Find Academic Supervisors

Abhishek HalderView profile

Associate Professor

• Stochastic Systems
• Control Theory
• Optimal Transport
• +7 more

Abhishek Halder is an Associate Professor in the Department of Aerospace Engineering at Iowa State University and an Associate Adjunct Professor in the Department of Applied Mathematics at the University of California, Santa Cruz. He is also a member of the Translational AI Center at Iowa State University. His academic journey includes joining Iowa State University as an Assistant Professor in July 2023 and previously serving as faculty at UC Santa Cruz starting from October 2017. Dr. Halder's educational background includes studies at IIT Kharagpur and Texas A&M University, where he developed expertise in systems and control theory with applications to matrix analysis, probability, and optimization. His research has been recognized with prestigious awards including the O. Hugo Schuck Best Application Paper Award from the American Automatic Control Council, Applied Mathematics Research Award from UC Santa Cruz, Outstanding Doctoral Student Award from Texas A&M, and Best Dual Degree Thesis Award from IIT Kharagpur. His research focuses on stochastic systems, control and optimization with applications to large scale cyber-physical systems. Dr. Halder has made significant contributions to the fields of optimal transport, Schrödinger Bridge theory, distributional control, and uncertainty propagation in dynamical systems. His work bridges theoretical developments with practical applications in power systems, aerospace engineering, and machine learning. He has secured multiple research grants from NSF, including a CPS Frontier project on Computation-Aware Algorithmic Design for Cyber-Physical Systems. Dr. Halder has demonstrated leadership in the control systems community through editorial roles including Associate Editor for IEEE Transactions on Automatic Control (2025-present), ASME Journal of Dynamic Systems, Measurement, and Control (2025-present), Systems & Control Letters (2022-present), and previously for IEEE Control Systems Society Conference Editorial Board (2019-2025) and IEEE Transactions on Aerospace and Electronic Systems (2019-2022). He is a Senior Member of IEEE and a member of IFAC, SIAM and ASME. His research group has produced numerous publications in top-tier journals and conferences, with recent work focusing on connections between optimal transport theory, stochastic control, and machine learning. The publication trends show increasing integration of Schrödinger Bridge formulations with machine learning techniques for distributional control problems across various domains including power systems, aerospace applications, and resource allocation. O. Hugo Schuck Best Application Paper Award (2024) Applied Mathematics Research Award from UC Santa Cruz (2022) IEEE Senior Member (2021) Outstanding Doctoral Student Award from Texas A&M Best Dual Degree Thesis Award from IIT Kharagpur Dr. Halder has mentored numerous PhD students including Alexis, Georgiy, Iman, Shadi, and Kenneth, many of whom have received prestigious fellowships. His research group maintains strong collaborations with national laboratories including Lawrence Livermore National Lab and Los Alamos National Lab, as well as industry partners. Dr. Halder is also committed to education and outreach, having created and taught the 'Feedback Control' course for high school students in the California State Summer School for Mathematics and Science (COSMOS), introducing complex control theory concepts without calculus or linear algebra.

James ShucksmithView profile

Senior Lecturer

• Urban Flooding Hydrodynamics
• Water Quality Modeling
• Sustainable Drainage Systems (SuDS)
• +3 more

Dr James Shucksmith is a Senior Lecturer in Water Engineering at the School of Mechanical, Aerospace and Civil Engineering, University of Sheffield. After completing his undergraduate degree and PhD at the same department, he joined the academic staff in 2010 following a KTP associate role with Yorkshire Water. His research focuses on urban flooding hydrodynamics, water quality modeling, and sustainable drainage systems. Co-director of EPSRC Centre for Doctoral Training in Water Infrastructure and Resilience Current projects: Real Time Abstraction Management (with Severn Trent Water), Centaur FloodInteract Research interests include: Urban flood hydrodynamics and drainage-surface flow interactions Water quality forecasting tools for surface water abstraction Development of local real-time control systems for urban drainage Experimental validation of flood models using PIV measurements His publications (2010-2025) cover topics like contaminant transport in flooded sewer systems, longitudinal dispersion modeling, and real-time control optimization. Recent work focuses on data-driven approaches for Cryptosporidium prediction and E. coli forecasting.

