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Gaik AmbartsoumianView profile

Associate Professor

• Computerized Tomography
• Integral Geometry
• Inverse Problems
• +1 more

Gaik Ambartsoumian is an Associate Professor in the Department of Mathematics at The University of Texas at Arlington. He holds a Ph.D. in Applied Mathematics from Texas A&M University (2006) and a B.S. in Mathematics from Obninsk Institute of Nuclear Power Engineering (2001). His research focuses on computerized tomography, integral geometry, inverse problems, and mathematical methods in imaging. His research spans integral geometry, inverse problems, and mathematical foundations of imaging technologies, with applications in medical tomography and remote sensing. Recent publications demonstrate a consistent focus on developing novel mathematical transforms (V-line, conical, spherical mean) for tomographic reconstruction, with increasing emphasis on higher-dimensional generalizations and tensor tomography. The works feature sophisticated numerical implementations and theoretical advancements in inversion algorithms. Scientific Awards: Guest Editor for Inverse Problems journal (2018-2019) Plenary speaker at Radon Transform centennial conference (2017) US Junior Oberwolfach Fellow (2014) Outstanding Faculty Teaching Award (2012-2013) Multiple research fellowships and prizes including the L.F. Guseman Prize He has supervised 25+ graduate students (PhD and Master's) and numerous undergraduates, securing significant grant funding including NSF DMS-1616564 ($197,628) and serving as PI on a $385,127 NIH BRAIN Initiative subaward. Leads research in computational tomography and mentors students through the Institute for Scientific Computation.

Chen KanView profile

Associate Professor

• Advanced Manufacturing
• Quality Control
• Anomaly Detection
• +5 more

Dr. Chen Kan is an Associate Professor in the Department of Industrial, Manufacturing, and Systems Engineering at The University of Texas at Arlington. His research integrates data science with engineering for advanced manufacturing and smart health applications. He is affiliated with the Center on Stochastic Modeling, Optimization, and Statistics (COSMOS) and leads the Sensing, Analytics, and Intelligence Laboratory (SAIL). PhD in Industrial Engineering (2018), Penn State University MS in Industrial Engineering (2012), University of South Florida BS in Electrical Engineering (2010), China University of Mining and Technology Dr. Kan’s research focuses on sensor-based modeling, anomaly detection, and machine learning for manufacturing systems and biomedical applications. His work spans additive manufacturing, metamaterials, ECG signal analysis, and IoT-enabled smart systems. Recent projects include blockchain-integrated security for cyber-physical manufacturing and AI-driven driver readiness monitoring. His publications emphasize 3D point cloud analytics, dynamic network modeling, and deep learning applications in manufacturing and healthcare. Key trends include cybersecurity in AM (2023-2025), metamaterials optimization (2022-2024), and ECG anomaly detection (2012-2023). Scientific awards include: SME Susan Smyth Outstanding Young Manufacturing Engineer Award (2024) NSF CAREER Award (2025) NSF CPS Program Travel Support (2024) Dr. Kan has advised five PhD students and secured over $325k in federal grants, including NSF and USDOT funding. His lab SAIL develops methodologies for real-time process monitoring and optimization across manufacturing and healthcare domains.

Walter McNeilView profile

Associate Professor

• Radiation Detection Systems
• Neutron Sensors
• Semiconductor Detectors
• +5 more

