Dr. Calum Gabbutt is a Chapman-Schmidt AI in Science Postdoctoral Research Fellow at Imperial College London's Department of Mathematics (Faculty of Natural Sciences) and a Postdoctoral Training Fellow at the Institute of Cancer Research (ICR), London. He holds a PhD in Mathematical Biology from Queen Mary University of London (2017–2021) and an MPhys from the University of Oxford (2013–2017). His research focuses on mathematical and computational methods to understand clonal dynamics and cancer evolution, particularly leveraging genomic and lineage tracing data. He develops Bayesian inference models to analyze evolutionary processes in cancer, aiming to improve clinical outcomes through precision medicine. Key areas include genetic barcoding, methylation-based molecular clocks, and phylogenetic reconstruction of tumor evolution. Recent work spans large-scale genomic analyses of cancer evolution, computational tools like PISCA-box for somatic chromosomal alterations, and studies on phenotypic plasticity in metastasis and therapy resistance. His research integrates AI-driven approaches to decode cancer heterogeneity and temporal dynamics in human tissues. Collaborations span institutions like the ICR and Queen Mary University of London, emphasizing interdisciplinary methods at the intersection of mathematics, computational biology, and oncology.
Dr. Todd D. Murphey is a Professor of Mechanical Engineering at Northwestern University's Robert R. McCormick School of Engineering and Applied Science. He serves as Director of Transformative Research and Director of the Master of Science in Robotics Program at Northwestern, leading initiatives in computational dynamics, control systems, and robotics. His work bridges engineering, neuroscience, and biomedical applications, with a focus on developing systems that interact effectively with humans and their environments. Dr. Murphey received his Ph.D. in Control and Dynamical Systems from the California Institute of Technology in 2002, with a thesis titled "Control of Multiple Model Systems." Prior to that, he earned a B.S. in Mathematics, summa cum laude, from the University of Arizona in 1997. Dr. Murphey's research centers on computational methods in dynamics and control, with applications spanning neuroscience, health science, robotics, and automation. His work in the Interactive & Emergent Autonomy Lab focuses on computational models of embedded control, biomechanical simulation, dynamic exploration, and hybrid control. The group develops mathematical approaches that lead to orders of magnitude improvement in computational efficiency for real-time implementation. Key application areas include assistive exoskeleton control, stabilization of energy networks, bio-inspired active sensing, entertainment robots, robotic exploration, and software-enabled stroke rehabilitation. Analysis of Dr. Murphey's recent publications reveals a strong emphasis on human-swarm interaction, algorithmic matter, and control of cyber-physical systems in uncertain environments. His work increasingly integrates information theory with physical systems, exploring how both autonomous and biological systems interact with environments to learn and improve behaviors. Recent trends show growing applications in rehabilitation technology, with particular focus on human-machine interaction in biomedical devices and embodied intelligence. Dr. Murphey has received numerous honors and awards for his contributions to robotics and engineering: Named Director of Transformative Research at Northwestern University (2025) Appointed IEEE Robotics and Automation Society Vice President of Publication Activities (2022) Co-recipient of Best Paper Award for IEEE Transactions on Robotics (2020) Appointed to Air Force Scientific Advisory Board (2019) Recipient of ABB Best Student Paper Award for CPL-SLAM research (2019) Cole-Higgins Award from Northwestern Engineering (2015) Dr. Murphey has supervised numerous graduate students including Taosha Fan, Giorgos Mamakoukas, and Ian Abraham, with research spanning robotic exploration using electrosense and mechanical contact, human-in-the-loop control, and shared control for rehabilitation devices. His lab has secured significant funding from the National Science Foundation, DARPA, and industry partners including Siemens and Ekso Bionics, supporting research in algorithmic matter, emergent behavior, and human-swarm collaboration. The Interactive & Emergent Autonomy Lab, led by Dr. Murphey, investigates how both autonomous systems and biological systems interact with their environments to learn and improve behaviors. Current projects include active learning and data-driven control, active perception in human-swarm collaboration, algorithmic matter and emergent computation, control for nonlinear and hybrid systems, cyber physical systems in uncertain environments, harmonious navigation in human crowds, information maximizing clinical diagnostics, reactive learning in underwater exploration, robot-assisted rehabilitation, and software-enabled biomedical devices. The lab collaborates with researchers across Northwestern and institutions including Georgia Tech, MIT, and industry partners.