• Digital Signal Processing
• Sensor Array Processing
• Spectrum Analysis
• +6 more

James McClellan is a Professor at the School of Electrical and Computer Engineering at Georgia Institute of Technology . He earned his B.S. from Louisiana State University (1969) and M.S./Ph.D. from Rice University (1972/1973). Prior roles include research at MIT Lincoln Laboratory (1973-74), faculty at MIT (Electrical Engineering and Computer Science Department), and industry work at Schlumberger Well Services (1982-1987) before joining Georgia Tech in 1987. Education B.S. in Electrical Engineering, Louisiana State University (1969) M.S. and Ph.D. in Electrical Engineering, Rice University (1972, 1973) His research focuses on digital signal processing , including algorithms for sensor array processing , spectrum analysis , time-frequency representations , and multi-dimensional signal processing , with applications in radar and geophysics . He also develops computer software for DSP design and emphasizes undergraduate curriculum innovation. Prof. McClellan co-authored influential books such as Number Theory in Digital Signal Processing , Computer-Based Exercises for Signal Processing , and DSP First: A Multimedia Approach . His academic contributions have been recognized through IEEE Fellow status and awards from the ASSP Technical Achievement (1987) and Signal Processing Society (1996).

Vassilios AngelidisView profile

Professor

• Power Electronics
• Renewable Energy Sources
• Electrical Power Systems
• +1 more

Professor Vassilios Angelidis is a distinguished academic in the field of Electrical Engineering, currently serving as a Professor at the Department of Electrical and Computer Engineering of Democritus University of Thrace. He joined the university in June 2024, bringing with him extensive international experience from prestigious institutions including the University of Glasgow, University of Sydney, University of New South Wales (UNSW), and the Technical University of Denmark. Professor Angelidis received his educational foundation with a degree in Electrical Engineering from Democritus University of Thrace, followed by a Master of Applied Science from Concordia University in Canada, and a PhD from Curtin University in Western Australia. He further enhanced his expertise with an MBA from Curtin Graduate School of Business. His research interests span Power Electronics, Renewable Energy Sources, Electrical Power Systems, and Autonomous Electricity Networks. Professor Angelidis has made significant contributions to the development of advanced power conversion techniques, grid integration of renewable energy sources, and the application of artificial intelligence in power system analysis and prediction. His work has been instrumental in advancing the field of smart grid technologies and sustainable energy systems. An analysis of his recent publications reveals a strong focus on power electronics applications in renewable energy integration, particularly in photovoltaic systems and electric vehicle charging infrastructure. His research demonstrates expertise in developing advanced algorithms for power system monitoring and control, with particular emphasis on frequency estimation, phase angle calculation, and power quality enhancement in modern grids with high penetration of distributed energy resources. Among his notable scientific achievements, Professor Angelidis has been recognized as an IEEE Fellow for his significant contributions to power electronics and the conversion and integration of renewable energy sources into power grids. He has also received the prestigious Advanced Research Fellowship for young researchers from the Engineering and Natural Sciences Council of the United Kingdom. Throughout his career, Professor Angelidis has secured research funding from various organizations across Australia, the United Kingdom, Denmark, China, India, and Malaysia, as well as industry partners with global reach. He has served on the International Councils of Beijing Jiaotong University and Universiti Tenaga Nasional (UNITEN), and currently holds the position of Vice President-elect of the IEEE Power Electronics Community. At Democritus University of Thrace, Professor Angelidis is affiliated with the Electrical Machines Laboratory within the Energy Systems Sector, where he leads research on Control and Diagnostic Methods of Electrical Machines. His current work focuses on advancing the integration of renewable energy sources into electrical power systems while maintaining grid stability and reliability.

