Prof. Roger Wattenhofer is a Full Professor at the Department of Information Technology and Electrical Engineering, ETH Zurich, Switzerland, and Deputy head of the Computer Engineering and Networks Lab. He holds a doctorate in Computer Science from ETH Zurich (1998) and has held positions at Brown University and Microsoft Research before returning to ETH. His research focuses on distributed computing, wireless networks, and algorithmic systems design, with contributions to Byzantine agreement protocols, blockchain technologies, and neural network architectures. He teaches courses such as Distributed Systems and Computational Thinking. Education: Ph.D. in Computer Science (ETH Zurich, 1998). Research interests include distributed systems, network algorithms, and the intersection of machine learning with distributed computing. His work spans both theoretical foundations and practical implementations, addressing challenges in fault tolerance, consensus mechanisms, and algorithmic efficiency. Recent publications explore topics like adversarial robustness in voting systems, privacy in reinforcement learning, and generative music models. He actively contributes to open-source frameworks and benchmarks for neural algorithmic reasoning.
Subhasish Mitra is the William E. Ayer Professor of Electrical Engineering and Computer Science at Stanford University, holding dual appointments in both departments. He leads the Stanford Robust Systems Group and serves on the leadership team of the Microelectronics Commons AI Hardware Hub under the US CHIPS and Science Act. His research spans Robust Computing, NanoSystems, Electronic Design Automation (EDA), and Neurosciences, with breakthroughs in X-Compact test compression, carbon nanotube computing, and 3D integration. He has held international roles like the Carnot Chair at CEA-LETI and Visiting Professorships globally. Education & Honors: Recipient of over 40 awards including the IEEE Computer Society’s Harry H. Goode Memorial Award, ACM/IEEE’s A. Richard Newton Technical Impact Award, and the Intel Achievement Award. He earned top academic accolades from IIT Kharagpur and Jadavpur University, and is a Fellow of ACM and IEEE. Research Impact: Pioneered first-of-their-kind systems like the carbon nanotube computer and monolithic 3D integration. His work on robust computing techniques like QED validation and X-Compact compression has industry-wide adoption, saving billions in manufacturing costs. Collaborates with industry leaders like Intel, Google, and Samsung. Publications & Grants: Over 400 publications, including award-winning papers in DAC, ISSCC, and IEEE journals. Leads grants from NSF, DoE, and industry partnerships. His lab explores cutting-edge topics like neuromorphic computing, 3D thermal scaffolding, and AI hardware acceleration. Administration & Outreach: Serves as Associate Chair (Faculty Affairs) for Stanford’s Computer Science Department. Recognized by students for mentorship, and frequently invited to global forums like the World Economic Forum and National Academy of Engineering.
Aleksandar Jeremic is an Associate Professor in both the Electrical & Computer Engineering department and the McMaster School of Biomedical Engineering at McMaster University. His research focuses on biomedical signal processing, statistical signal processing, and biometrics, with applications in healthcare and biomedical systems. He holds a Dipl. Ing. from the University of Belgrade, and an M.S. and Ph.D. from the University of Illinois at Chicago. His expertise spans physiological signal analysis (e.g., ECG/EEG), medical imaging, and machine learning for biomedical applications. He has authored over 50 technical articles and two book chapters, and has been recognized with a teaching award from the McMaster Electrical and Computer Engineering Society (2018). He supervises graduate students in both theoretical and applied research areas. Dr. Jeremic teaches advanced courses such as Biomedical Signal Modeling and Processing and Advanced Probability and Random Processes , emphasizing practical applications of signal processing in healthcare. His work includes clinical implementations like neonatal seizure monitoring software and microwave imaging for breast cancer detection.