Christopher G. Healey is the Goodnight Distinguished Professor of Analytics in the Institute for Advanced Analytics and a Professor in the Department of Computer Science at North Carolina State University. His research spans visualization, data analytics, text analytics, sentiment analysis, machine learning, cognitive psychology, computer graphics, and social media analytics. He has graduated 15 Ph.D. and 26 master's students and secured over $6 million in research funding from agencies including the National Science Foundation, Department of Defense, National Security Agency, Army Research Office, and various industry partners. He has published over 100 peer-reviewed articles and is a senior member of both IEEE and ACM, as well as a member of the NC State Academy of Outstanding Teachers. His research focuses on developing visualization techniques that leverage visual perception to support rapid, accurate, and effective analysis of large, complex datasets. More recently, he has been investigating machine learning for natural language processing and text analytics. His work includes projects on visualizing election results, sentiment estimation for social media, and wildfire narratives using large-scale social media data. His publications demonstrate a strong trend toward integrating machine learning with visualization, particularly for text analytics and social media analysis. He has made significant contributions to visualizing deep neural networks, cyber situation awareness, and pandemic response analytics, showing how visualization can enhance understanding of complex systems and large datasets across multiple domains. IBM Faculty Award (2007, 2008, 2010, 2011, 2012) Senior member, Association of Computing Machinery (ACM) (2007) Senior member, Institute of Electrical and Electronics Engineers (IEEE) (2007) NC State Academy of Outstanding Teachers inductee (2003) National Science Foundation Faculty Early CAREER Award (2001) He has successfully mentored numerous graduate students and secured significant research funding across multiple projects. His work with the Laboratory for Analytic Sciences, National Science Foundation, and Department of Defense demonstrates strong industry and government partnerships. His recent projects focus on visualizing social media narratives, deep neural networks for text understanding, and predictive analytics for large document collections. He leads research groups focused on visualization and analytics, working with teams to develop innovative approaches for data exploration and analysis. His current work continues to push the boundaries of how visualization can be used to enhance understanding of complex data across domains including public health, cybersecurity, and social media analysis.
Abhinav Shrivastava is an Associate Professor in the Department of Computer Science at University of Maryland, College Park, with a joint appointment in the Institute of Advanced Computer Studies (UMIACS). Previously, he served as an Assistant Professor at the same institution from August 2018 to June 2024, and spent one year as a Visiting Research Scientist at Google Research from September 2017 to August 2018. His educational background includes: PhD in Robotics and Artificial Intelligence from Carnegie Mellon University (2017), advised by Abhinav Gupta, with thesis titled 'Discovering and Leveraging Visual Structure for Large-scale Recognition' MS in Artificial Intelligence from Carnegie Mellon University (2011), supervised by Alyosha Efros and Martial Hebert BTech in Computer Science and Engineering from Jaypee Institute of Information Technology (2010) Professor Shrivastava's research focuses on computer vision and machine learning, with particular expertise in object detection, image recognition, and neural representations. His work bridges theoretical advances with practical applications, exploring how visual systems can discover and leverage structure in large-scale recognition problems. He has made significant contributions to understanding the role of supervision in vision transformers, developing novel approaches for object-state composition recognition, and creating efficient neural representations for videos and 3D scenes. His research often addresses fundamental challenges in visual recognition, including handling novelty in open-world environments and improving the efficiency of visual systems. An analysis of his recent publications reveals a strong emphasis on neural representations, particularly for dynamic content like videos and 3D scenes. His work demonstrates increasing sophistication in handling open-world vision problems, with research spanning object discovery, localization, and representation learning. The publications show a clear progression toward more efficient and scalable models, with recent work focusing on model compression, sparse representations, and addressing the challenges of working with limited annotations. His scientific contributions have been recognized with several prestigious awards: Best Paper Award (Applications) at IEEE Winter Conference on Applications of Computer Vision (2020) Microsoft Research PhD Fellowship (2014-2016) Best Student Paper Award at IEEE Winter Conference on Applications of Computer Vision (2014) Outstanding Reviewer Award at IEEE CVPR (2015) Professor Shrivastava has successfully mentored numerous graduate students, many of whom have become prominent researchers in computer vision. His Amazon Research Awards (2020 and 2023) have supported innovative projects including 'The pursuit of knowledge: discovering and localizing new concepts using dual memory' and 'Audio-conditioned Diffusion Models for Generating Lip-synchronized Videos.' He has served as Area Chair for major conferences including ICCV, CVPR, and AAAI, demonstrating his leadership in the computer vision community. His research has attracted significant funding from both academic and industry sources, supporting his exploration of fundamental questions in visual recognition and representation learning.
