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Miltos AlamaniotisView profile

Associate Professor

• Applied Artificial Intelligence
• Nuclear Security
• Smart Grids
• +5 more

Miltos Alamaniotis is an Associate Professor and GreenStar Endowed Fellow in the Department of Electrical and Computer Engineering at the University of Texas at San Antonio (UTSA). His research focuses on applied artificial intelligence in nuclear security, smart grids, and radiation detection systems, with a particular emphasis on maritime nuclear applications and nonproliferation. Academic Appointments: Associate Professor (2023–Present), UTSA Education: Ph.D. in Applied Intelligent Systems, Purdue University Research interests include: Nuclear Security and Nonproliferation Smart Grids and Distributed Energy Systems Explainable AI for Radiation Detection Quantum Machine Learning Applications Intelligent Control of Nuclear Reactors Fuzzy Logic in Energy Management Recent publications highlight trends in AI-driven nuclear security systems, matrix profile methods for radiation anomaly detection, and quantum neural networks for thermographic image analysis. His work bridges nuclear engineering, cybersecurity, and smart city technologies. Scientific honors include: Top 0.5% ScholarGPS Ranking (2024) Best Paper Award at IEEE Texas Power and Energy Conference (2025) Luthcher Brown Fellowship (2023) GreenStar Endowment Fellowship (2023) NAE Frontiers of Engineering Symposium Selection (2023) UTSA President’s Distinguished Achievement Award (2022) He has supervised PhD students like Thanos Arvanitidis and secured over $15M in grants from DOE, NSF, and NRC for projects including the $25M NNSA Consortium. His AI Lab at UTSA collaborates with Argonne, Idaho, and Los Alamos National Laboratories.

• Control Theory
• Optimization
• Machine Learning
• +7 more

Michael M. Zavlanos is the Yoh Family Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University's Pratt School of Engineering. He also holds secondary appointments in the Department of Computer Science and the Department of Electrical and Computer Engineering. Currently serving as the Director of the Healthcare Systems Optimization program with Duke AI Health and as an Amazon Scholar with Amazon Robotics, his academic career spans control theory, optimization, and artificial intelligence with applications across multiple domains. Dr. Zavlanos received his educational foundation from prestigious institutions: Diploma in Mechanical Engineering from the National Technical University of Athens (NTUA), Greece (2002) M.S.E. in Electrical and Systems Engineering from the University of Pennsylvania (2005) Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania (2008) His research program spans multiple interconnected domains, with a strong foundation in control theory, optimization, and learning methodologies . This theoretical work directly enables applications in robotics and autonomous systems , where his team develops algorithms for multi-robot coordination, motion planning under complex constraints, and network connectivity maintenance. A significant portion of his work addresses networked and distributed control systems , focusing on how multiple agents can coordinate effectively with limited communication. More recently, he has expanded his research into cyber-physical systems with healthcare applications, leveraging his expertise to optimize healthcare delivery systems through the Duke AI Health initiative. Dr. Zavlanos' work demonstrates a consistent trajectory from theoretical foundations to real-world applications. His early work established fundamental principles for maintaining connectivity in mobile robot networks, which evolved into more sophisticated approaches for temporal task planning and risk-averse decision making in uncertain environments. The most recent phase of his research integrates machine learning with traditional control theory to address complex healthcare system optimization problems. His significant contributions to the field have been recognized through prestigious awards: Office of Naval Research Young Investigator Program (YIP) Award (2014) National Science Foundation Faculty Early Career Development (CAREER) Award (2012) National Science Foundation Faculty Early Career Development (CAREER) Award (2011) Duke University Distinguished Faculty Rank (2019) Duke University Distinguished Professor designation (2018) As an educator, Dr. Zavlanos has taught courses including ME 627: Linear System Theory, ME 592: Research Independent Study, ECE 391/291: Projects in Electrical and Computer Engineering, and CEE 627: Linear System Theory. His research program has been supported by multiple grants from the National Science Foundation and the Office of Naval Research, enabling him to mentor numerous graduate students and postdoctoral researchers in the development of cutting-edge control and optimization algorithms. Dr. Zavlanos leads research efforts at the intersection of control theory, optimization, and artificial intelligence, with particular focus on translating theoretical advances into practical applications. His recent work with Duke AI Health represents a strategic expansion of his research portfolio into healthcare systems optimization, where he applies his expertise in algorithmic decision making to improve patient scheduling, resource allocation, and operational efficiency in medical settings. Through his Amazon Scholar role, he also contributes to advancing robotics technologies for real-world applications.