Artur W. Dubrawski is an Alumni Research Professor of Computer Science and Director of the Auton Lab at Carnegie Mellon University's School of Computer Science. He leads interdisciplinary research on Artificial Intelligence, Machine Learning, and Robotics with real-world applications in healthcare, nuclear safety, food safety, and counter-human trafficking. His work focuses on bridging gaps between data-driven AI and empirical sciences through probabilistic modeling, predictive analytics, and time-series intelligence. Lab: Auton Lab (founded 1993) Collaborations: Allegheny County Health Department, USDA, CDC, U.S. Army Research Impact: AI for wastewater-based COVID-19 forecasting, radiological inspection systems, and hospital infection detection His students and affiliates include current PhD candidates Angela Chen, Emma Erickson, Cecilia Morales, Willa Potosnak and past researchers like Benedikt Boecking (co-inventor of Interactive Weak Supervision). The lab has spun off startups like Marinus Analytics (IBM XPrize finalists) and developed open-source tools like auton-survival for survival analysis. Key Grants: $10.5M U.S. Army contract for AI-driven predictive maintenance research.
Belkis Ezgi Arikan is a researcher at Justus Liebig University Giessen , affiliated with the Department of Psychology and Sports Science . Her work focuses on sensorimotor integration, tactile suppression, and neural mechanisms underlying action-outcome monitoring. She collaborates with Prof. Dr. Fiehler and Dr. Voudouris on project A4 (Predictive somatosensory processing during voluntary movements). Department: Psychology and Sports Science Collaborators: Dr. Dimitris Voudouris, Prof. Dr. Katja Fiehler Her research investigates: How the brain processes self-generated vs. externally generated sensory feedback Neural correlates of tactile suppression networks Role of cerebellum and angular gyrus in temporal recalibration Modulation of BOLD responses during action monitoring Recent publications reveal trends in predictive coding , sensorimotor recalibration , and neural suppression mechanisms across tactile, visual, and multisensory domains. Her work employs neuroimaging techniques like fMRI and behavioral paradigms to study self-motion perception and feedback processing. She contributes to understanding how the brain distinguishes self-initiated from external sensory events, with implications for motor learning and perceptual timing mechanisms.
Mahdi Khodayar is an Assistant Professor of Computer Science at the University of Tulsa . He holds a Ph.D. in Electrical Engineering from Southern Methodist University (2020) and M.Sc./B.S. degrees in Artificial Intelligence and Software Engineering from K.N. Toosi University of Technology (2015, 2013). His research focuses on AI and machine learning applications in power systems, transportation systems, computer vision, and spatiotemporal pattern recognition. B.Sc. in Computer Engineering (2013), K.N. Toosi University of Technology M.Sc. in Artificial Intelligence (2015), K.N. Toosi University of Technology Ph.D. in Electrical Engineering (2020), Southern Methodist University Khodayar’s research bridges deep learning with energy systems , including fault detection in power grids, renewable energy forecasting, and traffic scene understanding. His work leverages graph neural networks , reinforcement learning , and generative models for robust spatiotemporal analysis. Recent publications highlight his focus on graph-based architectures for power systems, hybrid deep reinforcement learning in energy forecasting, and unsupervised domain adaptation in remote sensing. His work integrates physics-informed modeling with probabilistic frameworks . Scientific Awards : NSF ECCS Division Funding (2022) US DOT FHWA Grant (2023) Zelimir Schmidt Award for Early Career Research (2023) Honors Student Award (2015), K.N. Toosi University Khodayar has been funded by the NSF , US Department of Transportation , and the TU Cyber Fellows program . He serves as an associate editor for IEEE Transactions on Transportation Electrification and other journals.
