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Erik KomenderaView profile

Assistant Professor

• Autonomous Systems
• Space Robotics
• Field Robotics
• +3 more

Erik Komendera is an Assistant Professor in the Department of Mechanical Engineering at Virginia Tech. He leads the Field and Space Experimental Robotics (FASER) Laboratory, focusing on autonomous assembly, space robotics, and field robotics. His research integrates perception, machine learning, and multi-agent systems to enable precise robotic operations in large workspaces and off-nominal scenarios. Education: Ph.D. in Computer Science, University of Colorado (2014) M.S. in Computer Science, University of Colorado (2012) B.S.E. in Aerospace Engineering, University of Michigan (2007) Research Interests: Autonomous assembly and construction Space robotics and in-space assembly Field robotics for disaster response and environmental monitoring Perception and state estimation for large-scale operations Multi-agent motion planning and collaborative manipulation Machine learning for anomaly detection in robotics Key Projects: Built On-Orbit Robotically Assembled Gigatruss (BORG) system Development of lightweight manipulators for in-space operations Autonomous damage detection in constructed structures COVID-19 response efforts: PPE production via robotic prototyping Awards: NASA Early Career Achievement Medal (2018) Center Achievement Award (NASA Langley, 2017) First NASA Space Tech Fellow to earn a Ph.D. (2014) Outstanding Research Award (University of Colorado, 2014) Labs/Teams: Director of the FASER Lab, which advances robotics for space and terrestrial applications. Current projects include modular space systems, autonomous assembly algorithms, and robotic collaboration frameworks.

Benjamin NegrevergneView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Data Mining
• +3 more

Benjamin Negrevergne is an Associate Professor at the LAMSADE laboratory within Paris Dauphine University (PSL Research University), actively engaged in Machine Learning research. He co-leads the MILES research team and serves as co-director of the IASD Master's Program in AI and Data Science. He also contributes to PSL's computer science graduate program and is launching Nautia, a startup focused on reinforcement learning for sailing automation. His research spans Generative Modeling, Adversarial Machine Learning, and Constraint Programming, with over 15 recent publications in top conferences (NeurIPS, ICML, ACL). He has advised notable Ph.D. students including Alexandre Araujo (postdoc at NYU), Boris Doux (AWS), Alexandre Vérine (École Normale), Lucas Gnecco (ANR project DELCO), and Kunhao Zheng (Meta/FAIR). Key article trends include improving language model diversity, adversarial robustness, diffusion model optimization, and precision-recall metrics for generative systems. His work often combines theoretical rigor with practical applications in cybersecurity, game AI, and parallel computing. Benjamin holds a Ph.D. from the University of Grenoble (2011) with a thesis on parallel pattern mining algorithms. He collaborates with researchers like Yann Chevaleyre, Gabriel Synnaeve, and Tristan Cazenave, with publications in journals such as DMKD and KAIS.

• Machine Learning
• Artificial Intelligence
• Stochastic Games
• +5 more

Xian Chen is an active academic researcher with an extensive publication record spanning over 30 years from 1994 to 2025. Their research demonstrates significant contributions across multiple disciplines within computer science, engineering, and applied mathematics. The publication pattern indicates sustained scholarly activity at a high level, with numerous papers in top-tier journals and conferences. Chen's research interests span a remarkably diverse range of fields, with particular emphasis on machine learning, artificial intelligence, stochastic processes, and their applications across various domains. Their work bridges theoretical foundations with practical applications, as evidenced by publications in both theoretical journals like SIAM Journal on Control and Optimization and applied venues like IEEE Access . The research portfolio shows evolution from early work in parallel computing and wireless networks toward contemporary AI and machine learning applications. The publication record reveals significant trends in Chen's research trajectory. Early work focused on parallel computing, wireless networks, and database systems. Over time, there's a clear shift toward machine learning applications, optimization techniques, and interdisciplinary work connecting computer science with fields like biomedical engineering, renewable energy, and education technology. Recent publications (2024-2025) show strong focus on large language models, federated learning, hate speech detection, and educational AI applications, reflecting current trends in artificial intelligence research. The consistent output across multiple domains suggests a highly collaborative research approach with numerous co-authors across different institutions. Chen has established productive collaborations with researchers worldwide, as evidenced by the diverse author lists across publications. The research demonstrates both theoretical depth in areas like stochastic games and Markov decision processes, as well as practical applications in fields ranging from medical diagnostics to renewable energy systems. The interdisciplinary nature of the work indicates versatility in applying computational methods to solve domain-specific problems.

