his contributions to image restoration and noise modeling.

He was the Editor-in-Chief of the IEEE Transactions on Image Processing from 2021 to 2023. He previously served as a Senior Area Editor for the IEEE Transactions on Computational Imaging and as an Associate Editor for the IEEE Transactions on Image Processing, the SIAM Journal on Imaging Sciences, and the IEEE Transactions on Computational Imaging.

Talk on Phase Retrieval: computational imaging in the machine learning era
2025-04-21 16:00 - 17:00 | B-303,EE
A talk on

Phase Retrieval: computational imaging in the machine learning era

by Jonathan Dong, Biomedical Imaging Group, Ecole polytechnique federale de Lausanne, Switzerland

Venue: B303, Electrical Engineering Department (second floor), IISc

Date: April 21, 2025; Time: 4 PM (Coffee will be served at 3.45 PM)

Host: Prof. Chandra Sekhar Seelamantula (EE)

Abstract:
Phase retrieval is a fundamental nonlinear inverse problem that appears across a wide range of computational imaging applications, from X-ray and electron ptychography to phase imaging in optical microscopy. Because it is often addressed through nonlinear optimization techniques, it has deep links with modern machine learning theory. In this talk, I will provide a unified overview of phase retrieval models and algorithms, highlighting the connections between different applications. I will also discuss recent theoretical insights on reconstruction guarantees derived from random matrix theory. Finally, we’ll explore practical implementations, and I’ll share how these extend to our recent work on differentiable physical models and open-source computational imaging tools.

Biography of the speaker:
Jonathan Dong is an SNF Ambizione Fellow with Prof. Michael Unser at the Biomedical Imaging Group, EPFL, Lausanne, Switzerland. He received his Ph.D. degree in Physics in 2020 from Ecole Normale Supérieure in Paris, France. His research interests include nonlinear inverse problems and computational imaging, with a focus on physics-based models, reconstruction algorithms, and statistical analysis methods.

Poster Presentation, MTech(SP)
2025-06-23 09:00 - 17:30 | EE Department
Poster Presentation, MTech(SP)
Date & time : 23rd June 2025, Full day

CCE Workshop
2025-06-25 - 2025-06-27 | EE Department
CCE Workshop
Host : Prof Dr. Sarasij Das
Date & Time : 25th to 27th June 2025, Full day

Colloquium : Ms. Tanuka Bhattacharjee
2025-07-01 16:00 - 17:30 | MMCR, Hall C 241, 1st floor, EE department
Supervisor : Prof. Prasanta Kumar Ghosh
Student : Ms. Tanuka Bhattacharjee

Summer School 2025
2025-07-10 - 2025-07-12 | B308,2nd floor, EE Dept.
Date : 10th July – 12th July 2025
Venue : EE Department, Room B – 308

Alumni Meet
2025-07-14 | B308,2nd floor, EE Dept.

Colloquium : Student: Ms. Vibhuti Sahu
2025-07-18 11:00 - 12:00
Thesis Title: Time Domain Cascading Analysis Framework with Integrated Substation Configurations and Protection Systems
Student: Ms. Vibhuti Sahu, EE, IISc
SR. No: 04-03-00-10-12-19-1-17173
Faculty Advisor: Prof. Gurunath Gurrala.
Join the meeting now
Meeting ID: 478 448 375 461 6
Passcode: [REDACTED]

Oral Defense : Mr. Varun Krishna P S
2025-07-21 09:00 - 11:00 | MMCR, Hall C 241, 1st floor, EE department
Student : Mr. Varun Krishna P S
Advisor : Prof. Uday Kumar
Find this link to join below :
https://teams.microsoft.com/l/meetup-join/19%3ameeting_NmJiZGQzMzQtY2JkOC00MTllLWE3NGEtNzQ5Y2RjOTQ5ZDFi%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%22378ce499-3df5-4e0b-a5f9-539164b7b28b%22%7d

