جست‌وجوی استاد و پژوهشگر

• Network Performance
• Computer Networks
• Cloud Computing
• +۷ مورد دیگر

Dr. Theophilus A. Benson is a Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU) and CMU-Africa. His research focuses on improving network performance and availability in data centers, clouds, and edge networks. He leads projects addressing the digital divide in Africa through initiatives like the African Internet Observatory (AIO), which analyzes connectivity challenges and infrastructure resilience. His work spans programmable networks (eBPF/P4), CDN optimizations, and network management frameworks. Education: B.S. from Tufts University, Ph.D. from University of Wisconsin-Madison, Postdoc at Princeton University. Research Interests: Network state management, programmable substrates, digital equity, measurement systems, and network security. His group develops tools like NetEdit (eBPF management), JSBench (mobile web performance), and the African Internet Observatory's probe network. Recent Trends: Publications emphasize eBPF management frameworks, African network analysis, and data-driven CDN improvements. Key projects include subsea cable impact studies, QUIC protocol analysis, and deploying measurement probes across Africa. Awards: NSF CAREER Award, Google/FA Faculty Awards, SIGCOMM Test of Time Award, and DARPA ISAT membership. Advising & Grants: Active in mentoring MS/PhD students and postdocs. Current grants include NSF funding for data-driven web performance and IoT security. Collaborations with Meta and industry partners enhance real-world system deployments. Labs & Teams: Leads the AIO initiative with local African stakeholders. Research group includes collaborators from National Taiwan University and partnerships with institutions like TU Delft (keynote on eBPF).

• Biomedical Signal Processing
• Electroencephalography (EEG)
• Mental Workload Assessment
• +۱۲ مورد دیگر

Tarmo Lipping is a Professor in the Department of Computer Science and Engineering at the Faculty of Information Technology and Electrical Engineering, University of Oulu. His work bridges computing sciences with biomedical engineering, environmental modelling, and data-driven societal applications. Doctor of Science (Technology), Information Technology – Awarded 14 Feb 2001 Master of Science (Technology), Information Technology – Awarded 10 Sept 1993 His research focuses on electroencephalography (EEG) , mental workload assessment , depth of anesthesia monitoring , and machine learning applications in healthcare and human-computer interaction. He also contributes to environmental informatics , particularly in land uplift modelling and radionuclide transport , aligning with UN Sustainable Development Goals. Recent publications highlight trends in transformer networks for EEG analysis , wearable HCI systems , data-driven food safety , and participatory municipal governance . His work integrates deep learning, signal processing, and real-world deployment. Scientific awards include: CIMO opettajavaihto (2017) Lipping has supervised numerous master’s students and served as an examiner in diverse topics including data vault modelling , telecom revenue estimation , and EEG hyperscanning . He has evaluated funding applications, acted as a journal reviewer (65 times), and contributed to editorial work. His activities reflect strong engagement in academic service and interdisciplinary research mentorship. He has contributed datasets on Fennoscandian land uplift , lake isolation , and archaeological shorelines to PANGAEA, supporting open science in geosciences and environmental history.

Kristin Y. Pettersenمشاهده پروفایل

استاد

• Nonlinear control theory
• Marine robotics
• Snake robotics
• +۵ مورد دیگر

Kristin Y. Pettersen is a Professor at the Norwegian University of Science and Technology (NTNU) in the Department of Engineering Cybernetics, Faculty of Information Technology and Electrical Engineering. She holds a PhD and MSc in Engineering Cybernetics from NTNU and serves as an Adjunct Professor at the Norwegian Defence Research Establishment (FFI). She co-founded and led Eelume AS as its first CEO. PhD in Engineering Cybernetics, NTNU MSc in Engineering Cybernetics, NTNU Her research focuses on nonlinear control theory, motion control of mechanical systems, and marine robotics. Key areas include autonomous vehicles, underactuated systems, and cooperative control. Her recent work involves snake robotics, vehicle-manipulator systems, and safety-critical control algorithms. Her publications demonstrate trends in marine robotics , nonlinear control systems , autonomous navigation , formation control , and adaptive algorithms . Emerging topics include energy-shaping control , extremum-seeking optimization , and task-priority frameworks for complex robotic systems. 2025: Norwegian Academy of Science and Letters (DNVA) 2020: ERC Advanced Grant 2017: IEEE Fellow 2016-2021: Board member, Eelume AS 2013-2023: Key scientist, NTNU AMOS She has supervised 30 PhD graduates and currently mentors 16 PhD candidates. Her grants include ERC PoC UR4energy (€150k), ERC AdG CRÈME (€2.5M), and CAROS (NOK 45M) for subsea autonomy. She leads teams at NTNU's Applied Underwater Robotics Laboratory and contributes to the Cluster of Excellence IntCDC.