• Stochastic systems
• Applied probability
• Queueing systems
• +4 more

Ohad Perry is an Associate Professor in the Operations Research and Engineering Management (OREM) department at Southern Methodist University. His research focuses on stochastic systems, applied probability, and their applications in service systems, healthcare, and inventory management. He has contributed to the theoretical foundations of queueing systems and their practical implementations in healthcare and service industries. Education: B.Sc., Mathematics and Statistics, University of Haifa, 2004 (summa cum laude) M.S., Operations Research, Columbia University, 2005 Ph.D., Industrial Engineering and Operations Research, Columbia University, 2010 Research Interests: Perry’s work emphasizes performance analysis and control of stochastic systems, including queueing systems, healthcare operations optimization, and inventory management strategies. His methodologies integrate fluid and diffusion approximations, stochastic averaging principles, and heavy-traffic analysis. Grants and Advising: PI of NSF grants totaling over $1M, focusing on service systems, patient flow optimization, and queueing dependencies Advised students now at Tsinghua University, HKUST, Stanford, and Colorado School of Mines Labs/Teams: Collaborates with Northwestern Memorial Hospital and academic teams on healthcare capacity modeling and prehospital stroke diagnosis systems.

Aleksandar NikolovView profile

Associate Professor

• Theoretical Computer Science
• Algorithm Design
• High-Dimensional Geometry
• +6 more

Aleksandar Nikolov is an Associate Professor in the Department of Computer Science at the University of Toronto, holding the Canada Research Chair in Algorithms and Private Data Analysis. He is affiliated with the Vector Institute and a faculty fellow at the Schwartz Reisman Institute. His research focuses on theoretical computer science, algorithm design, and their connections to high-dimensional geometry, differential privacy, discrepancy theory, and approximation algorithms. Nikolov completed his PhD at Rutgers University under Muthu's advisement and was a postdoctoral researcher at Microsoft Research. Research Interests: Nikolov explores high-dimensional geometry's applications in algorithm design, particularly in private data analysis (differential privacy), discrepancy theory, and experimental design. His work bridges geometric tools with computational challenges, including nearest neighbor search and geometric optimization. He also delves into approximation algorithms and sublinear/parallel algorithms for large-scale data analysis. Awards: Nikolov received the Best Paper Award at SoCG 2015 and a $100 prize from Joel Spencer for resolving Beck's 3-permutations conjecture. His contributions span theoretical computer science with a focus on algorithmic foundations and privacy. Supervision and Education: He has mentored several graduate and undergraduate students, including current PhD candidates Haohua Tang and Lily Li. Teaching includes advanced courses like CSC2420 (Algorithm Design) and a discrepancy theory course. His research spans over 50 publications, with recent work emphasizing differential privacy and geometric algorithms. Affiliations: Beyond his academic role, Nikolov collaborates with industry and academic institutions, contributing to Canada's AI ecosystem through Vector Institute and interdisciplinary efforts at Schwartz Reisman.

Belgin Ergenç BostanoğluView profile

Associate Professor

• Database Systems
• Data Mining
• Privacy-Preserving Techniques
• +3 more

Belgin Ergenç Bostanoğlu is an Associate Professor in the Computer Engineering Department at Izmir Institute of Technology (Turkey). Her research focuses on query optimization in distributed databases, association rule mining, privacy-preserving data mining, and graph-based algorithms. She leads the Dworld research laboratory and has held academic and industry roles since the 1980s. Education: B.Sc. in Computer Engineering, Middle East Technical University (1983) M.Sc. in Computer Engineering, Izmir Institute of Technology (2002) Ph.D. in Computer Engineering, Paul Sabatier University, France (2008) Research Interests: Dynamic frequent itemset mining and hiding under multiple support thresholds Subgraph mining in evolving graphs Federated query processing over linked data Privacy-preserving techniques in distributed databases Medical NLP applications (e.g., TurkMedNLI dataset) Her recent work emphasizes large-scale graph analysis, medical NLP dataset development, and adaptive join operators for federated SPARQL queries. She has contributed to over 30 peer-reviewed publications and led projects like the TÜBİTAK ARDEB 3501 platform for dynamic frequent itemset mining. Teaching: Courses include Advanced Database Management Systems, Knowledge Discovery, and Privacy-Preserving Data Mining. Labs & Projects: Manages Dworld lab and coordinates projects such as 'Turkish Medical NLP Model Development' (BAP-funded) and the Behavioral Next Generation Wireless Networks COST Action.

