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Chandraprakash Chindam is an Associate Professor in the Department of Mechanical Engineering at the Indian Institute of Technology (IIT) Kanpur. He joined IIT Kanpur as a Visiting Assistant Professor in 2017, became an Assistant Professor from 2018-2024, and was promoted to Associate Professor in 2024. His academic journey includes a PhD in Engineering Science and Mechanics from Pennsylvania State University, an M.Tech in Product Design from IIT Madras, and a B.Tech in Mechanical Engineering also from IIT Madras. Prior to his academic career, he worked as a Project Officer at the Centre for Non-Destructive Evaluation at IIT Madras and as a Technical Manager at TATA Motors' Engineering Research Centre. Education: 2017: PhD in Engineering Science and Mechanics, Pennsylvania State University 2010: M.Tech in Product Design, Mechanical Engineering, IIT Madras 2008: B.Tech in Mechanical Engineering, IIT Madras Dr. Chindam's research focuses on the intersection of wave mechanics, thermal diffusion, and material characterization. His primary research interests include acoustic metamaterials, thermal nondestructive evaluation, soft robotics, multifunctional materials, instrumentation, and computer vision. His work combines computational approaches with experimental techniques to develop innovative materials and characterization methods. He has pioneered research in phononic crystals, acoustic foams, and advanced surface characterization techniques, with applications in noise control, structural health monitoring, and sustainable materials development. His recent publications demonstrate a strong trend toward interdisciplinary research combining materials science, acoustics, computer vision, and sustainable engineering. He has made significant contributions to the understanding of phononic crystals for sound absorption, development of biodegradable acoustic materials from agricultural waste, and computer vision applications for non-destructive evaluation. His work bridges fundamental material properties with practical engineering applications, particularly in the areas of noise control and structural health monitoring. Scientific Awards: Best PhD thesis award 2017 Distinguished Teaching Fellow 2015-16 during PhD Dr. Chindam teaches several courses at IIT Kanpur including ME222A (Nature and properties of materials), ME321A (Advanced mechanics of solids), ME698E (Fabrication and mechanics of thin films), ME621A (Introduction to solid mechanics), ME683A (Techniques in non-destructive evaluation), and ME723A (Wave propagation in solids). His teaching approach integrates theoretical concepts with practical applications, particularly in the areas of material characterization and wave mechanics. While specific grant information isn't detailed in the provided text, his research output suggests involvement in projects related to acoustic metamaterials, non-destructive evaluation techniques, and sustainable material development. His laboratory work focuses on developing advanced characterization techniques for materials, particularly in the areas of acoustic metamaterials and thermal nondestructive evaluation. His team appears to work at the intersection of computational mechanics, experimental characterization, and sustainable materials development, with emphasis on practical applications for noise control and structural health monitoring.

• Acoustic metamaterials
• Thermal nondestructive evaluation
• Soft robotics
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Navaneeth Poonthottathil is an Assistant Professor in the Department of Physics at the Indian Institute of Technology Kanpur (IIT Kanpur), specializing in Experimental High Energy Physics with a focus on Neutrino Physics and Long-baseline neutrino experiments. His research contributes significantly to the Deep Underground Neutrino Experiment (DUNE) collaboration and related neutrino physics initiatives. Dr. Poonthottathil earned his PhD from Cochin University of Science and Technology and Fermilab, followed by postgraduate studies at Cochin University of Science and Technology and undergraduate education at Kannur University. Prior to joining IIT Kanpur, he served as a Postdoctoral Research Associate at Iowa State University (2018-2022), Visiting Physicist at Brookhaven National Lab (2018), Adjunct Faculty at Kannur University (2017), and Visiting Scientist at Fermilab (2012-2016). His research spans multiple areas of high energy physics with particular emphasis on neutrino interaction studies, vertex reconstruction techniques using deep learning, supernova neutrino detection capabilities, and long-baseline neutrino oscillation physics. He has also made contributions to cosmological research, particularly examining scalar field dominated models and dark energy phenomena. His methodological approach frequently integrates machine learning techniques with traditional particle physics analysis methods, demonstrating interdisciplinary innovation. Analysis of his recent publications reveals a strong focus on the DUNE experiment, with significant contributions to detector performance, neutrino interaction measurements, and analysis techniques. His work spans both experimental and theoretical aspects, with publications in journals such as The European Physical Journal C, Physical Review D, and Journal of Cosmology and Astroparticle Physics. He maintains active collaborations with major international physics laboratories including Fermilab and Brookhaven National Laboratory. Dr. Poonthottathil's research has important implications for understanding fundamental particle properties, neutrino characteristics, and cosmological phenomena. His work contributes to advancing detector technologies and analysis methodologies in high energy physics, with potential applications across multiple scientific domains.

