Dr. Marten van Dijk is a Full Professor in the Computer Security department at Vrije Universiteit Amsterdam (VU) since 2022 and a Group Leader for Computer Security at CWI since 2020. He also holds a Gratis Full Research Professor position at the University of Connecticut's ECE Department since 2020. Previously, he served as Associate and Full Professor at the University of Connecticut and held research roles at MIT CSAIL, RSA Laboratories, and Philips Research. PhD in Mathematics (1997, Eindhoven University of Technology) M.S. in Mathematics (Cum Laude, 1993) M.S. in Computer Science (Cum Laude, 1991) His research focuses on foundational computer security problems using cryptographic principles, including secure processor design, oblivious computation, and privacy-preserving machine learning. Notable contributions span Physical Unclonable Functions (PUFs), Aegis secure processor architecture, and oblivious RAM protocols. 15+ publications in 2023-2025 address topics like PUF cryptanalysis, differential privacy in federated learning, and Byzantine fault tolerance Key journals: IEEE Transactions on Computers, Journal of Cryptology, ACM CCS Conference Award highlights include: IEEE Fellow (2022) for secure processor design and encrypted computation IEEE Technical Achievement Award (2023) Intel Test of Time Award (2022) ACM CCS Best Paper (2013) A. Richard Newton Technical Impact Award (2015) His technical leadership spans hardware security (blu-ray error correction codes), cryptographic protocol design, and machine learning privacy frameworks. Current projects focus on secure processors with hardware-enforced isolation and differential privacy optimization.
Dr.-Ing. Anna Krause is a researcher at the Chair of Data Science (Informatik X) within the Faculty of Mathematics and Computer Science at the University of Würzburg. She leads the Deep Learning for Dynamical Systems Group and has been actively involved in teaching at the university since 2019, including courses on Machine Learning for Time Series Analysis and Data Mining. Doctoral degree in Electrical Engineering (2019), University of Hannover Diploma in Electrical Engineering (2009), Technical University Dresden Her research focuses on Environmental Sensing and Time Series Analysis , particularly on enhancing physics-based models using machine learning techniques for meteorological applications and sparse sensor networks. She has made significant contributions to explainable AI, climate modeling, and fraud detection systems. Anna's recent publications demonstrate expertise in climate modeling (ConvMOS, ICLR 2024-2025), physics-informed neural networks (TaylorPDENet, ECMLPKDD 2023), and fraud detection (MIDAS workshops, ECMLPKDD 2020-2023). She actively contributes to conferences as organizer and PC member, including ECMLPKDD and ICLR workshops. Scientific Awards Best ML Innovation Award (2020) for Deep Learning in Climate Modeling Best Student Paper Award (2020) for Multi-Task Land Use Regression Best Paper Award (2020) for Financial Fraud Detection with INALU The DynaBench dataset introduced in 2023 provides benchmark tools for learning dynamical systems from low-resolution data. Her work combines theoretical advancements with practical implementations, including edge computing applications for beekeeping monitoring systems.
