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Muhammad Ali GulzarView profile

Assistant Professor

• Software Engineering
• Automated Debugging
• Testing
• +6 more

Muhammad Ali Gulzar is an Assistant Professor in the Department of Computer Science at Virginia Tech and an Amazon Scholar at Amazon Web Services. He holds a Ph.D. (2020) and B.S. (2014) in Computer Science from UCLA and Lahore University of Management Sciences, respectively. His research focuses on improving developer productivity through automated debugging and testing for emerging software domains, including data-intensive systems, machine learning applications, and computational notebooks. Education Ph.D., Computer Science, University of California, Los Angeles (2020) B.S., Computer Science, Lahore University of Management Sciences, Pakistan (2014) Research Interests Dr. Gulzar redesigns software productivity tools for emerging applications across three areas: (1) automated tracking-code localization in web applications, (2) re-engineering testing/debugging for data-intensive systems (e.g., Apache Spark), and (3) advancing testing/debugging in federated learning. His work includes projects like TraceFL for federated learning interpretability, NoT.JS for JavaScript tracking blocking, and MeanCache for LLM caching. Recent Contributions Recent papers address accessibility issues in ad-driven web pages (ICSE 2025), interpretability in federated learning (ICSE 2025), and notebook executability (MSR 2025). His team received the ACM CCS 2024 Distinguished Artifact Award for tracking JavaScript function analysis and a 2024-25 Amazon-VT Award for semantic caching research. Grants & Projects CCI-funded project on web ad transparency/accessibility engineering Amazon Scholar collaboration on productivity tools Student Contributions Recent advisees include Abdul Haddi Amjad (ad accessibility), Waris Gill (Federated Learning), and Tien Nguyen (notebook analysis). Students have contributed to industry-relevant tools like GitMeter for evidence-based hiring and DeSQL for SQL debugging.

Andrea L'AfflittoView profile

Associate Professor

• Autonomous Robots
• Control Theory
• Unmanned Systems
• +3 more

Dr. Andrea L'Afflitto is an Associate Professor in the Grado Department of Industrial and Systems Engineering at Virginia Tech. His primary research focuses on autonomous robots, unmanned systems, control theory, and lightweight robotics. He holds a Ph.D. in Aerospace Engineering from Georgia Tech and has held roles including Assistant Professor at the University of Oklahoma (2015–2019) and Summer Faculty Fellow at the Army Research Lab (since 2017). His work spans funded projects with ARL, DARPA, NSF, and ONR, emphasizing applications like autonomous aerial support for ground troops. Education B.S. & M.S. in Aerospace Engineering, University of Napoli 'Federico II' (2004, 2006) M.S. in Mathematics, Virginia Tech (2011) Ph.D. in Aerospace Engineering, Georgia Tech (2015) His research interests include robust adaptive control, model reference adaptive control (MRAC), barrier Lyapunov functions, and nonlinear systems. He has authored 1 monograph, 2 book chapters, and over 40 peer-reviewed publications. Notable awards include the 2018 DARPA Young Faculty Award and the Domenica Rea D'Onofrio Scholarship (2011–2015). Grants & Collaborations His projects are funded by Army Research Lab (ARL), DARPA, NSF, and ONR. He has developed an open-source framework for nonlinear control of UAVs and contributed to terrain-informed motion planning for tactical vehicles. His work integrates cutting-edge control theory with practical applications in autonomous systems. Labs & Teams Dr. L'Afflitto leads research in autonomous systems at Virginia Tech, focusing on UAV control and adaptive algorithms. He collaborates with Army Research Lab on tactical autonomous systems and has ties to institutions like FermiLab and Thales Alenia Space through prior industry experience.

Steven J. GortlerView profile

Professor

• Computer Science
• Computer Graphics
• Computational Geometry
• +6 more

Steven J. Gortler is the Robert I. Goldman Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS) . His research focuses on computational geometry, computer graphics, and rigidity theory , with significant contributions to mesh parameterization, sensor networks, and geometric algorithms. He leads the Graphics, Vision & Interaction Lab , advancing interdisciplinary work at the intersection of mathematics and computer science. His technical work spans theoretical foundations (e.g., universal rigidity, reconstruction algorithms) and applied innovations (e.g., 3D scene conversion systems, mesh optimization). Recent publications emphasize geometric learning, neural networks for structured data, and algorithmic solutions to problems in unlabeled data analysis and sensor localization. His work bridges abstract mathematical concepts with practical computational tools for real-world applications in visualization and engineering. While no awards are explicitly listed here, his prolific publication record (spanning 20+ years) and professorship indicate sustained academic leadership. His advising and grant activities are embedded in his research output, though specific student names or grant details are not provided in available texts. Ongoing interests include geometric deep learning, invariant embeddings, and interdisciplinary applications of computational methods.

