Find Academic Supervisors

Myra B. CohenView profile

Professor

• Software Testing
• Search-Based Software Engineering
• Combinatorial Designs
• +4 more

Myra B. Cohen is a Professor and the Lanh and Oanh Nguyen Chair in Software Engineering within the Department of Computer Science at Iowa State University's College of Engineering. She previously served as a Susan J. Rosowski Professor at the University of Nebraska-Lincoln and leads the LaVA-OPs Laboratory for Variability-Aware Assurance and Testing of Organic Programs. Her research spans software testing of highly-configurable systems, search-based software engineering, combinatorial design applications, and synergies between software engineering and synthetic biology. She investigates assurance techniques for self-adaptive systems through bio-inspired algorithms and examines software testing representations of natural processes like chemical reaction networks. Her 15 most recent publications demonstrate strong focus on cyber-physical systems (particularly drone safety), biological computing, and configuration-aware testing. These works reveal interdisciplinary trends combining software engineering with synthetic biology, emphasizing real-world applications in autonomous systems and computational biology. NSF CAREER Award AFOSR Young Investigator Award ACM Distinguished Scientist Best Student Paper Award at SPLC 2019 ACM Distinguished Paper Award at ASE 2020 Best Paper Award at GI@ICSE 2021 Professor Cohen advises PhD students Salil Purandare, Md Obaidul Kabir, and Michael Gerten, with research focusing on cyber-physical systems and biological software applications. She leads multiple significant projects including the DOE-funded Dependable, Explainable, Reusable, AI-Driven Computational Biology initiative and blockchain fault tolerance research. Her LaVA-OPs laboratory develops assurance techniques for highly-configurable and self-adaptive programs through bio-inspired algorithms.

Christian López BencosmeView profile

Assistant Professor

• Machine Learning
• Data Science
• Persuasive Technologies
• +7 more

Christian López Bencosme is an Assistant Professor of Computer Science with an affiliation in Mechanical Engineering at Lafayette College. He holds a Ph.D. from the Harold and Inge Marcus Department of Industrial and Manufacturing Engineering at the Pennsylvania State University and a Master of Science in Industrial and Systems Engineering from the Rochester Institute of Technology, NY. Prior to his academic career, he worked in both the Service and Manufacturing sectors. Dr. López's research focuses on the design and optimization of intelligent decision support systems and persuasive technologies to augment human proficiencies. His work integrates technologies and methods from science and engineering, particularly Machine Learning, Virtual Reality, and Gamification, to create systems that improve decision-making and task performance across education, business, health, and wellness domains. He has established several significant research projects including the Adaptable Game-based, Interactive Learning Environments for STEM Education (AGILE STEM), a Machine Learning and Gamification project for personalized-adaptive educational applications, and the VR CLICK Approach Project for improving spatial visualization skills. His scholarly work demonstrates consistent innovation in educational technology, with particular emphasis on developing machine learning algorithms that can personalize learning experiences and adapt pedagogical content to individual students' needs. His research on multilingual Twitter datasets related to the COVID-19 pandemic has generated significant scholarly interest, resulting in over 3 billion collected tweets and multiple publications analyzing global reactions to pandemic policies. Dr. López's work consistently bridges theoretical computer science with practical educational applications, creating systems that not only advance knowledge but also have immediate impact in classroom settings. Best Paper Award from Consortium for Computing Sciences in Colleges Regional Conference New IE Educator Outstanding Paper Award (awarded twice) ASME CIE Virtual Environments & Systems Best Paper Award Mid-Atlantic ASEE Committee Best Paper Award As an educator, Dr. López teaches courses including Introduction to Engineering: Design & Data Mining (ES 101), Introduction to Game Programming (CS 104), Introduction to Machine Learning (CS 414), and Principles of Data Science (DS 201). He has mentored numerous undergraduate students including Nathan Dinh, Alexa Gould, and Malolan Vasu on research projects related to gamification, machine learning, and virtual reality applications. His students have presented their collaborative work at major conferences including the ASME International Design Engineering Technical Conferences. Dr. López leads the VR/AR Community of Practice at Lafayette College, fostering interdisciplinary collaboration around virtual and augmented reality technologies. His research lab focuses on developing innovative educational applications that leverage machine learning algorithms to personalize learning experiences and improve student outcomes in STEM fields, with current projects including AI-driven educational game development and VR applications for spatial visualization training.