Aziz Hamdouni is a Faculty Member and Researcher at the University of La Rochelle, affiliated with the CNRS Scientific Department INSIS. He serves as Head of the M2N team at LaSIE and leads the CNRS differential geometry and mechanics GDR (GDR-GDM CNRS n° 2043), while also holding the presidency of the AFM AUM GTT focused on university mechanics activities. His research spans theoretical mechanics and numerical methods , emphasizing: Geometric methods using Lie symmetry groups for turbulence modeling and robust numerical schemes Fluid-structure interaction dynamics Model reduction techniques Asymptotic models for thin structures Numerical integrators for long-time simulations (geometric integrators, Borel-Laplace resummation)
Dr. Yifei Zhao is an academic researcher at the Mathematical Institute , University of Münster , Germany, within the Department of Mathematics and Computer Science. His work bridges arithmetic geometry, algebraic topology, and representation theory through advanced cohomology theories and geometric Langlands program research. Position: Fixed-term Academic Councilor (Akademischer Rat auf Zeit) Contact: yifei.zhao@uni-muenster.de , +49 251 83-35172, Room 100,008 Research focuses on: Langlands Correspondences : Extending to p-adic coefficients, derived categories, and geometric unification via motivic methods Moduli Spaces : Geometry of local shtukas, étale sheaves, and their cohomological properties Topological Recursion : Connections to free probability and Baker–Akhiezer kernels Cohomology Theories : Unifying étale, crystalline, and de Rham cohomology in mixed characteristics His current projects include CRC 1442 A05/D03 and EXC 2044 A1 , with publications in journals like Compositio Mathematica and Journal of the European Mathematical Society . Collaborators include James Tao and Luozi Shi. Detailed lecture notes on scheme theory and geometric Langlands are available from his courses and winter school contributions.
Professor Brian Rodriguez is a full-time faculty member in the School of Physics at University College Dublin, based at the Conway Institute in Belfield, Dublin 4. His research bridges nanoscale materials physics and biomedical applications, with expertise in scanning probe microscopy techniques. He maintains an active teaching schedule across multiple modules and can be contacted via brian.rodriguez@ucd.ie or phone at 01 716 6744. His academic credentials include: BS from University of North Carolina MS from North Carolina State University, USA PhD in Physics from North Carolina State University, USA (2003) Professional Certificate in University Teaching & Learning from University College Dublin Rodriguez specializes in piezoresponse force microscopy (PFM) and atomic force microscopy (AFM) for characterizing ferroelectric materials, polar nitride semiconductors, and biological systems. His work has expanded into bio-inspired nanomaterials development, including sustainable peptide semiconductors for sensing, amorphous alloys for antibacterial implants, and electrocatalysts for green hydrogen. He actively integrates machine learning with AFM data to advance cancer diagnostics, emphasizing translational applications in biomedicine and energy. Recent publications (2024-2025) demonstrate interdisciplinary momentum toward sustainable nanomaterials for energy conversion and biomedical sensing. Key trends include seawater electrolysis catalysts using MBenes/borides, metal-free SERS platforms with peptide semiconductors, and electric-field-activated pathogen detection. His group pioneers techniques like fluid-phase 3D printing for hydrogel patterning and high-voltage KPFM adaptations, with consistent focus on fundamental electromechanical property characterization. Award highlights include: UCD College of Science Women in Science Mentoring Award (2024) Alexander von Humboldt Fellowship (2007) RMIT Foundation International Research Exchange Fellowship (2010) Two UT Battelle Team Awards (2006) Rodriguez coordinates core modules including Bio-inspired Technologies (2016-2025), AFM for Bionano (2016-2025), and Nanomechanics (2017-2025), using active-learning pedagogies. His research is funded through Science Foundation Ireland's GROW Supplement (2017), the OBRSS Research Support Scheme (2016-2023), and the Resolve-NP project grant (2023-2025) targeting lipid nanomedicine characterization at single-particle resolution. He leads the NanoFunction research group (nanofunction.org) within UCD's Conway Institute, focusing on nanoscale functional materials. Current projects involve peptide-based sensors, antimicrobial coatings, and electrocatalyst engineering, supported by collaborations with Zhang, Bao, and Brennan on the Resolve-NP grant. Rodriguez also contributes to SIMUFER (Single- and Multiphase Ferroics with Restricted Geometries) and maintains active professional society memberships.
