Ebru Turanoglu Bekar is a Senior Lecturer at the Department of Industrial and Materials Science, Chalmers University of Technology, specializing in Smart Maintenance and Production Systems. She contributes to the Production Service Systems & Maintenance research group. Research Interests: Total Productive Maintenance (TPM), Artificial Intelligence applications in manufacturing, Multi-Criteria Decision Making, Performance Measurement systems Recent Focus: Development of data-driven algorithms for predictive maintenance, integration of digital twins in industrial contexts Key Projects: Factory SensAI (2025–2028) - Data integration for AI in manufacturing Trustworthy Predictive Maintenance TPdM (2022–2025)
Jung-Eun Kim is an Assistant Professor in the Department of Computer Science at North Carolina State University, where she conducts research at the intersection of artificial intelligence, machine learning, and cyber-physical systems. Her work focuses on creating trustworthy, interpretable, and efficient AI systems, particularly for safety-critical applications. Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (2017) M.S. in Computer Science and Engineering, Seoul National University (2009) B.S. in Computer Science and Engineering, Seoul National University (2007) Dr. Kim's research primarily investigates how to make AI systems more trustworthy, interpretable, and efficient, with particular emphasis on understanding failure modes, safety risks, vulnerabilities, and biases in deep learning models. Her work bridges theoretical understanding with practical applications in safety-critical systems. She explores how efficiency considerations interact with these issues, seeking to fundamentally anatomize neural networks to understand what causes failure modes and how to mitigate them. Her approach has been described as 'like a heart surgeon, we open the heart of a neural network architecture, look into it, interpret it, and cure it.' Her recent publications demonstrate a strong focus on safety alignment in large language models, mitigation of spurious correlations, privacy preservation against membership inference attacks, and sustainable AI development. Her work spans theoretical foundations of trustworthy AI while addressing practical challenges in model deployment, particularly for resource-constrained environments. She has made significant contributions to understanding how model compression techniques like pruning and quantization can inadvertently amplify biases and vulnerabilities. Scientific Awards: ICLR Spotlight, 2025 IBM Faculty award, 2023 CRA Early & Mid Career Mentoring Workshop, 2023 Cloud GPU provided by Lambda, worth $17,280, for course, Spring 2023 NeurIPS Spotlight and nomination for Best Paper Award, 2022 CRA Career Mentoring Workshop, 2022 GPU Grant by NVIDIA Corporation, 2018 The MIT EECS Rising Stars, 2015 The Richard T. Cheng Endowed Fellowship, 2015-2016 Dr. Kim actively mentors PhD students, currently advising Xingli Fang, Varun Mulchandani, Jianwei Li, Rishi Singhal, and Minseon Kim. She has secured significant research funding, including an NSF SaTC (Secure and Trustworthy Cyberspace) grant as Co-PI for 'Partition-Oblivious Real-Time Hierarchical Scheduling' ($281,629.00, 2022-2024). Her research has also been supported by an NVIDIA GPU Grant and cloud resources from Lambda. She serves on program committees for top AI conferences including ICLR, ICML, NeurIPS, AAAI, and IJCAI, and has held roles such as Publicity Chair for IJCAI 2024. Her research group focuses on developing methods to make AI systems more trustworthy, interpretable, and efficient, with particular attention to safety-critical applications. The group investigates how to identify and mitigate failure modes in neural networks while maintaining efficiency, exploring the fundamental relationship between model architecture, safety risks, and computational constraints.
