جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Machine Learning
• Anomaly Detection
• +۳ مورد دیگر

Dr. Victoria J Hodge is a Research Fellow in the Department of Computer Science at the University of York, affiliated with the Centre for Assuring Autonomy (CfAA). She holds a PhD in Computer Science from the University of York and has industry experience in software architecture for medical diagnostics and industrial anomaly detection systems. Her research focuses on artificial intelligence (AI), anomaly detection, machine learning, and safety assurance for autonomous systems, particularly in robotics. Her work emphasizes through-life safety assurance of AI-driven autonomous systems, including robotics platforms operating in uncertain environments. She has authored over 70 publications across disciplines like AI, machine learning, robotics, and safety engineering. Notable projects include SCALOFT (drone safety analysis) and ASUMI (UAV safety assurance in mining). Her research trends show a strong emphasis on interdisciplinary applications of AI, with recent focus on autonomous systems in high-risk environments (e.g., mining, healthcare) and real-time decision support systems. Collaboration with industry ensures practical impact in domains like medical diagnostics and industrial automation.

Carla Dirkschneiderمشاهده پروفایل

استاد مدعو

• Healthcare Administration
• Health Information Management
• Enrollment Strategies
• +۳ مورد دیگر

Carla Dirkschneider, M.S., RHIA, serves as Vice President of Enrollment and Retention and Adjunct Faculty in Healthcare Business at Clarkson College. She leads departments including Enrollment and Advising, Financial Aid, and Student Life, while maintaining an adjunct teaching role. Her career began at Clarkson in 2003, progressing from faculty to Program Director (2008) before her current VP role (2023). Education: BS in Health Information Management (College of Saint Mary), MS in Health Care Business Leadership (Clarkson College), EdD in Interdisciplinary Leadership (Creighton University). Credentials include Registered Health Information Administrator (RHIA). Research interests focus on healthcare leadership, enrollment strategies, and accreditation. She actively contributes to CAHIIM's accreditation processes and serves on the Nebraska Health Collaborative Board. Awards include the 2017 40 Under 40 Award and 2018 NHIMA Distinguished Member Award. Professional engagement includes presentations on data-driven enrollment tactics, leadership in turbulent times, and healthcare communication. She volunteers with Clarkson Alumni Association and NHIMA. Outside work, she enjoys family activities, Broadway plays, and Nebraska football.

• Machine Learning
• Optimization
• Control Systems
• +۳ مورد دیگر

Bo Li is an Assistant Professor in the Department of Statistics at the University of California, Berkeley . His research bridges statistical theory with applications in machine learning, optimization, and autonomous systems. Education: PhD in Statistics (2006), advised by Peter Bickel Email: boliboli@gmail.com Bo Li's research focuses on machine learning , control systems , and energy-efficient optimization . His work addresses challenges in autonomous vehicle navigation, solar panel positioning, and stochastic energy routing problems. Key themes include predictive control frameworks, multi-agent coordination, and uncertainty handling in dynamic systems. Recent publications highlight 2025 and 2024 advancements in model predictive control (MPC) , generalized Nash equilibrium for autonomous racing, and stochastic optimization for energy systems. His work spans vehicle routing , solar energy tracking , and multi-agent control , emphasizing data-driven approaches and real-world validation.