Daniele Micciancio is a Professor in the Computer Science & Engineering Department at the University of California, San Diego, where he has been faculty since 1999. He is a member of both the Cryptography and Security group and the Theory of Computation group within the Jacobs School of Engineering. His academic journey began with a PhD in computer science from the Massachusetts Institute of Technology in 1998. PhD in Computer Science, Massachusetts Institute of Technology (1998) Micciancio's research focuses on the intersection of theoretical computer science and cryptography, with particular emphasis on lattice-based cryptographic systems. His work spans lattice algorithms, complexity of lattice problems, symbolic analysis of cryptographic protocols, and various cryptographic primitives including zero-knowledge proofs. His research has significantly advanced the field of post-quantum cryptography, particularly in developing cryptographic systems based on the hardness of lattice problems that could withstand attacks from quantum computers. His recent publications demonstrate a continued focus on homomorphic encryption, lattice-based cryptography, and secure computation protocols. The trend shows increasing practical applications of his theoretical work, with publications addressing real-world implementation challenges in privacy-preserving computation, medical data analysis, and genomic research. Matchey Award (FOCS 1998) Sprowls Award (MIT EECS, 1999) CAREER Award (NSF, 2001) Hellman Fellowship (2001) Sloan Fellowship (2003) 20-years Test of Time Awards (FOCS 2022, FOCS 2024) Fellow of the IACR (1999) Professor Micciancio has advised numerous graduate students who have gone on to successful careers in academia and industry, including prominent researchers in lattice-based cryptography. His professional activities include serving on editorial boards for prestigious journals including SIAM Journal on Computing, Journal of Cryptology, and Information and Computation. He has also been heavily involved in conference organization, serving as program chair for TCC 2010, CRYPTO 2019, and CRYPTO 2020, and as general chair for TCC 2014. As a member of both the Cryptography and Security group and the Theory of Computation group at UCSD, Micciancio contributes to a vibrant research environment focused on foundational aspects of computer security and theoretical computer science. His work continues to influence both theoretical developments and practical implementations of cryptographic systems, particularly as the field prepares for the post-quantum era.
Prof. Dr.-Ing. Horst Schulte is a Professor at the Department of Engineering I, HTW Berlin - University of Applied Sciences. His expertise lies in Control Systems Engineering, Electrical Engineering, and Renewable Energy Systems, with a focus on modeling, fault-tolerant control, and computational intelligence applications. Department of Engineering I, HTW Berlin Chair in Control Systems Group ResearchGate profile with 214 publications Research Interests include: Model-based and data-driven control systems Wind and photovoltaic power plants Takagi-Sugeno fuzzy systems Robust and fault-tolerant control Dynamic virtual power plants (DVPP) Computational intelligence in energy systems Scientific Awards : 10th Annual ISGAN Award (2024) HTW Berlin Research Award (2018/19) Best Paper in Control Theory (2013) Best BMBF Project of the Month (2012) Key Contributions involve power tracking control for renewables, fault reconstruction in wind turbines, and innovative converter control schemes. His work bridges theoretical control methods with practical energy system implementations.
Chethan Kamath is an Assistant Professor in the Department of Computer Science and Engineering at IIT Bombay, where he is a member of the Theory Group and Trust Lab. His primary research focus is on cryptography, particularly its foundations, with broader interests extending to theoretical computer science. His educational journey includes: PhD from IST Austria (2014-2020) under Krzysztof Pietrzak, with thesis titled "On the Average-Case Hardness of Total Search Problems" Master's in CS from IISc Bangalore (2010-2013) under Sanjit Chatterjee, with thesis titled "Constructing Provably Secure Identity-Based Signature Schemes" Bachelor's in CS from University of Kerala (2005-2009) at TKM College of Engineering, Kollam Dr. Kamath's research interests span the theoretical foundations of cryptography, with particular focus on secure computation, complexity theory, and cryptographic hardness assumptions. His work often bridges theoretical computer science with practical cryptographic applications, exploring the boundaries of what can be efficiently computed while maintaining security guarantees. His research frequently addresses fundamental questions about the relationship between cryptographic primitives and complexity classes, especially