جست‌وجوی استاد و پژوهشگر

Yee Whye Tehمشاهده پروفایل

استاد

• Statistical Machine Learning
• Probabilistic Learning
• Deep Learning
• +۴ مورد دیگر

Yee Whye Teh is a Professor at the Department of Statistics, University of Oxford, and a research scientist at DeepMind. His work focuses on statistical machine learning, including probabilistic learning, Bayesian nonparametrics, deep learning, and Monte Carlo methods. He co-directs the ELLIS programme on Robust Machine Learning and has held roles such as Programme Co-chair for ICML 2017. Teh has delivered keynotes at UAI 2019, an IMS Medallion Lecture at JSM 2019, and the Breiman Lecture in 2017. His research emphasizes scalable inference algorithms, hierarchical models, and applications in genetics and natural language processing. Teh's educational background includes a PhD from the University of Toronto (2003) and a Master's from the same institution (2000). He has contributed to widely used software tools like the Sequence Memoizer and has been recognized for his work through prestigious lectureships. Research interests span Bayesian nonparametric models, MCMC methods, and their applications in genetics and data compression. His lab collaborates on projects like fragmentation-coagulation processes for genetic variation modeling and Mondrian forests for online learning. Teh advises students through Oxford's graduate programs, though he notes high demand for mentorship. His work often bridges theory and practice, addressing challenges in big data learning and small data problems.

Andrew Childsمشاهده پروفایل

استاد

• Quantum Algorithms
• Quantum Information Processing
• Quantum Simulation
• +۲ مورد دیگر

Andrew Childs is a Professor at the University of Maryland, affiliated with the Department of Computer Science and the Institute for Advanced Computer Studies (UMIACS). He serves as Director of the NSF Quantum Leap Challenge Institute for Robust Quantum Simulation (RQS) and is a Fellow at the Joint Center for Quantum Information and Computer Science (QuICS). His research focuses on quantum algorithms for simulating physical systems, algebraic problems, and quantum walk protocols, with applications in quantum computing and computational complexity. University of Maryland Institute for Advanced Computer Studies (UMIACS) Joint Center for Quantum Information and Computer Science (QuICS) NSF Quantum Leap Challenge Institute for Robust Quantum Simulation Childs' research spans quantum simulation, quantum Fourier transform, phase estimation, and Hamiltonian dynamics. He has developed techniques to reduce quantum computational resources for simulating quantum systems and explored limitations of quantum computers through hidden subgroup problems and non-unitary dynamics. His publications cover diverse areas including quantum walk optimization, Hamiltonian simulation methods, and applications to cryptography and condensed matter physics. Recent works address spatial search algorithms, product formulas for commutators, and quantum routing protocols. As an educator, Childs has taught courses on quantum algorithms and information processing at both the University of Maryland and University of Waterloo, with lecture notes and materials spanning multiple years. Contact: amchilds@umd.edu | Office: ATL 3359 | Affiliated with University of Maryland's quantum research institutes.

Hayden Kwok Hay SOمشاهده پروفایل

دانشیار

• Reconfigurable Computing
• FPGA-Based Systems
• Computer Architecture
• +۷ مورد دیگر

Prof. Hayden Kwok Hay SO is an Associate Professor at the University of Hong Kong (HKU), affiliated with the Department of Electrical and Electronic Engineering. He currently serves as Acting Director of the School of Innovation and previously co-directed the Computer Engineering Program. His research focuses on reconfigurable computing systems, FPGA-based architectures, and their applications in AIoT, medical imaging, and high-performance computing. He holds a B.S., M.S., and Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley (1998–2007). Prof. So has been recognized with awards such as the IEEE-HKN Teaching Award (2021), Croucher Innovation Award (2013), and multiple teaching excellence awards. He leads the Computer Architecture & System Research Lab (CASR) and co-founded the Joint Lab on Future Cities (JLFC). His work spans FPGA overlay architectures, graph processing systems, and hardware-software co-design for efficient computing. Key research contributions include advancements in FPGA-based reconfigurable systems, sparse dataflow architectures, and medical imaging accelerators. He has secured grants for projects like 'Advanced machine vision guided aquatic surface vehicles' and 'Efficient and Productive Parallel Data Processing in Hybrid FPGA-CPU Clusters.' Prof. So has advised numerous students and researchers, contributing to over 150 peer-reviewed publications. His current projects explore AI hardware acceleration, neuromorphic computing, and FPGA-driven solutions for big data challenges.