Rajpal SodhiView profile

Professor

• Mechanical Engineering
• Robotics
• Engineering Education
• +3 more

Rajpal Sodhi is a Professor in the Department of Mechanical and Industrial Engineering at New Jersey Institute of Technology (NJIT), affiliated with the College of Engineering. He specializes in mechanical systems design, mechanism synthesis, and engineering education. His work integrates MATLAB and CAD tools for teaching and research. Education: Ph.D. in Mechanical Engineering, University of Houston, 1980 M.S. in Industrial Administration, Union College, 1976 B.S./BSME in Mechanical Engineering, Thapar Institute of Technology, 1971 Research Interests: Professor Sodhi focuses on mechanism design, particularly four-bar linkages and their applications in robotics and automotive systems. He emphasizes sensitivity analysis, tolerance design, and robust engineering solutions. His work also extends to educational initiatives, including pre-engineering programs for K-12 teachers and ethics in engineering design. Publications Trends: His articles span mechanism synthesis, MATLAB-based simulation tools, engineering education reforms, and sustainable product design. Recent work addresses genetic algorithms in automotive design and motion generation for robotic systems. Awards/Grants: No specific awards or grants are explicitly listed in the provided text. Advising & Labs: No student advisees or laboratory affiliations are mentioned. His contributions lie primarily in course development and outreach programs like NJIT's Pre-Engineering Initiative.

• Machine Learning
• Data Mining
• Graph Neural Networks
• +5 more

Hanghang Tong is a Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign. He also holds affiliations with the National Center for Supercomputing Applications (NCSA) and the Center for Digital Agriculture. His research interests span machine learning, data mining, graph theory, and ethical AI, with a focus on network analysis, fairness, and large-scale data systems. Tong has been recognized with prestigious awards including the IEEE Fellow (2022), ACM Distinguished Member (2020), and NSF CAREER Award (2019). His recent work explores topics like LLM safety, time series analysis, and network alignment through tools like PLANETALIGN. Tong actively contributes to open-source libraries and benchmarks, advancing reproducibility and ethical standards in AI. Education : Not explicitly listed in provided texts. His research emphasizes practical applications of AI in climate science, healthcare, and social networks. Tong's work often addresses challenges in model fairness, scalability, and cross-domain adaptation. He collaborates widely, contributing to both foundational theory and real-world systems. Key projects include CLIMB (class-imbalanced learning benchmark) and Saffron-1 (LLM safety assurance). Tong’s lab at NCSA and Siebel School drives innovation in AI-driven data science and computational methods.

Michael WelzlView profile

Professor

• Network Congestion Control
• Internet Transport Protocols
• Recursive Networking Architecture (RINA)
• +7 more

Michael Welzl is a Full Professor in the Networks and Distributed Systems group at the Department of Informatics , University of Oslo. He is also involved with Netflix from March to September 2025 at a varying percentage. Research Interests: Network congestion control, transport protocols, recursive networking (RINA), multipath TCP, data center networking, and machine learning applications in networking. Scientific Awards: 2024: Outstanding paper award, Usenix NSDI 2024: Community award, Usenix NSDI 2018: Best of workshop presentation award 2017: Best Paper Award, IFIP NETWORKING 2016: Best Paper Award, IEEE IMCEC 2014: Best Paper Award, ACM SIGCOMM CSWS 2007: University of Innsbruck Recognition Award 2004: GI/ITG KuVS Award 2003: NATWI-Award for Young Researchers Ph.D. Students: Wen Cai (ongoing: Internet sustainability) Jie Bian (ongoing: NLP in IETF) Kristjon Ciko (defended 2022: RINA architecture) Runa Barik (defended 2020: Middlebox impact) Safiqul Islam (defended 2017: Congestion control coupling) Humaira Ijaz (defended 2016: P2P peer selection) Sven Hessler (external: Congestion control on UDP) Simone Ferlin (defended 2017: Multipath TCP) Recent Publications focus on energy-efficient networking, protocol design, machine learning for networking, and internet architecture. Key topics include TCP packet loss prediction, in-network performance enhancements, and edge AI challenges.