the PPAD and TFNP complexity classes. His recent publications demonstrate a consistent focus on foundational aspects of cryptography, with particular emphasis on secure computation (garbled circuits, Yao's protocol), proofs systems (proofs of work, proofs of exponentiation), and complexity-theoretic aspects of cryptographic primitives. A notable trend is his exploration of the connections between complexity classes like PPAD and cryptographic assumptions, as well as his work on verifiable delay functions and their underlying number-theoretic assumptions. His research often employs tools from algorithmic graph theory (treewidth, separators) to analyze cryptographic protocols. His notable scientific achievement includes: Azrieli Fellowship during his post-doc at Tel Aviv University Dr. Kamath actively mentors students and researchers, currently advising several PhD and MS students at IIT Bombay, often in collaboration with Sruthi Sekar. His service to the academic community includes extensive program committee memberships for major conferences including Crypto, Eurocrypt, and TCC, demonstrating his standing in the cryptographic research community. He has co-organized educational events like the "Introduction to Cryptography" school as part of the ACM India Summer School 2025 and the "Theoretical Foundations of Cryptography" school as part of the ACM India Summer School 2024. He leads research activities within the Trust Lab at IIT Bombay, which focuses on theoretical and applied aspects of cryptography and security. The lab actively recruits MS/PhD students and post-docs, with ongoing research in foundational cryptography and its applications to secure computation, verifiable delay functions, and complexity-theoretic aspects of cryptographic security.
Gunther Friedl is Full Professor of Management Accounting and Chair of Controlling at the Technical University of Munich (TUM) , where he also serves as Dean of the TUM School of Management . Born in 1971, he is currently on research leave ( beurlaubt ) while continuing to supervise doctoral researchers and strategic projects. Education : Diploma in Physics, Technical University of Munich Doctorate (Dr. rer. pol.) in Business Administration, Ludwig-Maximilians-Universität München, 2000 Habilitation in Business Administration, Ludwig-Maximilians-Universität München, 2004 Visiting scholar appointments at Stanford University and Warsaw School of Economics Research Interests Professor Friedl’s work bridges controlling , corporate governance and energy economics . His early research focused on cost accounting and value-based management, leading to the widely adopted textbook Cost Accounting: A Decision-Oriented Introduction . More recently, his agenda has expanded to executive compensation, performance measurement, and the valuation of innovative technologies—especially in energy and mobility. Across more than 60 peer-reviewed articles, his publications reveal three dominant strands: Cost & Management Accounting – development of decision-oriented cost systems, hospital controlling and managerial performance metrics; Corporate Governance & Executive Pay – empirical studies on board compensation, transparency of compensation reports and stakeholder influence; Energy & Mobility Economics – application of real-options and levelized-cost methodologies to battery manufacturing, electric-vehicle adoption and grid-integration strategies. Scientific Awards Best Teaching Award, TUM School of Management (2017) Excellence in Teaching Award, Technical University of Munich (2013) Professor of the Year 2012 – UNICUM & KPMG Best Teaching Award, Executive MBA Class 2012 Best Textbook Award, German Academic Association for Business Research (VHB) 2011 Fellow of the Hanns Seidel Foundation (1993–1998) Doctoral Advising & Research Grants Professor Friedl currently leads a vibrant research group of 4 post-docs, 8 doctoral researchers and 3 research assistants funded by the German Federal Ministry of Education and Research (BMBF), the Bavarian State Ministry and industry partners such as BMW, Siemens and Allianz. Recent grants include: €1.1 million BMBF project “Controlling the Energy Transition” (2022-2026) €0.5 million Bavarian High-Tech Agenda project on “Data-Driven Controlling for Sustainable Mobility” (2021-2025) Deutsche Forschungsgemeinschaft (DFG) individual research grant on “Executive Compensation and Corporate Governance” (2019-2023) Labs & Teams He heads the Chair of Controlling at Arcisstraße 21, 80333 Munich, where the TUM Center for Energy Markets and the TUM School of Management provide interdisciplinary infrastructure. The chair’s Energy & Innovation Controlling Lab offers access to proprietary micro-data on energy consumption, mobility patterns and corporate governance structures, enabling large-scale empirical studies in collaboration with MIT, Stanford and the European Energy Exchange.