Ali Vakilianمشاهده پروفایل

عضو هیئت علمی

• Algorithmic Foundations of Machine Learning
• Data Science
• Streaming Algorithms
• +۱۸ مورد دیگر

Ali Vakilian is a Research Assistant Professor at the Toyota Technological Institute at Chicago (TTIC), with a strong academic background in theoretical computer science and algorithms. He will join the Department of Computer Science at Virginia Tech as an Assistant Professor in Fall 2025. His research bridges algorithmic theory and machine learning, focusing on scalable, fair, and efficient algorithms for massive data. Education: Ph.D. in EECS, Massachusetts Institute of Technology (MIT), advisors: Erik Demaine and Piotr Indyk M.S. in Computer Science, University of Illinois at Urbana-Champaign (UIUC), advisor: Chandra Chekuri B.S. in Computer Engineering, Sharif University of Technology Research Interests: Ali Vakilian's work centers on the algorithmic foundations of machine learning and data science. He develops streaming, sketching, and sublinear-time algorithms for massive datasets, and pioneers learning-augmented algorithms that use machine learning predictions to improve performance while maintaining worst-case guarantees. His research in trustworthy ML includes algorithmic fairness, fair clustering, and learning with strategic agents. He also contributes to combinatorial optimization and approximation algorithms for network design, set cover, and low-rank approximation. His recent publications (2023–2025) show a consistent focus on fair clustering (individual and group fairness), streaming graph algorithms , learning-augmented methods , and frequency estimation . These works appear in top venues such as NeurIPS, ICML, SODA, and ICALP, often with recognitions like oral or spotlight presentations. Scientific Awards: Outstanding Student Paper Highlight Award, AISTATS 2024 Notable-top-25% paper, ICLR 2023 Oral presentation, AISTATS 2024 Spotlight presentation, NeurIPS 2023 Advising and Grants: Ali Vakilian mentors several students and interns, including summer interns at TTIC and Fatima Fellows. His research is supported by the National Science Foundation (TRIPODS program), as noted in the press coverage of his work on LearnedSketch. He actively contributes to the academic community through advising, organizing workshops (e.g., Algorithms with Predictions, Learning-Augmented Algorithms), and serving on program committees (e.g., NeurIPS, ICML, AISTATS). Labs and Teams: He is affiliated with the theory and algorithms group at TTIC and collaborates with researchers at MIT, UIUC, and other institutions. His work on learning-augmented algorithms has led to influential workshops and collaborations with leading figures such as Piotr Indyk and Erik Demaine.

Daniel M. Kaneمشاهده پروفایل

استاد

• Number Theory
• Combinatorics
• Complexity Theory
• +۳ مورد دیگر

Daniel M. Kane is a Professor at the University of California, San Diego (UCSD), holding a joint appointment in the Department of Mathematics and the Department of Computer Science and Engineering (CSE). His research spans mathematics and theoretical computer science, with a focus on number theory, combinatorics, complexity theory, and computational statistics. He earned a Ph.D. in Mathematics from Harvard University (2011) and dual BS degrees in Mathematics with Computer Science and Physics from MIT (2007). Prior to UCSD, he was a postdoctoral researcher at Stanford University (2011–2014) on an NSF fellowship. His research interests include robust statistics, machine learning, polynomial threshold functions, and algorithmic methods for high-dimensional data. Notable achievements include co-authoring the book Algorithmic High-Dimensional Robust Statistics (Cambridge University Press, 2023) and receiving the Best Paper Award at the Conference on Computational Complexity (2013), as well as gold medals at the International Mathematical Olympiad (2002 and 2003). Current teaching includes courses such as Math 96 (Putnam Seminar), Math 154 (Graph Theory), CSE 101 (Algorithms), and CSE 203A (Randomized Algorithms). He has consulted for companies like CASPER Labs and AIble, and his work extends to cryptographic protocols, including quantum money schemes based on quaternion algebras. Key contributions include breakthroughs in robust mean estimation, list-decodable learning, and the development of efficient algorithms for statistical problems. His research often bridges foundational theory with practical applications in machine learning and data analysis.