• Production Systems Optimization
• Flow Simulation
• Energy-Efficient Optimization
• +5 more

Abdelkader Mekhalef Benhafssa serves as a Teacher-Researcher at CESI Engineering School within the Engineering and Digital Tools research team. His work spans industrial engineering, robotics, and sustainable manufacturing systems. Education: Doctorate in Electrical Engineering (2017) Master's degree in Electrical Engineering specializing in Electrical Networks and High Voltage Techniques (2013) His research focuses on optimizing production systems through multi-agent simulations, human-robot collaboration in Industry 5.0 contexts, and energy-efficient manufacturing. Key areas include flow simulation, autonomous vehicle scheduling in logistics, and electrostatic separation techniques for plastic waste recycling. His experimental work examines tribocharging mechanisms and particle behavior in recycling processes. Publications reveal a strong trend toward human-centric manufacturing systems, with recent work (2023-2025) emphasizing collision avoidance algorithms, dynamic scheduling for autonomous vehicles, and energy-conscious production planning. Earlier research (2014-2018) established expertise in electrostatic separation for plastic waste recycling. Supervision & Projects: Supervised Kader Sanogo's 2024 thesis on optimizing transport tasks for collaborative robots in Industry 5.0 Currently supervising Nesrine Hebbadj's research (2024-2027) on human-centered production planning Leading DYNALOG project (2025-2027) on robotic intra-logistics systems His work integrates industrial engineering with environmental sustainability, particularly through advanced recycling technologies for plastic waste and energy-efficient production systems.

David SchubertView profile

Professor

• Vocal Performance
• Opera
• Art Song
• +3 more

David Schubert serves as Professor of Voice and Department Chair in the School of Music at Wittenberg University, joining the faculty in 2007. He holds a D.M.A. from the University of Oklahoma and degrees from Baldwin-Wallace College and Boston University. His educational background: Baldwin-Wallace College Boston University University of Oklahoma, D.M.A. A distinguished baritone, Schubert specializes in English art song (particularly Gerald Finzi) and German Lieder, with extensive performance experience in opera roles including Faust and Don Giovanni. His research integrates vocal pedagogy with historical performance practices, emphasizing lyric diction and art song repertoire. He actively collaborates through the Doscher Vocal Quartet and international projects focused on United Kingdom music. Honors include the competitive 1994 NATS Internship Program selection. As Department Chair, he oversees curriculum development while maintaining an active performance schedule, including premieres like David Caudill's The Shepherd's Story. His mentorship extends to vocal coaching with renowned artists including George Shirley and John Wustman.

Nicolas MansardView profile

Researcher

• Robotics
• Humanoid Robotics
• Sensor-Based Control
• +6 more

Nicolas Mansard is a permanent researcher at LAAS-CNRS in Toulouse, France, where he has been working since October 2008. He is a member of the Gepetto research group alongside Philippe Souères, Florent Lamiraux, Olivier Stasse, and Jean-Paul Laumond. He defended his Habilitation à Diriger des Recherches (HDR) in July 2013 on the topic of motion semiotics. In 2013, he was an invited researcher at Emo Todorov's lab at the University of Washington, Seattle. His research focuses on sensor-based control, particularly the integration of sensor-based schemes into humanoid robot applications. His work spans the intersection of robotics, automatic control, signal processing, and numerical mathematics, with humanoid robotics as his primary application field. Mansard has made significant contributions to hierarchical quadratic programming for fast online humanoid-robot motion generation, inverse dynamics control, and sensor-based control systems. Mansard has received prestigious awards including the CNRS Bronze Medal in 2015 and the Grand Prix de l'ANR in 2016 for his project ANR Entracte. His research has resulted in numerous publications in top robotics journals and conferences, with a focus on motion generation, control theory, and humanoid robotics applications. His work has been particularly influential in developing efficient algorithms for hierarchical task control and inverse dynamics. 2015 CNRS Bronze Medal for research in robotics Grand Prix de l'ANR in 2016 for project ANR Entracte Associate Editor for IEEE TRO since July 2013 Mansard has advised numerous PhD students including Justin Carpentier, Mathieu Geisert, Oscar Ramos, and Sovannara Hak. He has secured significant research funding including leading the ANR project ENTRACTE (starting November 2013) and serving as CNRS coordinator and work package leader for the FP7 EuRoc project. He has also taught courses in advanced robotics at Supaero, mathematics for motion generation at École Normale Supérieure, and experimental humanoid robotics at INSA, all in Toulouse. His research group has been involved in developing open-source software for motion generation, notably the Stack of Tasks framework, which has been widely adopted in the robotics community. His work on humanoid robot dance with HRP-2 demonstrated the practical applications of his theoretical contributions to motion generation and control.