PhD Thesis Colloquium: Durvesh Kalke
2025-07-21 14:30 - 17:00 | MMCR, Hall C 241, 1st floor, EE department
Thesis Title: Emulation of Grid Level Energy Storage Systems on a Laboratory Experimental Testbed
Speaker: KALKE DURVESH PRASHANT . of Ph.D. (Engg) in Electrical Engineering under Electrical Engineering
Research Supervisor: Prof. Gurunath Gurrala
Meeting Link :
https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZmI3ZTZjNTYtNzgzMS00ZTMwLTg2ZjAtY2M1ZWE2ZTRlMjAw%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%22df1edb23-47bc-4868-9b1f-5eee3a3b1232%22%7d

Oral Defense : Mr. Anupam Verma
2025-07-22 15:00 - 17:00 | MMCR, Hall C 241, 1st floor, EE department
Student : Mr. Anupam Verma
Advisor : Prof. Narayan G

Colloquium
2025-08-21 15:00 - 16:00 | MMCR, Hall C 241, 1st floor, EE department
Title: Medium Voltage AC to Low Voltage DC Converters: Topology, Modulation & Design

Speaker: Harisyam P V

Ph.D. Thesis Open Defense
2025-08-22 10:00 - 12:00 | MMCR, Hall C 241, 1st floor, EE department
Student Name : Mrs. Baby Sindhu A . V.

Thesis Title : Developmental Studies on Polymeric Nano/Micro Composite Insulation for Various High Voltage Power Applications

Professor : Prof. Joy Thomas. M.

https://teams.microsoft.com/l/meetup-join/19%3ameeting_YjZiNGVkYzYtODA1ZC00NmMxLWEzYTctYWRjNjM4OGMwZTNk%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%22aff5f912-6b5f-4511-96bb-925683e426e0%22%7d

PhD Oral Examination of Mr. Kamisetti N V Prasad
2025-09-17 15:30 - 17:30 | MMCR, Hall C 241, 1st floor, EE department
Thesis Title: “Modeling, Design and Control of Power-Electronic-Actuated Electromagnetic Bearings”
Name of the Student: Kamisetti N V Prasad
Research Supervisor: Prof. G. Narayanan
Team’s link:
https://teams.microsoft.com/l/meetup-join/19%3ameeting_MTdhNmUyYjYtMjEwOS00NWRlLTliZTgtODc2OGI5NTZkODRh%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%22f711300f-120c-447e-8a76-45f7c1a483a8%22%7d

PhD Thesis Defence : MANISH MANDAL
2025-09-19 15:00 - 17:00 | MMCR, Hall C 241, 1st floor, EE department
Thesis Title: Modelling the Switching Dynamics of Advanced Power Semiconductor Devices: From Silicon Superjunction to Wide Bandgap Technologies
Name of the Student: Manish Mandal
Research Supervisor: Prof. Kaushik Basu
Teams Meeting Link:
https://teams.microsoft.com/l/meetup-join/19%3ameeting_OWY4MTljNTQtNzg0NS00YWQ0LTljZWMtOTBkYmQ5ZDJiNDM5%40thread.v2/0?context=%7b%22Tid%22%3a%226f15cd97-f6a7-41e3-b2c5-ad4193976476%22%2c%22Oid%22%3a%22eb94fa47-7fce-40ed-bc9a-2e29c75a99ec%22%7d

EE PhD Colloquium - Vishal Anand A G
2025-10-10 10:00 - 11:00 | MMCR, Hall C 241, 1st floor, EE department
Title: Topology, Design and Dynamic Modeling of Resonant-based DC-AC and DC-DC Converters
Speaker: Vishal Anand A G

Mid term Interviews
2025-11-17 - 2025-11-19 | B – 218 and Chairman’s room, EE department
MID TERM INTERVIEWS
17th, 18th and 19th November
Venue : 1. Chairman’s room, Ground floor A wing
2. B-218, 1st floor B wing