• Experimental High Energy Physics
• Neutrino Physics
• Long-baseline neutrino experiments
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Purushottam Kar is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. He completed his PhD from IIT Kanpur in 2014 and his B.Tech. from the same institution in 2008. His academic career includes a post-doctoral research position at Microsoft Research India from July 2013 to August 2015, following a doctoral research internship there from May to August 2012. Dr. Kar's research focuses on the theoretical and practical aspects of machine learning. His primary interests include Machine Learning , Optimization , and Statistical Learning Theory . More specifically, he works on online and stochastic learning and optimization, multi-label classification, high dimensional statistics, kernel methods, and statistical learning theory. His work bridges theoretical foundations with practical applications in artificial intelligence. His recent publications demonstrate a strong focus on advancing machine learning methodologies, particularly in robust regression, non-decomposable performance measures, precision optimization, multi-label classification, and the theoretical foundations of online learning. His research spans both theoretical developments in statistical learning and practical implementations for complex classification problems. International Conference on Machine Learning (ICML) Reviewer Award (2015) Indian Unit of Pattern Recognition and Artificial Intelligence (IUPRAI) Doctoral Dissertation Award (2014) Yahoo! Key Scientific Challenges (Yahoo! KSC) Program Honourable Mention (2012) Microsoft Research (MSR India) PhD Fellowship (2010) Academic Excellence Awards by IIT Kanpur (2005, 2006) Dr. Kar teaches courses in Machine Learning, Online Learning, and Statistical Learning Theory. His research profile suggests he mentors PhD and Master's students in machine learning and related fields. His work with Microsoft Research indicates significant industry collaborations and grant funding in artificial intelligence research. His office is located in Room 509 of the CSE Extension building (Rajeev Motwani Building) at IIT Kanpur.

• Machine Learning
• Optimization
• Statistical Learning Theory
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K S Venkatesh is a Professor in the Department of Electrical Engineering at Indian Institute of Technology Kanpur (IIT Kanpur). His research spans multiple areas of signal and image processing with applications in computer vision and robotics. Education: PhD from IIT Kanpur M.Tech from IIT Kanpur B.E. from Bangalore University Professor Venkatesh's research focuses on signal processing, image and video processing, computer vision with applications in robotics, and signal and system theory . His work bridges theoretical foundations with practical applications, particularly in the domain of visual perception systems. He has made significant contributions to areas such as object occlusion handling, gesture recognition under challenging lighting conditions, image enhancement techniques, and efficient stereo depth computation. His recent publications demonstrate a strong focus on computer vision problems with practical applications. The research shows progression from fundamental signal processing techniques to more complex vision systems capable of operating in real-world conditions. Several of his papers have received recognition, including Best Paper Awards at prominent conferences. Scientific Awards: Best Paper Award at Second Michael Faraday IET India Summit 2013 Best Paper Award at IEEE INDICON, IIT Bombay, December 2013 Professor Venkatesh maintains active research collaborations, as evidenced by his co-authored publications with researchers from various institutions. His work appears in prestigious international conferences including IJCAI, IEEE INDICON, and specialized computer vision conferences. His office is located in room 207-A/ACES at the Department of Electrical Engineering, IIT Kanpur, where he leads research activities in signal and image processing.