Chinmay Kulkarni is an Assistant Professor at Carnegie Mellon University's Human-Computer Interaction Institute, where he leads the Expertise@Scale lab. His research integrates large-scale data and automation to transform learning, work, and mentoring systems. Education : Ph.D. in Computer Science from Stanford University (recipient of the Arthur P Samuel Award) Previous Affiliations : Microsoft Research, Barcelona Supercomputing Center His research spans: Human-Computer Interaction design for massive collaboration Voice-controlled interfaces and AI tools Future of work in remote/hybrid environments Behavioral economics through tech interventions Creative entrepreneurship support systems Algorithmic feedback in education Recent publications with AI and education focus show strong trends in voice technology, peer feedback mechanisms, and scalable learning platforms. His lab's systems have been used by >100,000 users across 150 countries. Scientific Awards : Arthur P Samuel Award (Stanford thesis award) Advising & Grants : NSF grant recipient US Department of Education funding Office of Naval Research support Departmental fellowship Labs : Directs Expertise@Scale lab developing systems adopted by Coursera and edX. Current research group includes PhD students Yasmine Kotturi, Julia Cambre, Pranav Khadpe and Masters student Sayan Chaudhry.
Jelena Mirkovic serves as Principal Scientist at USC Information Sciences Institute (USC/ISI) and Research Associate Professor at the University of Southern California's Thomas Lord Department of Computer Science. She has held faculty positions at USC since 2010, progressing from Research Assistant Professor to her current role as Research Associate Professor since 2017, while also serving as Project Leader at USC/ISI. Her educational background includes: PhD in Computer Science from UCLA (2003) MS in Computer Science from UCLA (2000) B.Sc. in Computer Science from University of Belgrade, Serbia (1998) Mirkovic's research spans network security, human-centered attacks, and cybersecurity experimentation infrastructure. Her work focuses on critical security challenges including botnets, denial-of-service attacks, IP spoofing, vulnerability scanning, and user-centric privacy. She has pioneered methodologies for security experiments and led major infrastructure projects including the DETER testbed and SPHERE (Security and Privacy Heterogeneous Environment for Reproducible Experimentation). Analysis of her recent publications reveals consistent innovation across multiple security domains. Her work demonstrates strong technical depth in DDoS defense systems (particularly DNS protection), binary vulnerability analysis, privacy-preserving systems, and security experimentation infrastructure. A notable trend is her focus on bridging theoretical security concepts with practical implementation through large-scale testbeds and real-world data analysis. Her significant scientific achievements include: IEEE Senior Member distinction Best paper award at IEEE COMSNETS 2023 for DNS DDoS defense research Mirkovic has secured substantial research funding as Principal Investigator or Co-PI on numerous grants from NSF, DHS, and other agencies. Current major projects include SPHERE (Security and Privacy Heterogeneous Environment for Reproducible Experimentation), DISCERN (Datasets to Illuminate Suspicious Computations), and modernizing DeterLab education infrastructure. She has successfully led multiple REU sites focused on cybersecurity education and workforce development. She directs the STEEL (Security Research Lab) at USC/ISI, which develops innovative security solutions through interdisciplinary research in network security, human factors in security, and cybersecurity experimentation infrastructure. The lab emphasizes practical implementations that address real-world security challenges while advancing theoretical understanding of security systems.