Chih-Chun Wang is a Professor at the Elmore Family School of Electrical and Computer Engineering , Purdue University, with additional leadership roles as Associate Head for Facilities, Planning, and Staff. He earned his Ph.D. in Information Sciences and Systems from Princeton University in 2005, following an M.S. (2002) and B.S. (1999) in Electrical Engineering from Princeton and National Taiwan University, respectively. Research Interests: His work spans Network coding (graph-theoretic capacity, wireless network coding, feedback mechanisms) Coding theory (LDPC codes, Reed-Solomon decoding, iterative algorithms) Information theory (multi-user detection, network information theory) Signal processing (turbo equalization, space-time codes) Control theory (optimal stopping theory) Scientific Contributions: He has published extensively on Age-of-Information (AoI) minimization, low-latency coding, and multi-hop relay optimization. His research trends include Integrating machine learning with network coding Wireless security for Beyond-5G systems Distributed storage networks with intelligent helper selection Delay-constrained communication protocols Awards: Recognized as an IEEE Fellow in 2024 for contributions to network coding and information theory. Teaching: He teaches undergraduate courses like ECE301: Signals and Systems and graduate courses such as ECE639: Error Control Coding , with a focus on iterative decoding, LDPC codes, and network information theory. Advising: Supervised 15+ Ph.D. students, including current advisees Pin-Wen Su (delay-oriented coding), Wonjun Lee (cyber-physical systems), and Giles Bischoff (low-latency systems). Former students hold prominent roles at institutions like Google, Meta, and Intel.
Prof. Blerim Rexha is a full professor at the University of Prishtina's Faculty of Electrical and Computer Engineering, Kosovo. With a Ph.D. in Computer Engineering from Vienna University of Technology (2004), he has led research in cybersecurity, blockchain, machine learning, and electronic voting systems. His teaching portfolio includes data, computer, and internet security courses. Education : Ph.D. in Computer Engineering (Vienna), Electrical Engineer MSc (Prishtina), specialized certifications in software engineering, biometrics, and .NET programming. His research spans cybersecurity (DDoS mitigation, face authentication attacks), blockchain applications (electronic voting bridges, transaction privacy), and machine learning integration (boosted trees for intrusion detection, LSTM for vulnerability scanning). He has contributed to cloud security through novel encryption methods and AI-driven attack detection. Recent publications focus on energy efficiency in cloud vs on-premises systems, XGBoost/CatBoost/LightGBM comparisons for network security, and blockchain bridges for e-voting. His work has addressed privacy preservation in video data, SMS encryption, and eID card pseudo-profiles. Awards include the 2024 Marin Barleti Prize for academic contributions and Best Paper Awards in election security (2015) and Kosovo website vulnerabilities (2013). Honors : Marin Barleti Prize (2024) Cyber Security Ambassador (2018) ICT Academician of the Year (2016) Best Paper Awards (2015, 2013) As academic advisor to the KosovaCyberTeam , he mentors students like Korab Keqekolla and Abian Morina. His leadership extends to Kosovo's Cyber Security State Training Center curriculum development and jury roles in Albanian ICT Awards .
Lilja Øvrelid is a Professor at the Department of Informatics, University of Oslo, leading the Language Technology Research Group. Her research focuses on syntactic and semantic text processing using machine learning techniques such as dependency parsing, negation analysis, and sentiment analysis. She teaches courses including IN1140: Introduction to Language Technology , IN5550: Neural Methods in NLP , and INF5830: Natural Language Processing . Her academic interests span natural language processing, machine learning, and computational linguistics, with a particular emphasis on Norwegian language technology. Recent publications highlight work in sentiment analysis (including patient feedback), event extraction from Norwegian news, benchmarking language models, emotion analysis for under-resourced languages (Pashto, Farsi-Dari), and bias detection in multilingual models. She actively contributes to the development of Norwegian language resources such as NorBench, NorQuAD, and NoReC. Current projects include BigMed and SIRIUS , focusing on biomedical text mining and AI infrastructure. Collaborations with colleagues like Erik Velldal, David Samuel, and Vladislav Mikhailov are frequent in her work. Despite no explicit mention of scientific awards, her contributions to NLP and computational linguistics are substantial through publications, datasets, and tool development.