Burak Kurkcuمشاهده پروفایل

استادیار

• Robust Control
• Soft Robotics
• Switched Neural Networks
• +۳ مورد دیگر

Burak Kurkcu is an Assistant Professor in the Department of Electrical and Computer Engineering at Santa Clara University's School of Engineering. He previously served as an Assistant Professor at Hacettepe University and as a Senior Control System Design Engineer at Aselsan Inc. Education: Ph.D., TOBB University of Economics and Technology (2019) M.S., TOBB University of Economics and Technology (2015) B.S., Istanbul Technical University (2010) Research Interests: Dr. Kurkcu specializes in robust control systems, soft robotics, switched neural networks, and autonomous systems. His work focuses on disturbance estimation, simultaneous learning algorithms, and control of nonlinear systems. Recent Publication Trends: His research includes soft pneumatic actuator modeling, disturbance observer-based control methods, and evolutionary optimization for state-space models. Key themes involve soft robotics, autonomous control, and computational intelligence applications. Scientific Awards: IEEE Turkey Ph.D. Thesis Award (2020) Editorial Roles: Associate Editor for TIMC, Measurement and Control , and Turkish Journal of Electrical Engineering and Computer Science . Principal Investigator for defense-related control system projects.

• Machine Learning
• Artificial Intelligence
• Data Science
• +۵ مورد دیگر

Pan Xu is a tenure-track assistant professor with joint appointments in the Department of Biostatistics & Bioinformatics, Department of Computer Science, and Department of Electrical & Computer Engineering at Duke University's Pratt School of Engineering. Prior to joining Duke, he was a Postdoctoral Scholar Research Associate at the California Institute of Technology, and he earned his Ph.D. in Computer Science from UCLA. His research bridges theoretical foundations with practical applications in machine learning and artificial intelligence. Dr. Xu's research focuses on developing computationally- and data-efficient machine learning algorithms with strong theoretical guarantees, particularly in reinforcement learning, optimization, and high-dimensional statistics. His work addresses two fundamental challenges in sequential decision-making: efficient exploration with minimal interactions and robustness against distributional shifts. His research spans theoretical algorithm design, practical implementation, and real-world applications in bioinformatics and healthcare. His publication record demonstrates consistent high-impact contributions to top-tier conferences including ICML, NeurIPS, ICLR, AAAI, and AISTATS. The research trends show a progression from foundational work in non-convex optimization and multi-armed bandits toward increasingly sophisticated frameworks for robust reinforcement learning, with particular emphasis on distributional robustness, efficient exploration strategies, and practical applications. His work often bridges theoretical guarantees with empirical validation. NSF award on approximate sampling based exploration for sequential decision making Whitehead Scholar award from Duke University School of Medicine PIMCO Postdoctoral Fellowship in Data Science UCLA Outstanding Graduate Student Research Award Rising Stars in Data Science by University of Chicago Best Paper Award for Queer In AI: A Case Study in Community-Led Participatory AI at FAccT 2023 Featured Certification for Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits at TMLR Oral Presentation award at AAAI 2024 Dr. Xu actively mentors students and researchers, seeking highly motivated individuals with strong mathematical backgrounds for Ph.D. programs in Biostatistics & Bioinformatics, Computer Science, and Electrical & Computer Engineering at Duke. He has received multiple research grants including an NSF award on approximate sampling based exploration for sequential decision making. His service to the academic community includes roles as area chair for NeurIPS, ICML, ICLR, and AISTATS, as well as action editor for Transactions on Machine Learning Research. His research group develops algorithms that address fundamental challenges in sequential decision-making, with applications spanning healthcare, bioinformatics, and multi-agent systems. Current research directions include distributionally robust reinforcement learning, efficient exploration strategies, and applications of graph neural networks to biological problems.