Jeffrey L. Krichmar is a Professor in the Department of Cognitive Sciences and Department of Computer Science at the University of California, Irvine. His academic journey includes a B.S. in Computer Science from the University of Massachusetts Amherst (1983), an M.S. in Computer Science from The George Washington University (1991), and a Ph.D. in Computational Sciences and Informatics from George Mason University (1997). Prior to UCI, he served as Assistant Professor at George Mason University (1997-1999) and Senior Fellow at The Neurosciences Institute (1999-2007). University of California, Irvine (2007-present) George Mason University (1997-1999) The Neurosciences Institute (1999-2007) His research focuses on neurorobotics , exploring how embodied cognition and biologically plausible neural models can enhance robotic systems. Key areas include spiking neural networks , neuromodulation , path planning , and interactive tactile robots for therapeutic applications. His work bridges neuroscience , robotics , and cognitive science , with applications in autonomous vehicles , neuroprosthetics , and AI explainability . Recent publications emphasize spiking neural networks for navigation , neuromodulated attention , and neuromorphic hardware integration. The development of CARLsim, a GPU-accelerated spiking neural network simulator now in version 6.0, represents a major technical contribution. His team's work on socially assistive robots like CARL-SJR targets therapeutic applications for autism and ADHD. Scientific Awards IJCNN 2020 Best Paper Award Finalist for Best Student Paper at IJCNN 2018 Best Paper Award at IEEE IJCNN 2009 Grants include National Science Foundation funding for neural models of decision-making (2009). His lab (Cognitive Anteater Robotics Laboratory) develops systems that use large-scale brain simulations for autonomous behavior , with applications in adaptive robotics , sensorimotor learning , and neuroethology . Current projects explore neuromodulatory influences on attention systems and cognitive flexibility .
Güneş Acar is a tenured Assistant Professor in the Digital Security group at Radboud University's Faculty of Science. He is also affiliated with iHub, Radboud's interdisciplinary research hub on digitalization and society. His research focuses on security and privacy threats from websites, mobile apps, and IoT devices, with special emphasis on online tracking mechanisms, anonymous communication networks like Tor, and deceptive design patterns. Dr. Acar completed his PhD at KU Leuven under the supervision of Claudia Diaz and Bart Preneel. Prior to joining Radboud University, he was a Postdoctoral Research Fellow at KU Leuven's COSIC group and a Postdoctoral Research Associate at Princeton University's Center for Information Technology Policy. His research spans web security, privacy, online tracking, IoT security, Tor network analysis, and dark patterns. He investigates how websites, mobile apps, and IoT devices compromise user privacy through various tracking mechanisms and deceptive interface designs. His work combines large-scale measurements with user studies to understand both technical vulnerabilities and their real-world impact on users. Dr. Acar's publications reveal an evolving research trajectory from foundational web tracking mechanisms to increasingly sophisticated privacy threats. His work has expanded from browser fingerprinting to IoT privacy, children's online safety, and manipulative design patterns in subscription interfaces. A consistent theme across his research is the examination of how third-party trackers operate and circumvent privacy protections. 2022 CNIL-Inria Award for Privacy Protection (for Leaky Forms paper) Top Reviewer Award, Privacy Enhancing Technologies Symposium (2022) Future of Privacy Forum's Annual Privacy Papers for Policymakers Award (2020) Runner-up for multiple Caspar Bowden Awards for Outstanding Research in Privacy Enhancing Technologies Dutch Research Council (NWO) Vidi Grant (2025-2030) for "A Web Security and Privacy Observatory" Dr. Acar actively supervises PhD students including Zahra Moti, Tim Vlummens, and Luqman Zagi, with Asuman Senol recently completing her PhD in 2024. He has secured significant research funding including the NWO Vidi Grant and a project grant from armasuisse for the Mobile Web Inspector project. His research has influenced policy discussions with organizations including the OECD, US Federal Trade Commission, and European consumer protection authorities. As part of the Digital Security group at Radboud University, Dr. Acar contributes to a vibrant research ecosystem focused on practical security and privacy solutions. His work bridges technical security research with human-centered perspectives, often collaborating across disciplines to address complex privacy challenges in real-world contexts.