Dr. Walter J. McNeil is an Associate Professor and Steve Hsu Keystone Research Scholar in the Nuclear Engineering Department at Kansas State University. He holds a Ph.D. (2010) and B.S. (2004) in Nuclear and Mechanical Engineering from K-State. Prior to academia, he served at the Space and Naval Warfare Systems Command (2010–2015), leading R&D on radiation detection systems. At K-State, he established four laboratories, secured $8.17M in research funding, and advised 11 graduate students. His work focuses on semiconductor neutron sensors, gamma-ray spectrometers, and radiological detection systems. Education: Ph.D., Nuclear Engineering, Kansas State University, 2010 B.S., Mechanical Engineering with Nuclear Option, Kansas State University, 2004 Research Interests: Radiation detection systems design, semiconductor neutron sensors, CdZnTe gamma-ray spectrometers, and cryogenic sensor technology. He emphasizes applications in nonproliferation, radiological source detection, and military systems. His innovations include doubling He-3 neutron sensor sensitivity and developing lightweight HPGe spectrometers. Articles Trends: Recent publications (2019–2020) focus on neutron detector simulation (charge transport, pixelated designs), strain measurement via micro-nuclear-mechanical systems, and radiation transport modeling for inspection systems. Earlier work includes foundational research on microstructured detectors and Frisch collar CdZnTe technology. Scientific Awards: R&D 100 Awards (2005, 2009, 2014) DAWIA Level I Certification Extraordinary Leadership Award (2004) Advising & Grants: Advised 11 graduate students and 20 undergraduates, securing $5.5M as PI. Key grants include the $26M NNSA Consortium for Nonproliferation Enabling Capabilities (CNEC). Projects include radiological mapping via drones and 3D photogrammetry, neutron interrogation systems for explosives detection, and additive-manufactured electronics. Labs & Teams: Heads the Radiological System Integration Laboratory (RSIL), Kansas State University Material Interrogation Facility (KSUMI), and a mobile field laboratory. Collaborates with Honeywell, Radiation Detection Technologies, and national labs on sensor development and nonproliferation tools.

Vasiliki BitsouniView profile

Assistant Professor

• Differential Equations
• Dynamical Systems
• Applied Mathematics
• +1 more

Vasiliki Bitsouni is an Assistant Professor in the Department of Mathematics at the University of Patras, specializing in the Division of Applied Analysis. She is currently on parental leave. Her academic qualifications include a Ph.D. from the University of Dundee (2017), an M.Sc. and B.Sc. from the National and Kapodistrian University of Athens (2014 and 2010, respectively). She contributes to undergraduate teaching at the Department of Mathematics. Her research focuses on Differential Equations , Dynamical Systems , Applied Mathematics , and Mathematical Biology , with an emphasis on modeling biological and epidemiological systems. Her work integrates mathematical theory with real-world applications in areas such as cancer biology and public health policy analysis. Recent publications (2022–2024) emphasize epidemiological modeling of age-based interventions, cancer dynamics, and pure mathematical analysis. Key themes include disease transmission dynamics, tumor-immune interactions, and enzyme kinetics. Her work bridges theoretical mathematics with applied problems in healthcare and policy. Currently, she does not list any advisees or grants in her profile. Teaching responsibilities include undergraduate courses, though specific course details are not provided here. No lab or team affiliations are explicitly mentioned in the available information.

• Computational Materials Science
• Composite Structures
• Lattice Materials
• +3 more

Stefano Valvano serves as an Associate Professor in Integrated Computational Materials Engineering within the College of Science and Engineering. His academic appointment focuses on advanced computational methodologies for materials design and structural analysis. Valvano's research spans computational materials science , composite structures , and lattice materials engineering . His work integrates multiscale modeling with mechanical analysis of advanced materials, particularly focusing on high entropy alloys, additively manufactured lattice structures, and viscoelastic composite systems. Key research thrusts include thermo-mechanical optimization of aerospace components, noise/vibration control in multilayered structures, and development of higher-order finite element formulations for complex material systems. Analysis of his publication record reveals strong emphasis on Computational homogenization techniques for lattice structures Advanced finite element modeling of multilayered composites Thermo-mechanical behavior of high entropy alloys Passive damping systems for aerospace applications Optimization of variable-angle-tow composite structures His scientific contributions demonstrate consistent output in high-impact journals including Composite Structures , Computational Materials Science , and Mechanical Systems and Signal Processing , with recent work increasingly addressing space propulsion and lunar outpost applications.

John L. VolakisView profile

Professor

• Wireless Communications
• Antennas and Arrays
• Ultra-Wideband RF Techniques
• +7 more