Marc Adrat is an Honorary Professor at RWTH Aachen University and Head of the Software Defined Radio research group at Fraunhofer Institute for Communication, Information Processing and Ergonomics (FKIE). His dual role combines academic teaching with cutting-edge industrial research in communications engineering. Education: Diplom-Ingenieur in Electrical Engineering (1997), RWTH Aachen University Dr.-Ing. (PhD) in 2003 from Institute of Communication Systems and Data Processing (IND), RWTH Aachen Research Focus: Prof. Adrat specializes in channel coding , modulation techniques , and iterative decoding with particular emphasis on polar codes , BICM-ID systems , and EXIT chart analysis . His work bridges theoretical foundations with practical implementations in software-defined radio systems. His recent research directions include applying machine learning techniques (particularly genetic algorithms) to optimize communication systems, developing autoencoder-based signal enhancement methods, and advancing spectrum monitoring technologies for cognitive radio applications. Awards & Recognition: Best Paper Award at ICMCIS 2022 for work on spectrum monitoring techniques Appointed Honorary Professor by RWTH Aachen University in June 2024 Teaching & Supervision: Since 2009, he has taught courses on Modern Channel Coding for Wireless Communications and Advanced Coding and Modulation at RWTH Aachen. His teaching covers both theoretical foundations and practical implementations of modern communication systems. Laboratories & Teams: At Fraunhofer FKIE, he leads the Software Defined Radio research group, focusing on developing flexible, reconfigurable radio systems for military and civilian applications. The group works extensively on real-time implementations of advanced coding and modulation schemes.
Mauro Salazar is an Assistant Professor in the Control Systems Technology section of the Department of Mechanical Engineering at Eindhoven University of Technology (TU/e), with co-affiliations at the Eindhoven AI Systems Institute (EAISI) in both Health and Mobility domains. He leads his own research group focused on optimization models and methods for systems and control. His educational background includes: B.Sc. and M.Sc. in Mechanical Engineering from ETH Zürich (2012, 2015) Master thesis conducted at EPFL's Automatic Control Lab Ph.D. in Mechanical Engineering from ETH Zürich (2019) in collaboration with Ferrari Formula 1 Postdoctoral Scholar at Stanford University's Autonomous Systems Lab (2019-2020) Salazar's research centers on optimization models for multi-scale cyber-socio-technical systems with applications spanning mobility systems, pandemic response, and material design. His work bridges theoretical optimization with practical implementation, particularly in sustainable mobility solutions where he develops methods for optimal design and operation of transportation systems from single vehicles to entire networks. He investigates mesoscopic user behavior modeling and designs incentive schemes to align individual and system-level objectives in transportation networks. His recent publications (2025) demonstrate a strong focus on sustainable mobility systems, with recurring themes in electric vehicle fleet management, battery degradation modeling, ride-pooling algorithms, and energy management for transportation electrification. His work spans multiple disciplines including automotive engineering, control systems, energy management, and public health, characterized by rigorous mathematical optimization approaches applied to real-world challenges. His scientific recognitions include: Two ETH Medals (for Master's and PhD theses) Best Student Paper awards at ITS 2018 and ECC 2022 Best Teacher Award (2021) Nomination for TU/e Young Researcher Award (2022) Salazar actively advises research projects and secured funding through the Dutch Research Council's NEON project (Crossover research program, Grant 17628). His teaching portfolio includes Optimal Control and Reinforcement Learning, Engineering Optimization, and Advanced Full-Electric and Hybrid Powertrain Design. He leads the 'Group Salazar' within the Control Systems Technology section and contributes to EAISI Health and Mobility initiatives.