Alejandro RussoView profile

Professor

• Computer Science
• Information Security
• Functional Programming
• +3 more

Alejandro Russo is a Professor at Chalmers University of Technology , specializing in Information Flow Control (IFC) , Secure Programming Languages , and Functional Programming . His research bridges theoretical foundations and practical implementations, focusing on mitigating timing channels , covert channels , and data leakage in concurrent systems. Developed novel frameworks for Differential Privacy with provable accuracy bounds Pioneered COWL integration for browser security and instruction-based scheduling to prevent cache timing attacks Led major projects like HIPSTER (hybrid static/dynamic IFC) and AppFlow (practical IFC deployment) His publications reveal expertise in security libraries for Haskell and Python , with a focus on faceted execution , label manipulation , and mechanized security proofs . Students under his supervision have explored topics ranging from secure eDSLs to privacy-preserving compilation techniques . Scientific awards : Google Research Award (2011) for Python taint analysis Advising and grants : Principal Investigator for VR , STINT , and Google Research Award Supervised 12+ PhD and Master’s students in security and functional programming research

Evangelia KalyvianakiView profile

Senior Lecturer

• Cloud Computing
• Big Data Processing
• Autonomic Computing
• +6 more

Evangelia (Eva) Kalyvianaki is a Senior Lecturer (equivalent to Associate Professor) in the Department of Computer Science and Technology at the University of Cambridge , where she is also a member of the Systems Research Group / netos group . Previously she held faculty positions as Lecturer at City University London and as post-doctoral researcher at Imperial College London. Education Ph.D. in Computer Science, Computer Laboratory (SRG/netos group), University of Cambridge M.Sc. in Computer Science, University of Crete, Greece B.Sc. in Computer Science, University of Crete, Greece Research Interests Her research spans the broad areas of Cloud Computing , Big Data Processing , Autonomic Computing , and Distributed Systems . A central theme is the design and management of next-generation, large-scale cloud applications, with an emphasis on applying mathematical reasoning—particularly control-theoretic techniques such as Kalman and H-infinity filtering—to address the complexity and uncertainty inherent in modern distributed infrastructures. Topics of active investigation include adaptive CPU and resource provisioning for virtualized servers, fairness and overload management in federated stream-processing systems, explicit state management for big-data frameworks, and distributed optimization algorithms for large-scale networked systems. Publications & Research Impact Across more than thirty peer-reviewed papers, her work demonstrates a consistent trajectory toward bridging rigorous control theory with practical systems challenges in the cloud. Signature contributions include the THEMIS framework for fair federated stream processing, dynamic block-sizing algorithms for data-stream engines, and robust resource-provisioning schemes based on advanced filtering techniques. Recent publications extend these ideas to fully distributed, finite-time coordination protocols that operate under quantized communications and time-varying delays, reflecting an expanding scope toward large-scale networked control systems. Scientific Awards No specific awards or fellowships are listed in the provided material. Advising & Funding While individual student names are not disclosed, her extensive publication record with numerous co-authors indicates active supervision of doctoral and master’s researchers. Funding acknowledgements in papers suggest support from UK research councils, EU projects, and industrial partnerships, although explicit grant details are not provided. Labs & Teams She is affiliated with the Systems Research Group (netos) within the Cambridge Computer Laboratory, a leading collective focused on networked and operating systems research, providing a collaborative environment for experimental cloud and distributed-systems work.