Nathalia Peixotoمشاهده پروفایل

دانشیار

• Neural Engineering
• Brain-Computer Interfaces
• Biomedical Sensors and Actuators
• +۴ مورد دیگر

Nathalia Peixoto is an Associate Professor in the Department of Electrical and Computer Engineering and Affiliate Faculty in Bioengineering at George Mason University. Her work bridges neural engineering, biomedical applications, and assistive technology development with international collaborations across Israel, Ireland, Peru, and Korea. Educational background: PhD in Electrical Engineering, Universidade de Sao Paulo MS, University of Campinas Research Interests: Dr. Peixoto specializes in neural engineering with focus on brain-computer interfaces using wearable devices. Her lab develops: Neural prosthetics and implantable systems Bioimpedance-based medical sensors Low-cost electrophysiological recording platforms Community-centered engineering design solutions Publication Trends: Her 2022-2025 publications demonstrate strong interdisciplinary convergence between neuroscience, biomedical engineering, and AI. Key trends include machine learning for seizure detection in zebrafish models, electrochemical optimization of neural interfaces, and community-engaged design projects addressing societal challenges through transdisciplinary graduate training. Grants and Projects: Principal investigator for multiple NSF-funded initiatives: NRT-HDR: Transdisciplinary Graduate Training (2019-2024) Smart and Connected Communities: Networked Devices (2017-2019) Bioimpedance for retinal implants (2015-2017) C2MW: Classroom to Makers Week (2015-2016) Additional funding from VA STEM CoNNECT and Longwood University. Laboratory: The Neural Engineering Lab integrates chemistry, physics, and engineering disciplines through team-based projects involving high school to graduate students. Current work includes sustainable food-waste solutions, tremor-capturing robots for low-resource areas, and neural implants with international academic partnerships.

• Power Electronics
• Building Automation
• Smart Grids
• +۷ مورد دیگر

Dr. Andrzej Ożadowicz is a University Professor at the Department of Power Electronics and Automation of Energy Conversion Systems within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków, Poland. His office is located in room 510, building C-1, with contact details including phone +48 12 617 50 11 and email ozadow@agh.edu.pl. He holds PhD, DSc, and Engineering degrees, reflecting his dual expertise in academic research and practical engineering applications. His research spans Power Electronics, Building Automation, Smart Grids, and IoT-driven energy systems. Key interests include energy efficiency optimization through digital twins and BIM, distributed energy resource integration , and AI-enhanced demand management . Notably, he pioneers applications of deep reinforcement learning in home energy systems and develops frameworks for Smart Readiness Indicator implementation. His work bridges theoretical innovation with practical case studies in building thermal modeling and dynamic façade systems. Recent publications (2021-2025) reveal three dominant trends: (1) Convergence of digital twin technology with building automation for real-time energy management; (2) Critical analysis of IoT security and interoperability in smart infrastructure; (3) Pedagogical innovations in engineering education through blended learning methodologies post-COVID-19. His scholarly output demonstrates consistent focus on energy transition challenges and smart grid evolution. Professor Ożadowicz actively contributes to the Discipline Council for Automation, Electronics, Electrical Engineering and Space Technologies at AGH. He is instrumental in the AutBudNet initiative —a network of certified laboratories for energy efficiency assessment that implements "learning by doing" principles in building automation education. His work with this consortium emphasizes practical validation of smart grid technologies and demand response systems.

Thomas Longdenمشاهده پروفایل

دانشیار

• Brain blood flow
• Neurovascular coupling
• Functional hyperemia
• +۱۲ مورد دیگر

Thomas Longden is an Associate Professor in the Department of Physiology at the University of Maryland School of Medicine. He leads a research group focused on neurovascular interactions in health and disease, with particular emphasis on understanding how blood flows through the brain under normal conditions and how this process is disrupted in diseases like Alzheimer's. Dr. Longden received his B.Sc (Hons) and Ph.D. in Pharmacology from the University of Manchester in the UK (2006 and 2010), followed by postdoctoral training at the University of Vermont under Professor Mark Nelson (2011-2015). He was promoted to Assistant Professor at Vermont in 2015 before joining the University of Maryland in February 2019. His research focuses on the control of blood flow in the brain, particularly the mechanisms of neurovascular coupling where neuronal activity triggers changes in blood flow. His lab has made significant discoveries including identifying the brain's capillary network as a 'sensory web' that translates neural activity into vasodilatory electrical signals, and demonstrating how pericytes function as metabolic sentinels that control blood flow through KATP channel-dependent mechanisms. Analysis of Dr. Longden's recent publications reveals a strong focus on pericyte function in neurovascular coupling, electrical signaling in the capillary network, and how these mechanisms are disrupted in Alzheimer's disease and other dementias. His work increasingly incorporates advanced imaging techniques, computational approaches, and innovative tools to study vascular plasticity. 2023: Fellow of the American Physiological Society Cardiovascular Section 2020: NIH Director's New Innovator Award 2017: American Heart Association Scientist Development Grant Multiple travel awards and postdoctoral fellowships Dr. Longden currently mentors several graduate students and postdoctoral fellows in the Longden Lab, which is supported by multiple NIH grants including an NINDS New Innovator Award and an NIA R01 grant. His lab develops and employs advanced techniques including multiphoton microscopy, electrophysiology, optogenetics, and molecular biology to study vascular cells in the brain. The lab is particularly focused on understanding vascular signaling plasticity and how pericytes control brain blood flow in health and Alzheimer's disease.