John L. Volakis is a Professor of Electrical & Computer Engineering at Florida International University (FIU) and former Dean of the College of Engineering and Computing (2017-2023). He is an IEEE, ACES, AAAS, NAI, and URSI Fellow. His career spans over four decades, including roles at Boeing, University of Michigan, Ohio State University, and FIU. He has pioneered advancements in antennas, electromagnetic compatibility, and biomedical sensing. His research group has produced over 400 journal papers, 800 conference papers, and 8 books. He has mentored 95 PhD/postdoc students, many of whom became faculty or industry leaders. Education: Ph.D. (1982), M.S. (1979), B.E. (1978) from The Ohio State University and Youngstown State University. Key Roles: Former Director of Ohio State's ElectroScience Laboratory, Co-Director of FIU's RFCOM Lab. Research Interests: Focuses on wearable antennas, ultra-wideband systems, metamaterials, neurosensing, and electromagnetic compatibility. His work includes innovations in millimeter-wave arrays, EMI shielding, and bioelectromagnetics. Recent projects involve battery-free neural recording systems and STAR radio interference cancellation. Publications: Over 1,200+ publications across antennas, computational electromagnetics, and biomedical engineering. His 2025 papers address EMI shielding, LPD waveforms, and TCDA arrays. Awards: IEEE Antennas & Propagation Society Distinguished Award (2014), Chen-To Tai Educator Award (2011), and leadership roles in URSI and IEEE. Advising & Grants: Guided 43 students to best paper awards and secured $125M in external funding during his deanship. His RFCOM Lab develops advanced fabrication and measurement capabilities for phased arrays and biomedical sensors.

Malikiosis Romanos-DiogenesView profile

Professor

• Harmonic Analysis
• Number Theory
• Convex and Discrete Geometry
• +3 more

Malikiosis Romanos-Diogenes is a Professor and Chairman of the Department of Mathematics at Aristotle University of Thessaloniki. He holds a PhD in Mathematics from UCLA (2010), with a thesis on Convex and Discrete Geometry supervised by Prof. Don Blasius, and a Master’s degree in Mathematics from Aristotle University of Thessaloniki (2007). PhD in Mathematics, University of California, Los Angeles (2010) MA in Mathematics, University of California, Los Angeles (2008) MSc in Mathematics, Aristotle University of Thessaloniki (2007) BSc in Mathematics, Aristotle University of Thessaloniki (2002) His research focuses on Harmonic Analysis, Number Theory, and Convex and Discrete Geometry. He has contributed to topics such as Gabor frames, formal duality in cyclic groups, and lattice-point enumeration, with applications in signal processing and Fourier analysis. His publications span journals like Combinatorica , Applied and Computational Harmonic Analysis , and Constructive Approximation , reflecting interdisciplinary work bridging pure mathematics and computational applications. Alexander von Humboldt Postdoctoral Fellow (2015–2017) He has organized international conferences on Discrete and Applied Fourier Analysis (2025, Thessaloniki) and Tiling and Fourier Bases (2025, Xi'an, China), highlighting his leadership in advancing harmonic analysis and geometric research.

• Artificial Intelligence
• Optimization Techniques
• Reinforcement Learning
• +2 more

Michele Ciavotta is an Associate Professor at the University of Milano-Bicocca's Department of Computer Science, Systems, and Communication, specializing in AI-driven optimization for complex systems. His research integrates reinforcement learning, graph neural networks, and metaheuristics applied to distributed computing and physical systems like smart mobility and production lines. Research spans cloud/edge computing optimization, industrial production systems, smart city applications, and graph-based learning methods. Recent publications demonstrate focus on decentralized AI systems, geospatial data processing, and hypergraph neural networks for chemical and urban applications. Extensive involvement in European R&D projects addresses challenges in cloud computing infrastructure, Industry 4.0 implementations, and distributed AI solutions.

Sheela RamannaView profile

Professor

• Artificial Intelligence (AI)
• Machine Learning
• Multimodal Information Processing
• +5 more

Dr. Sheela Ramanna is a Professor and Chair of the ACS Graduate Program in the Department of Applied Computer Science at the University of Winnipeg, and an Adjunct Professor at the University of Manitoba. She holds a Ph.D. in Computer Science from Kansas State University, and completed her earlier education at Osmania University, India. Her research focuses on AI, machine learning, and soft computing with applications in natural language processing, multimodal information processing, and topological data analysis. She has been recognized with numerous awards, including multiple UW Merit Awards and Senior Member status in the International Rough Set Society. Dr. Ramanna leads projects funded by NSERC and MITACS, including work on precipitation forecasting, microplastics analysis, and cannabinoid medicine studies. She supervises over 20 graduate students and has authored/co-authored over 150 publications in top journals/conferences. Key roles include editorial positions for EAAI and KES journals, and program chairs for major conferences like IJCRS and RSCTC. Education: B.S. Electrical Engineering (Osmania University), M.S. Computer Science (Osmania University), Ph.D. Computer Science (Kansas State University) Research Interests: Machine Learning, Natural Language Processing, Multimodal Deep Learning, Computational Topology, Rough Set Theory, Social Network Analysis Grants & Projects: NSERC Alliance/Engage Grants, MITACS Accelerate Projects, WeatherLogics collaborations on precipitation forecasting and road condition mapping