Monika Henzingerمشاهده پروفایل

استاد

• Algorithms
• Data Structures
• Dynamic Algorithms
• +۳ مورد دیگر

Monika Henzinger is Professor at the Institute of Science and Technology Austria (ISTA), heading the research group of Theory and Applications of Algorithms. She also serves as Vice President for Technology Transfer at ISTA since 2024. Previously, she held professorships at the University of Vienna (2009-2023) and EPFL, Switzerland (2005-2009), was Director of Research at Google (1999-2005), and served as Assistant Professor at Cornell University. Professor Henzinger's research centers on efficient algorithms and data structures with three main thrusts. First, she investigates dynamic settings where program inputs are repeatedly updated, seeking solutions faster than restarting computations. Second, she develops privacy-preserving algorithms that add minimal noise to protect input data while maintaining efficiency. Third, she translates theoretically optimal algorithms into practical implementations for dynamically changing inputs. Her work consistently addresses resource conservation in data processing, particularly computing time and memory space, while exploring the theoretical limits of possible savings. Henzinger's recent publications (2024-2025) reveal strong trends in dynamic algorithms, differential privacy, and graph theory. Her research consistently bridges theoretical computer science with practical applications, focusing on algorithms that adapt to changing inputs while preserving computational efficiency and data privacy. She has made significant contributions to problems like dynamic matching, minimum cut computation, and privacy-preserving data analysis across various domains. Professor Henzinger has received numerous prestigious awards and honors: Wittgenstein Award (2021) Two ERC Advanced Grants (2014, 2021) Carus Medal of the German Academy of Sciences Leopoldina (2019) SIGIR Test of Time Award Fellow of the Association of Computing Machinery (2016) Member of the Austrian Academy of Sciences (2017) CAREER Development Award of the National Science Foundation Best paper Award at the Symposium on Discrete Algorithms (2024) Professor Henzinger currently advises PhD students Bardiya Aryanfard, Antoine El-Hayek, and Roodabeh Safavi Hemami, along with postdocs Anamay Chaturvedi and Niklas Hahn. Her research is supported by multiple significant grants including an ERC Advanced Grant for 'Design and Evaluation of Modern, Fully Dynamic Data Structures,' the FWF Wittgenstein Prize, and the WEAVE Project on 'Static and dynamic hierarchical graph decompositions.' She also serves as Principal Investigator for the FWF project 'Fast algorithms for a reactive network layer,' providing substantial funding for her innovative work in algorithms and data structures. Professor Henzinger leads the Theory and Applications of Algorithms research group at ISTA, which focuses on developing practical algorithms for dynamic environments. Her team investigates resource conservation in data processing, specializing in dynamic algorithms that efficiently handle changing inputs, privacy-preserving algorithms that minimize noise while protecting data, and translating theoretical algorithms into practical implementations. The group maintains a strong presence in theoretical computer science through regular publications in top conferences and journals, and collaborates extensively with institutions worldwide to advance algorithmic research.

Jonathan A. Kelnerمشاهده پروفایل

استاد

• Algorithms
• Spectral Graph Theory
• Combinatorial Optimization
• +۷ مورد دیگر

Jonathan A. Kelner is a Professor of Applied Mathematics in the Department of Mathematics at the Massachusetts Institute of Technology (MIT) and a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on applying techniques from pure mathematics to solve fundamental problems in algorithms and complexity theory, with the goal of developing practical algorithms for real-world questions. Dr. Kelner received his undergraduate degree from Harvard University and his Ph.D. in Computer Science from MIT in 2006. Before joining the MIT faculty, he spent a year as a member of the Institute for Advanced Study. His educational background has provided a strong foundation for his interdisciplinary research spanning mathematics and computer science. His research interests include combinatorial optimization, mathematical programming, spectral graph theory, distributed computing, machine learning, computational geometry and topology, computational biology, signal processing, and random matrix theory. Kelner's work demonstrates how deep theoretical insights can lead to practical algorithmic improvements, particularly in graph algorithms and optimization problems. His approach often involves connecting seemingly disparate areas of mathematics to create novel algorithmic techniques. Analysis of his recent publications reveals a strong focus on spectral graph theory, optimization algorithms, and the Sum-of-Squares method. His work frequently addresses fundamental questions in theoretical computer science with practical implications for algorithm design. A notable trend in his research is the development of nearly-linear-time algorithms for various graph problems, which represents significant improvements over previous approaches. NSF CAREER Award Alfred P. Sloan Research Fellowship NEC Award for Research in Computers and Communication Sprowls Doctoral Dissertation Award Best Student Paper Award at STOC 2004 Best Paper Award at STOC 2011 Kokusai Denshin Denwa Junior Faculty Chair 2008 Harold E. Edgerton Faculty Achievement Award 2011 School of Science Award for Excellence in Undergraduate Education 2012 Professor Kelner has been actively involved in mentoring students and has received recognition for his teaching excellence, including the School of Science Award for Excellence in Undergraduate Education in 2012. His research has been supported by prestigious grants including the NSF CAREER Award. He has collaborated extensively with researchers across multiple institutions, often working with other leading figures in theoretical computer science to produce groundbreaking results in algorithm design. At MIT, Kelner is part of both the Mathematics Department and CSAIL, positioning him at the intersection of theoretical mathematics and practical computer science. This dual affiliation reflects the interdisciplinary nature of his work, which bridges pure mathematical theory with concrete algorithmic applications.

