Professor Subrahmanya Sastry Challa is affiliated with the Department of Mathematics at Indian Institute of Technology Hyderabad. His academic journey includes a PhD from IIT Kanpur under Prof. P. C. Das, an M.Sc(Tech) from JNT University, and a B.Sc from Hindu College, Machilipatnam. Research Focus: He specializes in Wavelets and Sparse Optimization Theory Frame Theory and Data-driven Learning Methods Applications in Medical Imaging and Signal Processing His recent work explores sparsity-driven optimization techniques with applications in tomography, ECG signal recovery, and machine learning algorithms. Publications & Collaborations: He has contributed to advancements in compressive sensing, inverse problems, and numerical linear algebra through collaborations with researchers like Dr. Phanindra Jampana and Dr. Praveen Pradhan. Key journals include IEEE Transactions on Signal Processing , Inverse Problems , and Neurocomputing . Teaching: Courses taught include Wavelets & Applications, Compressive Sensing, Numerical Linear Algebra, and Mathematics Behind Machine Learning, emphasizing both theoretical and applied aspects. Administrative Roles: Served as Associate HoD/HoD (2010-2014), Chief Vigilance Officer (2015-2019), and participated in policy-drafting committees during IIT Hyderabad's formative years.
Dr Francesca Pianosi is an Associate Professor in Water & Environmental Engineering at the University of Bristol 's School of Civil, Aerospace and Design Engineering. She contributes to the Cabot Institute for the Environment and leads research on data analysis, mathematical modelling, and uncertainty quantification for hydrology and water engineering. Specialises in simulation and optimisation methods for water resource management Focuses on uncertainty propagation in natural hazard models Developed the open-source SAFE Toolbox for sensitivity analysis Research Trends Her recent publications (2023-2025) demonstrate expertise in: Groundwater flow and recharge in data-scarce regions Digital Twin applications for watershed management Climate change impact on landslides and droughts Multi-objective optimisation for reservoir operations Integration of machine learning with hydrological models Scientific Awards Arne Richter Award for Outstanding Young Scientists (2015) Best Research Oriented Paper - Journal of Water Resources Planning and Management (2024) Early Career Research Excellence (ECRE) award (2014) Francesca leads the Water Management and Adaptation based on Watershed Digital Twins project (2024-2027) and contributes to the USARIS project on uncertainty quantification for infrastructure systems (2023-2025).
Nectarios Koziris is a Professor at the Department of Computer Science , National Technical University of Athens (NTUA) , and former Dean of the School of Electrical and Computer Engineering . His research focuses on Parallel and Distributed Systems , Computer Architecture , and Cloud Computing . Key Research Themes: Compiler-OS-Architecture Interaction, Datacenter Hyperconvergence, Sparse Matrix Optimization, Quantum Computing, FPGA Virtualization Leadership: Founder of ~okeanos (Europe's largest public Cloud IaaS), Co-founder of GFOSS , Member of IEEE Computer Society Greece, Advisor to Arrikto Inc. His work has led to over 180 publications with 5800+ citations (h-index 33) , including two Best Paper Awards (IPDPS 2001, CCGRID 2013) and Intel Recognition (2015). He has supervised 12 PhD students and participated in 15+ EU projects as coordinator or consortium partner. Scientific Leadership: Program Co-Chair for Europar 2012 , Organizer for IPDPS , ICPP , SC conferences, and active member in Cloud Computing Expert Groups for the European Commission.