Eilyan Bitarمشاهده پروفایل

دانشیار

• Robust Optimization
• Stochastic Optimization
• Control Theory
• +۹ مورد دیگر

Eilyan Bitar is an Associate Professor and David D. Croll Sesquicentennial Faculty Fellow at Cornell University's School of Electrical and Computer Engineering. He received his BS and PhD from UC Berkeley, advised by Kameshwar Poolla, and completed postdoctoral work at Caltech and UC Berkeley. His research focuses on robust/stochastic optimization , control theory , and game theory applied to sustainable transportation and electric power systems . Key themes include renewable energy integration , electric vehicle grid coordination , and market design for uncertain energy systems. Recent publications address convex optimization for energy storage robust control of electric vehicle charging data-driven chance-constrained programming market transparency in networked systems His work bridges theoretical advances in optimization with real-world implementations like the OptimizEV pilot program with NYSEG. Scientific recognition includes NSF CAREER Award (2014) Einhorn Community-Engaged Innovation Award (2024) Bezos Earth Fund Award for AI climate research (2025) NSERC Postgraduate Scholarship for student Feras Al Taha (2022) As advisor to students like Feras Al Taha (Best Student Paper Award, IEEE CDC 2023) and Polina Alexeenko (NSF GRFP recipient), he mentors the next generation of energy systems researchers.

Siddhartha Banerjeeمشاهده پروفایل

دانشیار

• Data-driven decision-making
• Stochastic control
• Economics and computation
• +۳ مورد دیگر

Sid Banerjee is an Associate Professor in the School of Operations Research and Information Engineering at Cornell University , with field memberships in the Computer Science Department , Electrical and Computer Engineering (ECE) , and the Center for Applied Mathematics . His research focuses on the intersection of data-driven decision-making , stochastic control , economic theory , and large-scale network algorithms , with applications to ride-sharing platforms, online marketplaces, and smart transit systems. His work has been recognized with prestigious awards including the NSF CAREER award , multiple grants from NSF (CNS 1955997, CNS 1952011, DMS 1839346), and support from ARL Network Sciences and Engaged Cornell . He has advised numerous PhD students and postdoctoral researchers, many of whom have transitioned to academic positions at institutions like MIT Sloan , Facebook Research , and Amazon . His recent publications analyze problems in fair allocation , information design , reinforcement learning , and market robustness , often combining tools from online algorithms , Bayesian game theory , and large-scale optimization . During 2021-2022, he was on sabbatical at the Simons Institute in Berkeley, co-organizing the program on Data-Driven Decision-Making .

Alberto J. Lamadridمشاهده پروفایل

استاد

• Energy Economics
• Cyber-Physical Systems
• Stochastic Optimization
• +۴ مورد دیگر

Professor Alberto J. Lamadrid is a faculty member in the Economics Department at Lehigh University with a joint appointment in Industrial and Systems Engineering. He serves as Director of the Institute for Cyber Physical Infrastructure and Energy (I-CPIE) and as a Research Affiliate at MIT's Laboratory for Information and Decision Systems (LIDS). His work focuses on electricity economics, cyber-physical systems, and stochastic optimization in interdependent networks. James T. Kane Faculty Fellow Principal Investigator for $2.5M ARPA-E grant Research Affiliate at MIT LIDS His research examines resilient infrastructure design, renewable energy integration, and market mechanisms for uncertain environments. Current projects address environmental change, risk management in networks, and critical infrastructure valuation. Methodologically, he combines optimization techniques, GIS analysis, and engineering-economic modeling. Recent publications span: 2025 studies on hurricane evacuation behavior and electricity market pricing; 2024 work on smart grid-transit co-optimization; 2023 analysis of pandemic social cohesion; 2022 market convexity studies; and 2021 cybersecurity models. Articles appear in The Energy Journal , IEEE Transactions , and European Journal of Operational Research . Scientific recognition includes: James T. Kane Faculty Fellowship ARPA-E $2.5M grant for electricity risk management His extramural funding sources span NSF, DOE, EPA, EPRI, and MIT collaborations. Teaching includes PhD-level Mathematical Economics, Electricity Economics, and MBA courses in Economics & Government.