Zhixin PanView profile

Assistant Professor

• Explainable Machine Learning
• Cybersecurity
• Quantum Computation
• +4 more

Zhixin Pan is an Assistant Professor in the Department of Electrical & Computer Engineering at Florida A&M University/Florida State University. Their research focuses on explainable machine learning, cybersecurity, quantum computation, and hardware security. They hold a Ph.D. in Computer Science from the University of Florida (2022), an M.S. in Computer Science from the University of Florida (2017), and a B.E. in Software Engineering from Huazhong University of Science and Technology (2015). Research interests include developing trustworthy AI systems through explainable machine learning techniques, securing hardware components against trojan attacks, and advancing quantum state preparation algorithms. Their work bridges theoretical foundations with practical applications in integrated circuit design and medical imaging. Publications emphasize cutting-edge topics like zero-shot learning for hardware security (TCAD 2024), adversarial attacks on Bayesian neural networks (TNNLS 2021), and quantum computing feedback mechanisms (QCE 2023).

Robert KeeView profile

Professor

• Chemically Reacting Flow Modeling
• Clean Energy Technologies
• Fuel Cells
• +5 more

Robert Kee holds the George R. Brown Distinguished Professor chair in Mechanical Engineering at Colorado School of Mines. His research focuses on chemically reacting flow modeling and simulation for clean energy applications, including fuel cells, photovoltaics, combustion, and electrochemistry. He is renowned as the principal architect of the CHEMKIN software, a leading global tool for chemically reacting flow simulation. Developed computational methods for stiff differential equations and chemical kinetics Work spans solid-oxide fuel cells, advanced combustion, and catalytic processes Current efforts address electro-chemo-mechanical coupling in batteries and hydrogen production Scientific Contributions: Recent publications emphasize Li-ion battery optimization, protonic ceramic electrochemical cells, and hydrogen production technologies. His work integrates thermodynamics, transport phenomena, and chemical kinetics across multiple energy domains. Research Trends: Focus areas include multi-physics battery modeling, solid-state electrolysis, ammonia synthesis, and catalytic hydrocarbon processing. Methodologies combine computational modeling, experimental validation, and materials optimization. Awards: George R. Brown Distinguished Professor Software Development: Creator of CHEMKIN, a foundational tool for reacting flow simulations. His NSF-funded work demonstrates ongoing contributions to computational thermochemistry and energy systems.

Rezaul ChowdhuryView profile

Associate Professor

• Algorithms and Data Structures
• Cache-efficient Computing
• Computational Biology
• +8 more

Rezaul Chowdhury is an Associate Professor in the Department of Computer Science at Stony Brook University (SBU), with a joint appointment at the Institute for Advanced Computational Sciences (IACS). His research focuses on algorithms and data structures for efficient serial and parallel computing, computational biology, and experimental algorithmics. He leads the Theoretical and Experimental Algorithmics (TEA) Group, emphasizing both algorithm design and engineering. Notable contributions include the Pochoir stencil compiler and the AutoGen system for dynamic programming algorithms. Chowdhury earned his Ph.D. from UT Austin, working on cache-efficient algorithms, and held postdoctoral positions at MIT and UT Austin. He has received prestigious awards, including the NSF CAREER Award and a Best Paper Award at IPDPS 2010. His work spans parallel programming, cache-oblivious algorithms, and bioinformatics applications like protein-protein docking (F2Dock). Teaching includes advanced courses on algorithms, parallel computing, and supercomputing. He advises multiple Ph.D. and master’s students and actively contributes to competitive programming through the SBU teams. His research projects are NSF-funded, focusing on stencil computations and resource-oblivious algorithms. Key software contributions include Pochoir (for stencil computations), F2Dock (protein docking), and AutoGen (automated algorithm discovery). His work bridges theory and practice, addressing challenges in multicore and distributed systems.