Adam Zylbersztejn is an Assistant Professor (Maître de conférences HDR) at Université Lumière Lyon 2 and affiliated with GATE Lyon Saint-Etienne, a research group in economics. He holds a PhD from the Paris School of Economics (2013) and previously conducted postdoctoral research at WU Wien (Vienna University of Economics and Business). His research focuses on behavioral economics using experimental methods to study individual and interactive decision processes, particularly in contexts involving trust, strategic uncertainty, and cultural variability. Key research areas include behavioral game theory, experimental finance, and the impact of nonverbal communication in economic interactions. His work has been published in top journals such as Journal of Economic Psychology , Games and Economic Behavior , and Experimental Economics . Recent studies examine topics like anonymity effects in charitable giving, trust in market exchanges under oath, and cognitive processes in asset markets using eye-tracking technology. His ongoing projects include research on risk-incentive tradeoffs and ambiguity framing in decision-making. While no formal academic awards are listed, his work has received press coverage in outlets like Le Monde and CORTEX Mag . He collaborates widely with institutions such as GATE-LSE and the Paris School of Economics, contributing to both theoretical and applied economic inquiry.
Vijay Raghunathan is a Professor in the Department of Electrical and Computer Engineering at Purdue University's College of Engineering. His work focuses on hardware and software architectures for embedded systems, wireless sensors for IoT, and wearable/implantable electronics with emphasis on low power design, energy harvesting, emerging memory technologies, and secure system design. Academic Rank: Professor Department: Electrical and Computer Engineering University: Purdue University Research Focus: Energy-efficient embedded systems, IoT, wearable devices His research explores low power design at both board-level and system-on-chip scales, micro-scale energy harvesting , and reliable/secure system design for medical devices. Recent work focuses on Processing-in-Sensor/Memory for battery-free AIoT devices, state space models for neural processing units, and security coprocessor integration in autonomous systems. Analysis of his publications reveals trends in energy-efficient neural network acceleration , approximate computing for edge inference , and compute-in-memory architectures . Key subfields include collaborative edge-cloud partitioning , sparse DNN accelerators , and security frameworks for medical devices. Vijay's work also addresses energy-accuracy tradeoffs in multimodal cognitive systems and intermittent computing using non-volatile memory technologies. His contributions span from microcontroller energy management to security protocols for implantable electronics.
Ronald McGarvey is a Full Professor at IÉSEG School of Management since 2022, with prior roles including Associate Professor with Tenure at the University of Missouri (2013-2022) and Senior Operations Researcher at RAND Corporation (2002-2022). He holds a Ph.D. in Industrial Engineering and Operations Research from Pennsylvania State University and an H.D.R. in Industrial Engineering from the University of Paul Sabatier, France. Education : H.D.R. (2024), Industrial Engineering, University of Paul Sabatier Ph.D. (2002), Industrial Engineering and Operations Research, Pennsylvania State University His research focuses on Operations Research and Robust Optimization applied to diverse domains: Healthcare Logistics : Modeling access disparities in rural obstetric care for American Indian populations Environmental Sustainability : Analyzing biomass energy systems, forest carbon impacts, and waste management frameworks Network Design : Developing optimization models for supply chain resilience, military logistics, and urban transit safety Policy Analysis : Evaluating multi-state environmental collaborations and localized food systems His publications demonstrate strong interdisciplinary applications of mathematical programming and spatial optimization to real-world challenges in healthcare access, energy sustainability, and transportation systems. Key Collaborations : Working with researchers in public health (Thorsen, Noble), environmental science (Aguilar), and engineering (Mirzaee, Gutierrez-Lopez)
Ji Zhu is the Susan A. Murphy Collegiate Professor of Statistics at the University of Michigan, Department of Statistics. He holds affiliations with the Michigan Institute for Data Science (MIDAS) and the Michigan Integrated Center for Health Analytics and Medical Prediction (MiCHAMP). His research focuses on statistical machine learning, network analysis, and health science applications. Education: B.Sc. in Physics (Peking University, 1996), M.Sc. and Ph.D. in Statistics (Stanford University, 2000 and 2003). Notable awards include the NSF CAREER Award (2008), Fellowships from the ASA (2013) and IMS (2015), and recognition as a Web of Science Highly Cited Researcher (2014–2020). Research interests span statistical methodologies for networks, survival analysis, and high-dimensional data. He co-authored influential papers on community detection, network cross-validation, and latent space models. Current editorial roles include Editor-in-Chief of the Annals of Applied Statistics and Action Editor for the Journal of Machine Learning Research. Advising: Supervised over 50 students and postdocs, many now in academia and industry. Notable former advisees include Tianxi Li (University of Minnesota), Yuan Zhang (Ohio State University), and Weijing Tang (Carnegie Mellon University). Labs/Teams: Active in interdisciplinary projects at MIDAS and MiCHAMP, focusing on healthcare analytics and predictive modeling for diseases like hepatitis and cardiovascular outcomes.