Michele Cosciaمشاهده پروفایل

دانشیار

• Network Science
• Complex Networks
• Digital Humanities
• +۱۰ مورد دیگر

Michele Coscia is an Associate Professor in the Department of Computer Science at the IT University of Copenhagen (ITU), where he conducts research at the intersection of network science, digital humanities, and data analytics. He leads the NERDS research group and supervises PhD students and postdoctoral researchers working on financial crime detection, archaeological networks, and work environment modeling. PhD in Computer Science, University of Pisa (2012) Former researcher at the Center for International Development (CID), Harvard University (6 years) Visiting researcher at Barabási Lab, Northeastern University His research focuses on developing and applying network science methodologies such as noise-corrected backboning , node attribute analysis , and network variance to study complex systems. His work spans diverse domains including: Archaeology : Inferring social and biological relationships from material culture at Neolithic sites like Çatalhöyük. Cultural Analytics : Mapping Italian music networks, analyzing Wikipedia’s gender bias, and studying ideological polarization on social media. Social Media Dynamics : Investigating meritocracy vs. topocracy, intolerance feedback loops, and information virality on platforms like Reddit and Twitter. Sports Analytics : Analyzing predictability trends in team sports and the impact of economic systems on league competitiveness. His publications appear in high-impact journals such as Science Advances , EPJ Data Science , and Applied Network Science . He is the author of The Atlas for the Aspiring Network Scientist , a comprehensive open-access textbook now in its second edition, which covers graph theory, machine learning on graphs, and statistical foundations of network analysis. Recent trends in his work show a growing emphasis on interdisciplinary applications of network science, particularly in archaeology and cultural studies, often in collaboration with institutions such as Aarhus University and the National Research Center for Work Environment. His research consistently promotes open science, with datasets and code publicly shared. Co-PI on a Villum Synergy project applying network analysis to Roman Empire archaeological data Active contributor to the CUDAN (Cultural Data Analytics) community Developing methods for uncertain and incomplete network data Michele Coscia’s work demonstrates a strong commitment to methodological innovation and real-world impact across the humanities, social sciences, and computational domains.

Frank Lagorمشاهده پروفایل

دانشیار

• Estimation and Control for Autonomous Systems
• Fluid Dynamics
• Aerospace Engineering
• +۳ مورد دیگر

Frank Lagor is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of Virginia. He joined UVA in 2023 after serving as faculty at the State University of New York at Buffalo. His research focuses on estimation and control for autonomous systems interacting with fluid environments, including gust mitigation in aerospace systems and bio-inspired flow sensing for robotics. Dr. Lagor holds a Ph.D. in Aerospace Engineering from the University of Maryland (2017), with prior degrees from the University of Pennsylvania (M.S., 2009) and Villanova University (B.S., 2006). Before academia, he worked at Lockheed Martin as a Certified Principal Engineer for satellite solar array systems. His research interests emphasize unsteady flow estimation, optimal maneuver design in gust encounters, and reduced-order modeling techniques. Key contributions include sensor placement strategies for data-driven flow estimation and closed-loop control methodologies for autonomous underwater vehicles. He has received prestigious awards including the AFOSR Young Investigator Award (2021) and UB SEAS Early Career Teacher of the Year (2020). Courses taught include advanced control systems theory, dynamics, and stochastic estimation methods.

• Operations Research
• Robust Optimization
• Queueing Theory
• +۷ مورد دیگر

Chaithanya Bandi is an Associate Professor at the National University of Singapore (NUS) in the Analytics and Operations Department of the NUS Business School, with a joint appointment in the Department of Mathematics. His research focuses on decision-making under uncertainty, robust optimization, and their applications in operations management, healthcare, e-commerce, and energy systems. He develops robust optimization models for queueing control, risk optimization, and mechanism design. Key research areas include robust queue inference, two-stage distributionally robust optimization, and multi-item auction mechanisms. He has contributed to operational challenges in healthcare (e.g., patient re-entry scheduling), energy systems (electricity generation optimization), and e-commerce (price optimization for fashion products). His work integrates theoretical advancements with practical implementations in large-scale systems. Recent publications emphasize adversarial evaluation of large language models, dynamic scheduling algorithms, and robust policies for uncertain environments. His methodologies often involve novel optimization frameworks and scalable computational approaches. Dr. Bandi holds a PhD in Operations Research and has collaborated with industry leaders like Flipkart and healthcare providers to apply his models in real-world settings. His contributions bridge theoretical rigor and practical applicability in complex operational systems.