Mario HarperView profile

Assistant Professor

• Machine Learning
• Data Science
• Robotics
• +2 more

Mario Harper is an Assistant Professor in the Department of Computer Science at Utah State University. His research emphasizes Machine Learning, Data Science, Robotics, and their intersections with Finance and Artificial Intelligence. He specializes in autonomous systems, energy-efficient robotics, and AI-driven solutions for transportation and urban sustainability. His work includes developing tools like simulators for electric vehicle systems and stealth-centric navigation algorithms inspired by biological systems. Key research areas include multi-robot coordination, reinforcement learning applications in robotics, and algorithmic approaches to environmental and economic challenges. He has contributed to projects like POSEIDON-SAT for satellite-based fishing vessel detection and electrified transportation equity analysis in urban settings. His publications span topics from trajectory planning in legged robots to AI modeling for economic systems. Beyond research, he designs interactive visualization tools to support policy decisions on sustainable transportation infrastructure. No scientific awards are explicitly listed in the provided information. Mario Harper’s advising and grants focus on robotics, energy systems, and AI applications, though specific student advisees or grant details are not detailed here. He collaborates on projects involving lab tools such as the Unknown Building Exploration Simulator (UBES) and Stealth Centric Autonomous Robot Simulator (SCARS), advancing robotic autonomy in unstructured environments.

• Combinatorial Optimization
• Algorithm Design and Analysis
• Game Theory and Social Choice
• +4 more

Jannik Matuschke serves as Associate Professor in the Department of Decision Sciences and Information Management at KU Leuven's Faculty of Economics and Business (FEB). He is an active member of both the KU Leuven Institute for Artificial Intelligence (Leuven.AI) and the KU Leuven Institute for Mobility (LIM), holding office at Hogenheuvelcollege (HOGC) 04.123 in Leuven, Belgium. His academic career spans multiple prestigious institutions across Europe, reflecting his international recognition in operations research and theoretical computer science. Dr. Matuschke completed his PhD in Mathematics in 2013 at Technische Universität Berlin under Martin Skutella and Britta Peis. His academic journey included a postdoctoral position at Universidad de Chile (2014), a DAAD P.R.I.M.E. fellowship at the University of Rome 'Tor Vergata' (2015), and a junior group leader/lecturer position at Technische Universität München (2016-2018) before joining KU Leuven in 2019. His research focuses on the intersection of discrete mathematics, theoretical computer science, and operations research, with specific expertise in combinatorial optimization, algorithm design and analysis, game theory and social choice, robustness and uncertainty, and logistics applications. Dr. Matuschke has made significant contributions to network flow theory, security games, and robust optimization frameworks, developing both theoretical foundations and practical applications. Analysis of his recent publications reveals a strong emphasis on robustness under uncertainty, with increasing focus on security applications and theoretical aspects of scheduling problems. His work demonstrates consistent innovation in decomposing complex optimization problems and developing approximation algorithms for challenging combinatorial settings. 2024 Meritorious Service Reward of the journal Operations Research Dr. Matuschke actively mentors several PhD students and postdocs, including Yanfei Chen, Léonie Gallois, Phablo Moura, Felix Rauh, and Fei Wu. He leads significant research projects such as 'Rigidity and Flexibility: Structures and Algorithms' (starting 2025), 'Designing resilient and recoverable infrastructures via multi-level and multi-stage optimization' (2022), and 'Optimization and analytics for stochastic and robust project scheduling' (2020), with funding from diverse sources including KU Leuven start-up grants, FWO research projects, and DAAD fellowships. As an integral member of the academic community, Dr. Matuschke serves as Associate Editor for Omega (since 2023), Operations Research Letters (since 2024), and OR Spectrum (since 2019). He co-chairs the program committee for WAOA 2025 and has participated in numerous prestigious conference committees. At KU Leuven, he coordinates the 'Master's Thesis in Production and Logistics' program and serves on the Teaching Portfolio Peer Review Committee.