Shaurya Agarwal is an Associate Professor in the Department of Civil, Environmental, and Construction Engineering at the University of Central Florida (UCF), where he has been a faculty member since 2018. He is the founding director of the URBANITY Lab (Urban Intelligence and Smart City Lab) and currently serves as the director of the Future City Initiative. Prior to joining UCF, he was an Assistant Professor in the Electrical and Computer Engineering Department at California State University, Los Angeles (2016–2018). Ph.D. in Electrical Engineering, University of Nevada, Las Vegas (2015) Postdoctoral Research, New York University (2016) B.Tech. in Electronics and Communication Engineering, Indian Institute of Technology (IIT), Guwahati Dr. Agarwal's research lies at the intersection of cyber-physical systems, intelligent transportation systems, and smart cities. He employs interdisciplinary methodologies integrating control theory, data-driven techniques, physics-informed machine learning, and mathematical modeling to address challenges in connected and autonomous mobility. His work emphasizes real-world applications such as traffic state estimation, signal-free intersections, and pedestrian safety using LiDAR perception. His recent publications demonstrate a strong trend in applying physics-informed deep learning and Koopman operator theory to model complex traffic dynamics. These works leverage both Lagrangian and Eulerian data frameworks and aim to improve accuracy under sparse sensor conditions. The research spans transportation, public health, and social systems, indicating a broad interdisciplinary impact. Dr. Agarwal is a senior member of IEEE and serves as an Associate Editor for IEEE Transactions on Intelligent Transportation Systems . His research has been funded by agencies including the Federal Highway Administration (FHWA), Florida Department of Transportation (FDOT), and Oculus. Senior Member, IEEE Associate Editor, IEEE Transactions on Intelligent Transportation Systems He actively mentors Ph.D. students in the Civil, Environmental, and Construction Engineering Department and leads the URBANITY Lab, a research team focused on next-generation urban mobility solutions. The lab develops real-time 3D object detection algorithms, operates a small-scale CAV test-bed, and explores hybrid approaches bridging theory, simulation, and practice.
Dr Jack Betteridge is an Honorary Research Fellow in the Department of Mathematics, Faculty of Natural Sciences, at Imperial College London. His work bridges computational mathematics with environmental sciences, focusing on numerical methods for atmospheric and oceanic systems. His research interests include: Numerical and Computational Mathematics Atmospheric Sciences Oceanography Physical Geography and Environmental Geoscience Computation Theory and Mathematics Distributed Computing Analysis of his 2019-2024 publications reveals deep engagement with finite element methods, particularly through the Firedrake project for automated PDE solutions. His work emphasizes high-performance computing applications in geophysical fluid dynamics, developing novel preconditioners and solvers for atmospheric modeling while contributing to computational education for mathematicians.
Mehtaab Sawhney is a Clay Research Fellow and a tenure-track assistant professor at Columbia University specializing in combinatorics, probability, analytic number theory, and theoretical computer science. His academic journey began at the University of Pennsylvania where he enrolled in a Bachelor of Engineering in Computer Science (2016-2017), then continued at MIT where he earned a Bachelor of Science in Mathematics with Minor in Computer Science (2017-2020), followed by a Doctor of Philosophy in Mathematics (2020-2024) under the advisorship of Yufei Zhao. His research spans probabilistic combinatorics, random matrix theory, additive number theory, and theoretical computer science. Sawhney's work bridges theoretical mathematics with computational applications, focusing on random structures, additive combinatorics, and spectral properties of discrete objects. His publications demonstrate a strong interdisciplinary approach that connects number theory with probabilistic methods to solve complex combinatorial problems. The analysis of his publication record reveals a consistent focus on foundational mathematical structures with applications across multiple domains. His work on random graphs, additive bases, and arithmetic progressions has established him as a leading researcher in modern combinatorics, often collaborating with prominent mathematicians including Ashwin Sah, Yufei Zhao, and Vishesh Jain. His research output shows remarkable depth and breadth, with contributions to both pure mathematics and theoretical computer science. 2024 Clay Research Fellow 2021 Frank and Brennie Morgan Prize for Outstanding Research in Mathematics by an Undergraduate Student (joint with Ashwin Sah) Churchill Scholar 2020 Best Student Paper STOC 2021 (Joint with Ryan Alweiss, Yang Liu) Best Student Paper ITCS 2022 (Joint with Yang Liu, Ashwin Sah) 2023 Hartley Rogers Jr. Prize 2022 Charles W. and Jennifer C. Johnson Prize (joint with Ashwin Sah) NSF Graduate Fellowship Sawhney has established a robust research program with significant contributions across multiple mathematical disciplines. His frequent collaborations with top researchers worldwide indicate an active and influential research network. While specific advisees aren't listed in available information, his extensive publication record with numerous co-authors suggests active mentorship of junior researchers through collaborative projects.