• Cloud microphysics
• Aerosol-cloud interactions
• Turbulence effects on microphysics
• +4 more

Dr. Jens Voigtländer is a Research Fellow in the Department of Atmospheric Microphysics at the Leibniz Institute for Tropospheric Research (TROPOS) in Leipzig, Germany. Affiliated with the Cloud Working Group since completing his PhD in 2010, his work centers on cloud-aerosol interactions, droplet activation processes, and instrumentation development for atmospheric measurements. His research directly contributes to understanding climate-relevant microphysical processes through laboratory and field studies. His academic background includes teacher training in mathematics and physics at the University of Leipzig (1999-2000), followed by meteorology studies culminating in a 2005 diploma thesis on traffic impacts on urban particle distributions. He earned his PhD in 2010 with research on hygroscopic growth and cloud condensation nuclei activation of organic/inorganic compounds using LACIS experimental data and computational modeling. Voigtländer's research spans cloud microphysics, aerosol-cloud-turbulence interactions, and spatiotemporal aerosol distribution analysis. He specializes in droplet activation mechanisms, ice nucleation processes, and miniaturization of atmospheric instrumentation. His work bridges laboratory experiments with real-world atmospheric observations to improve climate model parameterizations. Analysis of his 2014-2018 publications reveals dominant themes in ice nucleation instrumentation (notably the SPIN spectrometer development), aircraft-based aerosol characterization (CARIBIC program), and fundamental studies of Criegee intermediate chemistry. His work consistently integrates experimental measurements with computational modeling to address aerosol impacts on cloud formation. Scientific contributions include: Development of the SPIN ice nucleation spectrometer Parameterization of sea-salt hygroscopic growth Characterization of biological ice nuclei from pollen Investigation of Criegee intermediate reaction kinetics As a core member of TROPOS' Cloud Working Group, Voigtländer leverages the LACIS facility for controlled microphysical studies while contributing to international collaborations like CERN's CLOUD experiment. His instrumentation miniaturization efforts enable novel spatiotemporal atmospheric measurements critical for climate research.

Tobias HeerView profile

Professor

• Network Security
• IT Security
• Offensive Security
• +5 more

Prof. Dr. Tobias Heer serves as Professor and Dean of Computer Science and Engineering at Esslingen University of Applied Sciences, where he also holds Senate and Building Committee roles. Based in Room F 01.312 at Campus Esslingen (Flandernstraße 101, 73732 Esslingen), he leads the IT Security Lab while teaching Network Security, IT Security, Offensive Security, and Security of Industrial Networks courses. His research spans industrial network security with emphasis on time-sensitive networking (TSN), deterministic networking (DetNet), and offensive security methodologies. Key focus areas include spoofed traffic detection, secure resource allocation protocols, internet-wide scanner analysis, and global software patching practices. His work bridges theoretical protocol design with industrial implementation challenges in critical infrastructure systems. Recent publications (2022-2024) reveal strong trends in industrial network security, particularly TSN/DetNet integration and real-time traffic protection. His team develops evaluation frameworks like GeNESIS for industrial scenarios while analyzing global patching discipline across sectors. Earlier foundational work includes Host Identity Protocol standards (RFC 7401, 8002) and distributed hash table applications for mesh networks. No scientific awards are documented in the provided materials. While advising activities aren't specified, his patent portfolio indicates active industry collaboration. He heads the IT Security Lab which focuses on industrial network security testing and protocol development, with recent projects examining PRP-based transmission systems and time-deterministic firewall mechanisms.