Gidon ErnstView profile

Assistant Professor

• Formal Methods
• Software Verification
• Software Testing
• +5 more

Gidon Ernst is a Junior Professor (Assistant Professor) for Software Verification at the Department of Computer Science, LMU Munich, where he leads research in the Software and Computational Systems Lab. His work focuses on formal methods for software engineering, with particular emphasis on verification, testing, and security analysis of complex systems. His research interests span formal methods for software engineering, software testing, interactive and automated proofs, hybrid systems, and falsification. Ernst's work addresses challenges in relational proofs for substitution principle, software testing with Legion (a tool combining concolic execution and fuzzing), concurrent information flow security with SecCSL and SecC, and falsification of temporal logic requirements for hybrid systems. His approach often combines theoretical foundations with practical tool development. Ernst's publication record shows a strong focus on verification of security properties, particularly information flow security in concurrent systems, and falsification of hybrid systems. His recent work has advanced techniques for adaptive probabilistic search in falsification, compositional vulnerability detection, and contract-based software specification. He actively contributes to the verification community through competitions like VerifyThis and ARCH. Best Master Lecture 2022 in Computer Science (awarded by Gruppe Aktiver Fachschaftika) Ernst has supervised PhD student Dongge Liu on software fuzzing with machine learning (2018-2022), co-supervised with Toby Murray and Ben Rubinstein from the University of Melbourne. His teaching includes courses on formal specification and verification, methods in software engineering, and various seminars on software quality assurance and deductive verification. He has also served on numerous program committees and organized major verification events including SPIN 2025 and the VerifyThis challenge series. Ernst maintains active collaborations with researchers worldwide, particularly in the areas of hybrid systems verification (with Ichiro Hasuo, Sean Sedwards, and Zhenya Zhang) and information flow security (with Toby Murray). His work bridges theoretical computer science with practical software engineering challenges, developing tools that have seen adoption in both academic and industrial settings.

Santosh PandeView profile

Professor

• Compiler Analysis
• Software Optimization
• Hardware Security
• +6 more

Santosh Pande is a Professor and Associate Chair for Graduate Studies at the School of Computer Science , Georgia Institute of Technology. He is affiliated with the Center for Experimental Research in Computer Systems (CERCS) and the Online Master of Science Computer Science (OMSCS) program. His research focuses on compiler analysis and optimizations, particularly in enhancing software properties through static and dynamic analysis. Key areas include hardware security (e.g., side-channel attacks on secure processors), real-time systems (converting interactive software behavior into quantifiable metrics), and parallel computing (optimizing SAT solvers and GPU scheduling). His work bridges compiler design with security, embedded systems, and machine learning, supported by grants from NSF, ONR, DARPA, and industry. Research Highlights: Side-channel attack mitigation on XOM secure processors Framework for soft real-time software analysis Compiler-guided security defenses (e.g., Pythia, Decker) High-performance RF emulation architectures His publications emphasize compiler-driven security, real-time computing, and algorithmic optimization. Over 100+ papers and open-source tools reflect his commitment to advancing compiler theory and practice. Current projects apply these techniques to machine learning algorithms, enhancing their efficiency and security.

• Computational Chemistry
• Molecular Modeling
• Force Field Development
• +3 more

Toon Verstraelen is an Associate Professor at Ghent University, Belgium, specializing in computational chemistry and molecular modeling. His research focuses on the development of force-field models, atoms-in-molecules schemes, and polarizable force fields, with additional expertise in scientific software development. Verstraelen's research interests span across computational chemistry, with particular emphasis on: Force-field model development for accurate molecular simulations Atoms-in-molecules partitioning schemes for charge analysis Polarizable force fields that capture electronic response properties Machine learning potentials for materials science applications Software development for quantum chemistry and molecular modeling Analysis of Verstraelen's recent publications (2022-2024) reveals a strong focus on advancing computational methods in chemistry. His work demonstrates growing integration of machine learning techniques with traditional quantum chemistry approaches, particularly in developing more accurate force fields and potentials. Key research areas include metal-organic frameworks, zeolites, and reactive systems, with applications spanning materials science, catalysis, and environmental chemistry. His publications show consistent contributions to methodological advancements in computational chemistry, with increasing emphasis on software tools that make these methods accessible to the broader scientific community. Verstraelen maintains active research collaborations with institutions worldwide, including McMaster University (Canada), KU Leuven and VUB (Belgium), Eindhoven University of Technology (Netherlands), Pennsylvania State University (USA), and Université de Lille (France). His work has resulted in numerous software contributions to the computational chemistry community, including HORTON, Yaff, QuickFF, and other tools that facilitate molecular modeling and simulation.