Yoann Altmann is Professor in the School of Engineering & Physical Sciences at Heriot-Watt University and a member of the Institute of Sensors, Signals & Systems. Since 2024 he holds the Chair in Electrical, Electronic & Computer Engineering (EECE), directing a research programme that bridges statistical signal processing, computational imaging and quantum & neuromorphic sensing. Education & career: 2010 – Eng. degree (Electrical Engineering), ENSEEIHT, Toulouse, France 2010 – M.Sc. (Signal Processing), National Polytechnic Institute of Toulouse 2013 – Ph.D. (Signal & Communications), IRIT Laboratory, Toulouse 2014-2017 – Post-doctoral Research Fellow, Heriot-Watt University 2017 – Royal Academy of Engineering Research Fellow & Assistant Professor, HWU 2024 – promoted to Professor, School of Engineering & Physical Sciences, HWU Research interests: Prof. Altmann develops mathematical and algorithmic tools for Bayesian inverse problems, with emphasis on single-photon LiDAR, low-illumination imaging, neuromorphic computational sensing, variational inference and sparse reconstruction. His work combines principled statistical modelling with efficient computational schemes to enable imaging in extreme scenarios such as underwater scattering, photon-starved environments, quantum metrology and real-time 3-D scene reconstruction. Publication trends: Across 160 outputs (2011-2025) his recent articles reveal a clear trajectory toward integrating modern machine-learning paradigms—variational autoencoders, diffusion generative models, spiking neural networks—with rigorous physics-based forward models. Applications span quantum parameter estimation, multimode-fiber endoscopy, hyperspectral & Compton imaging, nuclear safeguards and cultural-heritage spectroscopy, demonstrating both methodological breadth and high-impact interdisciplinary deployment. Honours & recognition: Royal Academy of Engineering Research Fellowship – competitively awarded (2017) Grants & datasets: He has generated four open datasets supporting reproducible research in quantum sensing, variational autoencoders, underwater single-photon LiDAR and multispectral fluorescence imaging, reflecting sustained funding and commitment to open science. Continuous peer-review service for IEEE and Elsevier journals since 2013 underlines his standing within the signal-processing community. Labs & teams: He leads the Bayesian Imaging & Sensing Computing (BISC) group ( https://bisc.site.hw.ac.uk ) which hosts post-docs, PhD researchers and international visitors working on statistical machine-learning for imaging, sensing and quantum technologies.
Cen Wu serves as Associate Professor in the Department of Statistics at Kansas State University and Faculty Scientist at the Johnson Cancer Research Center. His methodological research focuses on developing robust statistical machine learning approaches for high-dimensional cancer genomic data integration, addressing challenges where measurement dimensions far exceed sample sizes. Dr. Wu earned his Ph.D. in Statistics from Michigan State University in 2013, followed by a postdoctoral fellowship in Biostatistics at Yale School of Public Health (2013-2015). He joined Kansas State University as Assistant Professor in 2015, was promoted to Associate Professor in 2021, and has maintained dual appointments in Statistics and Cancer Research since 2016. His research program centers on Bayesian sparse learning methods for cancer genomics, with particular emphasis on robust variable selection techniques that accommodate outliers and heavy-tailed distributions common in genomic studies. He develops integrative approaches for multi-platform genomic data (mRNA expression, copy number variations, DNA methylation) to elucidate cancer etiology and identify prognostic markers. His work bridges theoretical statistics with practical clinical applications, including adaptive prediction of patient recruitment in clinical trials. Analysis of his recent publications reveals consistent focus on gene-environment interaction modeling through advanced Bayesian frameworks, with increasing emphasis on longitudinal data structures and robust inference procedures. His methodological innovations frequently translate into practical R packages that implement these complex statistical techniques for broader research communities. Dr. Wu actively contributes to the academic community as Associate Editor for TEST and BMC Genomics, and previously served as Guest Editor for a special issue on Bayesian Learning in Entropy. He maintains active collaborations with cancer researchers at the Johnson Cancer Research Center, applying his statistical expertise to real-world cancer genomics problems. His laboratory develops and implements cutting-edge statistical methods through R packages including 'mixedBayes', 'pqrBayes', 'roben', and 'interep', which address specific challenges in high-dimensional data analysis for cancer research. Current projects focus on extending robust Bayesian frameworks to handle increasingly complex genomic data structures while maintaining computational efficiency.