Dr. Radu Jianu is a Lecturer in the Department of Computer Science at City, University of London , where he has been a faculty member since 2016. He is affiliated with the giCentre , a leading research group in information visualization. He earned his PhD and MSc in Computer Science from Brown University, USA, and a Diploma in Engineering from the Polytechnic University of Timisoara, Romania. His academic career includes a previous role as Assistant Professor at Florida International University (2012–2016). His research focuses on Data Visualisation, Visual Analytics, and Human-Computer Interaction . He conducts interdisciplinary collaborations with domains such as biology, food policy, and energy decarbonisation, aiming to develop interactive visual tools that enhance data understanding and decision-making. His methodological approach includes user studies, eye-tracking, and the design of novel visualization techniques. Dr. Jianu teaches Programming in Java and Cognition and Technologies , and he coordinates the Programming Bootcamp. He also holds administrative responsibilities as the Progression and Support Director in the Computer Science Department and is a member of its Executive Committee (ExCo). His recent publications reflect a growing interest in LLM-assisted visual analytics, gaze-aware systems, and collaborative human-AI analytical frameworks . He has published in top venues such as IEEE TVCG, CHI, EuroVis, and Nature Immunology, with several best paper awards. His work on the RAMPVIS project highlights his contributions to visualization in public health emergencies. Scientific Awards: Best Paper Award, Symposium on Graph Drawing (2018) Best Short Paper Award, EuroVis (2020) Advising and Grants: Dr. Jianu supervises multiple PhD and MSc students, including Dany Laksono (Energy Decarbonisation) and Maeve Hutchinson (NLP-mediated Visualization). His students have co-authored high-impact, award-winning papers. He has been involved in funded research initiatives such as RAMPVIS, which received support from UKRI/EPSRC for developing visual analytics infrastructure during the COVID-19 pandemic. Labs and Teams: He is an active member of the giCentre at City, University of London, a hub for visualization research. He also collaborates with interdisciplinary teams in epidemiology, immunology, and computer science, contributing to large-scale projects like the Immunological Genome Project and RAMPVIS.
Dr Lin Yue is a Lecturer at the University of Adelaide , affiliated with the Faculty of Sciences, Engineering and Technology and the School of Computer and Mathematical Sciences . She earned her PhD from Jilin University, with part of her doctoral studies completed as a joint PhD candidate at the University of Queensland. Past affiliations: Northeast Normal University, University of Queensland, University of Newcastle Her research focuses on Sequential Data Analysis and its applications in Medical Data Analytics, EEG Data Analysis, Brain-Computer Interfaces, Social Media Data Analytics, and Sentiment Analysis . She collaborates with academia, government, and professional organizations, supported by internal and external research grants. Dr Yue is eligible to supervise Masters and PhD students as a Co-Supervisor and contributes to advancing data mining and machine learning techniques in healthcare and time series analysis.
Hongbo Jiang is a Distinguished Professor and Vice Dean of the College of Computer Science and Electronic Engineering at Hunan University, China. He holds concurrent roles as Director of the Trusted Systems and Networking Key Laboratory of Hunan Province and Director of the Hunan International Technical Cooperation Base for High-Performance Computing and Distributed Systems. His academic journey includes tenures as a Professor at Huazhong University of Science and Technology and a Hong Kong Scholar Research Fellow at The Chinese University of Hong Kong. Education: PhD in Computer Science (Case Western Reserve University, 2008), B.S./M.S. in Mathematics (Huazhong University of Science and Technology, 2002). Research Interests: Distributed systems, mobile computing, smart sensing, wireless networks, IoT, and edge computing. Ongoing projects include mobile/wireless applications, data science in IoT, and edge computing platforms. His work emphasizes practical implementations such as DriverSonar for driving safety and SmileAuth for biometric authentication. Key Achievements: Elected Member of Academia Europaea (2022), Fellow of AAIA, IET, and BCS. Notable awards include the Wu Wenjun Science and Technology Award (2020) and multiple best paper recognitions. Over 100+ publications in top venues like ACM MobiCom, IEEE/ACM Transactions. Professional Contributions: Editorial roles across 8+ journals including IEEE Transactions on Mobile Computing and ACM Transactions on Sensor Networks. Conference leadership includes co-founding ACM TURC and EAI ICECI. Active in technical committees for INFOCOM, MOBIHOC, and ICDCS. Labs/Teams: Leads research groups focused on networking, IoT, and edge computing. Current openings for PhD/MSc students and PostDoc researchers with strong mathematical and systems backgrounds.