Kareen L K Coulombeمشاهده پروفایل

دانشیار

• Cardiovascular Regenerative Engineering
• Tissue Engineering
• Biomaterials
• +۳ مورد دیگر

Kareen L.K. Coulombe is an Associate Professor of Engineering at Brown University, affiliated with the Institute for Biology, Engineering and Medicine . She collaborates with researchers from the Department of Medicine and institutions like the University of Edinburgh and ScitoVation, Inc . Education: B.S. in Biomedical Engineering, Summa Cum Laude (University of Rochester, 2001) Ph.D. in Bioengineering (University of Washington, 2007) Her research focuses on cardiovascular regenerative engineering , including: Developing human iPSC-derived cardiac tissues for heart attack therapy Creating anisotropic biomaterial scaffolds to enhance tissue integration Designing in vitro cardiotoxicity testing platforms for pharmaceuticals and environmental chemicals Optimizing electrical coupling between engineered and native heart tissue Recent publications highlight advancements in: Predictive 3D cardiac microtissue models for arrhythmic risk assessment Custom polycaprolactone scaffolds for tailored mechanical properties Immunomodulatory biomaterials that reshape cardiac repair processes Computational strain continuum modeling of cardiac tissue mechanics Scientific Awards: 2023 - Brown University Innovation of the Year 2021 - NSF CAREER Award & Young Innovator Award (BMES) 2019 - Dean’s Award for Excellence in Mentoring 2017-2012 - Rising Star Award & NIH Pathway to Independence K99/R00 Dr. Coulombe mentors students through programs like: Brown Leadership Alliance (undergraduate research) Tougaloo College Partnership (student development) NIH-IMSD Programs (graduate mentoring)

Edmund Hollisمشاهده پروفایل

استادیار

• Spinal Cord Injury
• Neural Circuit Remodeling
• Motor Recovery
• +۵ مورد دیگر

Edmund Hollis is an Assistant Professor of Neuroscience at the Brain and Mind Research Institute within Weill Cornell Medical College . He has been affiliated with the institution since 2016 and leads a research lab focused on neural circuit remodeling and recovery after spinal cord injury. Education: Ph.D. in Neurosciences, University of California, San Diego, School of Medicine (2008) B.S., University of Southern California (2002) Research Interests: Hollis's lab investigates the neural mechanisms underlying movement and recovery from spinal cord injury. Using genetic, molecular, and behavioral tools, along with optogenetics and optical imaging, the lab explores how neural circuits respond to injury and how they can be therapeutically enhanced. Key areas include cortical plasticity, axon regeneration, astrocyte responses, and neuromodulation of motor circuits. Scientific Contributions: His recent publications demonstrate a strong focus on corticospinal tract function, spinal interneuron modulation, and the use of advanced behavioral assays like the Kinematic Deviation Index (KDI) to assess motor recovery in rodent models. His work spans from molecular signaling pathways (e.g., RANKL, Wnt, IGF-I) to large-scale circuit remodeling and rehabilitation strategies. Collaborations & External Roles: Hollis has professional affiliations with Texas A&M University and the National Institutes of Health, and has served as a speaker and consultant for various academic and governmental organizations.

Hao Yangمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Transportation Safety
• Transportation Equity
• +۴ مورد دیگر