Ali H. Sayed is the Dean of the School of Engineering (Faculté des sciences et techniques de l'ingénieur - STI) at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, where he also directs the Adaptive Systems Laboratory (Laboratoire de systèmes adaptatifs). Previously, he served as an emeritus professor and chair of the Electrical Engineering Department at UCLA. He is a highly cited researcher and a member of the US National Academy of Engineering and the World Academy of Sciences. Sayed served as president of the IEEE Signal Processing Society in 2018 and 2019. Professor Sayed's research focuses on adaptation and learning theories, data and network sciences, statistical inference, multi-agent systems, adaptive networks, and optimization. His work bridges theoretical foundations with practical applications in signal processing, machine learning, and network science. He has made significant contributions to distributed learning algorithms, social learning over networks, and adaptive signal processing techniques that have influenced both academic research and practical implementations. His recent publications demonstrate a strong focus on multi-agent systems, distributed learning, privacy-preserving techniques, and social learning over networks. The research trends show increasing emphasis on federated learning with privacy guarantees, graph-based learning approaches, and the intersection of social dynamics with information processing. His work consistently addresses fundamental theoretical questions while maintaining relevance to practical applications in communication networks, social media analysis, and distributed artificial intelligence systems. Professor Sayed has received numerous prestigious awards throughout his career, including: IEEE Fourier Award (2022) Norbert Wiener Society Award (2020) IEEE Signal Processing Society Education Award (2015) Papoulis Award from the European Association for Signal Processing (2014) Technical Achievement Award from IEEE Signal Processing Society (2012) Terman Award from the American Society for Engineering Education (2005) IEEE Donald G. Fink Prize (1996) Multiple Best Paper Awards from IEEE and EURASIP Sayed has authored or co-authored over 570 publications and six monographs. He has mentored numerous PhD students and researchers in the fields of signal processing and adaptive systems. His editorial leadership includes serving as Editor-in-Chief of IEEE Transactions on Signal Processing (2003-2005) and EURASIP Journal on Advances in Signal Processing (2006-2007), as well as Founding Editor-in-Chief of the Open Access Book Series on Information and Learning Sciences. At EPFL, Professor Sayed leads the Adaptive Systems Laboratory, which focuses on developing theoretical frameworks and practical algorithms for adaptive systems, networked learning, and distributed signal processing. The lab's research encompasses both fundamental theoretical investigations and applications to real-world problems in communications, social networks, and computational biology.
CHAN Mun Choon is a Professor at the School of Computing, National University of Singapore (NUS) , where he directs the NUS-NCS Joint Laboratory for Cyber Security . He previously worked at Bell Labs (1997-2003) and holds a PhD from Columbia University (1997). His research spans systems and networking with specific interests in mobile computing, software-defined networking, and cyber-physical systems . PhD, Electrical Engineering (1997), Columbia University M.Phil., Electrical Engineering (1993), Columbia University MS, Electrical Engineering (1993), Columbia University BS, Computer & Electrical Engineering (1990), Purdue University His recent work focuses on 5G network architecture , data center fault debugging , and energy-efficient mobile sensing . He has published over 100 papers and holds 7 US patents , including cache-based compaction techniques with 210+ citations. His projects include fronthaul slicing for 5G, network-wide packet history frameworks, and participatory indoor localization. Scientific recognition includes: Best Paper Awards: IEEE ICNP 2019, ACM SOSR 2019, ICDCN 2016 Best Demo: IPSN 2016 Distinguished Member, INFOCOM TPC (2016, 2020, 2021) He serves as Vice-Dean, Graduate Studies and Vice-Dean, Academic Affairs at NUS Computing, and has graduated 21 PhD students . His lab develops solutions for network security , latency-sensitive applications , and mobile sensing .