Ola Svenssonمشاهده پروفایل

دانشیار

• Approximation Algorithms
• Combinatorial Optimization
• Computational Complexity
• +۳ مورد دیگر

Ola Svensson is an Associate Professor at the School of Computer and Communication Sciences , EPFL. His research spans approximation algorithms, combinatorial optimization, computational complexity, and scheduling. He holds an ERC Consolidator Grant (2023–) and previously received an ERC Starting Grant (2014–2019) and SNF grant (2019–2023). Education: PhD in Computer Science from IDSIA, Università della Svizzera italiana (2009) M.Sc. from Uppsala University (2005) Research Focus: Svensson develops novel techniques for NP-hard problems, with emphasis on primal-dual methods, LP/SDP hierarchies, and hardness proofs. His work applies to clustering, scheduling, network design, and submodular optimization. Publications: His 15 most recent works (2018–2021) focus on learning-augmented algorithms, robust optimization, and improved approximations for clustering/TSP. Key trends include integration of ML with classical algorithms and quasi-polynomial methods for combinatorial problems. Awards: Best Paper Awards at FOCS (2011, 2017) and STOC (2018) I&C Teaching Award at EPFL Advising & Grants: He advises 6 current PhD students and graduated 8 others. Major grants include ERC Starting Grant 'OptApprox' (€1.4M) and ERC Consolidator Grant 'POTCO' (€2M). Teaching: Leads courses in Advanced Algorithms, Computational Complexity, and Approximation Algorithms. He developed pedagogical frameworks for scribe notes and project-based learning in theoretical computer science.

Erik Waingartenمشاهده پروفایل

استادیار

• Algorithms for Massive Datasets
• High-dimensional Geometry
• Property Testing
• +۵ مورد دیگر

Erik Waingarten is an assistant professor at the University of Pennsylvania in the Computer and Information Science department. His research focuses on algorithms for massive datasets, including similarity search, streaming/sketching, property testing, and distribution testing. Former postdoctoral researcher at Stanford's CS Department under Moses Charikar PhD from Columbia University advised by Xi Chen and Rocco Servedio Key research areas: High-dimensional geometry Streaming algorithms Property testing Sketching techniques Clustering and metric optimization Recent article trends show expertise in: 2025 publications on monotonicity testing and metric property analysis 2024 work on Earth Mover's Distance and kernel evaluations 2023 papers on clustering, optimal transport, and MST algorithms 2022-2020 foundations in sublinear algorithms and entropy estimation Scientific recognition: NSF CAREER Award (2023) CCC Best Paper Award (2017) Invited to Journal of the ACM (2017) Academic advising includes PhD students: Ashwin Padaki Tian Zhang Nicolas Menand Krish Singal Junkai Song