Hao Yang is an Assistant Professor in the Department of Civil and Systems Engineering at Johns Hopkins University, with dual affiliations at the Johns Hopkins Data Science and AI Institute and the Johns Hopkins Institute for Assured Autonomy. His research develops Trustworthy Machine Learning methods to enhance urban mobility systems, focusing on traffic safety, equity, and sustainability through ethical AI and human-machine cooperative systems. Yang earned dual bachelor's degrees in Electrical and Computer Engineering from Beijing University of Posts and Telecommunications and the University of London, followed by a Ph.D. in Civil Engineering (Transportation) from the University of Washington. His educational background bridges telecommunications, electrical engineering, and transportation systems. His research integrates spatio-temporal modeling, assured autonomous systems, and multimodal representation learning to address transportation equity and safety. Key projects include edge-AI-powered traffic surveillance, real-time crash identification, and cooperative signal assistance for vulnerable road users. His work emphasizes ethical AI deployment in cyber-physical infrastructure to create sustainable urban mobility solutions. Recent publications reveal a strategic shift toward large language models and multimodal AI for transportation challenges, with strong emphasis on explainability, reliability, and equity in traffic crash prediction, flow forecasting, and autonomous driving systems. This evolution demonstrates his commitment to adapting cutting-edge AI for real-world transportation problems. Yang's scientific contributions have earned significant recognition: Michael Kyte Outstanding Student of the Year Award (2022) High-Value Research Award from AASHTO (2022) Best Paper Award from TRB Information Systems Committee (2023) Best and Outstanding Dissertation Awards (2024) IEEE DTPI Outstanding Paper Award (2022) TRANSFOR22 Data Competition 2nd place (2022) ASCE Bridges Photo Contest First Place (2021) He actively mentors graduate researchers and seeks 2-3 PhD students for Fall 2025 to advance trustworthy AI in transportation. His research is supported by NSF, USDOT, and AASHTO grants including the Real-Time Truck Parking Information System project that received the High-Value Research Award. Current work focuses on edge-AI for traffic safety and multimodal data integration. Yang leads research within Johns Hopkins' Data Science and AI Institute and Institute for Assured Autonomy, collaborating with Transportation Research Board committees. His lab develops real-time perception systems using edge computing and representation learning, with active projects on non-motorized user safety and equitable traffic management for people with disabilities.

Anne Koelewijnمشاهده پروفایل

استادیار

• Human Movement
• Optimal Control
• In the wild movement analysis
• +۴ مورد دیگر

Dr. Anne Koelewijn is an Assistant Professor leading the Biomechanical Motion Analysis and Creation (BioMAC) group at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) since 2019. Her research bridges biomechanics, computational modeling, and wearable technology to analyze human movement. She holds a Junior Professorship in Computational Movement Science within the Department of Electrical-Electronic-Communication Engineering. Her educational background includes a Doctor of Engineering in Mechanical Engineering from Cleveland State University (focus: prosthesis design and gait simulations), an MSc in Mechanical Engineering (BioMechanical Design specialization), and a BSc in Aerospace Engineering , both from Delft University of Technology. She completed postdoctoral work at École Polytechnique Fédérale de Lausanne on neuromuscular control. Research interests center on human movement optimization , neuromuscular control mechanisms , and in-the-wild movement analysis . Her work integrates musculoskeletal modeling, optimal control theory, and machine learning to study gait adaptations, exoskeleton design, and pathological movement patterns (e.g., Parkinson’s disease). Publications emphasize predictive simulations , wearable sensor technology , and biomechanical energy optimization , with recent advances in radar-based motion capture, inertial pose estimation, and digital twin applications for medical engineering. Promising Scientist Award , International Society of Biomechanics (2023) Best Paper Award , 5th International Symposium on Wearable Robotics (2020) She leads the BioMAC research group, focusing on computational methods for movement science and collaborating internationally on projects involving exoskeletons, injury prevention, and neuroprosthetics.

Jan Engelhardtمشاهده پروفایل

استادیار

• Electric Vehicle Systems
• Battery Energy Storage
• Power System Integration
• +۴ مورد دیگر

Jan Engelhardt serves as Assistant Professor in the Department of Wind and Energy Systems at the Technical University of Denmark (DTU), specializing in e-mobility and prosumer integration within power and energy systems. His work directly contributes to UN Sustainable Development Goals through grid modernization and renewable energy integration. His research focuses on electric vehicle-grid integration , with core expertise in battery energy storage systems, DC microgrids, and virtual power plants. Key investigation areas include smart charging architectures for EV clusters, frequency response services, phase balancing techniques, and V2X management strategies. His experimental approach combines theoretical modeling with real-world validation of control systems for enhanced grid stability. Recent publications demonstrate a clear trend toward distributed control solutions for electric vehicle clusters, emphasizing user-centric scheduling while providing grid services. His work increasingly incorporates experimental validation of frequency control architectures and explores high-power reconfigurable battery applications for fast-charging infrastructure. Dr. Engelhardt actively supervises PhD candidates on projects including Hybrid Energy Solutions (EV charging and battery storage synergies) and Provision of Grid Services through EV Aggregation. He participates in major EU initiatives like EV4EU (Electric Vehicles Management for Carbon Neutrality in Europe) and GREAT (GRid Enhancement for Ancillaries in Tomorrow’s power systems), securing substantial research funding through Horizon Europe frameworks.