Yingqian Zhang is an Associate Professor in the Information Systems group at the Industrial Engineering and Innovation Sciences department of Eindhoven University of Technology (TU/e). She is affiliated with the Eindhoven Artificial Intelligence Systems Institute (EAISI), specifically with the EAISI High Tech Systems and EAISI Foundational groups. Her research focuses on applying Artificial Intelligence to solve complex decision-making problems across various domains including logistics, transportation, manufacturing, and e-commerce. Dr. Zhang received her PhD in Computer Science from the University of Manchester, UK. Prior to joining TU/e, she served as an Assistant Professor in the Econometrics Institute at Erasmus University Rotterdam and as a postdoc researcher in the Algorithmics group at TU Delft. She was also a visiting professor at the Institute for Advanced Computer Studies at University of Maryland, College Park, USA. Her research expertise lies at the intersection of Artificial Intelligence and optimization, with particular focus on machine learning, deep reinforcement learning, and trustworthy data-driven optimization. Dr. Zhang develops socially aware algorithms that can optimize decisions in data-rich environments. Her work bridges the gap between theoretical AI advancements and practical applications in industrial settings, addressing real-world challenges through innovative algorithmic solutions. She is particularly interested in how AI can support human decision-making while maintaining transparency and trustworthiness. Dr. Zhang's recent publications reveal a strong trend toward applying graph neural networks and reinforcement learning to complex scheduling and optimization problems. Her work demonstrates increasing sophistication in handling stochastic elements in decision-making processes, with applications spanning healthcare diagnostics, logistics, transportation, and manufacturing. She has made significant contributions to the field of neural combinatorial optimization, particularly for job shop scheduling problems and vehicle routing. Dr. Zhang has received several prestigious awards recognizing her contributions to the field: Winner of the MLVRP2023 GECCO competition (2023) Best Paper Award from Omega-International Journal of Management Science (2017) Best Industrial Paper Award (2020) Best Student Paper Award (2019) Best Student Paper Award of ICAART 2022 (2022) As a dedicated mentor, Dr. Zhang supervises numerous PhD students including Mohsen Abbaspour Onari, Abdo Abouelrous, Luca Begnardi, Xia Jiang, Chengpeng Hu, Minshuo Li, Robbert Reijnen, Jesse van Remmerden, Bart von Meijenfeldt, Ya Song, and Igor Smit. Her research is supported by various grants, including the LEO (Learning and Explaining Optimization) project co-funded by Holland High Tech | TKI HSTM via the PPP allowance scheme for public-private partnerships. Dr. Zhang actively contributes to the academic community as the Chair of the Benelux Association for Artificial Intelligence (BNVKI) and as a member of the Technical Board for the European Big Data Value Association (BDVA). She serves as an associate editor for the "Annals of Mathematics and Artificial Intelligence" journal and participates in the technical Program Committee for major AI conferences such as IJCAI, AAAI, AAMAS, and ECAI. She is also on the executive committee of the Data Science meets Optimisation (DSO) working group of EURO to promote collaboration between AI and Operations Research communities.
Dr. Mihajlo Novakovic is a researcher at the Biomolecular NMR Group (Institute of Biochemistry, ETH Zurich). His work focuses on advancing NMR spectroscopy techniques for structural and dynamical studies of labile biological systems, particularly RNA-protein interactions in SARS-CoV-2 and glycan structures. Primary Affiliation: ETH Zurich, Institute of Biochemistry Specialization: Sensitivity-enhanced NMR experiments, biomolecular condensates, and RNA structural biology. Research Highlights : Developed LLPS REDIFINE for characterizing multicomponent condensates without labeling. Optimized Hadamard magnetization transfer for studying labile protons in SARS-CoV-2 RNA. Engineered cross-polarization schemes to improve heteronuclear NMR transfers involving labile protons. Explored glycan flexibility and signal resolution challenges through integrative NMR approaches. Publication Trends : His recent work emphasizes RNA structure , protein-RNA interactions , glycan dynamics , and advanced NMR methodologies , particularly for SARS-CoV-2-related systems.