Fahmida RahmanView profile

Academic

• Transportation Safety
• Traffic Operation
• Congestion Management
• +3 more

Fahmida Rahman, Ph.D., is an Assistant Teaching Professor in the Department of Civil and Environmental Engineering at Rowan University's Henry M. Rowan College of Engineering. Her expertise lies in transportation engineering with focuses on safety, traffic operations, and data-driven solutions. She holds a Ph.D. from the University of Kentucky (2022), where she developed speed-based Safety Performance Functions (SPFs) for rural highways and applied machine learning for safety assessment. Education: Ph.D., Civil Engineering, University of Kentucky (2022) M.S., Civil Engineering, University of Kentucky (2019) B.S., Civil Engineering, Bangladesh University of Engineering and Technology (2016) Research Interests: Transportation safety engineering, traffic operation optimization, congestion management, big data analysis, and Intelligent Transportation Systems (ITS). She has contributed to tools like the Kentucky Road User Cost and Travel Time Savings models for the Kentucky Transportation Cabinet (KYTC). Professional Contributions: Developed SPF models using speed data, applied machine learning for crash prediction, and pioneered third-party data integration for congestion performance metrics. Her work bridges theoretical models with real-world transportation challenges. Affiliations: Member of ASCE, SWE, and ITE professional organizations.

Orlando HernandezView profile

Associate Professor

• VLSI Architectures
• Computer Vision
• Image Processing
• +4 more

Orlando Hernandez is an Associate Professor in the Department of Electrical and Computer Engineering at The College of New Jersey. He holds a Ph.D. in Electrical Engineering from Southern Methodist University (2002), an M.S.E.E. (1993) and B.S.E.E. (1991) from the University of South Florida. Prior to his academic career, he held industry positions at Texas Instruments and Maxim Integrated Products from 1993-2003, serving in design management roles for imaging systems, ASIC development, and microcontroller technologies. His research focuses on high-performance VLSI architectures for computer vision applications, including color image segmentation, digital signal processing, embedded systems, and mixed-signal design. Key research areas include hardware acceleration for image compression algorithms, real-time traffic monitoring systems, autonomous robotics, and advanced encryption implementations. His work consistently bridges theoretical algorithms with practical hardware implementations. Professional affiliations include Senior Membership in the Institute of Electrical and Electronics Engineers (IEEE). He has secured multiple research grants including a $93,320 National Science Foundation award for image processing instrumentation and several industry-sponsored equipment grants from Texas Instruments and Xilinx.

Daniel GrindleView profile

Teaching Professor

• Biomechanics
• Traffic Safety Engineering
• Computational Biomechanics
• +3 more

Daniel Grindle is an Assistant Teaching Professor in the Bioengineering department at Northeastern University, part of the College of Engineering. He holds a PhD in Engineering Mechanics from Virginia Tech (2023). His research focuses on biomechanics and mechanobiology, particularly using finite element modeling to investigate pedestrian-vehicle collision dynamics and injury mechanisms. Education: PhD in Engineering Mechanics from Virginia Tech (2023). Research interests include traffic safety engineering, computational biomechanics, and automotive safety. His work employs advanced simulation techniques to analyze injury risks and improve pedestrian protection systems. Recent publications emphasize finite element modeling of human body responses during collisions and gait-related injury dynamics. Collaborations include projects with industry and academic partners to validate biomechanical models for real-world applications. While no scientific awards are explicitly listed, his contributions to pedestrian safety research are notable. He advises students on capstone projects, such as the Flextab healthcare innovation developed with the Roux Institute. No grants are explicitly mentioned, but his research aligns with Northeastern’s emphasis on applied engineering solutions.