Youssef Marzouk is a Professor of Aeronautics and Astronautics at MIT, serving as co-director of the MIT Center for Computational Engineering and director of the Aerospace Computational Design Laboratory. His research focuses on integrating physical modeling with statistical inference, emphasizing Bayesian computation, uncertainty quantification, and optimal experimental design. He holds a SB, SM, and PhD from MIT and has been recognized with prestigious awards including the DOE Early Career Award and the Junior Bose Teaching Prize. Education: PhD in Aeronautics and Astronautics, MIT SM in Aeronautics and Astronautics, MIT SB in Aeronautics and Astronautics, MIT Research Interests: Uncertainty Quantification techniques for complex systems Bayesian computational methods and inverse problem solutions Optimal experimental design strategies Interdisciplinary applications in geophysics, environmental science, and engineering Awards: 2022: Report to the President, Center for Computational Science and Engineering 2021: Bayesian Inference Software Framework (hIPPYlib-MUQ) 2012: MIT School of Engineering Junior Bose Award 2010: DOE Early Career Research Award Labs & Leadership: Aerospace Computational Design Laboratory (Director) MIT Center for Computational Engineering (Co-Director) Editorial Board roles: SIAM Journal on Scientific Computing, Advances in Computational Mathematics
Dr. Amneet Bhalla serves as an Associate Professor in the Department of Mechanical Engineering within the College of Engineering at San Diego State University (SDSU). His primary contact email is asbhalla@sdsu.edu, with office located in Engineering Building Room 323-G, and phone number (619) 594-2043. Education: Ph.D., Mechanical Engineering, Northwestern University (2013) M.S., Mechanical Engineering, Indian Institute of Technology Kharagpur (2009) B.S., Mechanical Engineering, Indian Institute of Technology Kharagpur (2004-2008) Postdoctoral Training: University of North Carolina at Chapel Hill (Mathematics Department) and Lawrence Berkeley National Laboratory (Computational Research Division) Research Interests: Dr. Bhalla develops advanced numerical methods and high-performance computing techniques for computational fluid dynamics (CFD) and fluid-structure interaction (FSI) problems. His work spans aquatic locomotion, renewable energy device modeling, multiphase flows, vehicular aerodynamics, and bioengineering applications. He creates mathematical models to interrogate underlying flow physics for engineering design optimization, with emphasis on open-source software development through the IBAMR library. Publication Trends: Recent publications (2023-2025) focus on robust numerical frameworks for multiphase flows with phase change, acoustic streaming, and fluid-structure interaction. Key themes include mass conservation in level set methods, adaptive mesh refinement, and solvers for non-isothermal gas-liquid-solid systems. Applications range from aquatic locomotion and renewable energy devices to microfluidics and biomedical flows, demonstrating commitment to both theoretical advances and practical engineering solutions. Scientific Awards: No awards mentioned in the provided text Advising and Grants: Dr. Bhalla secured an NSF CAREER award (2023) for "Consistent Continuum Formulation and Robust Numerical Modeling of Non-Isothermal Phase Changing Multiphase Flows". As PI of the CFD Lab, he mentors graduate students in computational mechanics, leveraging prior industrial experience at ExxonMobil Upstream Research Company. His research integrates industrial practicality with academic rigor through collaborations with national laboratories. Laboratory and Team: The Computational Fluid Dynamics and Flow Physics Laboratory (CFD Lab) develops the open-source IBAMR software—a distributed-memory parallel implementation of the immersed boundary method with adaptive mesh refinement. The lab emphasizes transparency, community engagement, and reproducibility, establishing cross-institutional collaborations while advancing computational methods for complex flow phenomena in engineering and biological systems.