Margunn AanestadView profile

Professor

• Digital Transformation
• e-Health
• Information Systems
• +6 more

Margunn Aanestad is a Professor in Digital Innovation at the Department of Informatics (IFI), Faculty of Mathematics and Natural Sciences, University of Oslo (UiO). With 25 years of research experience focused on healthcare digitalization, she examines how digital technologies transform organizations and societies at multiple analytical levels. Her work spans healthcare information systems, digital innovation, and information infrastructures across the Nordic region and internationally. Her research interests center on understanding digital transformation through a sociotechnical lens, particularly examining how work practices evolve when based on digital monitoring rather than physical visits, how organizations become 'data-driven' through advanced analytics, and how sector-level ecosystems require new technical and regulatory solutions for data sharing. She investigates information infrastructures as interconnected systems rather than isolated technologies, working at the intersection of Information Systems (IS), Computer Supported Cooperative Work (CSCW), Science and Technology Studies (STS), and organizational theory. Her empirical work primarily focuses on healthcare digitalization, including patient records, collaboration solutions, patient portals, digital home monitoring, and emerging AI applications for personalized medicine. Professor Aanestad leads research within the Digital Innovation (DIN) group at UiO and has been involved in significant projects including KINT (From local breakthroughs in knowledge to integration into medical practice) and CORPUS (New competence requirements in the public sector: The consequences of digitalization for work with different degrees of specialization). She previously held a Professor II position at the University of Agder (2019-2022) and maintains active collaborations with NTNU, BI Norwegian Business School, Diakonhjemmet Hospital, and Copenhagen Business School. Her publication record demonstrates consistent high-impact contributions across information systems, healthcare innovation, and digital governance. Recent work explores critical perspectives on digital solutionism, data governance spaces for personal health information, and the sociotechnical dimensions of implementing data infrastructure in public sectors. She examines how digital technologies create both opportunities for innovation and complex challenges related to governance, inclusion, and sustainability. Professor Aanestad's educational background includes engineering degrees in medical technology (1992) and technical sciences (1994) from Stavanger University College. Before joining UiO as a PhD student in 1998, she worked as a department engineer at Rikshospitalet and as a senior engineer at Telenor. Her career trajectory reflects deep engagement with both technical implementation and theoretical understanding of digital transformation processes.

Kristina EngwallView profile

Senior Lecturer

• Social Work
• Disability Studies
• Child Welfare
• +4 more

Kristina Engwall is a Senior Lecturer at Uppsala University's Department of Social Work, with a dual background in history and social work. She transitioned from a decade-long research career at the Institute for Future Studies (2001-2010) to leadership roles at R&D Södertörn (2008-2020) before joining Uppsala University in 2020. Her work bridges historical analysis with contemporary social policy, focusing on marginalized groups. Current position: Senior Lecturer in Social Work Key affiliations: Uppsala University, R&D Södertörn, Forte research council Research Focus: Engwall specializes in disability studies, child welfare, and gender research. Her ongoing Forte-funded projects examine: Elderly care conflicts for intellectually disabled individuals Digital inclusion post-COVID Critical perspectives on the IBIC assessment model Child participation in foster care matching She has led major studies on: Swedish child welfare practices for disabled children Co-living arrangements for parents with disabled adult children Parent carer support systems Publications: Over 25 peer-reviewed articles and 5 books addressing disability history, foster care dynamics, and gender studies. Notable works include Utanförskapets historia and Frivillig barnlöshet . Collaborations: Works with scholars like Ann-Sofie Bergman, Gunnel Andersson, and Lill Hultman across Sweden's top universities. Secured multiple Forte grants for disability and child welfare research.

Vlad-Costin AndreiView profile

Researcher

• Integrated Sensing and Communications (ISAC)
• Digital Twinning
• Neuromorphic Computing
• +5 more

Vlad-Costin Andrei is a Researcher at the Chair of Theoretical Information Technology , Technical University of Munich (TUM), specializing in wireless communication systems and digital twinning. He joined the ACES Lab (TUM's Chair of Theoretical Information Technology) in late 2021 after 3.5 years in the aerospace and defense industry. Research Focus: Joint Communications and Sensing (6G), Neuromorphic PHY Layer, Digital Twins, MIMO-OFDM Resilience Projects: 6G-life, 6G Future Lab Affiliation: ACES Lab, TUM His work bridges theoretical foundations with practical implementations, including demonstrations of digital twinning platforms and sensing-assisted receivers. Recent publications emphasize anti-jamming frameworks, federated learning over wireless networks, and trajectory optimization for UAV-enabled ISAC systems. Scientific Awards: Best Paper Award, IEEE Symposium on Joint Communications and Sensing (2023) His research is supported by third-party grants such as BMBF's 6G-life, DFG's Gottfried Wilhelm Leibniz Prize, and multiple collaborative projects.

