Berrak Sisman is an Assistant Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, affiliated with the Data Science and AI Institute and the Center for Language and Speech Processing (CLSP). She leads the Speech & Machine Learning Lab (SmILe Lab), focusing on AI-driven speech technologies. She received her PhD from the National University of Singapore in 2020 and was previously a tenure-track faculty member at the University of Texas at Dallas (2022–2024). Research Interests: Her work spans artificial intelligence, speech synthesis, voice conversion, emotion analysis in speech, medical speech applications, and secure speech technology. She develops neural models for expressive and adaptive speech processing. Publications: Her recent articles (2024–2025) emphasize speech emotion recognition, zero-shot prosody control, accent conversion, and disentangled representations in TTS, reflecting a focus on cross-modal learning, robustness, and real-world applications. Awards & Grants: NSF CAREER Award (2024) Amazon Faculty Research Award (2022) Singapore Ministry of Education Award (2021) A*STAR Singapore International Graduate Award (2016–2020) Leadership: She directs the SmILe Lab, recruiting PhD/Master’s students for projects in neural speech modeling. Her grants include NSF and Amazon funding for voice conversion and emotion synthesis research.
LIN Meng is an Associate Professor at the Department of Mechanical and Energy Engineering , Southern University of Science and Technology (SUSTech) . He holds a Ph.D. in Mechanical Engineering from Swiss Federal Institute of Technology in Lausanne (EPFL) (2018) and has held postdoctoral positions at Caltech's Joint Center for Artificial Photosynthesis (2018-2019). Research Interests include solar thermal/thermochemical/(photo)electrochemical energy conversion devices , CO2 capture and utilization , and multi-scale modeling and simulation . His work focuses on optimizing energy systems through advanced computational models and cross-disciplinary integration of physics. Recent Publications highlight his contributions to solar fuel processing, CO2 conversion technologies, and hybrid electrochemical systems, with articles in Nature Communications , Joule , and Energy & Environmental Science . His research emphasizes scalable solutions for sustainable energy and carbon management. Scientific Awards ASME Graduate Student Award (2018) Outstanding Reviewer of Solar Energy (2018, 2015) Swiss National Science Foundation Postdoc Fellowship (2017) Shanghai Outstanding Master Thesis (2016) Shanghai Jiao Tong University Outstanding Graduate (2013)
Nedim Pervan is a Full Professor at the Faculty of Mechanical Engineering, University of Sarajevo, Bosnia and Herzegovina. His academic position focuses on mechanical engineering with emphasis on product design, structural analysis, and biomechanical applications. He maintains an active research profile with numerous publications and collaborations across various engineering disciplines, with office hours every workday from 09:00 to 10:00 in room 314. Professor Pervan's research interests span multiple domains of mechanical engineering. He has made significant contributions to additive manufacturing, particularly in polymer gear production and analysis. His work explores mechanical properties, failure mechanisms, and service life of polymer gears manufactured through additive processes. Additionally, he has conducted extensive research on external fixation devices used in orthopedic treatments, analyzing their biomechanical characteristics and structural stability under various loading conditions. His expertise extends to finite element analysis, structural optimization, and the application of 3D scanning technologies within Industry 4.0 contexts. His research demonstrates a strong connection between theoretical engineering principles and practical applications across automotive, medical devices, and manufacturing industries. His recent publication record reveals a strong trend toward interdisciplinary research bridging mechanical engineering with biomedical applications and advanced manufacturing technologies. A significant portion of his work focuses on polymer gears and additive manufacturing, examining material properties and performance characteristics. Another substantial research stream involves biomechanical engineering, particularly the analysis of external fixation devices. His publications demonstrate a methodological approach combining experimental testing with finite element analysis. More recently, his research has expanded into 3D scanning applications in manufacturing and the electrification of transportation systems in Bosnia and Herzegovina. Professor Pervan has been involved in numerous research projects that have advanced the capabilities of the Faculty of Mechanical Engineering. These include the "Integrated Intelligent CAD System for Interactive Design, Analysis and Prototyping of Compression and Torsion Springs" (2022), "Opremanje Laboratorije za razvoj i dizajn proizvoda" (2020), and "Modernizacija Laboratorije za ispitivanje mašinskih konstrukcija" (2019-2020). These projects have focused on developing advanced laboratory facilities, intelligent CAD systems, and equipment for mechanical design and analysis, with several specifically targeting 3D scanning technology implementation. His collaborative work extends across multiple research teams within the Department of Mechanical Constructions at the University of Sarajevo. He frequently collaborates with researchers including Adis Muminović, Elmedin Mešić, and Muamer Delić on projects related to additive manufacturing, biomechanical engineering, and structural analysis. His research group appears actively involved in both theoretical and applied engineering research with practical industrial and medical applications, contributing significantly to Bosnia and Herzegovina's engineering research landscape.