• Signal processing
• Image processing
• Video processing
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Avijit Khanra is an Assistant Professor in the Department of Management Sciences at the Indian Institute of Technology Kanpur. He holds a PhD (FPM) in Production & Quantitative Methods from IIM Ahmedabad and a BE in Mechanical Engineering from Jadavpur University, bringing both academic rigor and industry experience to his role. His research focuses on Scheduling, Stochastic Systems, and Production & Supply Chain Management, with specific interests in Operations Research and Operations Management. His work often explores optimization problems in supply chain contexts, particularly related to the newsvendor model and job-shop scheduling. His research demonstrates a strong theoretical foundation with practical applications in manufacturing and supply chain systems. Dr. Khanra's scholarly work has been published in leading operations research journals including Operations Research Letters and the European Journal of Operational Research, reflecting his expertise in mathematical modeling and optimization techniques for complex business problems. Excellence-in-Teaching Award 2021 (by IIT Kanpur) Prof. Tirath Gupta Memorial Thesis Award 2014-15 (by IIM Ahmedabad) Industrial Finance Corporation of India Thesis Proposal Award 2012-13 (by IIM Ahmedabad) Dr. Khanra has received significant sponsored research funding from Mitsubishi Heavy Industries for projects on Large-scale Job-shop Scheduling and Machine Learning Applications in Job-shop Scheduling. Prior to his academic career, he worked as an Engineer/Manager in Manufacturing at Tata Motors Ltd from 2005-2009, providing him with valuable industry experience that informs his research and teaching approach. He is actively affiliated with the Analytics Society of India, Operational Research Society of India, and Society of Operations Management, India. He teaches courses in Applied Probability, Operations Research, and Operations Management, focusing on both theoretical foundations and practical applications of quantitative methods in business decision-making.

• Scheduling
• Stochastic Systems
• Production & Supply Chain Management
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Anikesh Pal is an Associate Professor in the Department of Mechanical Engineering at Indian Institute of Technology Kanpur (IIT Kanpur). He earned his Ph.D. in 2016 from the University of California San Diego, USA, and holds an M.Tech (2010) and B.Tech (2007) from IIT Kanpur and Kalyani Government Engineering College, India, respectively. Research Interests: Turbulence, Machine Learning, Computational Fluid Dynamics (CFD), Atmospheric and Oceanic flows, Climate dynamics Contact: Office: 333, IIT Kanpur | Office Phone: 0512-259-6418 | Email: pala@iitk.ac.in

• Turbulence
• Machine Learning
• Computational Fluid Dynamics (CFD)
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Professor Zafar Ali Khan Mohammed is affiliated with the Department of Electrical Engineering at Indian Institute of Technology Hyderabad . His research spans advanced wireless communications, cyber-physical systems, and commensal radar technologies. Ph.D. from Indian Institute of Science, Bengaluru Visiting Professor at UC Irvine (2014), University of Southampton (2015) His research interests focus on: 6G Communication Systems Cyber-Physical Systems (CPS) Security Commensal Radar Applications Dynamic Spectrum Allocation Scientific Awards include: INAE Young Engineer Award (2006) Visvesvarya Young Faculty Research Fellowship (2015-2017) IEEE COMSOC/SP Chapter Appreciation Award (2013-2014) His projects involve JICA-funded M2Smart and multiple DST/DeiTy-funded initiatives in cognitive radio and CPS security. Publications highlight innovations in massive MIMO detection , cooperative spectrum sensing , and communication infrastructure sensing .