Martin ElsmanView profile

Professor

• Programming Languages
• Theory of Computation
• Functional Programming
• +5 more

Martin Elsman is a full-time Professor in the Programming Languages and Theory of Computation section at the Department of Computer Science, University of Copenhagen (DIKU). He serves as head of the PLTC section and head of studies for the BSc education in Computer Science and Economics. Elsman is also an active maintainer of several software tools including the MLKit and SMLtoJs. Joined DIKU in 2012 after 4 years at SimCorp (2008-2012) and previous Associate Professorship at IT University of Copenhagen (2003-2008). Co-developer of Futhark, TAIL APL compiler, SMLtoJs, and SMLserver. Education: M.Sc. in Engineering, Technical University of Denmark Ph.D. in Computer Science, University of Copenhagen (DIKU), supervised by Mads Tofte. Research Interests: Elsman works on programming language design and implementation, with a focus on functional programming, module systems, domain-specific languages for financial contracts, region-based memory management, compilation techniques for parallelism, program optimization, and static type systems. His work spans both theoretical and applied domains, including blockchain-based financial contract execution, web technology, and GPU programming using functional languages. Publication Trends: His recent articles focus on functional programming, array programming, parallelism, and memory management. Topics include region inference, type systems for data-parallelism, program optimization techniques, and domain-specific compilation for financial and quantum computing. He frequently collaborates with Troels Henriksen and others on tools like Futhark and MLKit.

Cameron BrownView profile

Associate Professor

• Pharmaceutical Manufacturing
• Crystallization Processes
• Digital Design Tools
• +4 more

Dr. Cameron Brown is a Reader (equivalent to Associate Professor) at the Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow. He specializes in developing digital design tools and strategies for pharmaceutical manufacturing. Brown joined Strathclyde in 2014 and has progressed through research associate, research fellow, and Chancellor's fellow positions. He currently coordinates the Drug Substance Manufacturing module for the Advanced Pharmaceuticals Manufacturing MSc program. Education: Brown holds a PhD in crystallization process characterization and a Chemical Engineering degree, both from Heriot-Watt University. Research Focus: His work centers on three primary areas: Hybrid modeling approaches : Integrating physics-based and data-driven models to enhance drug substance manufacturing efficiency Self-driving labs : Developing automated systems for drug substance process development with model-based experimental design Digital decision-making : Implementing coupled models through GenAI and LLMs for rapid pharmaceutical process development His research contributes to UN Sustainable Development Goals through improved medicine manufacturing sustainability. Publication Trends: Brown's recent articles focus on pharmaceutical crystallization, digital design methodologies, AI applications in manufacturing, and process optimization. His work demonstrates strong emphasis on translating computational models into industrial practice, particularly in continuous manufacturing and quality-by-design frameworks. Honors: Elected staff officer of British Association of Crystal Growth (2024) Research Leadership: Brown serves as Principal Investigator for PharmaCrystNet and co-investigator on multiple major initiatives including Digital Design and Manufacturing of Amorphous Pharmaceuticals, Future CMAC Manufacturing Hub, Accelerated Discovery and Development of New Medicines Prosperity Partnership, and ARTICULAR. He leads knowledge exchange projects with pharmaceutical companies and manages knowledge transfer partnerships. Professional Engagement: Brown is active in the Acceleration Consortium and serves on the committee of the British Association of Crystal Growth.

• Fintech
• Sustainable Finance
• Behavioural Finance
• +4 more

Professor Qingwei Wang holds a Personal Chair of Finance at Cardiff Business School, Cardiff University, where he serves as Head of the Accounting and Finance section. He is the co-founder and co-director of the Cardiff Fintech Research Group and has previously directed the Cardiff Empirical Finance Group and Centre for China Business Research. Before joining Cardiff in 2014, Professor Wang was a Lecturer in Finance at Bangor Business School and a researcher at the Centre for European Economic Research in Mannheim, Germany. Professor Wang earned his Ph.D. in Finance from the University of Mannheim, M.A. in Economics and Management Science from Humboldt University at Berlin, LL.B. in International Economic Law from Fudan University, and BA in Economics from Anhui Normal University. He also holds a PGCert in Higher Education (2014). His research focuses on three interconnected areas: Fintech (cryptoassets, machine learning applications, big data investing), Sustainable Finance (ESG, CSR, greenwashing, climate risk), and Behavioural Finance (investor sentiment, attention, biases in financial decision-making). His work has been published in top journals including Journal of International Business Studies, Journal of Empirical Finance, European Financial Management, and International Journal of Forecasting. Professor Wang's recent publications reveal a strong focus on cryptocurrency market efficiency, behavioral aspects of investing, and the intersection of technology with traditional finance. His work spans both theoretical and empirical approaches, with increasing emphasis on sustainable finance applications in recent years. Best AFS Paper Award at the AFS 2025 Conference Best Paper Award at the 8th China Finance and Accounting Conference (2024) Best (PhD) Paper Award at the 2024 Edinburgh World-Class Workshop in Fintech and Sustainable Finance Best Paper Award at the 22nd BAFA SIG-AFEE Workshop (2022) Multiple finalist nominations for Fintech Wales Awards (2021-2023) Professor Wang has supervised numerous PhD students who have secured positions at universities including Bangor, Cardiff, Southampton, Northampton, Greenwich, and Groningen. He serves on editorial boards of British Accounting Review, Finance Research Letters, and The European Journal of Finance, and has delivered invited talks at prestigious institutions worldwide including European Central Bank, University of Glasgow, and Xiamen University.