Professor Jo Smith is a Personal Chair at the University of Aberdeen ’s School of Biological Sciences , specializing in systems modelling, soil organic matter dynamics, and nutrient management. With over two decades at Aberdeen and prior roles at Rothamsted Research and British Petroleum, Smith focuses on carbon sequestration, greenhouse gas emissions, and agricultural sustainability. Research interests include: Soil organic matter and nutrient dynamics in tropical, temperate, and nutrient-depleted soils Climate-smart agriculture across global regions Decision support systems for nitrogen fertilizer optimization Impact of organic resource use on poverty alleviation in Sub-Saharan Africa Machine learning applications in predicting soil nutrient losses Recent publications highlight trends in: Stable isotope analysis for agricultural water management Modelling carbon-nitrogen interactions in data-limited agroforestry systems Policy frameworks for regenerative agriculture and net-zero transitions Tropical peatland emissions and exclosure-based soil restoration High-lignin biofuel byproduct utilization for soil sustainability Collaborations span institutions including: Centre for Ecology and Hydrology (nitrogen cycles, GHG measurements) Nanjing Agricultural University (biochar applications in China) Addis Ababa University (nutrient recycling in Ethiopia) Scottish Government (wind farm carbon calculator development) Makerere University (biogas impact studies in Uganda) Teaching responsibilities include coordinating the Honours Essay course (BI4017/BI4517) and contributing to modules in ecology, environmental science, and risk assessment.
Dr. Aliakbar Jamshidi Far is a Lecturer at the School of Engineering, University of Aberdeen, since December 2017. Previously, he served as a Research Fellow at the Aberdeen HVDC Centre (2012-2017) and as an Assistant Professor at the Iranian Research Organization for Science and Technology (2008-2012). Dr. Jamshidi Far holds a PhD in Electrical Engineering from AmirKabir University (2008), an MSc from Iran University of Science and Technology (1996), and a BSc from Sharif University of Technology (1992). He is an IET Chartered Engineer, Senior Member of IEEE, and member of CIGRE Working Group B4.76. His research focuses on Modeling and control of HVDC systems and DC grids High-power AC/DC and DC/DC converters Hybrid DC circuit breakers Ultrasound applications in Oil & Gas Renewable energy integration Recent publications highlight trends in Modular Multilevel Converters (MMC) for DC grids Flux Switching Machine optimization Ultrasound-based scale removal technology Wave energy for remote islands Smart microgrids for Indonesia and Nigeria Advanced DC circuit breaker designs Dr. Jamshidi Far has secured significant funding including £204k from the Oil & Gas Technology Centre (2019-2021, Co-PI) £42k Consultancy from BP PLC (2019-2020, PI) EU Horizon 2020 PROMOTion Project (2016-2017, Research Fellow) SSE-funded DC converter research (2015-2016, Research Fellow) ERC-funded DC systems modeling (2012-2014, Research Fellow) Teaching responsibilities include Undergraduate: EE3579 - Electrical and Electronic Engineering Design Postgraduate: EG551K/55M5 - Renewable Energy Integration to Grid (Coordinator) EG503V/503W - Solar Energy (Coordinator) EG501H/504B - Electrical Systems for Renewable Energy (Coordinator) He supervises MEng/BEng and MSc students in Renewable Energy, Oil & Gas, and Subsea programs.
Sean B. Andersson is a Professor in the Department of Mechanical Engineering at Boston University's College of Engineering. His research focuses on optimal estimation, system identification, single particle tracking, robotics, and control theory. He earned his Ph.D. from the University of Maryland, College Park. Education : Ph.D. in Mechanical Engineering (University of Maryland, College Park) His work integrates control algorithms with applications in microscopy, nanofabrication, and multi-agent systems. Recent research trends highlight persistent monitoring, trajectory optimization, MRI reconstruction, and dip-pen nanolithography. He has mentored numerous graduate and undergraduate students, many of whom now hold positions at institutions like MIT Lincoln Labs, University of Pennsylvania, and Juniper Networks. Scientific Contributions : Developed robust multi-agent control policies for data harvesting Advanced single particle tracking with real-time feedback Innovated in non-raster scanning probe microscopy Optimized sensor scheduling via minimax and semidefinite programming His lab team combines theoretical and applied research in robotics and control systems, with alumni contributing to academia, industry, and research labs globally.