Anupam Guptaمشاهده پروفایل

استاد مدعو

• Algorithms
• Complexity
• Artificial Intelligence
• +۴ مورد دیگر

Anupam Gupta is an Adjunct Professor in the Computer Science Department at New York University's Courant Institute of Mathematical Sciences. Previously, he held a faculty position at Carnegie Mellon University. His research focuses on algorithms, complexity, and theoretical computer science, with an emphasis on network design, metric embeddings, and approximation algorithms. He has received notable awards, including the ACM Fellowship (2021) and the Herbert A. Simon Award for Teaching Excellence (2019). Education: Ph.D., University of California, Berkeley (2000); B.Tech., Indian Institute of Technology Kanpur (1996) Teaching: Taught courses on algorithms, theoretical computer science, and real-world applications at both undergraduate and graduate levels. Research: Explores algorithms for uncertain environments, stochastic optimization, and online algorithms. His work bridges theoretical foundations with practical applications in networking and combinatorial optimization. Awards: ACM Fellow, Sloan Fellowship, and multiple teaching awards. Students: Advised over 15 Ph.D. students, including notable researchers in algorithms and theoretical computer science. Grants: Supported by NSF grants, including a CAREER Award, and collaborates on projects like the Indo-US Algorithms under Uncertainty initiative. His work on metric embeddings and graph algorithms has had significant impacts, with contributions to approximation algorithms and competitive analysis. He actively participates in academic conferences and serves as an organizer for programs like the Simons Institute's Algorithms and Uncertainty initiative.

Orest Iftimeمشاهده پروفایل

دانشیار

• Applied Mathematics
• Control Theory
• Systems and Control
• +۳ مورد دیگر

Orest Iftime is an Associate Professor in Applied Mathematics at the University of Groningen, affiliated with the Department of Economics, Econometrics, and Finance under the Faculty of Economics and Business. He holds a PhD from the University of Twente (2002) and has held academic positions since 2004, including roles at Delft University of Technology and the University of Groningen. His research focuses on control theory, systems and control engineering, optimization, and model reduction. Education: PhD (2002) and MSc/BSc from 'Al. I. Cuza' University, Iasi, Romania Awards: IEEE Outstanding Reviewer (2012), NWO Grants (2011–2022), Automatica Reviewer Award (2008) Research interests include moment matching, sliding mode control, PDE control, and robust control systems. Over 60 publications across conferences like CDC and ECC, and journals like Automatica. Active in teaching courses like Matrix Analysis, Stochastic Models, and Mathematics for Economics. Grants: Multiple NWO projects (2008–2022) Professional Experience: Postdoc (2002–2004), Lecturer/Researcher roles across institutions

Jiajun Xuمشاهده پروفایل

پژوهشگر

• Agricultural Engineering
• Robotics
• Machine Vision
• +۴ مورد دیگر

Jiajun Xu is a Research Associate in the Department of Biosystems and Agricultural Engineering (BAE) at Michigan State University's College of Engineering. His work focuses on advancing agricultural automation through machine vision systems, robotics, and path planning algorithms. Key areas of expertise include automated crop sorting, weed detection systems, and the development of cable-driven parallel robots for precision agriculture. His research integrates mechanical engineering principles with cutting-edge computer vision technologies, aiming to enhance efficiency in agricultural post-harvest processes and autonomous farming operations. Notable contributions include the OpenWeedGUI platform for weed imaging and the design of LiDAR-based systems for real-time crop quality assessment. Publications highlight trends in sensor integration, path optimization for mobile robots, and cross-season weed detection adaptations. Collaborative projects emphasize practical applications in agricultural automation, with a focus on open-source tools and industry-relevant solutions. Awards and grants are not explicitly mentioned in available texts, though his work indicates strong engagement with both academic and applied research in biosystems engineering.