David M. NicolView profile

Professor

• Cyber-security
• Network Security
• Parallelized Discrete-Event Simulation
• +5 more

David M. Nicol is the Herman M. Dieckamp Endowed Chair and Professor of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign. He directs the Information Trust Institute, Advanced Digital Sciences Center (Singapore), and Critical Infrastructure Resilience Institute. As a leader in cyber-security and large-scale system simulation, he oversees national centers funded by DHS, DOE, and NSA. Nicol’s research focuses on network security, infrastructure resilience, and parallel simulation, with contributions to startups like Network Perception and textbooks like Discrete-Event Systems Simulation . Education: B.A. Mathematics (Carleton College, 1979), M.S./Ph.D. Computer Science (University of Virginia, 1983/1985) Affiliations: Director of CIRI, CREDC, and ADSC; Co-PI of NSA Science of Security Lablet His research interests include cyber-risk assessment, mathematical modeling, and security policy analysis. Nicol has advised numerous PhD students and received accolades for teaching excellence at UIUC, Dartmouth, and William & Mary. Notable awards include ACM and IEEE Fellowships, and the Titan of Simulation distinction. He teaches courses on computer systems analysis and information assurance, and has led major conferences like Winter Simulation Conference (2006 General Chair). His work bridges academia and industry through grants totaling millions in cyber-security innovation.

Tiffani L. WilliamsView profile

Teaching Professor

• Computer Science Education
• Computational Biology
• Bioinformatics
• +3 more

Tiffani L. Williams is a Teaching Professor and Director of Onramp Programs in the Department of Computer Science at the University of Illinois at Urbana-Champaign, and serves as a Dean’s Fellow in Inclusion, Belonging, and Engagement in the Grainger College of Engineering. Previously, she held roles as Director of Computer Science Programs and Professor of the Practice at Northeastern University-Charlotte (2017–2020) and was a faculty member at Texas A&M University’s Department of Computer Science and Engineering (2005–2017). Her research focuses on computer science education , computational biology , and inclusive science communication , with a particular emphasis on phylogenetic tree inference , high-performance computing , and diverse educational pathways . She pioneered programs like the iCAN accelerator to broaden participation in computing for non-CS graduates. Her awards include the Denice Denton Emerging Leader ABIE Award , Radcliffe Institute Fellowship , and multiple teaching excellence honors from Texas A&M and Illinois. Her work bridges computational science and equitable education , with notable contributions to phylogenetic algorithms and reproducible research practices. Recent courses include advanced computer science fundamentals and excursions in computing. She leads initiatives to address systemic inequities in STEM through curriculum design and inclusive pedagogy.

Berkant SavasView profile

Associate Professor

• Scientific computing
• Numerical linear and multilinear algebra
• Large-scale graph/network computations
• +3 more

Berkant Savas is an Associate Professor at Linköping University's Department of Science and Technology (ITN) within the Physics, Electronics and Mathematics school. His research focuses on scientific computing, numerical linear/multilinear algebra, large-scale graph/network computations, and tensor analysis. He has developed influential algorithms like Clustered Matrix Approximation (CMAPP) and Grassmann manifold computational tools. His work bridges theoretical foundations with practical applications in data science and engineering. Key contributions include scalable methods for massive graphs, low-rank tensor approximations, and optimization on manifolds. Savas' MATLAB-based tools (e.g., Grassmann classes, tensor approximation packages) are widely used in academic and industrial research. He has published extensively in top journals and conferences such as SIAM Journal on Matrix Analysis and IEEE transactions. Current research explores applications in link prediction, recommendation systems, and high-dimensional data analysis. Savas collaborates internationally, with notable co-authors like Inderjit Dhillon and Lars Eldén. His work emphasizes computational efficiency and memory optimization for large-scale problems arising in networks and information retrieval. Though no explicit awards are mentioned, his impactful contributions highlight recognition within the computational mathematics community.