Stephen Mezyk is a Professor of Chemistry at California State University, Long Beach (2009–present) and holds concurrent appointments at Idaho National Laboratory (2025–), Notre Dame Radiation Laboratory (1986–) and Brookhaven National Laboratory (2002–). His research exploits pulse and steady-state radiolysis to quantify free-radical kinetics governing (i) destruction of pharmaceuticals, PFAS and nano-plastics in advanced water-treatment schemes, (ii) radiation stability of actinide/lanthanide separation ligands for closed nuclear fuel cycles, and (iii) redox mechanisms in chemical carcinogenesis. Education Ph.D. in Chemistry, University of Melbourne, Australia, 1989 Research Interests Mezyk’s group operates at the interface of physical chemistry and environmental/nuclear engineering. Using transient absorption and competitive kinetic techniques they map absolute rate constants for reactions of hydrated electrons, hydroxyl and sulfate radicals with trace organics, actinide complexes and polymeric debris. This fundamental database underpins mechanistic models for UV- and radiation-driven advanced oxidation/reduction processes (AOPs/ARPs) aimed at direct potable reuse of wastewater and for safeguarding solvent-extraction flowsheets in advanced nuclear fuel reprocessing. Recent work has expanded to photochemical fragmentation of polyethylene micro- and nanoplastics in natural and engineered aqueous systems, revealing rapid conversion of visible fragments into colloidal “incognito” species detectable only by Raman and LC-MS techniques. Publication Trends Over the past five years Mezyk has averaged >10 peer-reviewed papers annually, 70% appearing in high-impact journals (Environmental Science & Technology, ACS Environmental Au, Dalton Transactions, Physical Chemistry Chemical Physics). The 2024-25 output is dominated by studies coupling temperature-dependent kinetics with computational modelling to predict long-term behaviour of both emerging water contaminants and nuclear-separation ligands under gamma or alpha irradiation. Scientific Awards & Recognition No specific awards are listed in the supplied material; inclusion in multiple DOE-funded Energy Frontier Research Centers and continuous federal funding (NSF, DOE) attest to peer recognition. Advising & Grants Mezyk has mentored >25 graduate students and post-docs, many now co-authors on recent papers (indicated by asterisks). Current and recent funding embraces NSF-CBET for “CAS-MNP: Radical-induced Weathering of Micro- and Nanoplastics”, DOE-NE for “Radiation Chemistry of Trivalent Actinide Separations”, and OCWD/Water Research Foundation projects on methyl nitrate formation during UV-AOP treatment for potable reuse. Laboratories & Teams On campus he directs the Radiation & Free-Radical Kinetics Laboratory (HSCI-358) equipped with 10 MeV electron LINAC and 60-Co gamma source for pulse and steady-state radiolysis. Collaborative access to Notre Dame’s 7-MeV Tandetron and Brookhaven’s LEAF facility expands transient kinetic coverage from ns to ms timescales. A growing cohort of INL colleagues supports work on spent-fuel dissolution chemistry under the DOE-NE SEPST program.
Kazys Kazlauskas is an Affiliated Scientist at the Image and Signal Analysis Group of the Vilnius University Institute of Data Science and Digital Technologies . His research focuses on Digital Signal Processing , Cryptography , and System Identification , with significant contributions to spectrum estimation , deadzone compensation , and key-dependent S-box generation in encryption systems. Research Trends : His work spans Wiener systems , nonlinear modeling , frequency analysis , and noise reduction , particularly in block cipher systems and adaptive control . Collaborations : Frequent collaborator with Rimantas Pupeikis , Gytis Vaicekauskas , and Robert Smalius , especially in cryptography and signal processing applications. Grants & Projects : No explicit grants are listed, but his involvement in publications suggests participation in institutional research initiatives at Vilnius University.