• Wireless Communications
• MIMO Decoding
• Commensal Radar
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Dr. Mallikarjun Tatipamula is a distinguished academic professional currently serving as a Professor. His work spans cutting-edge technological domains at the intersection of telecommunications, cloud computing, and artificial intelligence. Research Focus: 5G networking and distributed systems Technical Expertise: Edge computing, SDN/NFV architectures Technological Innovation: AI/ML integration in IoT and network infrastructure

• 5G
• Distributed Cloud Computing
• Edge Computing
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Arnab Chakraborty is a Professor at the Indian Statistical Institute (ISI), where he teaches advanced courses in statistics and probability. His teaching portfolio includes Probability I & II (scheduled for 2025), Statistical Methods, Linear Statistical Models, Numerical Analysis, and specialized courses in Multivariate Statistics and Categorical Data Analysis. Dr. Chakraborty's research expertise spans several key areas of modern statistics: Multivariate Statistical Analysis with focus on high-dimensional data Copula and Vine modeling for complex dependence structures Machine Learning algorithms including SVM, CART, and k-NN methods Statistical pattern recognition and classification techniques Data mining methodologies and applications His scholarly contributions include comprehensive educational materials on advanced statistical topics, particularly detailed expositions on regular vines and their applications in statistical modeling. Dr. Chakraborty has supervised multiple graduate students on research projects involving printed digit recognition, handwritten letter classification, and kernel methods. Dr. Chakraborty emphasizes practical application of statistical methods, incorporating real-world data examples and computational implementations using R. His teaching philosophy values presentation skills development, often replacing traditional examinations with student presentations on advanced topics. He has also contributed to statistical education through YouTube content and various tutorials on computational statistics.

• Multivariate Statistical Analysis
• Probability Theory
• Machine Learning
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Kunal Narayan Chaudhury serves as an Associate Professor in the Department of Electrical Engineering at the Indian Institute of Science (IISc), Bangalore, specializing in Computational Imaging, Convex Optimization, and Machine Learning. His educational journey includes: Postdoc from Princeton University (2011-2014) PhD from Swiss Federal Institute of Technology Lausanne (EPFL), 2011 ME from Indian Institute of Science, India, 2005 BE from Jadavpur University, Kolkata, India, 2003 Chaudhury's research integrates algorithmic innovation with practical applications: Computational Imaging focuses on image reconstruction techniques; Convex Optimization develops scalable solvers for complex systems; and Machine Learning leverages optimization frameworks for model training. His work bridges theoretical foundations and real-world implementation in signal processing domains. Notable honors include: SERB-STAR Research Award (2021) from DST, Government of India Swiss National Science Foundation Postdoctoral Fellowship Gold medal for M.E. in System Science and Automation at IISc He actively contributes to academia through teaching core courses like Linear Algebra, Advanced Convex Optimization, and Computational Linear Algebra, while leading the Lab for Imaging Sciences and Algorithms (LISA) which drives interdisciplinary projects in imaging algorithm development.

• Computational Imaging
• Convex Optimization
• Machine Learning

Dr. Akanksha Rathore serves as Assistant Professor in the Department of Computer Science and Information Systems at Birla Institute of Technology & Science, Pilani (Hyderabad Campus) since April 2024. Her research bridges computational sciences with ecological applications through the DSCOvER Lab, focusing on data-driven approaches to understand natural systems and address environmental challenges. Her educational background includes a PhD from Indian Institute of Science, Bangalore (2015-2021) and B.Tech in Information Technology from Malaviya National Institute of Technology, Jaipur (2008-2012). Prior professional experience encompasses postdoctoral research at Max Planck Institute of Animal Behavior (Germany), project work at Nature Conservation Foundation, and software development at SAP Labs. Research centers on two interconnected domains: Collective Intelligence : Computational modeling of animal group coordination through collective movement, escape dynamics, and synchrony using multi-modal data collection (UAVs, GPS) and network analysis Eco-Informatics : Application of AI and machine learning for wildlife monitoring, habitat quality assessment, and developing early-warning indicators for environmental stress using drone and remote sensing technologies Currently leading an Indo-German project ( Emergent Collective Intelligence for Ephemeral Resource Tracking ) funded by IGSTC-WISER, she integrates reinforcement learning with agent-based modeling to study resource tracking in dynamic ecosystems. The DSCOvER Lab actively recruits Junior Research Fellows and mentors MSc students through interdisciplinary collaborations with Max Planck Institute of Animal Behavior.

• Collective Intelligence
• Eco-Informatics
• Computational Ecology
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