Silvère BonnabelView profile

Professor

• Systems and control
• Robotics
• Machine learning
• +5 more

Silvère Bonnabel is a Professor at École des Mines ParisTech (Mines ParisTech), a prestigious engineering school in France. He holds a Research Habilitation HDR (2014) and a PhD (2007) from Mines ParisTech, along with advanced degrees in financial mathematics and engineering. His professional experience includes roles as Professor at the University of New Caledonia (2019-2022), Visiting Fellow at the University of Cambridge (2017), and Assistant Professor at Mines ParisTech (2009-2016). Bonnabel’s research focuses on systems and control, robotics, machine learning, and navigation, with notable contributions to invariant Kalman filtering and optimization on manifolds. He has authored/co-authored over 100 articles, 9 patents, and achieved an h-index of 39. Notable awards include the 2024 George N. Saridis Award and 2021 European Control Award. His work bridges theoretical advancements with industrial applications, including collaborations with companies like Safran, Thales, and Manitowoc. Bonnabel has supervised numerous PhD students, contributing to breakthroughs in inertial navigation, radar tracking, and autonomous systems. Education: 2014: HDR in Mathematics (Paris VI Sorbonne) 2004-2007: PhD, Mines ParisTech 2003-2004: Master in Financial Mathematics (Paris XII) 2001-2004: Engineering Degree, Mines ParisTech Industrial Collaborations: Partnerships with Safran, Thales, Manitowoc, and startups like OFFROAD, focusing on crane control, inertial navigation, and autonomous systems. Editorial Roles: Associate Editor for IEEE Control Systems Magazine, European Control Conference, and Systems & Control Letters. His research emphasizes geometric control theory, stochastic algorithms, and sensor fusion, with applications to robotics, aerospace, and industrial automation. Bonnabel’s work on invariant extended Kalman filters has been commercialized in products like Safran’s inertial navigation systems.

Neil T. DantamView profile

Associate Professor

• Robot Planning and Control
• Task and Motion Planning
• Hybrid Systems
• +3 more

Neil T. Dantam is an Associate Professor of Computer Science at the Colorado School of Mines, focusing on robot planning and control at the intersection of symbolic and continuous domains. His work emphasizes mathematical verification, physical applicability, and user-friendly robot programming. Education: Ph.D. in Robotics from Georgia Institute of Technology (2014) Double B.S. in Computer Science and Mechanical Engineering from Purdue University (2008) Research Interests: Neil's research combines discrete and geometric planning, improves Cartesian control, and analyzes robot policies. He prioritizes methods connecting theoretical development with practical validation through robot manipulation and software design. Recent Trends: His publications address motion planning infeasibility proofs, robot team coordination under communication constraints, and hybrid task-motion planning frameworks. Keywords include Robotics, Algorithm Design, Computational Geometry, and Human-Robot Interaction. Scientific Awards: Georgia Tech President's Fellowship Georgia Tech/SAIC Paper Award American Control Conference '12 Presentation Award HUMANOIDS '14 Best Paper Finalist HUMANOIDS '14 Mike Stilman Award Finalist Students & Grants: Neil is actively recruiting students for research. He has secured grants for projects on robot team data collection, communication jamming resilience, and infeasibility proof scaling. Labs: He contributes to robotics research at Colorado School of Mines and previously at Georgia Tech, Rice University, and institutions like MIT Lincoln Laboratory and Raytheon.

Jakob HultgrenView profile

Associate Professor

• Complex Geometry
• Partial Differential Equations
• Monge-Ampère Equations
• +2 more

Jakob Hultgren is an Associate Professor in Mathematics at Umeå University's Department of Mathematics and Mathematical Statistics, specializing in complex geometry and partial differential equations. His research examines connections between geometry and PDEs, particularly focusing on Monge-Ampère equations and their applications to mirror symmetry and optimal transport. Research interests center on canonical metrics on complex manifolds, degenerating families of Calabi-Yau manifolds, and special Lagrangian torus fibrations. Hultgren's work frequently bridges pure mathematics with applications in signal processing and machine learning through geometric approaches. He currently supervises doctoral candidates researching SYZ mirror symmetry and canonical heights in Arakelov geometry. Teaching responsibilities include courses in calculus, discrete mathematics, and specialized graduate topics in optimal transport and geometry.