Eleni Staiمشاهده پروفایل

استادیار

• Optimization
• Communications Networks
• Data Analytics
• +۴ مورد دیگر

Eleni Stai is an Assistant Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), affiliated with the Division of Communication, Electronic and Information Engineering. She holds advanced degrees in Electrical Engineering, Mathematics, and Applied Mathematical Sciences from NTUA and the National and Kapodistrian University of Athens. Her academic credentials include: Diploma in Electrical and Computer Engineering, NTUA (2009) B.Sc. in Mathematics, National and Kapodistrian University of Athens (2013) M.Sc. in Applied Mathematical Sciences, NTUA (2014) Ph.D. in Electrical Engineering, NTUA (2015) Dr. Stai's research integrates advanced optimization techniques with communications networks and energy systems. She develops stochastic and deterministic optimization frameworks for network resource allocation, data analytics on complex topologies, and smart-grid control applications. Her work bridges theoretical foundations with practical implementations in energy-harvesting networks, network slicing, and reinforcement learning for distributed systems. Analysis of her recent publications reveals dominant research thrusts in AI-driven network management (particularly O-RAN and network slicing), energy-integrated communications, and optimization of energy communities. A significant portion of her work addresses the convergence of 5G/6G networking with power systems, emphasizing real-time control and sustainability. Her scientific contributions have been recognized through prestigious awards: Chorafas Foundation Best Ph.D. Thesis award Thomaidis Foundation Best M.Sc. Thesis award Best Paper Award at ICT 2016 Best Presenter Award at IEEE ENERGYCON 2022 Dr. Stai serves on technical program committees for major international conferences and has co-authored the book "Evolutionary Dynamics of Complex Communications Networks". She teaches undergraduate courses in Queuing Systems, Computer Networks, and Social Network Analysis, reflecting her expertise in network theory and applications. Her research trajectory demonstrates continuous evolution from fundamental network optimization to AI-enhanced solutions for next-generation communication-energy systems. Her work builds upon her postdoctoral experience at EPFL (2016-2020) and ETH Zurich (2020-2023), where she developed advanced frameworks for communications networks and energy systems.

• Computational tomographic reconstructions
• subsurface imaging
• shape/target tracking
• +۸ مورد دیگر

Naren Naik is a Professor in the Department of Electrical Engineering at the Indian Institute of Technology (IIT) Kanpur, specializing in computational tomographic reconstructions and analysis for subsurface imaging and shape/target tracking. His educational background includes: PhD from the Indian Institute of Science (IISc) Bangalore in 2000 M.E. in Electronics and Communication Engineering from IISc Bangalore in 1992 B.Sc. from Bangalore in 1988 Professor Naik's research focuses on development and analysis of reconstruction algorithms for nonlinear tomography , with particular emphasis on shape-based and dynamic tomography, tracking and battlefield surveillance, and numerical solutions to partial differential equations in electromagnetics. His work spans multiple imaging modalities including subsurface imaging with Ground Penetrating Radar (GPR), fluorescence optics, electrical impedance tomography, and photoacoustic tomography. His research bridges theoretical mathematics with practical applications in electromagnetic imaging and target tracking systems, addressing complex inverse problems in computational imaging. His publication record shows a clear progression from electromagnetic tomography to advanced Kalman filtering techniques for target tracking applications. The most recent works focus on wireless sensor networks and maneuvering target tracking, demonstrating his ability to adapt theoretical frameworks to evolving technological contexts while maintaining mathematical rigor in solving inverse problems. His professional recognition includes: Invited presentation at the special session on advances in model based inversion at the 2011 IEEE AP-S International Symposium on Antennas and Propagation Professor Naik maintains an active research program with consistent publication output in high-impact journals and conferences. His work demonstrates strong interdisciplinary collaboration, particularly with researchers in electromagnetics, signal processing, and imaging sciences. His research has significant applications in defense technology (battlefield surveillance), medical imaging, and subsurface exploration systems, contributing to both theoretical advances and practical implementations in these fields. He is based in Office 303A ACES (Advanced Centre for Electronic Systems) at the Department of Electrical Engineering, IIT Kanpur, where he leads research activities in computational imaging and tomographic reconstruction.