• Computational Fluid Dynamics
• Aerospace Engineering
• Turbulence
• +5 more

Bernardini Matteo is an Associate Professor at the Department of Civil and Environmental Engineering, Sapienza University of Rome. His research focuses on high-fidelity computational fluid dynamics (CFD), with particular emphasis on compressible turbulent flows, shock wave/boundary layer interactions, and exascale computing. He leads the development of the STREAmS solver, a high-performance compressible flow solver optimized for exascale architectures. His work addresses challenges in aerospace engineering, including planetary entry systems, cryogenic tank pressurization, and rocket nozzle dynamics. Key research areas include direct numerical simulation (DNS) and large eddy simulation (LES) of supersonic/hypersonic flows, aeroacoustics, and flow control using microramps and surface modifications. His numerical methods are applied to study phenomena such as liquid sloshing in spacecraft, aeroelastic interactions in parachutes, and thermal effects in boundary layers. He collaborates on projects involving solid rocket motors, wind turbine aeroelasticity, and HPC optimization for exascale platforms. Publications from 2023–2025 highlight advancements in exascale-ready CFD, cryogenic system modeling, and turbulence characterization under extreme conditions. His work bridges fundamental fluid dynamics research with aerospace engineering applications, emphasizing high-performance computing and multiphysics simulation.

Krzysztof SpalikView profile

Professor

• Phylogenetics
• Evolutionary Biology
• Plant Taxonomy
• +3 more

Prof. Krzysztof Spalik is a distinguished academic serving as Dean of the Faculty of Biology at the University of Warsaw and Head of the Scientific Council of the Biological-Chemical Research Centre. He leads the Committee of Environmental and Evolutionary Biology at the Polish Academy of Sciences (PAS). His research focuses on phylogenetics, evolution, taxonomy, and biogeography of Apiaceae, with particular emphasis on economically important plants like carrots and medicinal genera such as Ferula and Thapsia. His current project investigates ecological factors shaping secondary woodiness in Apioideae. Spalik has held numerous leadership roles, including deputy head of the Committee for Evolutionary and Theoretical Biology PAS and membership in expert panels for the National Science Centre Poland. He is a recipient of the 2013 Academic Teacher of the Year award for his contributions to education in exact and natural sciences at UW. Spalik actively engages in educational research, collaborating with the Institute of Educational Research and serving on the Central Examination Board. His teaching portfolio includes Botany, Ecological Education, Evolutionism, and Taxonomy, reflecting his commitment to both research and pedagogy. His research has led to groundbreaking taxonomic revisions (e.g., Laserocarpum in Greece) and advancements in phylogenetic methodologies, such as the Fast mvSLOUCH tool for analyzing multivariate trait evolution. Spalik’s work bridges molecular systematics with ecological adaptation, addressing topics like dispersal mechanisms, drought resistance, and inflorescence evolution in Apiaceae. Over 50 peer-reviewed publications and active roles in international collaborations underscore his influence in plant evolutionary biology.

Damek DavisView profile

Assistant Professor

• Optimization
• Machine Learning
• Signal Processing
• +4 more

Damek Davis is an Assistant Professor in the Department of Mathematics at Cornell University, affiliated with both the College of Engineering and the College of Arts and Sciences. He holds a Ph.D. in Mathematics from the University of California, Los Angeles (2015). His research focuses on developing and analyzing optimization algorithms for large-scale, nonconvex, and nonsmooth problems arising in machine learning and signal processing. He emphasizes theoretical guarantees on algorithm performance and practical implementations that leverage modern computing architectures. His work spans stochastic optimization, subgradient methods, and convergence analysis, with applications to computational microscopy, phase retrieval, and decentralized systems. Recent contributions include advancements in adaptive stepsize methods, sharpness-aware optimization, and global optimality conditions for mixture models. Davis collaborates on theoretical foundations and algorithmic innovations in high-dimensional statistics, nonlinear optimization, and parallel computing. His scholarly output includes over 50 peer-reviewed articles, with recent highlights in top venues such as SIAM Journal on Optimization, Mathematical Programming, and IEEE Transactions on Signal Processing. His research is supported by grants from NSF and AFOSR, focusing on topics like robust statistical estimation and algorithmic scalability.