Dr. Bei Jiang is an Associate Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta , Canada. She holds the Canada CIFAR AI Chair and is affiliated with the Alberta Machine Intelligence Institute (Amii) . Her academic journey includes a PhD in Biostatistics (2014) from the University of Michigan, MS (2008) and BS (2004) from the University of Alberta and Beijing University of Technology, respectively. Current Positions : 2021–Present (Associate Professor), 2022–Present (CIFAR AI Chair) Past Appointments : Assistant Professor (2015–2021), Postdoctoral Fellow at Columbia University (2014–2015), Research Assistant at University of Michigan (2009–2013) Research Interests : Dr. Jiang specializes in methods for joint modeling of longitudinal and health outcome data , Bayesian hierarchical modeling , functional and imaging data analysis , and statistical machine learning . Her work integrates kernel machine regression , differential privacy , and synthetic data generation to address challenges in heterogeneous health data and neuroimaging. Scientific Awards : Highlights include the 2015 SAMSI New Research Fellow , multiple Rackham Conference Travel Awards (2013, 2012), and prestigious NSERC scholarships (2009–2012). She has also received the J Gordin Kaplan Graduate Award (2008) and Statistical Society of Canada Travel Award (2008). Grants : $375,000 (CIFAR AI Chairs, 2022–2027), $480,000 (MITACS Accelerate, 2022–2025), and $210,000 (Canadian Statistical Sciences Institute, 2022–2025) Students and Postdocs : She mentors numerous PhD , MSc , and Postdoctoral Fellows , including Junxi Zhang (2023–Present), Enze Shi (2022–Present), and former advisees like Wenxing Guo (now Lecturer at University of Essex) and Yafei Wang (Assistant Professor at University of Alberta).
Vikram Iyer is an Assistant Professor at the Paul G. Allen School of Computer Science and Engineering and holds an Adjunct Appointment in Mechanical Engineering at the University of Washington. He co-directs the CS for Environment Initiative , focusing on interdisciplinary solutions that bridge computing, biology, and physical systems for environmental sustainability. Education : Ph.D. in Electrical & Computer Engineering (University of Washington), B.S. in Electrical Engineering and Computer Sciences (UC Berkeley) Research Interests revolve around bio-inspired wireless systems , environmentally sustainable electronics , and miniaturized autonomous robotics . His work includes: Biodegradable circuit boards Battery-free wireless sensors Insect-scale vision systems Wind-dispersed environmental monitors AI tools for sustainable design Article Trends highlight contributions to green hardware , energy-autonomous robotics , and environmental sensing networks , often integrating machine learning with physical world interaction . Awards include: NSF CAREER Award SIGMOBILE Dissertation Award Marconi Society Paul Baran Young Scholar Best Paper Awards (SIGCOMM 2016, Sensys 2018) Google/Amazon Research Awards Students advised include Kyle Johnson (NSF Fellow), Vicente Arroyos (GEM Fellow), and Qiuyue Xue (co-advised with Shwetak Patel). His lab collaborates with the Networks & Mobile Systems Lab and Urban Innovation Initiative .
Zukui Li is a Professor in the Department of Chemical and Materials Engineering at the University of Alberta's Faculty of Engineering, where he leads a research group focused on mathematical optimization, machine learning, and process systems engineering. His work spans oil sands extraction, steel production, biomedical applications, and advanced optimization methods. Education: Ph.D. in Chemical Engineering, Rutgers University (2010) M.Sc. in Control Theory and Control Engineering, University of Science and Technology of China (2005) B.Sc. in Automatic Control, University of Science and Technology of China (2002) Postdoctoral Training: Princeton University (2010-2012) Research Focus: Dr. Li's research integrates mathematical optimization and machine learning for complex process systems. His primary areas include: Advanced optimization techniques (robust, stochastic, and distributionally robust optimization) Machine learning applications in process monitoring and biomedical systems Industrial applications in energy, manufacturing, and resource extraction Specific innovations include physics-informed ML for anemia treatment, adaptive optimization for steel production, and distributionally robust methods for uncertainty management. Publication Trends (2019-2023): Recent articles demonstrate a strong focus on uncertainty-aware optimization methods, with increasing integration of machine learning techniques. Dominant themes include distributionally robust optimization, adaptive decision-making under uncertainty, neural network approximations for complex constraints, and applications in industrial process control and biomedical systems. Theoretical advancements are consistently coupled with practical implementations in energy and manufacturing sectors. Research Group: Leads an active team developing optimization frameworks and machine learning solutions for process engineering challenges. Group website: Dr. Zukui Li's Research Group