Jasper FuchsView profile

Lecturer

• Forest Economics
• Climate Change Impacts
• Bioeconomic Modeling
• +2 more

Jasper Fuchs is Lecturer at ETH Zurich's Department of Environmental Systems Science, where he researches climate impacts on forest ecosystems and sustainable resource management. He holds a PhD in Forest Sciences from the University of Göttingen (2024), an MSc in Forest Sciences (2018), and a BSc in Forest Sciences (2016) from the same institution. His research integrates economic and ecological approaches to address: Climate-adaptive forest management strategies Bioeconomic modeling of silvicultural systems Optimization of tree species portfolios under disturbance risks Development of R-based tools for forest decision-making Recent publications demonstrate strong focus on: Multifunctional forest management under climate change Economic valuation of biodiversity-ecosystem tradeoffs Advanced 3D modeling for treatment optimization Risk quantification in forest economics Awards and honors include: Gerhard Speidel Award for outstanding forest economics research (2024) Sayn-Wittgenstein Forestry Award (2019) German National Academic Foundation membership (2016) He supervises graduate theses on forest conversion economics, bark beetle management, and diversification strategies. Current projects include EU COST Action CA22141 on ecosystem service decision support systems and FunPotential (Biodiversa) on functional diversity for forest resilience.

• Transportation and Freight Services
• Artificial Intelligence and Image Processing
• Automotive Engineering
• +3 more

Felix Feng is a Research Fellow and Manager of the Intelligent Infrastructure and Transport Systems (IITS) Laboratory at Imperial College London's Department of Civil and Environmental Engineering. His work focuses on developing configurable automotive system models for autonomous vehicle safety validation and simulating interactions with road users. He holds a PhD in Mechanical Engineering (University of Bath, 2019), an MSc in Mechatronics (University of Bath, 2014), and a BEng in Mechatronics (Tongji University, 2013). Education: PhD in Mechanical Engineering, University of Bath (2015–2019) MSc in Mechatronics Engineering, University of Bath (2013–2014) BEng in Mechatronics Engineering, Tongji University (2009–2013) His research interests span autonomous vehicles , artificial intelligence , transportation systems , and control systems . Notable projects include driver model development using machine learning and safety validation frameworks for autonomous systems. His recent work explores multimodal traffic prediction, digital twin simulations, and reinforcement learning applications in urban logistics. Publications highlight advancements in traffic risk modeling, GNSS fault detection, and smart infrastructure solutions. Felix’s contributions address challenges in autonomous vehicle interaction safety, urban mobility optimization, and resilient air traffic networks. His IITS Lab at Imperial focuses on translating cutting-edge research into practical tools for safer, smarter transportation ecosystems.

Adam PiggottView profile

Associate Professor

• Geometric Group Theory
• Groups presented by rewriting systems
• Automorphism groups of groups
• +4 more

Adam Piggott is an Associate Professor and First-year Coordinator at the Mathematical Sciences Institute (MSI) of The Australian National University (ANU). He holds a D.Phil. in Mathematics from the University of Oxford and has extensive experience in teaching undergraduate mathematics within liberal arts frameworks, having worked in the USA for 13 years before returning to Australia in 2018. His research focuses on geometric group theory, rewriting systems, and automorphism groups of groups, with notable contributions to geodetic groups and algorithmic group theory. Affiliations: First-year Coordinator (ANU), Former positions at University of Queensland and Bucknell University Education: B.Math (Honours First Class), B.CompSci – University of Wollongong D.Phil. Mathematics – University of Oxford (2005) Research Interests: Adam specializes in geometric group theory, particularly exploring groups defined by rewriting systems and automorphism groups. His work bridges algebraic structures with geometric and computational methods, with recent focus on geodetic properties of groups and graphs. He also investigates interdisciplinary education initiatives, such as assessment frameworks and programming education in STEM contexts. Recent Research Trends: His publications span foundational group theory (geodetic graphs, rewriting systems) and educational innovations (authentic assessment, programming pedagogy). Collaborations include projects on PSPACE complexity in group detection and interdisciplinary science course impacts. Grants/Projects: Geodetic groups: foundational problems in algebra and computer science (2021–2024) Teaching Contributions: Leads first-year mathematics coordination at ANU, emphasizing equitable assessment practices and student belonging through initiatives like GPAM grading and formative assessment strategies. Labs/Teams: Engaged with ANU’s MSI research community, contributing to collaborative projects in algebraic structures and educational technology.