• Environmental Engineering
• Water Quality Management
• Sediment Dynamics
• +3 more

Chunyan Tang is a University Lecturer at the Department of Materials Science and Environmental Engineering, Faculty of Engineering and Natural Sciences, Tampere University. Her research focuses on environmental engineering with emphasis on water quality management, sediment dynamics, and hydrodynamic modeling. She has conducted extensive studies on Lake Taihu, investigating sediment resuspension mechanisms, algal bloom control, and the impact of human activities such as ship traffic and water diversion. Her work integrates field measurements, numerical modeling, and laboratory analyses. Key contributions include advancing understanding of turbulent burst dynamics in shallow lakes, evaluating ecological risks of heavy metal pollution, and developing novel sigma coordinate systems for hydrodynamic simulations. She collaborates on projects addressing eutrophication mitigation, sediment remediation, and climate change impacts on water resources. Notable methodologies in her research include ADV (Acoustic Doppler Velocimeter) measurements for suspended sediment quantification, coupled hydrodynamic-water quality modeling, and kinetic analysis of pyrolysis processes for waste conversion. Her studies often combine environmental chemistry, fluid dynamics, and geospatial analysis to provide interdisciplinary solutions for complex water systems.

• Aerospace Engineering
• Computational Engineering
• Artificial Intelligence
• +5 more

Professor Andy Keane is a Professor of Computational Engineering at the University of Southampton , focusing on advanced computational methods for aerospace system design and optimization. He is affiliated with the Computational Engineering and Design Group and has led numerous EPSRC and EU-funded research projects, including DECODE , DYNAMO , and ICAReS . Research spans Aerospace Engineering , Artificial Intelligence , and Computer-Aided Design (CAD) Key projects include Novel Passive Vibration Isolators and Multi-fidelity Simulation for Aero Engine Seals Active in mentoring PhD students such as Gerico Vidanes and Petru-Cristian Cimpoesu Research Trends : Recent publications highlight applications of surrogate modeling , generative adversarial networks , and hybrid machine learning in aerospace and CAD. Themes include uncertainty estimation , meta-learning , and vibration isolation . Scientific Awards : Fellow of the Institution of Mechanical Engineers (1998) Fellow of The Royal Institution of Naval Architects (2011) Collaborations : Partnered with institutions across the UK and EU on projects like CRESCENDO and E-Break . Supervision of PhD students focuses on AI-driven design and optimization . Labs & Teams : Member of the Computational Engineering and Design Group , integrating analytical, computational, and experimental techniques for multidisciplinary engineering.

• Performance Engineering
• Telecommunication Systems
• Enterprise Software
• +3 more

John Murphy is a Full Professor at the School of Computer Science, University College Dublin. He holds academic positions as an IBM Faculty Fellow and Fellow of multiple professional organizations, including the Institution of Engineering and Technology and Engineers Ireland. His research focuses on Performance Engineering, Telecommunication Systems, and Middleware, with a strong emphasis on cloud computing, distributed systems, and network optimization. Education: B.E. (Electronic Engineering) from University College Dublin (1988), M.Sc. (Electrical Engineering) from California Institute of Technology (1990), and Ph.D. (Electronic Engineering) from Dublin City University (1996). Research Interests: His work spans Performance Engineering, Telecommunication Systems, Enterprise Software, Middleware, Mobile Networks, and Queueing Theory. Notable contributions include energy-efficient cloud resource management and network-aware scheduling in data centers. Publications: Over 200 peer-reviewed articles, including recent work on intelligent computing in IoT, energy-efficient VM mapping, and load balancing in wireless networks. His research trends emphasize cloud optimization, distributed systems performance, and smart city infrastructure. Awards: Recognitions include the Real Time Correlation Engine award and prestigious fellowships. He has supervised 24 PhD students and secured over €9.5M in research grants. Teaching: Coordinates modules on Computer Networking, Databases, and Programming. He advocates for foundational learning to foster adaptability in students. Grants: Includes Horizon 2020 projects like NEO-QE and PRTLI grants for SimSci. Collaborates with industry partners for applied research. Labs: Leads the Performance Engineering Laboratory, advancing research in distributed systems and network performance.