Renata Medeiros de CarvalhoView profile

Assistant Professor

• Business Process Management
• Process Mining
• Declarative Business Processes
• +3 more

Renata Medeiros de Carvalho is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the Process Analytics and EAISI Health groups. She holds a PhD in Computer Science from Federal University of Pernambuco (Brazil), an MSc and BSc in Computer Engineering from University of Pernambuco, and has conducted postdoctoral research at UQAM (Canada). Her research focuses on adaptive and declarative business processes, with particular emphasis on healthcare and data privacy. Education: PhD in Computer Science, Federal University of Pernambuco (2015) MSc in Computer Engineering, University of Pernambuco BSc in Computer Engineering, University of Pernambuco Research Interests: Flexible business processes and Process Mining Declarative modeling (e.g., OCBC language) Healthcare process optimization GDPR compliance frameworks Key Projects: PATIENCE 2 : Patient-centric healthcare through nomadic sensing BPR4GDPR : GDPR compliance toolkit Awards: Xerox University Affairs Committee Grant NSERC Engage Grant Teaching & Leadership: Local coordinator for EIT Digital Data Science and Erasmus Mundus BDMA master programs Teaches courses like Advanced Process Mining and DBL Data Challenge

Mohammad T. KhasawnehView profile

Professor

• Healthcare Systems Engineering
• Operations Management
• Data Science
• +3 more

Mohammad T. Khasawneh is a SUNY Distinguished Professor and Director of the School of Systems Science and Industrial Engineering at Binghamton University. He leads the Watson Institute for Systems Excellence (WISE) and the Healthcare Systems Engineering Center. His roles include Director of the Manhattan Graduate Program in Health Systems. Education: BS and MS in Mechanical Engineering from Jordan University of Science and Technology (1998, 2000), PhD in Industrial Engineering from Clemson University (2003). Research focuses on healthcare systems engineering, operations management, and data science. His work optimizes healthcare systems for improved patient outcomes and cost efficiency. Key areas include predictive analytics, hospital resource utilization, and clinical performance improvement. He has generated over $15M in external funding and led projects with U.S. hospital systems. Notable achievements include developing the Executive Master of Science in Health Systems and an MS in Healthcare Systems Engineering. His research has produced 60+ journal articles and 120+ conference papers. Awards include SUNY Chancellor’s Awards for Teaching (2011) and Scholarship (2021), University Awards for Graduate Director (2015) and International Education (2016). He is an IISE Fellow and holds honorary visiting professorships at Hebei University of Technology (China) and Vellore Institute of Technology (India). Grants and funding: $2.5-3M annually via WISE, $39M in software/equipment grants. Lab/Initiatives: Healthcare Systems Engineering Center, WISE, and multiple hospital partnerships.

Markus KellerView profile

Professor

• Viticulture
• Grape Physiology
• Irrigation Management
• +4 more

Markus Keller is a **Château Ste. Michelle Distinguished Professor** in the **Department of Horticulture and Landscape Architecture** at **Washington State University (WSU)**, affiliated with the **Prosser Irrigated Agriculture Research and Extension Center (IAREC)**. His research focuses on grapevine physiology, viticulture production systems, and environmental stress management. He holds a **Ph.D. (1995)** and **M.S. (1989)** in Agricultural Engineering from the **Federal Institute of Technology (ETH Zurich), Switzerland**. **Research Interests**: Dr. Keller’s work addresses crop physiology, irrigation strategies, cold hardiness, berry shrivel, and mechanized viticulture. His projects include developing deficit irrigation frameworks, understanding water movement in berries, and predicting frost damage risks using machine learning. He collaborates with industry partners like the USDA and Washington wine producers. **Key Contributions**: His studies on grapevine water relations, berry development disorders, and cold tolerance have advanced sustainable viticulture practices. He leads teams investigating precision agriculture tools for vineyards, including hyperspectral imaging and decision-support systems. **Grants & Funding**: Supported by grants from USDA, Washington wine tax, and juice processors. His work integrates applied research with grower education on frost protection and yield optimization. **Lab & Collaborations**: Directs research at WSU’s Viticulture and Enology program, collaborating with international teams on climate resilience and grapevine health. Mentors Ph.D. and M.S. students in experimental design and field studies.