• Robotics in Gastronomy
• Educational Spaces Design
• Vocational Education Didactics
• +4 more

Prof. Birgit Peuker is a Professor of Nutrition and Home Economics at the European University Flensburg, serving as Dean of Studies for Faculty I. She holds a PhD from TU Dresden (summa cum laude) and has over 15 years in vocational education research. Her work focuses on integrating digitalization, robotics, and AI into gastronomy and home economics education, emphasizing interprofessional collaboration and sustainable practices. Education : 2001–2008: Diploma in Vocational Education, TU Dresden (specializing in Food Science and Ethics) 2015: PhD, TU Dresden ("The Teaching Kitchen as a Space for Career Orientation") Research Interests : Robotics in hospitality, educational space design (7 dimensions framework), cultural competency in home economics, and consumer literacy for vulnerable learners. Key projects include the BiLEV initiative on agricultural education and the Interreg GERDA sustainability project. Publications : Over 30 peer-reviewed articles and books, including works on AI-driven gastronomy, interprofessional care, and innovative educational spaces. Notable publications include Forschendes Lernen in der Universität (2023) and Grundbildung in der Lebenswelt verankern (2022). Grants & Awards : Lead researcher in EU-funded projects (e.g., FRESH, GERDA). Awarded summa cum laude for doctoral work. Active in academic governance roles including Senate committees and the German Home Economics Society. Labs & Initiatives : Directs the CREST research center's education projects. Co-convenes the Summer School for Nutrition Education, fostering cross-university collaboration in didactics.

Selvi KaraView profile

Assistant Professor

• Commutative Algebra
• Algebraic Combinatorics
• Graph Theory
• +3 more

Selvi Kara is an Assistant Professor of Mathematics at Bryn Mawr College. Her research focuses on Commutative Algebra and Algebraic Combinatorics, with particular emphasis on algebraic structures such as edge ideals, toric rings, and graph-theoretic applications. She holds a PhD in Mathematics and has contributed to advancements in algebraic invariants, combinatorial optimization, and computational algebra. Her work bridges abstract algebra with applied domains like remote sensing for agricultural pest monitoring. She has developed software tools such as the MorseResolutions package for Macaulay2, enhancing computational methods in algebraic geometry. Kara’s research also explores interdisciplinary topics including Stirling permutations, flow-firing dynamics, and rigid Gorenstein varieties derived from directed graphs. Publications span theoretical contributions (e.g., minimal cellular resolutions, multi-Rees algebras) and applied studies (e.g., satellite imagery analysis for crop monitoring). Her research demonstrates a strong focus on algebraic combinatorics with both pure and applied dimensions. Advising and grants: No specific advisees or grants listed in available records. Active participation in collaborative research projects such as LEAPS-MPS initiatives. Engaged in educational outreach through Bryn Mawr’s graduate programs. Labs/Teams: Collaborates with interdisciplinary teams in algebraic geometry, computational mathematics, and applied remote sensing research.

Anthony KleerekoperView profile

Senior Lecturer

• Databases
• Data Science
• Machine Learning
• +5 more

Dr Anthony Kleerekoper is a Senior Lecturer in the Department of Computing and Mathematics at Manchester Metropolitan University, where he specializes in databases and data science education. As Pathway Lead for the Data Analyst path in the Digital Technology Specialist Degree Apprentice programme, he focuses on developing innovative teaching methodologies and automated assessment tools for SQL and database systems. Developed an automated assessment tool used across multiple university units Research interests span database pedagogy, machine learning, and agricultural technology Active in performance modeling and wireless sensor networks research His publications demonstrate expertise in distributed computing, signal processing, and educational technology. Notable works include: Deep learning performance models for distributed environments (2024) Automated SQL assessment frameworks (2024) UAV-based crop disease detection using hyperspectral imagery (2022) Longitudinal athlete performance analysis (2020-2021)