Zhiqiang TaoView profile

Assistant Professor

• Machine Learning
• Computer Vision
• Computational Imaging
• +7 more

Zhiqiang Tao is an Assistant Professor in the School of Information at the Golisano College of Computing and Information Sciences, Rochester Institute of Technology (RIT). Prior to RIT, he served as an Assistant Professor at Santa Clara University (2020–2022). He holds a Ph.D. from Northeastern University (ECE Department) and B.E. and M.E. degrees from Tianjin University. Research Focus: His research group develops smart, scalable, and secure machine learning algorithms for healthcare, AI education, computational imaging, and data science. Key areas include large vision-language models (LVLMs), calibrated uncertainties in low-level vision, ranking fairness, and multimodal understanding. Recent works explore LLM fairness, medical question-answering systems, and diffusion models for text-video retrieval. Awards & Honors: RIT GCCIS Emerging Scholar Award (2025) Teaching: IDAI-780: Capstone Project ISTE-780: Data-Driven Knowledge Discovery Advising & Team: Actively seeking motivated graduate/undergraduate students to join his research group. His team has produced impactful work in NeurIPS, CVPR, ECCV, and IEEE TPAMI/TKDE. Labs & Collaboration: Collaborates across disciplines to advance AI applications in healthcare and education, leveraging computational imaging and fair ML principles.

• Cybersecurity
• Machine Learning
• Network Security
• +6 more

Dr. Zhiyuan Tan is an Associate Professor in the School of Computing at Edinburgh Napier University (ENU), specializing in cybersecurity research. He holds a PhD in Computer Systems from the University of Technology Sydney (UTS), Australia (2014), an MEng from Beijing University of Technology, China (2008), and a BEng with high distinction from North-eastern University, China (2005). Before joining ENU in 2016, Dr. Tan held research positions at the University of Twente (Netherlands), University of Technology Sydney (Australia), and La Trobe University (Australia). Dr. Tan's research focuses on cybersecurity, machine learning, data analytics, virtualisation, and cyber-physical systems. His work has resulted in over 44 scholarly publications with an H-Index of 13 and more than 830 citations according to Google Scholar. His recent publications demonstrate a continued focus on network security, intrusion detection systems, and the application of machine learning techniques to cybersecurity challenges, with publications spanning from 2022-2025 in top venues including IEEE Transactions and international conferences. Dr. Tan has received significant research funding, including AUD 27,800 from CSIRO and UTS for autonomous network intrusion detection research and £6,987 from ENU for securing future 5G health care systems. His research has been recognized with awards including the National Research Award 2017 from the Research Council of the Sultanate of Oman, a Best Paper Award, and the Kaspersky Lab's Annual Student Cyber Security Conference Finalist Award. National Research Award 2017 from the Research Council of the Sultanate of Oman Best Paper Award Kaspersky Lab's Annual Student Cyber Security Conference Finalist Award Dr. Tan has mentored 9 PhD students over the past 5 years, with 6 successfully completing their studies. His students have produced 12 journal and 10 conference publications. He has also served as an editorial board member for international journals, organized special issues, and participated as a technical program committee member for major international conferences. Dr. Tan is currently recruiting PhD students for research projects on network security, adversarial machine learning for anomaly/malware detection, virtualization security, and IoT security.