Brian Magerko is Professor of Digital Media in the School of Literature, Media, and Communication at Georgia Institute of Technology , where he also serves as Director of Graduate Studies for the Digital Media program and holds an adjunct appointment in the School of Interactive Computing . He directs the Expressive Machinery Lab and has led over $20 million in federally funded research at the intersection of cognition, computation, and creativity. Education Ph.D. Computer Science and Engineering, University of Michigan (2006) M.S. Computer Science and Engineering, University of Michigan (2001) B.S. Cognitive Science (minor Computer Science & Jazz Performance), Carnegie Mellon University (1999) Research Interests Dr. Magerko’s scholarship integrates cognitive science , AI , and computational media to investigate three core themes: (1) social and creative collaboration between humans and AI; (2) design of interactive narrative, music, and arts-based computational experiences; and (3) inclusive STEAM education that leverages personal expression—most notably through the widely-adopted EarSketch platform, which engages learners in computer science via music remixing and coding. Publication Trends Recent publications (2022-2025) reveal a surge in work on generative and co-creative AI systems , AI literacy frameworks , and accessible computing education . Studies span dance improvisation agents (LuminAI), inclusive design for blind and visually-impaired learners, and large-scale evaluations of creativity and learning outcomes in EarSketch classrooms across the United States. Awards & Honors Methods Paper Recognition, ACM CSCW 2022 Best Paper Award, ACM Creativity & Cognition 2021 & 2017 Ivan Allen College Researcher of the Year 2018 NCWIT Engagement Excellence Award 2017 Multiple Best Student Paper Awards (AIED 2021, ICCCI 2021) CETL Thank-a-Teacher Award 2008 Grants & Advising Dr. Magerko has served as PI or Co-PI on numerous NSF, NEA, and private foundation grants totaling more than $20 million. His projects fund interdisciplinary teams of graduate and undergraduate students, post-docs, and external collaborators, producing open-source software, museum installations, and K-12 curricula. Labs & Teams As head of the Expressive Machinery Lab , he mentors researchers creating AI partners for dance, drawing, music, and storytelling. The lab’s artifacts have been exhibited at the Smithsonian, ArtScience Museum Singapore, MoogFest, and other international venues.
Amine Mhedhbi is an Assistant Professor at Polytechnique Montréal , where he leads the Data & AI Systems Lab . He earned his PhD in 2023 from the University of Waterloo. His work bridges data management , graph databases , and AI systems , with a focus on performance, debuggability, and user interface design for data applications. Education : PhD (University of Waterloo, 2023) Research Interests center on modern analytical data systems , including multimodal data management , language model integration , and graph query optimization . His projects like FLockMTL and GraphflowDB aim to combine semantic analysis, AI, and traditional database operations. Scientific Awards include the NSERC Discovery Grant , the Cheriton School Distinguished Dissertation Award , the VLDB Best Paper Award , and fellowships from Microsoft and Meta . Key Collaborations : Semih Salihoğlu, Jimmy Lin, Elena L. Glassman Labs & Teams : Affiliated with DAIS Lab , IVADO , and co-founded the applied research team at Distyl AI in 2023.
Tim Baarslag is a Senior Researcher and leader of the Intelligent and Autonomous Systems group at CWI (Centrum Wiskunde & Informatica), a Part-Time Professor of Mathematics of Cooperative AI at Eindhoven University of Technology (TU/e), and an Associate Professor at Utrecht University. Additionally, he holds visiting roles as a Scholar at MIT, Associate Professor at Nagoya University of Technology, and Fellow at the University of Southampton. His research focuses on enabling autonomous systems to collaborate through joint decision-making, with applications in smart energy trading, the Internet of Things, and autonomous vehicles. Education: MSc in Mathematics (cum laude), Utrecht University BSc in Computer Science (cum laude), Utrecht University PhD in Automated Negotiation, Delft University of Technology (2014, cum laude) Tim Baarslag investigates foundational theories for cooperative artificial intelligence, particularly in automated negotiation. His work includes developing algorithms for multi-deal coordination, optimizing bidding strategies with reservation values, and creating frameworks like Genius and NegoLog to evaluate automated negotiators. He explores how AI can balance efficiency and fairness in complex, real-world scenarios such as procurement and energy trading. Recent research trends highlight his development of NegoLog, a Python-based negotiation framework with advanced analytics, and his work on multi-deal negotiation protocols. He also investigates preference uncertainty in user-agent interactions and designs optimal concession strategies for risk-seeking agents with high reservation values. Scientific Awards: Cor Baayen Young Researcher Award Academic Pioneer by Elsevier Young Talent by The Financial Daily Science Talent by New Scientist Tim Baarslag leads the Intelligent and Autonomous Systems group at CWI and organizes the International Automated Negotiating Agent Competition. He is involved in the ACM Future of Computing Academy, The Young Academy, and the Netherlands Academy of Engineering. His work is supported by the NWO Vidi grant COMBINE, focusing on coordinating multi-deal bilateral negotiations. Labs & Teams: Intelligent and Autonomous Systems group, CWI EAISI and Combinatorial Optimization groups, TU/e ACM Future of Computing Academy The Young Academy Netherlands Academy of Engineering
Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.