• Machine Learning
• Data Mining
• Social Network Analysis
• +۴ مورد دیگر

Yllka Velaj is an Assistant Professor in the Faculty of Computer Science, affiliated with the Research Group on Data Mining and Machine Learning. Her research focuses on machine learning, data mining, and network analysis, with applications in social networks, recommendation systems, and AI-enhanced education. She actively contributes to advancing methodologies for uncertain graphs, game-based learning in IT project management, and educational technologies targeting non-computer science students. Her scholarly activities include organizing events such as the Austrian Computer Science Day 2024. Her work bridges theoretical computer science with practical applications, emphasizing learner-centered approaches in data science education and innovative solutions for complex network problems. Key research areas include node embedding, cluster analysis, and algorithm design for social influence maximization. She collaborates on projects addressing multi-relational graph clustering, semi-supervised learning, and optimizing communication in datacenters.

Travis Martinمشاهده پروفایل

استادیار

• Computer Science Education
• Machine Learning
• Network Science
• +۳ مورد دیگر

Travis Martin is an Assistant Professor (Lecturer) in the School of Computing at the University of Utah. His primary focus is on computer science undergraduate education. His research interests include computer science theory, machine learning, network science, graph algorithms, and mechanism design. He holds a PhD in Computer Science and Engineering (CSE) from the University of Michigan, completed in June 2016 under the advisement of Mark Newman and Mike Wellman. Previously, Martin worked as a Software Engineer at Recursion Pharmaceuticals and Google, contributing to data engineering and machine learning integration in Android systems. His academic work bridges theoretical computer science with practical applications in networks and algorithms, evident from his publications in journals like Phys. Rev. E and World Wide Web. Despite his research background, Martin currently does not supervise undergraduate or graduate research. His professional activities include open-source contributions and maintaining an updated code portfolio.

John E. Kobzaمشاهده پروفایل

استاد

• Systems Engineering
• Risk and Uncertainty Modeling
• Quality Improvement
• +۷ مورد دیگر

John E. Kobza is Professor and Department Head of the Department of Industrial and Systems Engineering at the Tickle College of Engineering, University of Tennessee, Knoxville. He has held significant academic and administrative roles across multiple institutions, including Texas Tech University and Virginia Tech, and served as a Visiting Professor at the United States Military Academy. Education: PhD in Industrial and Systems Engineering, Virginia Polytechnic Institute and State University MS in Electrical Engineering, Clemson University BS in Electrical Engineering, Washington State University His research centers on modeling, analyzing, and designing complex systems under uncertainty and risk, with applications in security, healthcare, manufacturing, and communication networks. He applies quality tools to enhance system performance, particularly in organizational settings such as healthcare delivery systems. His methodological expertise lies in stochastic modeling, operations research, and reliability engineering. Though no recent publications are listed in the provided text, his research trajectory emphasizes quantitative decision-making in high-stakes, uncertain environments, bridging theoretical modeling with practical industrial and societal applications. Professional Affiliations: Sigma Xi Alpha Pi Mu INFORMS (Institute for Operations Research and the Management Sciences) IISE (Institute of Industrial and Systems Engineers) Registered Professional Engineer, Texas Dr. Kobza has taught a wide range of courses including Engineering Data Analysis, Operations Research II: Stochastic Modeling, Queuing Theory, Reliability Theory, and Senior Design Project. He has advised academic programs and led departmental development as Department Head since 2013. His leadership experience includes serving as Associate Department Chair, Interim Department Chair, and Senior Associate Dean at Texas Tech University. He has been involved in research centers and labs within the Department of Industrial and Systems Engineering at UT, particularly those focused on Quality, Analytics, and Optimization (QAO), and has contributed to initiatives like the Research Experience for Undergraduates. His work continues to influence both academic research and real-world system design in high-risk domains.