Harrison Huibin Zhou is the Henry Ford II Professor of Statistics and Data Science at Yale University. He has held leadership roles, including Department Chair of Statistics and Data Science (2018–present) and former Chair of Statistics (2012–2017). His academic career at Yale spans over two decades, with promotions from Assistant Professor (2004–2009) to Associate (2009–2010) and full Professor (2010–present). Research Interests: Dr. Zhou specializes in high-dimensional statistical theory, including nonparametric estimation, minimax theory, and applications in network analysis, machine learning, and functional data analysis. His work bridges theoretical foundations with computational methods, addressing challenges in modern statistical decision-making. Publications: His recent work focuses on spectral clustering, quantum state tomography, and optimal estimation in high-dimensional models. Notable contributions include theoretical guarantees for algorithms like the EM method in Gaussian mixtures and advancements in community detection in networks. Teaching: He teaches advanced courses such as Functional Data Analysis, Nonparametric Estimation, and Decision Theory, reflecting his expertise in statistical methodology and theory. Professional Service: Organized workshops on topics like Empirical Processes (2015) and High-Dimensional Data (2012), underscoring his role in fostering academic collaboration.
Abbas Khalili is a Professor in the Department of Mathematics and Statistics at McGill University, Montreal. His research focuses on statistical methodology in data science, particularly high-dimensional statistics, distributed learning in big data, latent variable models (e.g., finite mixtures, hidden Markov models), and time series analysis. He holds a PhD from the University of Waterloo and BSc/MSc degrees from Isfahan University of Technology. Education: PhD in Statistics, University of Waterloo (supervised by Jiahua Chen) MSc in Mathematical Sciences, Isfahan University of Technology BSc in Mathematical Sciences, Isfahan University of Technology His research interests include post-selection inference, neural networks, sparse network analysis, and change point detection. His work has been funded by NSERC (Canada) and Fonds de recherche du Québec-Nature et technologies. Recent articles focus on mixture models, regularization in autoregressive systems, and hub structure analysis in networks. His work bridges theoretical advancements with practical applications in drug discovery and network science. Dr. Khalili advises students in PhD, MSc, and undergraduate projects in his research areas. He can be contacted at abbas.khalili@mcgill.ca .
Dr. Wei Dai is a Senior Lecturer (Associate Professor) in the Department of Electrical and Electronic Engineering at Imperial College London, part of the Faculty of Engineering. He holds affiliations with the EPSRC Centre for Maths of Precision Healthcare and the Communications and Signal Processing group. His research focuses on sparse signal processing, machine learning applications in signal processing, linear and bilinear inverse problems, wireless communications, and random matrix theory. Notably, he contributed to the first compressive sensing DNA microarray prototype and has a highly cited 2009 paper on compressive sensing reconstruction. Dr. Dai's educational background includes a Ph.D. in Electrical and Computer Engineering from the University of Colorado at Boulder (2007) and postdoctoral research at the University of Illinois at Urbana-Champaign (2007-2010). His work bridges theoretical signal processing with practical applications in sensing, communication systems, and biomedical signal analysis. He leads research initiatives in gridless DOA estimation, robust beamforming, and cortico-muscular coupling analysis using advanced optimization techniques. His research outputs span topics like spectral compressed sensing, Bayesian methods for integrated sensing-communication systems, and dictionary learning for causal discovery. Ongoing work emphasizes low-rank matrix recovery, distributed compressed sensing, and mathematical frameworks for super-resolution localization. Dr. Dai collaborates across disciplines, leveraging signal processing innovations for healthcare technology and next-generation wireless systems.