Ed O'Donnell is the Emerson Groennert Professor of Accountancy at the School of Accountancy within the College of Business at Southern Illinois University (SIU). He joined SIU in Fall 2009, teaching courses in financial statement auditing, information systems assurance, and experimental accounting research methods. O'Donnell's research focuses on how accounting information influences decision-making, utilizing cognitive psychology theories to analyze professional judgment and diagnostic reasoning. His applied work addresses improving auditing decisions and frameworks for enterprise risk management and information technology governance. Prior to SIU, he held faculty positions at Mississippi State University, Arizona State University, University of Kansas, and served as a visiting professor at the University of Connecticut. His recent publications examine strategic systems audit approaches, compensating controls, and cross-cultural differences in audit risk assessment. O'Donnell holds a Ph.D. in accounting from the University of North Texas (1995) and combines academic rigor with 14 years of practical experience as a practicing accountant, including roles as controller for a diversified company and public accounting sole proprietor. Contact: Email: eod@siu.edu Phone: 618-453-1497 Office: Rehn Hall, 228A
Jenny Y. Yang is a Professor in the Department of Chemistry at the University of California, Irvine. Her research focuses on the development of inorganic electrocatalysts for chemical fuel generation and utilization, emphasizing bio-inspired secondary coordination sphere effects and thermochemical property optimization. Institution: University of California, Irvine Department: Chemistry Research Interests: Oxygen activation mechanisms Hydrogen production and oxidation Carbon dioxide reduction for fuel synthesis Thermochemical property effects on catalysis Secondary coordination sphere engineering Electrochemical carbon capture systems Scientific Trends: Recent work spans from 2023–2025, covering CO2-to-methane conversion, quantum dot hybrid systems for hydrogen evolution, and computational approaches to CO2 capture agent design. Articles highlight interdisciplinary methods combining inorganic chemistry, electrochemistry, and sustainability-focused engineering. Contact: Office: 4080 ISEB | Phone: 949-824-1533 | Email: j.yang@uci.edu
S. C. Srivastava is a Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur (IIT Kanpur) and serves as the Director of the IIT Kanpur-La Trobe University Research Academy, highlighting his leadership in international academic collaboration. Dr. Srivastava earned his PhD in Electrical Engineering from IIT Delhi in 1987 and completed his B.Tech from Banaras Hindu University in 1976. His research spans power system dynamics & stability studies, optimal power dispatch and state estimation, security analysis and control, smart grid technologies, and renewable energy integration including DC and AC micro-grids. His publication record shows consistent research output through 2018 with significant contributions to power system state estimation, microgrid protection, and wide area control systems. His work demonstrates a clear progression toward addressing contemporary challenges in power systems, particularly renewable integration and smart grid applications. Malaviya Award for Excellence in Power Systems (2018) Prof GK Dubey Memorial IEEE UP Section Life Time Achievement Award (2016) The Life time Achievement Award for Academic Excellence (2016) Dr. Srivastava serves in influential advisory roles including Chairman of the Expert Committee on Mission Innovation in Smart Grids (DST, New Delhi) and Member of the R&D Advisory Committee of NETRA, NTPC. His leadership in the IIT Kanpur-La Trobe University Research Academy demonstrates ongoing commitment to addressing global energy challenges through international collaboration.