Zhen XieView profile

Assistant Professor

• High-Performance Computing (HPC)
• Machine Learning
• Parallel Computing
• +3 more

Zhen Xie is an Assistant Professor in the Department of Computer Science at Binghamton University (SUNY), serving as Director of the Parallel Computing and Intelligent System (PCIS) Lab. He holds a PhD from the Chinese Academy of Sciences and a BA from Wuhan University of Technology. His research focuses on high-performance computing (HPC), machine learning, and their intersections, particularly optimizing performance for HPC and AI/DL applications across heterogeneous architectures. Research Highlights: Dr. Xie’s work emphasizes system-level performance optimization for ML and HPC, including GPU acceleration, memory optimization, and AI accelerator selection. His team has won the ACM Gordon Bell Special Prize (2022) for their GenSLMs project predicting SARS-CoV-2 evolution. Recent grants include a 2024 gift from OpenAI for AI testbed initiatives. Awards: ACM Gordon Bell Special Prize (2022), Impact Argonne Awards (2023) Lab: PCIS Lab explores middleware for parallel computing, targeting scientific simulations and big data analytics. Collaborations include Argonne National Lab and Lawrence Berkeley National Lab. Teaching: Teaches Distributed Systems (CS 457/557) and oversees independent studies. Previously trained researchers at Argonne’s ATPESC program. Grants & Collaborations: Subcontract with Lawrence Berkeley Lab (HEVI-LOAD), Argonne testbed expeditions, and OpenAI-funded projects. Active in DOE labs like Summit and Aurora supercomputers.

• Operations Research
• Data Analytics
• Machine Learning
• +4 more

Dr. Katie McConky is a Professor and Department Head of Industrial and Systems Engineering at Rochester Institute of Technology (RIT). She holds a Ph.D. in Industrial Engineering from SUNY Buffalo and prior experience as a research scientist at CUBRC Inc., where she worked on military and data mining projects. Her research focuses on operations research, machine learning, and energy systems optimization, addressing challenges in combinatorial optimization, cyberattack forecasting, and sustainable energy management. She has secured funding from agencies like ONR, AFRL, NASA, and NYSERDA. Education: BS and MS (RIT), Ph.D. (SUNY Buffalo) Key Roles: Department Head, Faculty Member, Research Scientist (CUBRC) Her work spans applications such as kidney exchange optimization, rover mission planning, and energy demand forecasting. She emphasizes interdisciplinary collaboration in her research, integrating machine learning with traditional optimization techniques. Dr. McConky’s publications highlight advancements in forecasting methodologies for cyber threats, energy systems, and transportation logistics. She teaches courses including Operations Research and Forecasting Methods, emphasizing practical software tools like Gurobi. Her contributions include patents on remote activity detection and energy storage optimization. Her research is supported by grants from federal agencies and industry partners, reflecting her expertise in both academic and applied domains.

• Pharmacology
• Toxicology
• Drug Abuse Research
• +7 more

Brenda Gannon, Ph.D., is an Assistant Professor in the Department of Pharmacology and Toxicology at the University of Arkansas for Medical Sciences (UAMS), within the College of Medicine. Her research focuses on the abuse-related effects of novel psychoactive substances (NPS), including synthetic cannabinoids, cathinones, and opioids. She also investigates regulatory policy and drug-drug interactions. Dr. Gannon’s work employs behavioral pharmacology techniques such as intravenous self-administration, drug discrimination, and telemetry-based physiological monitoring. Education: Ph.D., Interdisciplinary Toxicology, UAMS (2015) Graduate Certificate in Regulatory Sciences, UAMS (2014) Postdoctoral Fellowship at University of Texas Health Science Center-San Antonio (2015–2018) Research Interests: Dr. Gannon’s research bridges preclinical pharmacology and regulatory science, with emphasis on understanding the neurochemical and behavioral profiles of emerging drugs. Her lab evaluates abuse liability, polypharmacology, and translational strategies for drug development. Key areas include synthetic cathinones, psychedelics, and opioid alternatives. Grant Activity: She serves as Lab Manager (Co-Investigator equivalent) on NIH-funded projects (IDs 15DDHQ24A00000020 and 15DDHQ24A00000027) investigating hallucinogens and stimulants’ abuse potential using in vivo assays. Labs/Teams: Her laboratory focuses on translational drug research, combining behavioral and neurochemical analyses to inform regulatory policy and therapeutic design.