Basca JadambaView profile

Professor

• Inverse Problems
• Continuous Optimization
• Partial Differential Equations
• +3 more

Basca Jadamba is a Professor in the School of Mathematics and Statistics at the Rochester Institute of Technology (RIT), part of the College of Science. She serves as Associate Head of the Applied and Computational Mathematics program. Her research focuses on inverse problems, stochastic optimization, partial differential equations, numerical analysis, finite element methods, and mathematical modeling. She has advised undergraduate and graduate students in research and teaches courses at both levels. Jadamba holds a BS from the National University of Mongolia, an MS from the University of Kaiserslautern (Germany), and a Ph.D. from the University of Erlangen-Nuremberg (Germany). Her academic journey includes joining RIT’s School of Mathematics and Statistics in 2008. She is actively involved in academic leadership, including serving as the faculty advisor for RIT’s Student Chapter of the Association for Women in Mathematics. Her research contributions span theoretical and numerical methods for inverse problems, with applications in elasticity imaging and parameter identification in stochastic systems. She has co-authored books and peer-reviewed articles on topics such as uncertainty quantification in variational inequalities, optimization formulations for inverse problems, and numerical methods for partial differential equations. Jadamba’s work emphasizes bridging mathematical theory with practical applications, particularly in engineering and environmental science. Her recent publications highlight advancements in stochastic approximation methods, convex optimization frameworks, and the role of Inf-Sup conditions in inverse problems. She has explored applications ranging from tumor localization in elasticity imaging to congestion network analysis with random data. Her teaching portfolio includes courses like Multivariable Calculus, Mathematical Modeling, and Applied Inverse Problems, reflecting her expertise in both foundational and advanced mathematical topics.

Emrah AkyolView profile

Associate Professor

• Game Theory
• Communications
• Control Systems
• +3 more

Emrah Akyol is an Associate Professor in the Electrical and Computer Engineering Department at Binghamton University (SUNY). He joined in 2017 after postdoctoral research at the University of Illinois at Urbana-Champaign (UIUC) and the University of Southern California (USC). He holds a PhD from UC Santa Barbara (2011) and prior industry experience at HP and NTT Docomo. Education: PhD in Electrical and Computer Engineering, UC Santa Barbara (2011) Postdoctoral Researcher at USC (2013–2014) and UIUC (2014–2017) Research: Focuses on game theory, control systems, and secure cyber-physical systems. Key areas include strategic communication, opinion dynamics in social networks, and stealthy attacks in multi-agent systems. Recent work addresses bounded rationality in data gathering and network topology inference. Awards: NSF Career Award (communication over human networks), Binghamton's Interdisciplinary Collaboration Award (with Economics/Political Science), and CoCo Seed Grant (CPS security). Advising & Grants: Supervised PhD/Master’s students Anju Anand, Xilin Zhang, and Griffin Rule (recipient of ECE MS Research Award). Active grants include NSF Career and CoCo Seed. Labs/Teams: Leads research on secure cyber-physical systems and game theory applications. Collaborates with UIUC and organizes conferences like DSAA’20.

Clayton CafieroView profile

Senior Lecturer

• Theory of computation
• Artificial intelligence
• Software engineering
• +3 more

Clayton Cafiero is a Senior Lecturer in the Department of Computer Science at the University of Vermont's College of Engineering and Mathematical Sciences. He joined the CS department on a full-time basis in 2022 after beginning his teaching career in 2020. Dr. Cafiero's educational background includes an MS from the University of Vermont (2021), studies at the University of Chicago (1983-1986), and an AB from Dartmouth College (1983). His research and teaching interests focus on Theory of computation, Artificial intelligence, Software engineering, and Algorithms . He has developed extensive course materials covering foundational computer science concepts, mathematical fundamentals for CS, and practical programming tools. His teaching philosophy emphasizes clarity in explaining complex theoretical concepts while connecting them to real-world applications. For the 2025 fall semester, he is teaching CS 2210 (Computer Organization), CS 2250 (Computability and Complexity), CS 2510 (Introduction to Artificial Intelligence), and EDCI 5004 (Computer Organization for Educators). Dr. Cafiero is an active member of several professional organizations: Association for Computing Machinery (ACM) Institute of Electrical and Electronics Engineers (IEEE) and IEEE Computer Society American Association for the Advancement of Science (AAAS) Mathematical Association of America (MAA) Python Software Foundation He has developed educational resources including PyPI packages like the Two's Complement Practice Tool and maintains numerous UVM GitLab repositories containing lecture notes and course materials for CS 1210, CS 2240, CS 2210, CS 2250, and CS 2510 courses. He has also created materials for a non-credit self-assessment course covering mathematical fundamentals and CS tools.