David Clewett, Ph.D. , is an Assistant Professor of Psychology at University of California, Los Angeles (UCLA) , where he leads the Dynamic Arousal and Memory Lab. His research explores how emotional arousal, stress, and neuromodulatory systems like norepinephrine and dopamine shape the way we encode, organize, and retrieve memories. He joined UCLA in July 2020 after completing his Ph.D. in Neuroscience at the University of Southern California and a postdoctoral fellowship at NYU and Columbia University. Education: Ph.D. in Neuroscience, University of Southern California (2016) B.S. in Biopsychology with minor in English, University of California, Santa Barbara Research Focus: Dr. Clewett’s lab investigates how physiological arousal influences attention and memory, using a multimodal approach that includes fMRI, pupillometry, eye-tracking, and pharmacological methods. His work spans four major themes: The role of emotion and arousal in selective memory enhancement or suppression. How contextual shifts and emotional states structure episodic memory into meaningful events. Techniques to weaken or update traumatic or unwanted memories. The dynamic effects of attention and arousal states on neural and memory representations. Publications and Impact: His work has been published in top-tier journals such as Nature Communications , Journal of Neuroscience , Hippocampus , and Trends in Cognitive Sciences . His research has contributed to understanding how neuromodulators like norepinephrine and dopamine interact with memory systems to prioritize salient information, and how these processes can be leveraged for therapeutic intervention in PTSD, depression, and aging-related memory decline. Students and Lab Team: Dr. Clewett mentors a diverse team of graduate students and research assistants. Current graduate students include Jacinda Taggett, Ringo Huang, Erin Morrow, Bailey Harris, and Brandon Katerman. Former lab members have gone on to Ph.D. programs at Harvard, UC Berkeley, and other top institutions. Lab and Facilities: The lab is located in Pritzker Hall at UCLA, and is equipped with tools for fMRI, pupillometry, eye-tracking, and behavioral testing. The lab also collaborates with researchers across UCLA and other institutions, including NYU, Columbia, and USC.
Risto Miikkulainen is a Professor of Computer Science and Neuroscience at the University of Texas at Austin and VP of AI Research at Cognizant AI Lab. He directs the UTCS Neural Networks Research Group and is currently on leave from UT, working on Evolutionary Computation and Deep Learning at Sentient Technologies, Inc. Education: Ph.D. in Computer Science, UCLA, 1990 M.S. in Applied Mathematics, Helsinki University of Technology (now Aalto University), 1986 Risto Miikkulainen's research focuses on biologically-inspired computation such as neural networks and evolutionary computation. His work spans three main areas: (1) Neuroevolution, evolving complex deep learning architectures and recurrent neural networks for sequential decision tasks in robotics, games, and artificial life; (2) Cognitive Science, developing models of natural language processing, memory, and learning that shed light on disorders such as schizophrenia and aphasia; and (3) Computational Neuroscience, studying the development, structure, and function of the visual cortex, episodic memory, and language processing. His research combines theoretical understanding of biological information processing with practical applications for developing intelligent artificial systems. His recent publications (2025) show a strong focus on evolutionary approaches to AI development, particularly in neural architecture search, loss function optimization, and explainable AI. Many papers explore the intersection of evolutionary computation with deep learning, creating more efficient and transparent AI systems. His work spans theoretical foundations and practical applications in areas ranging from environmental control systems to cognitive modeling. Scientific Awards: College of Fellows, International Neural Network Society, 2024 Best Pathway to Impact Award, NeurIPS Climate Change workshop, 2024 AAAI Fellow, 2023 IEEE CIS Evolutionary Computation Pioneer Award, 2020 Gabor Award, International Neural Network Society, 2017 Outstanding Paper of the Decade Award, International Society for Artificial Life, 2017 IEEE Fellow, 2016 Multiple Best Paper Awards at GECCO, CIG, and CEC conferences Deployed Application Award, AAAI/IAAI-2013, AAAI/IAAI-2018 Miikkulainen has extensive experience mentoring students through undergraduate research courses like CS378 Computational Intelligence in Game Design I and II, where students develop independent research projects on the OpenNERO research platform. He has received multiple awards for deployed applications, demonstrating the practical impact of his research. His work has led to the development of the NERO game platform, which serves as both an educational tool and research platform for AI. He directs the UTCS Neural Networks Research Group, which focuses on neuroevolution, cognitive science models, and computational neuroscience. The group has developed the NERO (Neuro-Evolving Robotic Operatives) platform, a machine learning game that allows users to train intelligent agents through evolutionary computation. The group's work spans theoretical research and practical applications in AI, with connections to both academic and industry partners.