• Power System Control
• Smart Grids
• Communication Systems
• +۱۱ مورد دیگر

Lars Nordström is a Professor at the Division of Electric Power and Energy Systems within KTH Royal Institute of Technology, Stockholm, Sweden. His work bridges control systems , communication networks , and power systems , with a focus on future architectures, functionality, and quality aspects of ICT for power grid operations. He has led initiatives such as the Swedish Centre of Electric Power Engineering and served as Thematic Leader for Smartgrids in KIC InnoEnergy. In 2014, he was a Visiting Professor at Washington State University. Education : Ph.D., MSc.EE Nordström's research explores the intersection of smart grids , machine learning , and cybersecurity for power systems. Key areas include: Wide-Area Monitoring and Control (WAMC) systems Decentralized control strategies for DC microgrids Impedance modeling using neural networks Data-driven methods for islanding detection ICT reliability and protocol design for grid operations His recent publications emphasize machine learning applications in power systems, including LSTM networks for EV charging management, graph attention networks for stability monitoring, and digital twin approaches for cyber-attack mitigation. These works span disciplines such as Smart Grids, Power Electronics, and Data Science. Scientific Recognitions : Senior Member, IEEE Senior Member, CIRED Senior Member, Cigre Past Chairman, Swedish IEC TC57 Mirror Committee Nordström actively teaches and examines graduate courses like Communication and Control in Electric Power Systems and Computer Applications and Machine Learning in Electric Power Systems . His work influences industry practices through collaborations on digital substations, energy market analysis, and resilience strategies.

Alan Zaoxing Liuمشاهده پروفایل

استادیار

• Systems
• Networking
• Applied Algorithms
• +۹ مورد دیگر

Alan Zaoxing Liu is an Assistant Professor of Computer Science at the University of Maryland, College Park , with appointments at the University of Maryland Institute for Advanced Computer Studies (UMIACS) and Maryland Cybersecurity Center (MC2) . His research bridges systems, networking, and cybersecurity to design scalable, trustworthy approximate computing systems. Ph.D. in Computer Science from Johns Hopkins University (2018) Postdoctoral research at Carnegie Mellon CyLab (2018–2020) Research Interests : Networked and data-intensive systems Telemetry/analytics for heterogeneous networks Machine learning for network optimization Security in programmable networks Recent Publications include work on future-proof telemetry (PromSketch, VLDB’25), scalable caching (OctoCache, ASPLOS’25), and secure network analytics (TrustSketch, NDSS’24). His NSF-funded projects focus on optics-enabled DDoS defense and data-driven network management. Scientific Awards : USENIX FAST Best Paper (2019) USENIX ATC 'Best of Rest' (2021) Red Hat Collaboratory Research Awards (2022, 2023) Teaching : Leads Cloud Computing at Boston University , emphasizing agile development, open-source collaboration, and cloud infrastructure.

Mohammadtaghi Hajiaghayiمشاهده پروفایل

استاد

• Design and Analysis of Algorithms
• Approximation Algorithms
• Fixed-Parameter Algorithms
• +۸ مورد دیگر

Mohammadtaghi Hajiaghayi is the Jack and Rita G. Minker Professor of Computer Science at the University of Maryland, College Park. He is affiliated with the Robert H. Smith School of Business and holds Research Affiliate positions at MIT CSAIL and the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS). His research focuses on algorithms, game theory, and network design, supported by NSF, ONR, and industry grants. He has received prestigious awards including ACM Fellow (2018) and EATCS Nerode Prize (2015) for his work on bidimensionality theory. Education: PhD from MIT (2005), postdocs at CMU and MIT, MSc from University of Waterloo, and BSc from Sharif University. He teaches courses like Data Science and Algorithms at UMD. Industry experience includes roles at Amazon, Google, and AT&T Labs. Over 20 students have graduated under his advisement, many in academia and industry. His work spans approximation algorithms, game theory, and big data. Projects include BigDND with Erik Demaine. He serves on editorial boards of Algorithmica, SODA, and others. Awards also include IEEE Fellow (2020) and Blavatnik Honoree (2020).

Margret Keuperمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Deep Learning Model Robustness
• +۷ مورد دیگر

Prof. Margret Keuper is a Professor of Machine Learning at the University of Mannheim's School of Business Informatics and Mathematics, leading the Data and Web Science Group. She is also affiliated with the Max-Planck-Institute for Informatics and ELLIS (fellow since 2024). Her research focuses on robust deep learning, neural architecture search, and computer vision tasks like motion segmentation and adversarial defense. She holds a PhD from the University of Freiburg and previously held positions at the University of Siegen and the University of Mannheim. Her work spans projects funded by DFG and BMBF, including Climate Visions for social media analysis and TrackOpt for motion tracking. She teaches courses on computer vision, generative models, and reinforcement learning. She actively serves on program committees for top conferences like CVPR, ECCV, and NeurIPS, and is an associate editor for IEEE TPAMI and JAIR. Education: PhD in Computer Science from University of Freiburg (advisor: Thomas Brox) Research Projects: Learning to Sense (DFG), Climate Visions (BMBF), TrackOpt (BMBF) Key Roles: Head of Mannheim Master in Data Science Examination Board, Member of MSc Business Informatics Board Her research emphasizes robustness in AI systems, with contributions to adversarial attacks, domain generalization, and efficient solvers for large-scale problems. She advises over 15 PhD students across academic and industry partnerships.