Shan JiangView profile

Assistant Professor

• Urban Analytics
• Big Data Analytics
• Spatial Data Science
• +4 more

Shan Jiang is an Assistant Professor at Tufts University, holding dual appointments in the Department of Urban and Environmental Policy & Planning (School of Arts and Sciences) and the Department of Civil and Environmental Engineering (School of Engineering). She also holds the Eileen Fox Aptman, J90, and Lowell Aptman Professorship. Her research focuses on leveraging big data analytics, spatial data science, and computational methods to study urban systems, with an emphasis on sustainable city planning and smart infrastructure. Education: PhD, MCP, and MSc in Urban Planning and Transportation from MIT, alongside undergraduate degrees in Urban Planning and Economics from Peking University. Research Interests: Urban analytics, spatial data science, mobility patterns, environmental justice, and the application of ICT in urban policy. Jiang’s work bridges theory and practice, addressing global urban challenges through innovative data integration (e.g., mobile phone metadata, social media, and environmental sensors). Grants & Collaborations: Funded by NSF, Singapore National Research Foundation, Lincoln Institute of Land Policy, and others. Projects include flood modeling using taxi GPS data, carbon emissions analysis in Boston, and public transit data-sharing frameworks. Awards: Knez Faculty Fellow (2018). Active in professional service, including editorial roles at Transactions in Urban Data, Science, and Technology and Data and Policy . Teaching: Courses on urban analytics, data science for sustainability, transportation planning, and visualization labs.

Sharon GuniView profile

Assistant Professor

• Artificial Intelligence
• Intelligent Transportation Systems
• Reinforcement Learning
• +6 more

Dr. Sharon Guni is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University, affiliated with the College of Engineering. She directs the PiStar laboratory, focusing on advancing artificial intelligence (AI) theory and its practical applications, particularly in transportation systems and multi-agent coordination. Her research integrates reinforcement learning, combinatorial optimization, and game theory to address real-world challenges such as traffic congestion and autonomous vehicle management. Ph.D. in Information Systems Engineering, Ben-Gurion University (2015) M.S. in Information Systems Engineering, Ben-Gurion University (2012) B.S. in Information Systems Engineering, Ben-Gurion University (2011) Dr. Guni’s research interests span AI, intelligent transportation systems, reinforcement learning, and multiagent systems. Notable contributions include socially optimal traffic tolling mechanisms, conflict-based search algorithms for multi-agent pathfinding, and agent-based models for epidemiological inference. She has received prestigious awards, including the NSF CAREER Award (2023) and the AAAI Outstanding Paper Award (2016). Her lab’s work on self-optimizing traffic signal controllers has been featured in WIRED Magazine and multiple news outlets. Recent projects include socially optimal non-discriminatory policies for continuous-action games and curriculum generation for reinforcement learning. Awards: Bergmann Memorial Research Award (2024), Wilson Memorial Lecture (2025), AIJ Prominent Paper Award (2020) Grants: NSF CAREER Award, Texas A&M grants supporting traffic optimization and AI research PiStar collaborates on interdisciplinary projects, including autonomous driving demonstrations with Houston high schools and pandemic mitigation strategies via agent-based modeling. The lab hosts a dynamic team of graduate students and researchers advancing AI’s societal impact.