Prof. Raul Fangueiro is a Full Professor and Vice-Dean at the School of Engineering, University of Minho, Portugal. As a Senior Researcher, he leads FIBRENAMICS – Institute of Innovation on Fiber-Based Materials and Composites. His work spans advanced materials (nano, smart, composites) and structures (3D, auxetic, multiscale) with applications in defense, healthcare, construction, and automotive sectors. Supervised over 20 PhD and Post-Doc researchers Scientific coordinator of 20+ national/international research projects Author of 200+ journal papers (H-index: 47), 500+ conference publications, 36 books, 40 patents Research focuses on nanotechnology , electrospinning , and sustainable material systems : Auxetic composites for personal protection Biodegradable nanofibers for medical use Smart textiles for biological/chemical resistance Recycled mineral/wood-based composites Graphene-reinforced green materials Multiscale fiber architectures Scientific recognition includes: Top 2% most influential scientist (Elsevier/Stanford 2020) Founder of AUXDEFENSE and ICNF conferences Editorial board member of leading composite journals Advisor to European Defense Agency/NATO working groups Active in industry-academia partnerships through spin-offs (Sciencentris, B4Logic, Beyond Composites, Givaware, Pixartidea) and collaborative projects with institutions like Instituto Superior Técnico, University of Aveiro, and international universities.
Dr. Elisa Donati is a researcher and independent group leader at the Institute of Neuroinformatics , affiliated with both the University of Zurich and the Swiss Federal Institute of Technology Zurich . Her work bridges neuromorphic engineering, biomedical signal processing, and wearable healthcare technologies, with a focus on creating brain-inspired systems for neuroprosthetics and rehabilitation. Affiliation: Institute of Neuroinformatics, University of Zurich & ETH Zurich Email: elisa@ini.uzh.ch Elisa’s research emphasizes developing neuromorphic signal processing strategies for wearable and embedded systems, enabling real-time closed-loop interactions with the nervous system. She specializes in translating neuroscience insights into energy-efficient technologies for digital health applications, including neuroprosthetics and personalized biomedical devices using neuromorphic hardware. Her recent publications (2024–2025) highlight advancements in gesture recognition via EMG and event-based systems, neuromorphic heart rate monitoring , and spiking neural network architectures . These works span biomedical signal processing, low-power computing, and adaptive algorithms, reflecting her commitment to robust, real-time, and brain-inspired solutions for healthcare. Elisa’s contributions to neuromorphic computing are evident in her exploration of heterogeneous population encoding , event-driven processing , and ultra-low-power microcontrollers . Her projects often integrate wearable systems with neuroscience, aiming to improve prosthetic control and rehabilitation technologies .