Dr. Le-Nam Tran is a researcher at the UCD School of Electrical & Electronic Engineering , University College Dublin. His work focuses on optimizing the last hop of 5G/6G wireless networks through mathematical programming, with emphasis on energy efficiency, interference management, and security against eavesdropping. Develops low-cost, low-complexity transmission techniques Projects supported by Science Foundation Ireland Career Development Award Author of over 80 peer-reviewed publications Research Keywords: Wireless Communications Network Security Signal Processing Energy-Efficient Systems Beamforming Optimization Interference Mitigation
Thijs Defraeye is a Senior Scientist at Empa (Swiss Federal Laboratories for Materials Science and Technology) and Adjunct Professor at Dalhousie University. He holds a PhD in Building Physics from KU Leuven (2011) and a Master's in Civil Engineering (2006). His work focuses on optimizing food supply chains through multiphysics simulations and digital twins, addressing challenges in refrigerated transport, postharvest quality preservation, and energy-efficient food processing. He leads the SimBioSys group, developing solutions for perishable goods logistics and electrohydrodynamic technologies. Research interests include: Biophysics of food systems Digital twin applications in agriculture Electrohydrodynamic drying Thermal management in cold chains Sustainable food technologies Recent work emphasizes reducing food loss through physics-based modeling of refrigerated containers, ventilated packaging optimization, and scalable evaporative cooling systems. His studies bridge engineering principles with biological processes, aiming to enhance global food security and environmental sustainability.
Haibo Yang is an Assistant Professor in the Department of Computing and Information Sciences at Rochester Institute of Technology's Golisano College of Computing and Information Sciences. He earned his Ph.D. in Electrical and Computer Engineering from The Ohio State University under the supervision of Prof. Jia (Kevin) Liu. Rochester Institute of Technology , Golisano College of Computing and Information Sciences Ohio State University , Ph.D. in Electrical and Computer Engineering His research focuses on distributed and federated learning systems, examining how statistical and system variability affect algorithm performance under constraints like privacy and communication limitations. Key areas include optimization algorithms, communication-efficient frameworks, Byzantine robustness, and multi-modal adversarial attacks. He is actively involved in developing theoretically grounded solutions for scalable and intelligent distributed learning. Recent publications highlight advancements in multi-objective reinforcement learning, zeroth-order federated optimization, and robustness against heterogeneous client participation. His work has appeared in top venues like UAI, IJCAI, ICLR, AAAI, NDSS, ACM CCS-LAMPS, and ACM MobiHoc, with notable acceptance rates (e.g., 19.3% for IJCAI 2025). Current projects investigate exact convergence mechanisms and adaptive weighting strategies. Dr. Yang received the RIT AI Seed Funding and GWBC Award in February 2024. He supervises funded Ph.D. students and teaches advanced machine learning topics, including CSCI-635: Introduction to Machine Learning.
Darko Marinov is a Professor at the Siebel School of Computing and Data Science and a member of the Information Trust Institute at the University of Illinois at Urbana-Champaign. His research focuses on software testing methodologies, particularly regression testing, flaky tests, and software reliability engineering. He has contributed to frameworks like Ekstazi for regression test selection and DeFlaker for identifying flaky tests. Marinov holds an NSF CAREER Award (2008) for his work in this domain. His research interests span testing techniques for modern software systems, including configuration testing, test prioritization, and fault localization. He has pioneered studies on the characteristics of flaky tests in large-scale projects and developed tools to improve software quality assurance processes. Marinov collaborates across academic and industrial settings, addressing challenges in continuous integration, reproducibility of computational experiments, and hardware-software co-design for resilience. His work bridges theory and practice, with applications in cloud computing, AI workloads, and cybersecurity. Awards: NSF CAREER Award (2008) Key Contributions: Ekstazi, DeFlaker, FastFlip, and Ctest4J frameworks Labs/Teams: Active member of the Information Trust Institute and leads software testing research groups at UIUC