Cameron Muscoمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Machine Learning
• Numerical Linear Algebra
• +۳ مورد دیگر

Cameron Musco is an Assistant Professor in the Manning College of Information and Computer Sciences at the University of Massachusetts Amherst. He is affiliated with the Theory Group and conducts research at the intersection of theoretical computer science, numerical linear algebra, and machine learning. His work focuses on randomized algorithms, streaming, and distributed computation, with applications in data science. Education: PhD in Computer Science, MIT (2019) BS in Computer Science and Applied Mathematics, Yale University Research Interests: Musco's research emphasizes algorithm design for large-scale data analysis, including fast randomized methods for linear algebraic problems. He explores topics such as low-memory computation, matrix approximations, and graph algorithms, driven by applications in machine learning and distributed systems. Publications: His recent work spans advancements in hierarchical matrix approximation, graph-based nearest neighbor search, and fair resource allocation, reflecting expertise in both theoretical foundations and practical algorithmic innovation. Awards: He has received an NSF Career Award, Google Research Scholar Award, and recognition for anti-racism leadership. He actively reviews for top conferences in theoretical computer science and machine learning. Advising & Grants: Musco advises multiple PhD students and has grants from NSF and Google. His lab collaborates on projects like low-rank matrix approximation and causal discovery. Labs/Teams: He is part of the Theoretical Computer Science Group and the Center for Data Science at UMass.

• Homomorphic Encryption
• Big Data Analysis
• Database Systems
• +۶ مورد دیگر

Hayato Yamana serves as Professor at Waseda University's Faculty of Science and Engineering and concurrently holds the position of Vice President for IT Promotion and Chief Information Officer since October 2020. His academic journey began with a Dr. Eng. degree from Waseda University in 1993, followed by positions at the Electrotechnical Laboratory of MITI, before joining Waseda University as Associate Professor in 2000 and becoming full Professor in 2005. He has held significant leadership roles including Director of the Database Society of Japan and the Information Processing Society of Japan, as well as Vice Chair of IEICE's Information and Communication Society. Waseda University, Faculty of Science and Engineering (2005-Present) National Institute of Informatics, Visiting Professor (2005-Present) Waseda University, Vice President for IT Promotion (2020-Present) Deputy Chief Information Officer (2015-2020) His research spans homomorphic encryption, big data analysis, and computer architecture, with notable contributions in privacy-preserving computation, recommender systems, and secure data processing. His work bridges theoretical cryptography with practical applications in smart cities, healthcare, and e-commerce security. Recent publications demonstrate strong focus on accelerating homomorphic encryption operations, improving recommendation system diversity, and developing novel authentication mechanisms. Analysis of his 15 most recent publications reveals consistent emphasis on privacy-preserving technologies (particularly homomorphic encryption applications), innovative recommender system architectures, and biometric security solutions. His research group produces highly cited work at the intersection of cryptography, machine learning, and systems security, with practical implementations in real-world scenarios including smart grids, e-commerce, and healthcare. Fellow, Information Processing Society of Japan (IPSJ), 2020 Golden Core Award, IEEE Computer Society, 2018 Fellow, Institute of Electronics, Information and Communication Engineers (IEICE), 2018 IBM Faculty Award, 2009 Multiple Best Paper Awards from IEICE, IPSJ, and ITE Yamana has secured substantial research funding for projects in homomorphic encryption, smart city infrastructure, and privacy-preserving systems. His leadership extends to advising numerous doctoral students and directing major research initiatives including the Smart Systems and Services Innovative Professional Education Program. He maintains active collaborations with industry partners through projects involving secure computation and data analytics. His research group operates at the forefront of secure computing, with specialized laboratories focused on homomorphic encryption acceleration, privacy-preserving machine learning, and secure mobile authentication. The team actively develops practical implementations of cryptographic protocols for real-world applications in healthcare, finance, and smart city infrastructure, bridging theoretical cryptography with deployable security solutions.