Georg Martius is a Full Professor in the Department of Computer Science at the University of Tübingen's Faculty of Science and a Max Planck Research Group Leader at the MPI for Intelligent Systems. Since April 2023, he has been a core member of the DFG-funded Cluster of Excellence 'Machine Learning: New Perspectives for Science,' which received extended funding through 2032 for its mission to integrate machine learning into fundamental scientific discovery processes. His academic foundation includes a PhD from the University of Göttingen and Bernstein Center for Computational Neuroscience (2005), a Diploma in Computer Science from the University of Leipzig (2003), and a visiting research period at the University of Edinburgh's Division of Informatics. Postdoctoral positions followed at the Max Planck Institutes for Dynamics and Self-Organization (Göttingen, 2009), Mathematics in the Sciences (Leipzig, 2010), and IST Austria (2015). Professor Martius's research pioneers the intersection of reinforcement learning, robotics, and tactile sensing, with emphasis on developing autonomous systems capable of natural locomotion, dexterous manipulation, and physical-world understanding. His work bridges theoretical machine learning with practical hardware applications, particularly in creating differentiable simulators, superresolution tactile sensors, and biologically plausible learning frameworks for robotic control. Analysis of his 2024-2025 publications reveals dominant trends in offline reinforcement learning (especially goal-conditioned and diversity-maximization techniques), object-centric representation learning for video understanding, and tactile sensing innovations. A strong thread connects foundation models to world model construction, while his work on differentiable physics engines enables precise collision handling and contact dynamics for real-world robotic control. His leadership roles include directing the Distributed Intelligence research team at Tübingen and contributing to major collaborative initiatives like the Real Robot Challenge and Myochallenge 2022. The Cluster of Excellence appointment represents recognition of his contributions to transforming scientific methodology through machine learning, particularly in automating hypothesis generation and experimental design. Current projects focus on integrating large-scale machine learning with embodied intelligence, advancing tactile perception systems like the Minsight vision-based sensor, and developing neuroplasticity-inspired approaches for robust out-of-distribution detection. His work directly impacts fields requiring physical interaction intelligence, from autonomous navigation to medical robotics, with emphasis on sample-efficient learning from limited real-world data.
Christopher E. Nelson is an Assistant Professor in the Department of Biomedical Engineering at the University of Arkansas, College of Engineering. His lab focuses on developing biologically inspired strategies for controlled drug and gene delivery, particularly in the context of gene therapy and regenerative medicine. He is actively supported by the NIH, DoD, and Arkansas Bioscience Institute. Education: Postdoctoral Fellow – Duke University Ph.D. – Vanderbilt University B.S. – University of Arkansas Research Focus: Dr. Nelson’s lab integrates genome editing technologies with targeted delivery systems to address challenges in treating genetic diseases and promoting tissue regeneration. Major themes include CRISPR/Cas9 delivery , gene regulation in wound healing , and safe-harbor genome integration in skeletal muscle. His work spans viral and non-viral delivery vehicles , including lipid nanoparticles and AAV vectors, with a strong emphasis on preclinical validation in models of Duchenne muscular dystrophy and inflammatory disease. Scientific Awards: Controlled Release Society Postdoctoral Fellowship The Hartwell Foundation Postdoctoral Fellowship NIH Pathway to Independence Award (K99/R00) Funding & Support: The Nelson Lab is currently funded by: NIH NIGMS R35 DoD CDMRP DMD IDEA Award Arkansas Bioscience Institute University of Arkansas Engineering & Honors Colleges Lab & Team: The Nelson Lab is a dynamic, interdisciplinary team working at the intersection of gene editing, biomaterials, and regenerative medicine. They regularly present at national conferences such as ASGCT and NCUR, and mentor undergraduate researchers through SURF and Honors College grants.