Mario Berges is an Associate Professor in the Department of Civil and Environmental Engineering at Carnegie Mellon University (CMU), with a courtesy appointment in Electrical and Computer Engineering. He holds leadership roles as Co-Director of the IBM Smart Infrastructure Analytics Lab and Director of the Intelligent Infrastructure Research Lab (INFERLab). His work focuses on applying information/communication technologies to enhance the operational efficiency and resilience of built environments amid evolving resource constraints and climate changes. Education: PhD in Civil & Environmental Engineering from CMU (2010). Research Interests: Berges' research integrates smart infrastructure systems, energy efficiency, and machine learning. Key areas include non-intrusive load monitoring (NILM), structural health monitoring of pipelines, building automation systems, and urban heat risk modeling. He develops data-driven frameworks for energy disaggregation, sensor placement optimization, and real-time infrastructure diagnostics. Awards: Recognized with the 2010 FIATECH Outstanding Early Career Researcher Award and 2015 Dean’s Early Career Fellowship from CMU. Grants & Labs: Leads INFERLab, collaborating with IBM on smart infrastructure projects. His work spans academic-industry partnerships focused on building analytics, smart grid technologies, and sensor networks. Future Directions: Expanding research into AI-driven energy systems, resilient urban infrastructure, and cross-disciplinary solutions for climate adaptation.
Professor Simo Särkkä holds a position in Sensor Informatics and Medical Technology at the Department of Electrical Engineering and Automation (EEA), Aalto University. His research focuses on multi-sensor data processing, Bayesian filtering, machine learning, and their applications in medical technology, brain imaging, and inverse problems. He leads research groups including the Helsinki Institute for Information Technology (HIIT) and Sensor Informatics and Medical Technology. His work bridges theoretical advancements in probabilistic methods with practical implementations in healthcare and engineering. Key research interests include Gaussian processes, stochastic differential equations, quantum machine learning, and signal processing. He has contributed to advancements in algorithms for nonlinear state-space models, parallel computing techniques, and medical imaging technologies such as scatter correction in CT scans. His methodologies are applied across domains like autonomous systems, robotics, and bioengineering. Notable publications span topics like quantum-assisted Gaussian regression, physics-informed machine learning for industrial processes, and parallel-in-time numerical methods. His work emphasizes computational efficiency and robustness in high-dimensional and real-time systems.
Najim Dehak is an Associate Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, part of the Whiting School of Engineering. His research focuses on machine learning applied to speech processing, audio classification, and health applications. He is renowned for developing the I-vector representation for speaker recognition, introduced in 2008 during a workshop at Johns Hopkins’ Center for Language and Speech Processing. Prior to this role, he was a research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory. Dehak holds a PhD from the School of Advanced Technology in Montreal (2009). He is a Senior Member of IEEE and contributes to the IEEE Speech and Language Technical Committee. His work bridges AI, healthcare, and signal processing, with notable contributions to neurodegenerative disease detection via speech and handwriting analysis. Research interests include adversarial attacks on speech systems, multimodal biomarker discovery, and robust speech processing across demographics. His lab’s tools, like the Hermespeech Recorder, enable scalable data collection for clinical and research applications. Education: PhD in Advanced Technology (2009), Montreal Affiliations: Johns Hopkins University, IEEE Labs/Teams: Center for Language and Speech Processing (CLSP) His recent work explores AI’s role in aging research, including Alzheimer’s and Parkinson’s disease detection through speech, eye tracking, and handwriting analysis. Ongoing projects address fairness in speaker verification and robustness against adversarial attacks in ASR systems.