Dr. Grey Ballard is an Associate Professor in the Department of Computer Science at Wake Forest University . He earned a B.S. in Math and Computer Science (2006), M.A. in Math (2008) from Wake Forest, and PhD in Computer Science (2013) from the University of California, Berkeley. He was a Truman Fellow at Sandia National Laboratories before joining Wake Forest. Research Focus: Ballard develops communication-optimal algorithms for high-performance computing , particularly in tensor decompositions , symmetric matrix computations , and nonnegative matrix factorization . His work combines numerical linear algebra with parallel algorithm design to reduce data movement costs in distributed systems. Publications demonstrate expertise in communication lower bounds , randomized tensor rounding , and visualization tools for parallel algorithms. He has contributed software packages such as TuckerMPI , GentenMPI , and PLANC for large-scale data compression and clustering. Scientific Awards: Wake Forest Excellence in Research Award NSF CAREER Award SIAM Linear Algebra Prize Three Conference Best Paper Awards (SPAA, IPDPS, ICDM) C.V. Ramamoorthy Distinguished Research Award (UC Berkeley) ACM Doctoral Dissertation Award – Honorable Mention Teaching: Courses include Introduction to Computer Science , Numerical Linear Algebra , and Parallel Algorithms . He has developed educational tools using the Thread-Safe Graphics Library to visualize parallel dynamic programming and collective communication.
Heiner Giefers is a Professor for Cloud Computing at the Department of Computer Science and Natural Sciences at Southwestphalia University of Applied Sciences since 2018. Prior to this position, he worked as a Research Staff Member at IBM Research - Zürich (2013-2018), focusing on hardware acceleration in cloud environments, implementation of big data algorithms on FPGAs, and development of hardware platforms for approximate and in-memory computing. Dr. Giefers received his doctorate (Dr. rer. nat.) from Universität Paderborn in 2012 with a dissertation titled "Design and Programming of Reconfigurable Mesh based Many-Cores." His academic journey at Universität Paderborn includes serving as an Academic Council Member (Akademischer Rat a.Z.) from 2008-2013 and as a Scientific Staff Member from 2006-2012, where he taught digital technology and computer architecture. Professor Giefers' research focuses on energy-efficient computing, particularly through hardware acceleration using FPGAs for cloud and AI workloads. His work spans cloud computing infrastructure, hardware-software co-design, approximate computing, in-memory computing, and energy-efficient implementations of machine learning algorithms. He has made significant contributions to the field of reconfigurable hardware for high-performance computing applications. His recent publications show a strong trend toward applying hardware acceleration techniques to artificial intelligence and machine learning workloads, with a particular focus on energy efficiency. His work bridges the gap between theoretical computer science and practical hardware implementation, often resulting in patented technologies that address real-world computing challenges in cloud environments. Best Paper Award for "Stochastic Matrix-Function Estimators: Scalable Big-Data Kernels with High Performance" (2016) Best Paper Award Nomination for "Energy-Efficient Stochastic Matrix Function Estimator for Graph Analytics on FPGA" (2016) Best Paper Award Nomination for "Analyzing the energy-efficiency of dense linear algebra kernels by power-profiling a hybrid CPU/FPGA system" (2014) Best Paper Award Nomination for "A Triple Hybrid Interconnect for Many-Cores: Reconfigurable Mesh, NoC and Barrier" (2010) Professor Giefers actively supervises numerous Bachelor's and Master's students, with over 50 completed theses covering topics from machine learning and cloud computing to IoT systems and hardware acceleration. He leads the "Energy-efficient AI" project (eki), which aims to increase the energy efficiency of AI systems through approximation techniques for FPGA implementation. Additionally, he collaborates with Prof. Dr. Christian Plessl on the "Digital teaching materials with Jupyter Notebooks" project, creating interactive learning materials that integrate teaching content, program code, and results into a single document. His work extends to practical applications through multiple patents related to FPGA implementations, neural networks, and memory systems, demonstrating his commitment to translating research into real-world solutions.