Todd MartinezView profile

Professor

• Theoretical Chemistry
• Photochemistry
• Mechanochemistry
• +3 more

Todd Martinez is the David Mulvane Ehrsam and Edward Curtis Franklin Professor of Chemistry and Professor of Photon Science at Stanford University, affiliated with SLAC National Accelerator Laboratory. His research focuses on theoretical and computational chemistry, with emphasis on photochemistry, mechanochemistry, and quantum mechanical methods. He earned his PhD in Chemistry from UCLA in 1994, followed by postdoctoral work at UCLA and the Hebrew University in Jerusalem before joining Stanford in 2009. His group develops novel simulation tools, including GPU-accelerated quantum chemistry software (e.g., TeraChem) and virtual reality interfaces (InteraChem and MolAR) for interactive molecular modeling. Key interests include predicting molecular behavior under light and mechanical force, designing self-healing materials, and advancing reaction discovery through first-principles methods. Awards include the MacArthur Fellowship and recognition as a Fellow of the American Academy of Arts and Sciences. Education: PhD in Chemistry (UCLA, 1994); Postdoctoral Studies at UCLA and Hebrew University (Jerusalem) Research Themes: Molecular modeling, excited-state dynamics, mechanochemical reactions, and computational tools Publications span ultrafast spectroscopy, quantum mechanical modeling, and algorithm development for large-scale simulations. The Martinez Group’s work bridges theory and application, aiming to make molecular design predictive and accessible through interdisciplinary collaborations.

Annabel LathamView profile

Senior Lecturer

• Artificial Intelligence in Education
• Ethics and Trust of AI
• Computational Intelligence
• +8 more

Dr. Annabel Latham is a Senior Lecturer in Computer Science at Manchester Metropolitan University within the Faculty of Science and Engineering’s Computing and Mathematics department. She holds a PhD in Artificial Intelligence, an MSc in Computing, a Postgraduate Certificate in Academic Practice, a CIM Diploma in Marketing, and a BSc(Hons) in Computation. As a Fellow of the Higher Education Academy (FHEA) and Senior Member of IEEE (SMIEEE), she contributes extensively to AI research and education. PhD in Artificial Intelligence MSc in Computing PGC Academic Practice CIM Diploma in Marketing BSc(Hons) Computation FHEA (2015) SMIEEE (2018) Research Interests include Artificial Intelligence in Education , Ethics of AI , and Computational Intelligence . She specializes in conversational agents, intelligent tutoring systems, and public trust in AI. Her work addresses fairness, accountability, and accessibility in AI-driven education, leveraging technologies like large language models and fuzzy logic. Research Outputs span 15 years, with a focus on explainable AI, educational applications of conversational agents, and ethical frameworks. Recent work (2024-2025) explores postdigital citizen science, trustworthy AI implementation, and XAI usability for non-specialists. 2023 IEEE Region 8 Outstanding Women in Engineering Volunteer Award 2019 IEEE Region 8 Outstanding Women in Engineering Affinity Group of the Year 2018 Outstanding Peer Reviewer, Elsevier: Computers & Education Senior Member IEEE (2018) FHEA (2015) Teaching and Supervision includes undergraduate Databases, postgraduate units in Information Systems and AI Ethics. She supervises MSc and PhD students in areas like explainability-aware machine learning, data responsibility, and AI trustworthiness. Her grants involve collaborations with international funding bodies such as NAFOSTED (Vietnam) and Croatia’s National Council for Science. Labs and Groups include the Computational Intelligence Lab, Machine Intelligence research group, and the Data and AI Ethics research group, where she co-leads initiatives on ethical AI in education.

Anthony P. AustinView profile

Assistant Professor

• Numerical Linear Algebra
• High-Performance Computing
• Approximation Theory
• +3 more

Anthony P. Austin is an Assistant Professor in the Department of Applied Mathematics at the Naval Postgraduate School . His research focuses on numerical linear algebra, high-performance computing, approximation theory, and GPU-accelerated algorithms. He has held prior positions at Virginia Tech and Argonne National Laboratory as a J. H. Wilkinson Fellow. His work includes contributions to Chebfun, a software system for numerical computation. Education: D.Phil. in Mathematics , University of Oxford Mathematical Institute (2016) Ph.D. studies with active development in the Chebfun project Research Interests : Numerical methods for large-scale problems, high-order methods for PDEs, and computational techniques leveraging GPUs. His work emphasizes algorithmic innovation and stability in numerical simulations. Recent Contributions : Publications include advancements in spectral factorization, adversarial signal perturbations, and parallel algorithms for partial spectral computations. His seminar co-organization highlights collaboration in early-career faculty research initiatives. Professional Activities : Co-organizer of the NPS Junior Faculty Research Seminar (AY2022/Q3), focusing on fostering interdisciplinary collaboration among early-career faculty.