Timo Vesala is Professor of Meteorology and Academy Professor at the University of Helsinki’s Faculty of Agriculture and Forestry, Institute for Atmospheric and Earth System Research (INAR). He is also affiliated with the Viikki Plant Science Centre (ViPS) and serves as a supervisor in the Doctoral Programme in Atmospheric Sciences. Research Interests: Micrometeorology and biogeochemical cycles Ecosystem–atmosphere exchanges of greenhouse gases Eddy-covariance methodology and flux networks Boreal lakes, wetlands, and forests as components of the climate system Development of long-term observational infrastructures such as ICOS-Finland Recent research output (2023–2025) is dominated by high-impact articles in Advances in Atmospheric Sciences , Biogeosciences , Agricultural and Forest Meteorology , and Geophysical Research Letters , reflecting a balanced portfolio of process understanding, methodological advances, and large-scale synthesis studies. Scientific Awards: Academy Professor (Akatemiaprofessori) – awarded by the Academy of Finland Doctoral Advising & Grants: Supervised or co-supervised doctoral theses of Sheila Wachiye, E. Lopez-Blanco, and X. Li Principal Investigator on Academy of Finland project “The Hidden Role of Gases in Trees” (2021–2025) Project leader for “Kasvihuonekaasujen maa-ilmakehävaihto järvi- ja suoekosysteemeille” (2024–2026) Co-leader of the art-science initiative “Periferia – Metsätaiteeellinen asema” (2021–2031) Participant in EU flagship EMME-CARE (2017–2026) Labs & Teams: Timo Vesala heads the Micrometeorology Group at INAR and leads the Finnish ICOS (Integrated Carbon Observation System) network node. His team operates multiple eddy-covariance towers across boreal lakes, wetlands, and forests, integrating field observations with modeling and remote-sensing data.
Dominik Schörkhuber is a PreDoc Researcher at the Vienna University of Technology (TU Wien) in the Computer Vision department. With a background in Informatics (BSc, Dipl.-Ing.), he focuses on computer vision applications for autonomous driving, robotics, and human-machine interaction. His work spans driver action recognition, pedestrian prediction, and adaptive lighting systems. Current projects: Empathic Vehicle (2024–2026), SyntheticCabin (2021–2025), SmartProtect (2020–2025) Research themes: Video transformers, synthetic data transfer learning, multi-task learning, and sensor-lighting integration Specializes in 3D sensing, nighttime driving analysis, and mobile video creation tools
Ahmed Hassoon is an Assistant Research Professor at the Johns Hopkins Bloomberg School of Public Health, with a primary appointment in the Department of Epidemiology and joint appointments in the Department of Neurology at the School of Medicine and the School of Engineering. He is affiliated with several key research centers, including the Welch Center for Prevention, Epidemiology and Clinical Research, the Center for Diagnostic Excellence, and the Center for Humanitarian Health. MPH, Johns Hopkins Bloomberg School of Public Health, 2014 MD, Baghdad College of Medicine, 2004 Dr. Hassoon’s research is centered on leveraging data science and artificial intelligence to improve healthcare quality and safety, particularly in diagnostic accuracy. His work spans applications in lung cancer, stroke, and emergency medicine, with a strong emphasis on reducing diagnostic errors using frameworks like symptom-disease pair analysis (SPADE). He is deeply engaged in developing AI models that reason across multiple clinical modalities to enhance patient outcomes. His recent publications highlight a strong trend in diagnostic safety, AI-driven interventions, and epidemiological analysis of misdiagnosis harms. Key themes include the validation of computable phenotypes, measurement of diagnostic error burden, and the impact of mental health on acute care diagnostics. Hubert Humphrey Fellowship Award, U.S. Department of State (2010) US President Appreciation Certificate, The White House (2011) Atlas Corps Award (2012) Sommer Scholarship Award (2013) State of Maryland Cigarette Restitution Fund Faculty and Translational Research Awards (2016–2017) Institute for Healthcare Improvement (IHI) Fellowship (2024) Dr. Hassoon is a recipient of the AHRQ K08 Career Development Award focused on improving cancer diagnostic safety at Johns Hopkins Hospital. He co-instructs graduate-level courses in data science and AI with Professor Brian Caffo, contributing to the training of the next generation of public health data scientists. He has not formally advised students listed in the provided texts. His collaborative research spans multiple institutions and has been widely disseminated through policy, news, and social media, indicating significant real-world impact. His work is deeply integrated with the Welch Center and the Center for Diagnostic Excellence, where he contributes to translational research initiatives aimed at improving clinical decision-making and patient safety through innovative data-driven approaches.