• Autonomous Systems
• Bayesian Statistics
• Big Data
• +7 more

Professor Simon Maskell is a Professor of Autonomous Systems at the University of Liverpool, affiliated with the School of Electrical Engineering, Electronics and Computer Science and the Faculty of Science and Engineering. He holds degrees including MEng, MA, PhD, and is a Fellow of the Institution of Engineering and Technology (FIET) and Chartered Engineer (CEng). His research focuses on algorithmic solutions at the intersection of Computer Science, Engineering, and Statistics. Key areas include Bayesian statistics for decision-making under uncertainty, particle filters for high-dimensional parameter estimation, and applications in autonomous systems, big data analytics, and data fusion for sectors like defense, healthcare, and maritime surveillance. He leads the Liverpool Big Data Network and contributes to the Stone Soup open-source tracking framework. Maskell has secured grants from the Home Office, European Commission, and EPSRC, addressing challenges like combating cyber threats, improving drug interaction detection, and enhancing sensor technologies. His notable award includes an Honorary Research Fellow title from Imperial College (2010). In teaching, he oversees modules such as Instrumentation & Control and supervises research on topics like Bayesian methods in proteomics, traffic signal control, and antimicrobial resistance. He serves as an Associate Editor for IEEE Transactions on Aerospace and Electronic Systems and IEEE Signal Processing Letters. Professional activities include editorial roles and administrative leadership in interdisciplinary initiatives, emphasizing ethical AI deployment and data-driven healthcare solutions.

• Cybersecurity
• Terahertz Communications
• Access Control Systems
• +5 more

Dr. Bernard Butler is a Lecturer in the Department of Computing and Mathematics at the Walton Institute for Information and Communications Systems Science, South-East Technological University (SETU). He holds a PhD in Access Control System Specification (2016) and specializes in cybersecurity, terahertz communications, access control systems, and smart grid security. His research bridges theoretical frameworks with practical applications in 6G networks, vehicular systems, and augmented reality. Affiliations: SETU Walton Institute Key Focus Areas: Terahertz nanonetworks, UAV security, quantum machine learning, and climate change sensing via 6G Research Interests: Terahertz Engineering , Access Control Policies , and Vehicular Network Optimization . He explores disruptive technologies like the Internet of Paint (IoP) for environmental monitoring and AR applications in intelligent transportation systems. His 2024 work on False Data Injection Attacks on UAVs highlights cybersecurity vulnerabilities in drone delivery systems, while 2025's IoP research pioneers smart material-based communication networks. Recent projects include 'ATLAS' (access control system dimensioning) and 'SWAGGER' (mobile/web deployment platforms). Advising: Supervises PhD projects in UAV security, quantum machine learning, and smart grid security. Grants include leadership in two national projects (2008, 2014). Labs/Teams: Active in the Walton Institute's communications systems research group, collaborating on 6G applications and environmental sensing technologies.

James SullivanView profile

Researcher

• Transportation Modeling
• Network Disruption Analysis
• Winter Maintenance Strategies
• +3 more

James Sullivan is a Senior Research Analyst at the University of Vermont's Transportation Research Center (TRC), affiliated with the College of Engineering and Mathematical Sciences. He holds a professional engineering license and is pursuing a Ph.D. Previously, he worked as a consulting engineer in the private sector for 15 years. His expertise includes network disruption analysis, transportation energy efficiency, winter maintenance strategies, and infrastructure resilience. Education: M.S., University of Vermont B.S., Cornell University Research Focus: Sullivan's work centers on land-use and transportation modeling, critical infrastructure assessment under severe weather, and maintaining statewide travel-demand models for Vermont. He develops methodologies to evaluate network performance, emergency service accessibility, and winter maintenance optimization, with a focus on integrating risk-based approaches and climate adaptation strategies. Teaching: Sullivan instructs CE 295: Transportation Planning and Modeling (covering travel-demand modeling and statistical methods) and CE 133: Transportation Systems (focusing on traffic analysis, safety, and design). Key Contributions: Sullivan has authored over 15 peer-reviewed articles addressing transportation system robustness, snow/ice control algorithms, and infrastructure vulnerability. His work often bridges theoretical modeling with practical applications, such as optimizing salt facility locations and enhancing emergency response networks. Labs/Teams: Active collaborator within the UVM TRC, contributing to interdisciplinary projects with faculty and state agencies like the Vermont Agency of Transportation.